\documentclass[11pt,twoside]{article}\makeatletter

\IfFileExists{xcolor.sty}%
  {\RequirePackage{xcolor}}%
  {\RequirePackage{color}}
\usepackage{colortbl}
\usepackage{wrapfig}
\usepackage{ifxetex}
\ifxetex
  \usepackage{fontspec}
  \usepackage{xunicode}
  \catcode`⃥=\active \def⃥{\textbackslash}
  \catcode`❴=\active \def❴{\{}
  \catcode`❵=\active \def❵{\}}
  \def\textJapanese{\fontspec{Noto Sans CJK JP}}
  \def\textChinese{\fontspec{Noto Sans CJK SC}}
  \def\textKorean{\fontspec{Noto Sans CJK KR}}
  \setmonofont{DejaVu Sans Mono}
  
\else
  \IfFileExists{utf8x.def}%
   {\usepackage[utf8x]{inputenc}
      \PrerenderUnicode{–}
    }%
   {\usepackage[utf8]{inputenc}}
  \usepackage[english]{babel}
  \usepackage[T1]{fontenc}
  \usepackage{float}
  \usepackage[]{ucs}
  \uc@dclc{8421}{default}{\textbackslash }
  \uc@dclc{10100}{default}{\{}
  \uc@dclc{10101}{default}{\}}
  \uc@dclc{8491}{default}{\AA{}}
  \uc@dclc{8239}{default}{\,}
  \uc@dclc{20154}{default}{ }
  \uc@dclc{10148}{default}{>}
  \def\textschwa{\rotatebox{-90}{e}}
  \def\textJapanese{}
  \def\textChinese{}
  \IfFileExists{tipa.sty}{\usepackage{tipa}}{}
\fi
\def\exampleFont{\ttfamily\small}
\DeclareTextSymbol{\textpi}{OML}{25}
\usepackage{relsize}
\RequirePackage{array}
\def\@testpach{\@chclass
 \ifnum \@lastchclass=6 \@ne \@chnum \@ne \else
  \ifnum \@lastchclass=7 5 \else
   \ifnum \@lastchclass=8 \tw@ \else
    \ifnum \@lastchclass=9 \thr@@
   \else \z@
   \ifnum \@lastchclass = 10 \else
   \edef\@nextchar{\expandafter\string\@nextchar}%
   \@chnum
   \if \@nextchar c\z@ \else
    \if \@nextchar l\@ne \else
     \if \@nextchar r\tw@ \else
   \z@ \@chclass
   \if\@nextchar |\@ne \else
    \if \@nextchar !6 \else
     \if \@nextchar @7 \else
      \if \@nextchar (8 \else
       \if \@nextchar )9 \else
  10
  \@chnum
  \if \@nextchar m\thr@@\else
   \if \@nextchar p4 \else
    \if \@nextchar b5 \else
   \z@ \@chclass \z@ \@preamerr \z@ \fi \fi \fi \fi
   \fi \fi  \fi  \fi  \fi  \fi  \fi \fi \fi \fi \fi \fi}
\gdef\arraybackslash{\let\\=\@arraycr}
\def\@textsubscript#1{{\m@th\ensuremath{_{\mbox{\fontsize\sf@size\z@#1}}}}}
\def\Panel#1#2#3#4{\multicolumn{#3}{){\columncolor{#2}}#4}{#1}}
\def\abbr{}
\def\corr{}
\def\expan{}
\def\gap{}
\def\orig{}
\def\reg{}
\def\ref{}
\def\sic{}
\def\persName{}\def\name{}
\def\placeName{}
\def\orgName{}
\def\textcal#1{{\fontspec{Lucida Calligraphy}#1}}
\def\textgothic#1{{\fontspec{Lucida Blackletter}#1}}
\def\textlarge#1{{\large #1}}
\def\textoverbar#1{\ensuremath{\overline{#1}}}
\def\textquoted#1{‘#1’}
\def\textsmall#1{{\small #1}}
\def\textsubscript#1{\@textsubscript{\selectfont#1}}
\def\textxi{\ensuremath{\xi}}
\def\titlem{\itshape}
\newenvironment{biblfree}{}{\ifvmode\par\fi }
\newenvironment{bibl}{}{}
\newenvironment{byline}{\vskip6pt\itshape\fontsize{16pt}{18pt}\selectfont}{\par }
\newenvironment{citbibl}{}{\ifvmode\par\fi }
\newenvironment{docAuthor}{\ifvmode\vskip4pt\fontsize{16pt}{18pt}\selectfont\fi\itshape}{\ifvmode\par\fi }
\newenvironment{docDate}{}{\ifvmode\par\fi }
\newenvironment{docImprint}{\vskip 6pt}{\ifvmode\par\fi }
\newenvironment{docTitle}{\vskip6pt\bfseries\fontsize{22pt}{25pt}\selectfont}{\par }
\newenvironment{msHead}{\vskip 6pt}{\par}
\newenvironment{msItem}{\vskip 6pt}{\par}
\newenvironment{rubric}{}{}
\newenvironment{titlePart}{}{\par }

\newcolumntype{L}[1]{){\raggedright\arraybackslash}p{#1}}
\newcolumntype{C}[1]{){\centering\arraybackslash}p{#1}}
\newcolumntype{R}[1]{){\raggedleft\arraybackslash}p{#1}}
\newcolumntype{P}[1]{){\arraybackslash}p{#1}}
\newcolumntype{B}[1]{){\arraybackslash}b{#1}}
\newcolumntype{M}[1]{){\arraybackslash}m{#1}}
\definecolor{label}{gray}{0.75}
\def\unusedattribute#1{\sout{\textcolor{label}{#1}}}
\DeclareRobustCommand*{\xref}{\hyper@normalise\xref@}
\def\xref@#1#2{\hyper@linkurl{#2}{#1}}
\begingroup
\catcode`\_=\active
\gdef_#1{\ensuremath{\sb{\mathrm{#1}}}}
\endgroup
\mathcode`\_=\string"8000
\catcode`\_=12\relax

\usepackage[a4paper,twoside,lmargin=1in,rmargin=1in,tmargin=1in,bmargin=1in,marginparwidth=0.75in]{geometry}
\usepackage{framed}

\definecolor{shadecolor}{gray}{0.95}
\usepackage{longtable}
\usepackage[normalem]{ulem}
\usepackage{fancyvrb}
\usepackage{fancyhdr}
\usepackage{graphicx}
\usepackage{marginnote}

\renewcommand{\@cite}[1]{#1}


\renewcommand*{\marginfont}{\itshape\footnotesize}

\def\Gin@extensions{.pdf,.png,.jpg,.mps,.tif}

  \pagestyle{fancy}

\usepackage[pdftitle={Condition Monitoring of Wind Turbines: A Review},
 pdfauthor={}]{hyperref}
\hyperbaseurl{}

	 \paperwidth210mm
	 \paperheight297mm
              
\def\@pnumwidth{1.55em}
\def\@tocrmarg {2.55em}
\def\@dotsep{4.5}
\setcounter{tocdepth}{3}
\clubpenalty=8000
\emergencystretch 3em
\hbadness=4000
\hyphenpenalty=400
\pretolerance=750
\tolerance=2000
\vbadness=4000
\widowpenalty=10000

\renewcommand\section{\@startsection {section}{1}{\z@}%
     {-1.75ex \@plus -0.5ex \@minus -.2ex}%
     {0.5ex \@plus .2ex}%
     {\reset@font\Large\bfseries}}
\renewcommand\subsection{\@startsection{subsection}{2}{\z@}%
     {-1.75ex\@plus -0.5ex \@minus- .2ex}%
     {0.5ex \@plus .2ex}%
     {\reset@font\Large}}
\renewcommand\subsubsection{\@startsection{subsubsection}{3}{\z@}%
     {-1.5ex\@plus -0.35ex \@minus -.2ex}%
     {0.5ex \@plus .2ex}%
     {\reset@font\large}}
\renewcommand\paragraph{\@startsection{paragraph}{4}{\z@}%
     {-1ex \@plus-0.35ex \@minus -0.2ex}%
     {0.5ex \@plus .2ex}%
     {\reset@font\normalsize}}
\renewcommand\subparagraph{\@startsection{subparagraph}{5}{\parindent}%
     {1.5ex \@plus1ex \@minus .2ex}%
     {-1em}%
     {\reset@font\normalsize\bfseries}}


\def\l@section#1#2{\addpenalty{\@secpenalty} \addvspace{1.0em plus 1pt}
 \@tempdima 1.5em \begingroup
 \parindent \z@ \rightskip \@pnumwidth 
 \parfillskip -\@pnumwidth 
 \bfseries \leavevmode #1\hfil \hbox to\@pnumwidth{\hss #2}\par
 \endgroup}
\def\l@subsection{\@dottedtocline{2}{1.5em}{2.3em}}
\def\l@subsubsection{\@dottedtocline{3}{3.8em}{3.2em}}
\def\l@paragraph{\@dottedtocline{4}{7.0em}{4.1em}}
\def\l@subparagraph{\@dottedtocline{5}{10em}{5em}}
\@ifundefined{c@section}{\newcounter{section}}{}
\@ifundefined{c@chapter}{\newcounter{chapter}}{}
\newif\if@mainmatter 
\@mainmattertrue
\def\chaptername{Chapter}
\def\frontmatter{%
  \pagenumbering{roman}
  \def\thechapter{\@roman\c@chapter}
  \def\theHchapter{\roman{chapter}}
  \def\thesection{\@roman\c@section}
  \def\theHsection{\roman{section}}
  \def\@chapapp{}%
}
\def\mainmatter{%
  \cleardoublepage
  \def\thechapter{\@arabic\c@chapter}
  \setcounter{chapter}{0}
  \setcounter{section}{0}
  \pagenumbering{arabic}
  \setcounter{secnumdepth}{6}
  \def\@chapapp{\chaptername}%
  \def\theHchapter{\arabic{chapter}}
  \def\thesection{\@arabic\c@section}
  \def\theHsection{\arabic{section}}
}
\def\backmatter{%
  \cleardoublepage
  \setcounter{chapter}{0}
  \setcounter{section}{0}
  \setcounter{secnumdepth}{2}
  \def\@chapapp{\appendixname}%
  \def\thechapter{\@Alph\c@chapter}
  \def\theHchapter{\Alph{chapter}}
  \appendix
}
\newenvironment{bibitemlist}[1]{%
   \list{\@biblabel{\@arabic\c@enumiv}}%
       {\settowidth\labelwidth{\@biblabel{#1}}%
        \leftmargin\labelwidth
        \advance\leftmargin\labelsep
        \@openbib@code
        \usecounter{enumiv}%
        \let\p@enumiv\@empty
        \renewcommand\theenumiv{\@arabic\c@enumiv}%
	}%
  \sloppy
  \clubpenalty4000
  \@clubpenalty \clubpenalty
  \widowpenalty4000%
  \sfcode`\.\@m}%
  {\def\@noitemerr
    {\@latex@warning{Empty `bibitemlist' environment}}%
    \endlist}

\def\tableofcontents{\section*{\contentsname}\@starttoc{toc}}
\parskip0pt
\parindent1em
\def\Panel#1#2#3#4{\multicolumn{#3}{){\columncolor{#2}}#4}{#1}}
\newenvironment{reflist}{%
  \begin{raggedright}\begin{list}{}
  {%
   \setlength{\topsep}{0pt}%
   \setlength{\rightmargin}{0.25in}%
   \setlength{\itemsep}{0pt}%
   \setlength{\itemindent}{0pt}%
   \setlength{\parskip}{0pt}%
   \setlength{\parsep}{2pt}%
   \def\makelabel##1{\itshape ##1}}%
  }
  {\end{list}\end{raggedright}}
\newenvironment{sansreflist}{%
  \begin{raggedright}\begin{list}{}
  {%
   \setlength{\topsep}{0pt}%
   \setlength{\rightmargin}{0.25in}%
   \setlength{\itemindent}{0pt}%
   \setlength{\parskip}{0pt}%
   \setlength{\itemsep}{0pt}%
   \setlength{\parsep}{2pt}%
   \def\makelabel##1{\upshape ##1}}%
  }
  {\end{list}\end{raggedright}}
\newenvironment{specHead}[2]%
 {\vspace{20pt}\hrule\vspace{10pt}%
  \phantomsection\label{#1}\markright{#2}%

  \pdfbookmark[2]{#2}{#1}%
  \hspace{-0.75in}{\bfseries\fontsize{16pt}{18pt}\selectfont#2}%
  }{}
      \def\TheFullDate{2013-01-15 (revised: 15 January 2013)}
\def\TheID{\makeatother }
\def\TheDate{2013-01-15}
\title{Condition Monitoring of Wind Turbines: A Review}
\author{}\makeatletter 
\makeatletter
\newcommand*{\cleartoleftpage}{%
  \clearpage
    \if@twoside
    \ifodd\c@page
      \hbox{}\newpage
      \if@twocolumn
        \hbox{}\newpage
      \fi
    \fi
  \fi
}
\makeatother
\makeatletter
\thispagestyle{empty}
\markright{\@title}\markboth{\@title}{\@author}
\renewcommand\small{\@setfontsize\small{9pt}{11pt}\abovedisplayskip 8.5\p@ plus3\p@ minus4\p@
\belowdisplayskip \abovedisplayskip
\abovedisplayshortskip \z@ plus2\p@
\belowdisplayshortskip 4\p@ plus2\p@ minus2\p@
\def\@listi{\leftmargin\leftmargini
               \topsep 2\p@ plus1\p@ minus1\p@
               \parsep 2\p@ plus\p@ minus\p@
               \itemsep 1pt}
}
\makeatother
\fvset{frame=single,numberblanklines=false,xleftmargin=5mm,xrightmargin=5mm}
\fancyhf{} 
\setlength{\headheight}{14pt}
\fancyhead[LE]{\bfseries\leftmark} 
\fancyhead[RO]{\bfseries\rightmark} 
\fancyfoot[RO]{}
\fancyfoot[CO]{\thepage}
\fancyfoot[LO]{\TheID}
\fancyfoot[LE]{}
\fancyfoot[CE]{\thepage}
\fancyfoot[RE]{\TheID}
\hypersetup{citebordercolor=0.75 0.75 0.75,linkbordercolor=0.75 0.75 0.75,urlbordercolor=0.75 0.75 0.75,bookmarksnumbered=true}
\fancypagestyle{plain}{\fancyhead{}\renewcommand{\headrulewidth}{0pt}}

\date{}
\usepackage{authblk}

\providecommand{\keywords}[1]
{
\footnotesize
  \textbf{\textit{Index terms---}} #1
}

\usepackage{graphicx,xcolor}
\definecolor{GJBlue}{HTML}{273B81}
\definecolor{GJLightBlue}{HTML}{0A9DD9}
\definecolor{GJMediumGrey}{HTML}{6D6E70}
\definecolor{GJLightGrey}{HTML}{929497} 

\renewenvironment{abstract}{%
   \setlength{\parindent}{0pt}\raggedright
   \textcolor{GJMediumGrey}{\rule{\textwidth}{2pt}}
   \vskip16pt
   \textcolor{GJBlue}{\large\bfseries\abstractname\space}
}{%   
   \vskip8pt
   \textcolor{GJMediumGrey}{\rule{\textwidth}{2pt}}
   \vskip16pt
}

\usepackage[absolute,overlay]{textpos}

\makeatother 
      \usepackage{lineno}
      \linenumbers
      
\begin{document}

             \author[1]{Sachin  Sharma}

             \affil[1]{  Green Hill Engineering College Kumarhutti Solan (H.P.T.U.)}

\renewcommand\Authands{ and }

\date{\small \em Received: 7 December 2012 Accepted: 3 January 2013 Published: 15 January 2013}

\maketitle


\begin{abstract}
        


This paper presents condition monitoring in wind turbines, and related technologies currently applied in practice and under development for aerospace applications, are reviewed. Condition monitoring system estimate the current condition of a machine from sensor measurements, whereas prediction systems give a probabilistic forecast of the future condition of the machine under the projected usage conditions. Current condition monitoring practice in wind turbine rotors involves tracking rotor imbalance, aerodynamic, surface roughness and overall performance and offline and online measurements of stress and strain.

\end{abstract}


\keywords{wind energy, condition monitoring.}

\begin{textblock*}{18cm}(1cm,1cm) % {block width} (coords) 
\textcolor{GJBlue}{\LARGE Global Journals \LaTeX\ JournalKaleidoscope\texttrademark}
\end{textblock*}

\begin{textblock*}{18cm}(1.4cm,1.5cm) % {block width} (coords) 
\textcolor{GJBlue}{\footnotesize \\ Artificial Intelligence formulated this projection for compatibility purposes from the original article published at Global Journals. However, this technology is currently in beta. \emph{Therefore, kindly ignore odd layouts, missed formulae, text, tables, or figures.}}
\end{textblock*}


\let\tabcellsep& 	 	 		 
\section[{Introduction}]{Introduction}\par
ondition based maintenance has been described as a process that require technologies and people skills that integrates all available equipment condition indicators to make timely decisions about maintenance requirement of important equipment. Today most of the maintenance actions are carried out either by preventive maintenance or corrective maintenance approach. The preventive maintenance generally have fixed intervals to prevent the components from failure where as corrective maintenance is performed after a fault or breakdown has occurred. But these approaches proves very costly in many cases due to loss in production, cost of keeping spare parts , quality deficiencies etc. Condition based maintenance involves the measurement or monitoring of specific parameters which directly corresponds to machine. The main difference between preventive maintenance and condition based maintenance is that condition based maintenance uses various methods of monitoring for checking the condition of the machine to determine the actual mean time for failure where as preventive maintenance depends upon industrial average life statistics. Condition based maintenance has three complimentary levels of implementations: i. Data acquisition step, to obtain data relevant to system health. ii. Data processing: Data processing analyze the data for interpretation, also known as Diagnosis. iii. Maintenance decision making step, to implement best optimum maintenance policy (Prognosis).\par
iv. This methodology as having existed for many years. Thus methodology is recently developed and has evolved over the past three decades from precursor maintenance methods. On the basis of data acquisition and periodicity analyses machine protection system can be of folowing types: a. OFFLINE system: In this type of system the vibration specialists and maintenance managers determine the system for periodic maintenance of machines , mean data acquisition and analyses based on the time base. The periodicity of data b. ONLINE system:The permanently installed systemfor vibration signals acquisition and analyses must be an online system. In ONLINE system condition monitoring units continuously monitor the vibration as well as other process variables, to trend the condition of the equipment and assist in determining when it should be removed from the service for repair. They run as stand alone davice and periodicaly transfer measured data to storage computer. Then it trigger a alarm automaticaly to plant control system and generate a e-mail message. These systems are capable of automatically communicating through a variety of methods, from earthnet GSM modem and allow monitoring of remote locations anywhere in the world.\par
Figure1.2 : Different approaches to condition based maintenance for different equipment in the plan c. Remote Condition Monitoring System: In REMOTE condition monitoring machines the traditional way of collecting data on site is replaced with web based maintenance equipment and it gathers the data fast and data acquisition and data transfer is cheap. By introducing a high speed computer network into vibration monitoring system, vibration signals can be transferred into a specific database. Using this data maintenance managers and vibration specialists have a real time insight into a machine's condition. Web based condition monitoring can be applied to both OFFLINE and ONLINE monitoring system. However it is generally applied to ONLINE system and in case of OFFLINE system means only remote database and vibrations specialists while the presence of maintenance personnel on site is still required for purpose of vibration data collection.\par
CBM is performed to serve the following purposes:\par
? To identify the fault in the monitored object.\par
? To identify nature of fault.\par
? To determine the time up to which machine can serve without interruption before breakdown. ? To determine the degrading components.\par
? The core objectives of condition based maintenance are: ? To take necessary action before breakdown.\par
? To reduce the maintenance overhead costs.\par
? To perform maintenance when required. 
\section[{?}]{?}\par
To reduce number of breakdowns and failure frequency rate. 
\section[{II. How to Start Condition b ased Maintenance}]{II. How to Start Condition b ased Maintenance}\par
One of the most attractive as pect of CBM is the fact that it can be implemented in a relatively inexpensive, step by step approach. Because condition based maintenance is based on equipment oriented concepts of predictive maintenance, it can be applied gradually one system a time. a) Phase 1 i. Data collection and storage procedure Maintenance data and its uses creates the main key differences between the classical and modern maintenance methods. In past, a little attention was paid for the comparison and trending and the data was collected from maintenance results from any given interval which had been filed by reviewing. Trending and statistical analyses are the fundamental building blocks of CBM. By comparing data absolute values and data deviations via statistical analyses provide information never before available. Thus a statistically relevant data base is required. There are a lot of maintenance management software available which have same basic purpose and design are similar from one manufacturer to other. Fundamental equipment information is stored in a detailed manner. Information such as size, date of purchase, ratings, cost, maintenance cycle etc. are stored. Many power system operators have kept good paper records over the years. These paper records may be converted into computerized database which is time consuming but simple.  
\section[{Objectives of CBM}]{Objectives of CBM}\par
Following steps are involved in condition based maintenance:\par
ii. failure and outage information Equipment To access the risk of condition based maintenance program, compilation of this outage data is needed,. This information include items such as type of outage data, cause, length, cost, date etc. The condition based maintenance program implementation sequence can be prioritize by using this outage information. Equipment with high failure rate should be enrolled in the program first. 
\section[{iii. Equipment Database}]{iii. Equipment Database}\par
The equipment database is developed in parallel with other databases. The equipment database may be linked with master maintenance database. But more reliability is realized if they are kept separate. 
\section[{iv. Preliminary budget and plan}]{iv. Preliminary budget and plan}\par
The preliminary budget and planning is the final step in phase 1.Table \hyperref[tab_0]{3}.1 shows the steps include in preliminary plan.   
\section[{IV.}]{IV.} 
\section[{Condition Monitoring of Wind Turbines}]{Condition Monitoring of Wind Turbines}\par
A Change in process parameter is an indicative of a developing failure  {\ref [86]}. A modern condition monitoring system consist of sensors and a processing unit which continuously check and record the condition of the component. There are various techniques to access the component condition. These techniques include vibration analyses, acoustics, oil analyses, strain measurement, and thermography. In case of wind turbine these techniques are used to monitor the major components such as blades, gear box, tower, bearings etc. A condition monitoring may be ONLINE or OFFLINE. A ONLINE monitoring provide instantaneous feedback of condition while OFFLINE provide data collected at regular intervals. For a fast fault detection while the component is in operation require good data acquisition system and appropriate signal processing. Maintenance tasks may be planned and scheduled with great efficiency which increase the reliability, safety and maintainability of the system and reduce the downtime and operational costs  {\ref [87]}. Therefore CM techniques are widely adopted by the industry [88,89] and its most benefits are used in offshore wind farms [90]. For wind turbines this type of monitoring is applicable for monitoring the wheels and bearings of the gearbox, bearings of the generator and the main bearing. Signal analysis requires specialised knowledge. Suppliers of the system offer mostly complete systems which includes signal analysis and diagnostics. The monitoring itself is also often executed by specialised suppliers who also perform the maintenance of the components. The costs are compensated by reduction of production losses. Application of vibration monitoring techniques and working methods for wind turbines differ from other applications with respect to: ? The dynamic load characteristics and low rotational speeds In other applications, loads and speed are often constant during longer periods, which simplifies the signal analysis. For more dynamic applications, like wind turbines, the experience is very limited. ? The high investment costs in relation to costs of production losses. The investments in conditions monitoring equipment is normally covered by reduced production losses. For wind turbines, especially for land applications, the production losses are relatively low. So the investment costs should for a important part be paid back by reduction of maintenance cost and reduced costs of increased damage. 
\section[{V. Condition Monitoring Techniques}]{V. Condition Monitoring Techniques} 
\section[{b) Oil Analysis}]{b) Oil Analysis}\par
Oil analysis may have two purposes:\par
-Safeguarding the oil quality (contamination by parts, moist)\par
Safeguarding the components involved (characterisation of parts) Oil analysis is mostly executed off line, by taking samples. However for safeguarding the oil quality, application of on-line sensors is increasing. Sensors are nowadays available, at an acceptable price level for part counting and moist. Besides this, safeguarding the state of the oil filter (pressure loss over the filter) is mostly applied nowadays for hydraulic as well as for lubrication oil. Characterisation of parts is often only performed in case of abnormalities. In case of excessive filter pollution, oil contamination or change in component characteristic, characterisation of parts can give an indication of components with excessive wear. 
\section[{c) Thermography}]{c) Thermography}\par
Thermograhy is often applied for monitoring and failure identification of electronic and electric components. Hot spots, due to degeneration of components or bad contact can be identified in a simple and fast manner. The technique is only applied for of line usage and interpretation of the results is always visual. At this moment the technique is not interesting for on line condition monitoring. However cameras and diagnostic software are entering the market which are suitable for on-line process monitoring. On the longer term, this might be interesting for the generator and power electronics. 
\section[{d) Physical condition of materials}]{d) Physical condition of materials}\par
This type of monitoring is mainly focussed on crack detection and growth. Methods are normally off line and not suitable for on line condition monitoring of wind turbines. Exception might be the usage of optical fuses in the blades and acoustic monitoring of structures. 
\section[{e) Strain Measurement}]{e) Strain Measurement}\par
Strain measurement by strain gauges is a common technique, however not often applied for condition monitoring. Strain gauges are not robust on a long term. Especially for wind turbines, strain measurement can be very useful for life time prediction and safeguarding of the stress level, especially for the blades. More robust sensors might open an interesting application area. Optical fibre sensors are promising, however still too expensive and not yet state-of-the-art. Availability of cost effective systems, based on fibre optics can be expected within some years. Strain measurement as condition monitoring input will than be of growing importance. 
\section[{f) Acoustic Monitoring}]{f) Acoustic Monitoring}\par
Acoustic monitoring has a strong relationship with vibration monitoring. However there is also a principle difference. While vibration sensors are rigid mounted on the component involved, and register the local motion, the acoustic sensors "listen" to the component. They are attached to the component by flexible glue with low attenuation. These sensors are successfully applied for monitoring bearing and gearboxes.\par
There are two types of acoustic monitoring. One method is the passive type, where the excitation is performed by the component itself. In the second type, the excitation is externally applied. 
\section[{g) Electrical Effects}]{g) Electrical Effects}\par
For monitoring electrical machines MCSA (Machine Current Analysis) is used to detect unusual phenomena. For accumulators the impedance can be measured to establish the condition and capacity. For medium and high voltage grids, a number of techniques are available: ? Discharge measurements ? Velocity measurements for switches ? Contact force measurements for switches ? Oil analysis for transformers For cabling isolation faults can be detected. These types of inspection measurements do not directly influence the operation of the wind turbines. 
\section[{h) Process Parameters}]{h) Process Parameters}\par
For wind turbines, safeguarding based on process parameters is of course common practice. The control systems become more sophisticated and the diagnostic capabilities improve. However safeguarding is still largely based on level detection or comparison of signals, which directly result in an alarm when the signals become beyond predefined limit values. At present, more intelligent usage of the signals based on parameter estimation and trending is not common practice in wind turbines. 
\section[{i) Performance Monitoring}]{i) Performance Monitoring}\par
The performance of the wind turbine is often used implicitly in a primitive form. For safeguarding purposes, the relationship between power, wind velocity, rotor speed and blade angle can be used and in case of large deviations, an alarm is generated. The detection margins are large in order to prevent for false alarms. Similar to estimation of process parameter, more sophisticated methods, including trending, are not often used.  
\section[{VI. Possible Applications of Condition Monitoring}]{VI. Possible Applications of Condition Monitoring} 
\section[{Literature Review}]{Literature Review}\par
Palle Christensen and Gregor Gie bel (2001) \hyperref[b0]{[1]} introduces a new condition monitoring tool which provide a fully automatic supervision and control of the wind farm on internet and data can be accessed with common interface for all form of data from farm.\par
T Holroyd (2001) \hyperref[b1]{[2]} constructed a test rig to seeded the defect of varying sizes on outer races of bearing. A comparison has been done between AE (Acoustic Emission) and vibration analyses. It was concluded that AE not even detect earlier faults but can also provide indication of defect size.\par
N. Jamludin et al. (  {\ref 2001}) \hyperref[b2]{[3]} present a work in which they apply the stress waves analyses to detect early stages damages of bearing at a very low speed i.e. 1.12r/min. Shjn Dander (2002) \hyperref[b3]{[4]} presented a work in which they access the use of time domain model based fault detection and identification (FDI) method for application to a horizontal axis wind turbine (HAWT) that uses pitch to vane control. They use two approaches, the system identification approach and observer based approach using the kalman filter. They construct a horizontal axis wind turbine model and use the simulation to test various approaches. Two algorithms based on kalman filter are presented which provide a reliable estimate of the wind speed by including it in an augmented system state.\par
T.W. Verbruggen (2003) \hyperref[b4]{[5]} develop a inventory of available condition monitoring techniques and selecting a set which has added a value for wind turbines. The area for further development of sensors, algorithm for data analyses were investigated.\par
L. Mihet-Popa et al (  {\ref 2003}) \hyperref[b5]{[6]} proposed a technique based on steady state analyses and applied to induction generators. This technique identify interturn stator fault and rotor asymmetries.\par
L. W. M.M. Rademakers et al. (  {\ref 2004}) \hyperref[b6]{[7]} they present a condition monitoring system for fiber optic blades. They installed the system on a NORDEX turbine for about one year to get operational experience with it to optimize and extend the algorithm. The data is stored on turbine module with back up at ECN. Based on this historical data algorithm were tested before implementing on any turbine system.\par
Giurgiutiu, V \& Cuc, A. (2005) \hyperref[b7]{[8]} presented a damage detection method which was based on ultrasonic rely due to propagation and reflection of elastic waves within the material. They identify local damages and flaws via blade field disturbances. They propose the use of piezoelectric transducer in place of conventional non destructive transducer because piezoelectric transducer can act both transmitter and receiver of ultrasonic waves. Douglas H. (2005) \hyperref[b8]{[9]} presented a work in which a steady state technique has been applied e.g. Motor Current Signature Analyses (MCSA) and Extended Park's Vector Approach (EPVA) and a new technique which was combination of EPVA and discrete wavelet transform and statics to detect the turn faults in doubly fed induction generators (DFIG). The proposed technique shows that steady state technique is not effective when DFIG's operate under transient condition but stator turn faults can be detected under transient conditions.\par
Christopher A.   \hyperref[b9]{[10]} highlights the relevant issues of reliability for wind turbine power generation projects. They identify the cost elements associated with wind farm operation and maintenance. Causes of uncertainty in reliability estimation of wind turbine was also discussed.\par
C. Walfard, D. Robert (2006) \hyperref[b10]{[11]} present a work in which they conduct a cost benefit analyses and estimates that cost liability for failure of wind turbine after four to five years is \$75,000 to \$ 2,25,000 per event for megawatt scale turbine. R.W. \hyperref[b11]{Hyers et al. (2006)}  \hyperref[b11]{[12]} compare the condition monitoring in wind turbine with monitoring and prognosis in helicopter gearboxes. They evaluate the state of art of electronic control and power electronics and compare with state of art in aerospace.\par
Jesse A. Andrawus et al. (  {\ref 2006}) \hyperref[b12]{[13]} apply the RCM approach to horizontal axis wind turbine to detect various failure modes and their causes and effects on system operation. The failure consequences are estimated in term of financial terms by evaluation. Over the whole life cycle of wind turbine the CBM activities are identified and assessed to maximize the return on investment in wind farms.  {\ref David}   \hyperref[b14]{[15]} present a new technique i.e. fluid condition monitoring system in which temperature of oil filter and cooling subsystems are monitored with various sensors. The other parameters such as absolute pressure drop across the filter, dielectric constant and viscosity are also monitored to analyze oil degradation. The data was recorded for two years for two turbines. Then filter lifetime can be predicted by processing the recorded data with statistical and semantic method. The combination of statistical and semantic method make the technique hybrid.\par
Michael   \hyperref[b15]{[16]} develop a electomechanical test rig for assessment that which sensor and fault detection algorithm should be used in a condition monitoring system for operational wind turbines. The test rig has been driven at variable speed to investigate the behavior of wind turbine. A number of fault detection algorithms have been tested on each sensor signal.\par
Edwin \hyperref[b16]{Wiggelinkhuizen et al. (2007)}  \hyperref[b16]{[17]} set up a wind farm of five turbines and several condition monitoring systems has been installed. Traditional measurement systems are also used. A algorithm has been developed which can be integrated with a SCADA system.\par
German Wind energy Association (2007) \hyperref[b17]{[18]} highlights the objective and condition monitoring ( )  \hyperref[b19]{[20]} deployed wireless sensor technology on two wind turbines to construct better models of wind turbine dynamic behavior and response to loading.\par
Mike Woebbeking (2008) \hyperref[b21]{[22]} performed various kind of inspections at turbines. The inspection include periodic monitoring, operation and maintenance surveillance, inspection after commissioning of wind turbine etc. The results shows that 26\% of defects and damages are due to gearbox, 17\% from generator and 13\% from drive train.\par
Asif \hyperref[b23]{Saeed (2008)}  \hyperref[b23]{[23]} implemented various conventional and latest techniques of condition monitoring including signal processing methods for vibration analyses for early fault detection. As a result of the study, infrared thermography is applied as an online condition monitoring for wind system as a retrofit design to increase performance of early detection system.\par
William A. \hyperref[b24]{Vachon (2008)}  \hyperref[b24]{[24]} presented a work which focuses on joint wind project involving IMLD and IpsWich school District (ISD). They install a single MW scale wind turbine generator and the output of the generator is shared in proportion according to funds provided by each party, and value of power delivered to each party reflect the projected time of use of costs. Thus goal of the study was to project the economics of the project.\par
E Lie-Ahmar et al (2008) \hyperref[b25]{[25]} presented a work which investigate specific transient techniques suitable for electrical and mechanical failures in an induction generator based wind turbine. An experimental set up of 1.1kW has been constructed and investigations shows that proposed technique can diagnose failure under transient condition.\par
Cattin Rene et al.  {\ref (2009)} [26] concludes some results from turbines working in ice regions. They revealed that in cloud conditions the air temperature can be used as an indicator for detecting icing conditions. They point out that there is no ice detector in market which can measure icing reliably.\par
The results of Enrcon E-40 turbine shows that the ice detection via power curve can be a better method except for light icing and in case of low wind speed. They point out that it is not possible to melt ice during one heating cycle specially at leading edge of blades thus heat transfer to leading edge should be optimized.\par
Yassine Amirat et al.  {\ref (2009)} [27] discussed different types of faults, Their generated signature and their diagnostic scheme by keeping in mind the need for future research.\par
T.  {\ref Barszczet et al. (2009)} [28] states that there is lack of sufficient data to perform training of method, thus some new states should have created, when there is data different from all known states. Thus they apply Neural Network approach because Neural is a proper tool for classification of operational states in wind turbine and is also capable to recognize new states. R.F. Mesquita et al. (  {\ref 2010})  {\ref [29]} apply the Neural network to analyze all the wind turbine information to identify possible future failure, based on past data of turbine. They show that neural network is a valid tool to make an early detection of failure in some wind turbine equipments.\par
Emilio Miguelanez et al.  {\ref (2010)} [30] presents the role of SeeByte's RECOVERY system within the wind industry, specially focus on offshore turbines. Systems of today gives false alarms most of the time which results in incorrect diagnosis and unnecessary intervention and important warnings are ignored. A RECOVERY system has been developed to guide the fault detection process and better automate knowledge discovery to improve diagnostics. The diagnostics concept is represented on the basis of system observation design pattern. This holistic system improve the diagnostic correctness by taking care of events and sensors values for complete turbine system. Thus it reduces the no-fault-found situations.\par
Bincheng Jiang (  {\ref 2010} Milos Milovancevic (2010)  {\ref [34]} states that applied condition monitoring techniques on sub system levels produces useful information about wind turbine. By using advanced methods of signal analyses, significant changes in turbine behavior can detected at an early stages. They develop a approach which is based on general turbine parameters. Due to global nature of information specific diagnostic is difficult to achieve. Thus they develop a system for wind generator transmission gear vibration monitoring. They proposed that wind turbine systems are complex to monitor and there are very small margins for investments and number of systems is high hence adaptation should not only focused on dynamic load behavior but also on streamlining the system and integration.\par
Joon Young Park et al.  {\ref (2010)} [35] develop a simulator composed of blades, a step up gearbox and a generator. This simulator replaced the conventional simulator which consist of a motor and was not suitable for effective condition monitoring algorithm. They developed the condition monitoring system and also the procedure for designing the simulator.\par
Eunshin Byon et al.  {\ref (2010)} [36] present a work which shows the effect of dynamic weather conditions on feasibility of maintenance. That state that weather conditions makes the modeling and maintenance strategy season dependent. They formulate a model based on Markov decision process by accounting heterogeneous parameters and solve the model by backward programming method. They highlight the benefits of resulting strategy through a case study by collecting data from wind industry. The case study results shows that a optimal policy can be adapted to operating conditions, choosing the most effective action.\par
Richard Dupuis (2010)  {\ref [37]} explains that how bearing and gear rolling elements fails by surface fatigue mode and also study the characteristics of debris produced by failure mode. Their work present that how the accumulated debris damage limit can be counted on the basis of gear geometry.  Ivo Houtzager (2011)  {\ref [49]} addressed two research goals one deal with the data driven modeling methods using system identification and other deal with repetitive controller to reject periodic disturbances. In first method the recursive identification is performed via the subsequent solution of only three linear problems and they are solved by recursive least square. They use Tikhonov regularization to overcome possible numerical problems in practice. The real time implementation and ability to multi input and output systems with closed loop make this approach best suitable for unstable dynamics estimation.\par
Radoslaw Zimroz et al.  {\ref (2011)} [50] proposed the use of advanced signal processing techniques for measuring instantaneous shaft speed recovery from vibration signals generated.\par
Peng Gua (2012) [51] used the history data of Supervisory Control and Data Acquisition (SCADA) system and analyzed this data for detecting the failure of turbine generator bearing. A new condition monitoring method based on Nonlinear State Estimate Technique (NSET) is proposed which is used to construct the normal behavior model of generator bearing temperature. When the generator bearing has an incipient failure the residual between NSET model estimates and measured generator bearing temperature will become insignificant and when residual exceeds the thresholds, an incipient failure is flagged.\par
Wenxian Yang et al. (  {\ref 2012})  {\ref [52]} proposed that reliability centered maintenance is best for offshore wind turbines which include preventive and predictive maintenance techniques enabling wind turbine to achieve high availability and low cost of energy. They present the wind industry with a detailed analyses of the current challenges with existing wind turbine condition monitoring technology.\par
Simon  {\ref Gill et al. (2012)}[53] used the operational data from wind turbine to estimate bivariate probability distribution functions representing the power curves of existing turbines. Hence deviations from expected behavior can be detected. They proposed application of empirical copulas to reduce the complexity between active power and wind speed which was either impossible to approximate by any parameterized distribution.\par
Bill Zijun Zhang (2012) \hyperref[b26]{[58]} use three models for detecting abnormalities of wind turbine vibrations reflected in time domain. Supervisory Control and Data Acquisition (SCADA) data collected from various turbines is used to develop models. The vibrations of wind turbine is characterized on the basis of drive train and tower acceleration. An unsupervised data mining algorithm, the k mean clustering algorithm was applied   {\ref 2012}) \hyperref[b30]{[61]} installed a condition monitoring system on a utility scale wind turbine and. The system produces a number of unexpected results. The installation allowed testing of a microelectromechanical system based sensor technology and vibration data \& RPM data was analyzed. They observed that ability to easy measure changes in main rotor RPM as a result of tower shadow and wind shear phenomenology also facilitate the detection of icing on blade pitch error. K. \hyperref[b31]{Smarsly et al. (2012)}  \hyperref[b31]{[62]} Developed an autonomous software based on multi-agent technology to detect any malfunctioning in the system. The software is modularly integrated into the monitoring system to monitor the installed sensor. A e-mail notification were automatically composed and immediately send to the responsible personnel. As a result appropriate actions were undertaken in time.\par
Anoop Prakash verma (2012) \hyperref[b32]{[63]} presents the wind turbine performance monitoring using historical wind turbine data. A model was constructed using SCADA operational data and fault logs. Two research directions were proposed first is identification and production of critical turbine fault and second is to monitor overall wind farm. The performance of overall wind farm can be accessed daily, weekly or monthly basis.\par
Lain Dinwoodie et al. (2012) \hyperref[b33]{[64]} applies an auto regressive (AR) climate modeling approach to simulate both wind and wave time series coupled with Markov chain Monte Carlo (MCMC) based failure simulation. The AR approach produces rapidly a synthetic time series on the basis of site data available. Failures are simulated on the basis of failure rates and associated time to repair based on simulated weather climate. They investigate that how turbine size influence the breakdown of operational costs. The modeling methodology has been developed to investigate novel asset management techniques and to identify and quantify the sensitivity of overall cost to key operational parameter.\par
Takeshi Ishihara (2012) \hyperref[b34]{[65]} proposed a concept of physical model based condition monitoring (PCM) including system identification and dynamic response analyses using advanced wind field model. The model is introduced to cost effective and reliable condition monitoring. Damping effects of movable parts are identified and then evaluated. Various parameters of model are determined and difference of damping in fore and aft and side by side are expressed.\par
Schlechtingen, Meik et al (2012) \hyperref[b35]{[66]} in their approach employed Neuro-Fuzy interface system (ANFIS) models in training phase. Then trained models applied to product predict the target signals in application phase eg. Temperature, pressure, currents etc. The normal or abnormal behavior of component can be indicated by prediction error with respect to learned behavior. This method is applied to 18 onshore wind turbines of 2MW and results shows that proposed method is well suited to closely monitor a large variety of signals. The accuracy of developed model is high hence even a small behavior changes can be detected. Robert B. \hyperref[b38]{Randall et al. (2012)}  \hyperref[b37]{[68]} presented a work in which a number of ways for compensating for speed and load variations on basis of obtained signals from study of natural occurring faults in wind turbine transmission were discussed. The gearbox was mounted on a test rig and a "pseudo-encoder" signal is extracted from a vibration signal.\par
Pavle Boskoski and Dani Juricic (2012) \hyperref[b38]{[69]} propose an approach for gear fault prognosis in non stationary and unknown operating conditions. In this approach the evolution of statistical complexity of the generated vibration envelop are monitored. The wavelet coefficients are calculated from generated vibrations and statistical complexity is then obtained from wavelet coefficients. The approach estimates the remaining useful life of component without any information about operating conditions and its physical characteristics.\par
Mohamed Elforjani and David Mba (2012) \hyperref[b39]{[70]} presented a work to detect natural defect initiation and crack propagation in rolling element bearing. They construct a test rig to allow accelerated natural degradation of bearing race and concluded that AE is able to detect sub surface initiation and crack propagation.\par
to develop the first monitoring model and other two are developed by using the concept of control chart for detecting abnormal values of drive train and tower acceleration.\par
Shane \hyperref[b27]{Butler et al. (2012)}  \hyperref[b27]{[59]} develop a methodology to estimate the Remaining Useful life (RUL) of the main bearing of commercial wind turbine. A residual model was developed to indicate a faulty behavior, Which produce a signal on post processing for extrapolation using particle filters. The RUL is then specified in term of probability distribution which narrows as the failure point is approached.\par
Walter \hyperref[b37]{Bartelmus (2012)}  \hyperref[b36]{[67]} present a new way of diagnostic which is based on object and operation factor oriented analyses. The data is presented in the term of susceptibility characteristics. The system shows two types of susceptibility. With increase in load mean value of generated vibration given by acceleration signal increases, and change in object condition, create a eristics. parallel shift of susceptibility charact  \hyperref[b41]{[72]} presented a work to determine the influence of the gyroscopic couple on dynamic behavior of blades made of glass fiber reinforced (GRP).\par
Yassine Driss et al. (  {\ref 2012}) \hyperref[b42]{[73]} present a wind turbine with a helical three stage gear system having twenty eight degree of freedom. The aerodynamic forces are considered constant and excitation of the model is induced by periodic variation of mass stiffness. Then dynamic response is calculated by integration method. Finally variation of aerodynamic force is introduced in model and their effect on gear system behavior is studied.\par
Jin Zhang et al. (  {\ref 2012}) \hyperref[b43]{[74]} state that impulses in vibration signals and their spectral features are the key indicators for diagnosing gear damages. They proposed a new method time wavelet energy spectrum for extracting characteristic frequency of faulty gears. The experimental results shows that this method can highlight impulses and is very effective for extracting repeating frequency of periodic impulses and it can even extract the weaker fault feature of gear tooth wear. Mohamed \hyperref[b45]{Boufenar et al. (2012)}  \hyperref[b44]{[75]} presented a work in which they study, compare and modify the time frequency representation techniques such as Wigner Ville Distribution(WVD), Short Time Fourier Transformation (STFT) and wavelets to analyses non stationary phenomena.\par
Ilyes \hyperref[b46]{Khelf et al. (2012)}  \hyperref[b45]{[76]} proposed a diagnostic method of rotating machinery using vibration signature and residual basis function classifier. Wavelet Decomposition method is used to process the recorded signals and indicators are extracted both in temporal and frequency domain. To improve the performance of diagnostic two techniques reduce of indicators space were combined. The method was tested on real signature at various operating condition on a test rig and results shows that choice of indicators closely affect the diagnosis performance.\par
Michael G. Lipsett (2012) \hyperref[b46]{[77]} point out that an effective condition monitoring system depends on good observability of effects of a fault. Thus a simple framework has been described for incorporating damage accumulation into lumped parameter system models.\par
Ryszard Makowski et al. (2012) \hyperref[b47]{[78]} proposed a adaptive algorithm for vibration signal modeling to enhance local changes of signal statics. The approach is based on the normalized exact least square time variant lattice filter (Adaptive Schur Filter). This filter have a fast parameters tracking capability, fast start up and excellent convergence behavior. Thus this method is well adapted for diagnostics of gear box working in time varying load and speed conditions.\par
Radoslow Zimzoz et al.  {\ref (2012)} [79] apply statistical feature processing instead of simply comparing value with threshold. It has been observed that data behavior and good-bad separation of data is different for different load conditions. It proves that recognition efficiency is better for decomposed data than for all data taken together.\par
Michael  {\ref Wilkinson et al. (2013)} [80] proposed that operational costs get greatly reduced by monitoring the condition of major components in the drive train. They conduct the validation study on this method using five wind farms and conclude that a good detection accuracy and high detection rate is possible. Dr. Shaik Nafeez Umar et al.  {\ref (2013)} [81] apply acoustic emission technique for condition monitoring of wind turbine and they conclude that AE technique can be successfully applied for condition monitoring of low speed rotating components. They observed that technique is able to detect very small energy release rates due to incipient failure at starting stage.\par
Schlechtingen,  {\ref Meik (2013)} [82] present a system for wind turbine condition monitoring by using adaptive Neuro-Fuzy interference systems (ANFIS). To fulfill the purpose a normal behavior model for common SCADA are developed to detect abnormal signals.\par
Van Horenbeek, Adriaan et al.  {\ref (2013)} [83] state that it is difficult to implement condition monitoring system due to uncertain parameters. They take into account the performance of the condition monitoring system itself which had been neglected in most of available literature. The modeling is done on the base of P-F curve for for different failure modes and then implemented on turbine gearbox. The case study proves that condition monitoring system is beneficial as compare to other maintenance strategies and benefit depends directly on performance of CBM.\par
A.Augustin Rajasekar (2013) [84] presented a work to investigates online condition monitoring based on voltages and currents for diagnosis of mechanical wind turbine brake. They conclude that the application of online condition monitoring systems can optimize maintenance scheduling but by evolution of condition based maintenance procedure rather than corrective maintenance and minimizes the rest of catastrophic failures.\par
Tommi  {\ref Karkkainen et al. (2013)} [85] point out that there is no generally accepted widely deployed condition monitoring method for power converters. Thus in this study they investigate the benefit and desirability of condition monitoring for electronic converter.\par
Ridha \hyperref[b41]{Ziani et al. (2012)}  \hyperref[b40]{[71]} presented a work in which they compare the performance of bearing fault detection by using Artificial Neural Network (ANN) and Genetic algorithm(GA). The time domain signals from normal and defective gear boxes are extracted and used as input to ANN. The system features are selected on the basis of genetic algorithm and linear Discriminant analyses (LDA) is used as evolution function. Summary of Literature Survey \begin{figure}[htbp]
\noindent\textbf{11}\includegraphics[]{image-2.png}
\caption{\label{fig_0}Figure 1 . 1 :}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{1}\includegraphics[]{image-3.png}
\caption{\label{fig_1}Figure 1}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{61}\includegraphics[]{image-4.png}
\caption{\label{fig_2}Figure 6 . 1 :}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{}\includegraphics[]{image-5.png}
\caption{\label{fig_3}Condition}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{2}\includegraphics[]{image-6.png}
\caption{\label{fig_4}2 ©}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{}\includegraphics[]{image-7.png}
\caption{\label{fig_5}Condition}\end{figure}
  \begin{figure}[htbp]
\noindent\textbf{3} \par 
\begin{longtable}{P{0.7487304687499999\textwidth}P{0.0763671875\textwidth}P{0.01162109375\textwidth}P{0.01328125\textwidth}}
\tabcellsep \multicolumn{3}{l}{1 : Steps involved in preliminary plan}\\
\multicolumn{3}{l}{Field\# Linked to Result}\tabcellsep Comments\\
1\tabcellsep 4\tabcellsep 2\\
2\tabcellsep 5\tabcellsep 4.5\%\\
3\tabcellsep 6\tabcellsep 1\\
4\tabcellsep Etc .\tabcellsep \\
b) Phase 2\tabcellsep \tabcellsep \\
\multicolumn{4}{l}{Information generated in phase 1 will be put to}\\
\multicolumn{3}{l}{work as a living,, active CBM program.}\\
\multicolumn{4}{l}{i. Develop detailed evaluation criteria and methods}\\
\multicolumn{4}{l}{Based on test result data gathered in phase 1,}\\
\multicolumn{4}{l}{the development of control chart strategies and other}\\
\multicolumn{4}{l}{statistical evaluation techniques should begun. The}\\
\multicolumn{4}{l}{exact nature of which strategies should be used may}\\
\multicolumn{4}{l}{not be immediately apparent. Two options are available}\\
\multicolumn{2}{l}{as shown in table 3.2.}\tabcellsep \end{longtable} \par
 
\caption{\label{tab_0}Table 3 .}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{3} \par 
\begin{longtable}{P{0.85\textwidth}}
2 : Evaluation criteria options\\
1 Contract with CBM consultant for statistical criteria\\
implementation\\
2 Start simple. Use graphical trending in spreadsheet.\\
Contracting with qualified CBM consultant, who\\
can assist in selection and implementation of\\
appropriate strategies. Starting simple with a\\
commercially available spreadsheet may not be as\\
rigorous or technically satisfying as more sophisticated\\
statistical analyses, it may well provide a satisfactory\\
compromise between competing technical and\\
economic factors.\\
ii. Finalize recommended maintenance procedure and\\
intervals\\
Maintenance procedure can be added as\\
needed in future intervals, Using transformer as an\\
example table 3.3 lists typical, predictive results, types\\
of tests that might be used. The opinion and\end{longtable} \par
 
\caption{\label{tab_1}Table 3 .}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{} \par 
\begin{longtable}{P{0.85\textwidth}}
Ayetullah Gunel et al. (2007)\end{longtable} \par
 
\caption{\label{tab_3}}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{} \par 
\begin{longtable}{P{0.1269538034039597\textwidth}P{0.7230461965960403\textwidth}}
\tabcellsep The test rig is fit for experimental validation of dynamic\\
\tabcellsep load situation models.\\
\tabcellsep Bodil Anjar et al. (2011) [40] conducted a\\
\tabcellsep feasibility study and they investigate the possibilities of\\
\tabcellsep using thermal condition monitoring of the systems and\\
\tabcellsep components in wind turbines. They conclude that\\
\tabcellsep thermography is suitable for monitoring electrical\\
\tabcellsep systems, transformers and also for fire detection and\\
\tabcellsep fire extinguishing. The IR cameras can be mounted on a\\
\tabcellsep pan tilt unit for continuous monitoring. They show that\\
\tabcellsep as the size of wind power plant increases the cost for\\
\tabcellsep downtime and repair also increases.\\
\tabcellsep Shuangwen Sheng (2011) [41] present a study\\
\tabcellsep work which focuses on results obtained by various CM\\
\tabcellsep techniques from a damaged Gearbox Reliability\\
\tabcellsep Collaborative (GRC) test gearbox. The study shows the\\
\tabcellsep capabilities and limitations of each technique. The\\
\tabcellsep results from a test gear box under healthy condition are\\
\tabcellsep compared with damaged gearbox. A fieldtest of one\\
\tabcellsep GRC gearbox shows that damaged gearbox\\
\tabcellsep experienced two unexpected oil losses which damage\\
\tabcellsep its internal components. The damaged gearbox is reset\\
\tabcellsep by using different CM techniques which help in\\
\tabcellsep evaluating the different CM techniques.\\
\tabcellsep Pratesh Jayaswal et al. (2011) [42] present a\\
\tabcellsep work in which they acquired the vibration signals of\\
\tabcellsep bearings and analyzed them with the help of vibration\\
\tabcellsep analyses techniques. In present work they detect the\\
\tabcellsep earlier fault in bearing using vibration monitoring. By\\
\tabcellsep study the FFT spectrum of bearing vibration signal they\\
\tabcellsep access the condition of bearing.\\
\tabcellsep Peng Guo et al. (2011) [43] proposed a new\\
\tabcellsep technique Autoassociative Kernel Regression (AAKR)\\
\tabcellsep which is used to construct the normal behavior model of\\
\tabcellsep gearbox temperature. The measurement temperature\\
\tabcellsep become significant, when residual between AAKR\\
\tabcellsep estimates on incipient failure of gearbox. To detect the\\
\tabcellsep changes of the residual mean value and standard\\
\tabcellsep deviation in timely manner a moving window statistical\\
They develop an effective PHM technique by presenting actual data obtained from seeded fault bearing and gear test. Zhi Gang Tian et al. (2010) [38] develop a optimal CBM defined by two failure probability thresholds values at wind turbine level. The CBM decisions can be made by calculating failure probability values on the basis of condition monitoring and prognostic data. The cost of CBM technology is\tabcellsep method is used. As one of these parameters exceeds the predefined value the incipient failure flagged. Secil Vorbak Nese et al. (2011) [44] constructed a model of three blades horizontal axis turbine and fault due to possible blade deformation was studied. By applying Continuous Wavelet Transform (CWT) approach a comparison is done between generator rotor speed and torque for healthy and damaged blades.\\
evaluated by simulation.\tabcellsep Suratsavadee Koonlaboon Korkua et al. (2011)\\
\tabcellsep {}[45] develop a Zig Bee based wireless sensor network\\
\tabcellsep for health monitoring of induction machines. The\end{longtable} \par
  {\small\itshape [Note: F.D. Coninck et al. (2010) [39] constructed a back to back gearbox setup which is one of the largest in world.]} 
\caption{\label{tab_5}}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{8} \par 
\begin{longtable}{P{0.32776807544533704\textwidth}P{0.0035626964722319247\textwidth}P{0.518669228082431\textwidth}}
Refno.\tabcellsep Author, Year\tabcellsep Investigated Problem Type\\
\multicolumn{2}{l}{Palle Christensen et al.(2001)}\tabcellsep Automatic supervision and control of wind turbines\\
\multicolumn{2}{l}{T. Holroyd (2001)}\tabcellsep Defect of varying size on outer races of bearings.\\
\multicolumn{2}{l}{N.Jamludin et al. (2001)}\tabcellsep Stress wave analyses to detect early stages damages\\
\multicolumn{2}{l}{Shjn Dander (2002)}\tabcellsep Time domain model based fault detection\\
\multicolumn{2}{l}{T.W. Verbruggen (2003)}\tabcellsep Develop inventory of available condition monitoring\\
\tabcellsep \tabcellsep techniques\\
\multicolumn{2}{l}{L. Mihet-Popa et al. (2003)}\tabcellsep Steady state technique for detection of interturn stator\\
\tabcellsep \tabcellsep faults\\
\multicolumn{2}{l}{L.W.M.M. Rademakers et al.}\tabcellsep Condition monitoring of fibers blades\\
(2004)\tabcellsep \tabcellsep \\
\multicolumn{2}{l}{Giurgiutiu,V (2005)}\tabcellsep Fault detection method based on ultrasonic rely\\
\multicolumn{2}{l}{Douglas H. (2005)}\tabcellsep Fault detection in Doubly Fed induction generator\\
\multicolumn{2}{l}{Christopher A. Walfard (2006)}\tabcellsep Reliability of wind power generation\\
\multicolumn{2}{l}{C. Walfard (2006)}\tabcellsep Cost estimation of failure of wind turbine\\
\multicolumn{2}{l}{R.W. Hyers et al. (2006)}\tabcellsep Comparison of CM techniques of turbine and\\
\tabcellsep \tabcellsep helicopter gearbox\\
\multicolumn{2}{l}{Jesse A. Andrawus et al.}\tabcellsep Detection of wind turbine faults using RCM\\
(2007)\tabcellsep \tabcellsep \\
\multicolumn{2}{l}{David Mc Millan et al. (2007)}\tabcellsep Measurement of benefits of CM quantitatively\\
\multicolumn{2}{l}{Ayetullah Gunel et al. (2007)}\tabcellsep Fluid condition monitoring technique\\
\multicolumn{2}{l}{Michael Wilkinson et al. (2007)}\tabcellsep Selection of sensors and algorithm\\
\multicolumn{2}{l}{Edwin Wiggelinkhuizen et al.}\tabcellsep Algorithm based on SCADA\\
(2007)\tabcellsep \tabcellsep \\
\multicolumn{2}{l}{German wind energy}\tabcellsep Requirement of a CM plan\\
\multicolumn{2}{l}{association (2007)}\tabcellsep \\
\multicolumn{2}{l}{Michael R. Wilkinson (2007)}\tabcellsep Signal Processing technique\\
\multicolumn{2}{l}{R.Andrew Swaetz et al. (2008)}\tabcellsep Dynamic behavior and loading of wind turbine\\
\multicolumn{2}{l}{Scott. J. Johnson et al. (2008)}\tabcellsep Active flow control\\
\multicolumn{2}{l}{Mike Woebbeking (2008)}\tabcellsep Percentages of various type of damages responsible\\
\tabcellsep \tabcellsep for failure\\
\multicolumn{2}{l}{Asif Saeed (2008)}\tabcellsep Fault detection by signal processing techniques\\
\multicolumn{2}{l}{William A. Vachon (2008)}\tabcellsep Project Economics\\
\multicolumn{2}{l}{E Lie-Ahmar et al. (2009)}\tabcellsep Transient Techniques\\
\multicolumn{2}{l}{Cattin Rene et al. (2009)}\tabcellsep CM techniques for wind turbines in icy regions\\
\multicolumn{2}{l}{Yassine Amirat et al. (2009)}\tabcellsep Faults and their signatures\\
\multicolumn{2}{l}{T. Barszczet et al. (2009)}\tabcellsep Fault detection by Neural Network approach\\
\multicolumn{2}{l}{R.F. Mesquita et al. (2010)}\tabcellsep Fault detection by Neural Network approach\\
\multicolumn{3}{l}{Emilio Miguelanez et al. (2010) Role of See Byte's Recovery system in wind industry}\\
\multicolumn{2}{l}{Bincheng Jiang (2010)}\tabcellsep Dynamics behavior of gear boxes in transient\\
\tabcellsep \tabcellsep conditions for fault detection\\
\multicolumn{2}{l}{Wenxian Yang et al. (2010)}\tabcellsep Continuous Wavelet Transform\\
\multicolumn{2}{l}{Yassine Amirat et al. (2010)}\tabcellsep Fault detection based on amplitude demodulation of\\
\tabcellsep \tabcellsep three phase generator\\
\multicolumn{2}{l}{Milos Milovancevic (2010)}\tabcellsep Dynamic load behavior and streamlining\\
\multicolumn{2}{l}{Joon Young et al. (2010)}\tabcellsep Designing of a new simulator\\
\multicolumn{2}{l}{Eunshin Byon et al. (2010)}\tabcellsep Effect of weather conditions on feasibility of\\
\tabcellsep \tabcellsep maintenance\\
\multicolumn{2}{l}{Richard Dupuis (2010)}\tabcellsep Characteristics of debris produced after failure mode\\
\multicolumn{2}{l}{Zhigang Tian et al. (2010)}\tabcellsep CBM decisions made by calculating failure\\
\tabcellsep \tabcellsep probabilities\\
\multicolumn{2}{l}{F.D. coninck et al. (2010)}\tabcellsep Reproduction of dynamic load for wind turbine\\
\tabcellsep \tabcellsep gearbox\\
\multicolumn{2}{l}{Bodil Anjar (et al.2011)}\tabcellsep Feasibility of thermal condition monitoring\\
\multicolumn{2}{l}{Shuangwen Sheng (2011)}\tabcellsep Condition monitoring of wind turbine gearbox\end{longtable} \par
  {\small\itshape [Note: 4552. Wenxian Yang et al. (2012) Wind turbine condition monitoring: Technical and commercial challenges.56]} 
\caption{\label{tab_7}Table 8 .}\end{figure}
 			\footnote{© 2013 Global Journals Inc. (US)} 			\footnote{© 2013 Global Journals Inc. (US) Condition Monitoring of Wind Turbines: A Review} 		 		\backmatter  			 			 			  				\begin{bibitemlist}{1}
\bibitem[ Undergraduate Academic Research Journal (UARJ)]{b29}\label{b29} 	 		\textit{},  	 	 		\textit{Undergraduate Academic Research Journal (UARJ)}  		 (1)  p. 2012.  	 
\bibitem[ Germanischer Lloyd Industrial Services GmbH ()]{b22}\label{b22} 	 		\textit{},  	 	 		\textit{Germanischer Lloyd Industrial Services GmbH}  		2008.  	 
\bibitem[Butler ()]{b27}\label{b27} 	 		‘A feasibility study into prognostics for the main bearing of a wind turbine’.  		 			Shane Butler 		.  	 	 		\textit{IEEE International Conference on Control Applications (CCA)},  				2012. p. 2012.  	 
\bibitem[Scott and Johnson (2008)]{b20}\label{b20} 	 		\textit{Active load control techniques for wind turbines},  		 			. J Scott 		,  		 			Johnson 		.  		2008. Aug.2008.  		 			Sandia National Laboratories 		 	 
\bibitem[Christensen ()]{b0}\label{b0} 	 		\textit{Availability of wind turbines in remote places: A statistical and real time view},  		 			Palle Christensen 		.  		2001.  	 	 (Preprint from the EWEC 2001 in Copenhagen) 
\bibitem[Ziani ()]{b41}\label{b41} 	 		‘Bearing fault diagnosis using Neural Network and Genetic Algorithm with trace criterion’.  		 			Ridha Ziani 		.  	 	 		\textit{Proceedings of the 2 nd International Conference},  				 (the 2 nd International Conference)  		2012. 2012. p. .  	 	 (Condition monitoring of machinery operations) 
\bibitem[Khelf ()]{b46}\label{b46} 	 		‘Combining RBF-PCA-Relief F Filter for a better diagnosis performance in rotating machines’.  		 			Ilyes Khelf 		.  	 	 		\textit{Proceedings of 2 nd International Conference},  				 (2 nd International Conference)  		2012. 2012. p. .  	 	 (Condition monitoring of machinery in non stationary operations) 
\bibitem[Hyers ()]{b11}\label{b11} 	 		‘Condition monitoring and prognosis of utility scale wind turbines’.  		 			R W Hyers 		.  	 	 		\textit{Energy materials}  		2006. 2006. 1  (3)  p. .  	 
\bibitem[Wilkinson ()]{b15}\label{b15} 	 		\textit{Condition monitoring of offshore wind turbines},  		 			Michael Wilkinson 		.  		2007. 2007.  		 			Durham University School of Engineering 		 	 
\bibitem[Michael and Wilkinson ()]{b18}\label{b18} 	 		\textit{Condition monitoring of offshore wind turbines},  		 			R Michael 		,  		 			Wilkinson 		.  		2007. 2007.  		 			Durham University School of Engineering 		 	 
\bibitem[Jamludin ()]{b2}\label{b2} 	 		‘Condition monitoring of slow speed rolling elements bearings using stress waves’.  		 			N Jamludin 		.  	 	 		\textit{Proceedings of the institution of the Mechanical Engineering}  		2001. 200. 215  (4)  p. .  	 
\bibitem[Mihet-Popa (2003)]{b5}\label{b5} 	 		‘Condition monitoring of wind generators’.  		 			L Mihet-Popa 		.  	 	 		\textit{proceedings of IEEE IAS'03},  				 (IEEE IAS'03)  		2003. oct. 2003. 3 p. .  	 	 (Salt lake city (USA)) 
\bibitem[Walfard ()]{b10}\label{b10} 	 		\textit{Condition monitoring of wind turbines, Technology overview, seeded fault testing and cost benefit analyses},  		 			C Walfard 		.  		2006. Electric Power Research Institute.  	 
\bibitem[Schlechtengen ()]{b35}\label{b35} 	 		\textit{Condition monitoring with wind turbine SCADA data using NEURO-FUZY normal behavior model},  		 			Meik Schlechtengen 		.  		2012.  	 
\bibitem[Wiggelinkhuizen ()]{b16}\label{b16} 	 		\textit{CONMOW: Condition monitoring for offshore wind turbines},  		 			Edwin Wiggelinkhuizen 		.  		2007.  	 
\bibitem[Elforjani ()]{b40}\label{b40} 	 		‘Detecting AE signals from Natural Degradation of slow speed rolling element bearings’.  		 			Mohamed Elforjani 		.  	 	 		\textit{Proceedings of the 2 nd International Conference},  				 (the 2 nd International Conference)  		2012. 2012. p. .  	 	 (Condition monitoring of machinery in non stationary operations) 
\bibitem[Andrawus (2006)]{b12}\label{b12} 	 		‘Determining an appropriate condition based maintenance strategy for wind turbines. The 2 nd joint international conference’.  		 			Jesse A Andrawus 		.  	 	 		\textit{Sustainable energy and Environment}  		2006. 2006. 23 Nov, 2006.  (SEE) .  	 
\bibitem[Driss ()]{b43}\label{b43} 	 		‘Dynamic behavior of an aerodynamic turbine coupled to a multiplier helical three stage gear box’.  		 			Yassine Driss 		.  	 	 		\textit{Proceedings of 2 nd International Conference},  				 (2 nd International Conference)  		2012.  	 	 (Condition monitoring of machinery in non stationary operations. CMMNO'2012. Pages 213-220) 
\bibitem[Hamdi ()]{b42}\label{b42} 	 		‘Effects of Gyroscopic coupling on dynamics of a wind turbine blade with horizontal axis’.  		 			Hedi Hamdi 		.  	 	 		\textit{Proceedings of 2 nd International Conference},  				 (2 nd International Conference)  		2012. 2012. p. .  	 	 (Condition monitoring of machinery in non stationary operations) 
\bibitem[Giurgiutiu and Cuc ()]{b7}\label{b7} 	 		\textit{Embedded non destructive evolution for structural health monitoring. Damage detection and failure prevention. The shock and vibration Digest},  		 			V \& Giurgiutiu 		,  		 			A Cuc 		.  		2005. 37 p. .  	 
\bibitem[Vachon ()]{b24}\label{b24} 	 		‘Estimated energy production and economics for a large wind turbine generator installed at the IPSWICH municipal light department site in IPSWICH’.  		 			William A Vachon 		.  	 	 		Massachusetts. IPSWICH IMLD-ISD Wind Study  		2008. 2008.  	 
\bibitem[Danders ()]{b3}\label{b3} 	 		‘Fault detection and identification for wind turbine systems: A closed loop analyses’.  		 			Shjn Danders 		.  	 	 		\textit{Faculty of applied Physics system and Control Engineering},  				2002. p. .  	 	 (Master's Theses University of Twente) 
\bibitem[Rademakers (2004)]{b6}\label{b6} 	 		‘Fiber optic blade monitoring’.  		 			L W M M Rademakers 		.  	 	 		\textit{Proceedings of European wind energy conference},  				 (European wind energy conferenceLondon)  		2004. Nov 2004.  	 
\bibitem[Zhang ()]{b44}\label{b44} 	 		‘Gearbox fault diagnosis using Time-Wavelet Energy spectral analyses’.  		 			Jin Zhang 		.  		 223-230.  	 	 		\textit{Proceedings of 2 nd International Conference},  				 (2 nd International Conference)  		2012. 2012.  	 	 (Condition monitoring of machinery in non stationary operations) 
\bibitem[Bechoefer ()]{b30}\label{b30} 	 		\textit{Initial condition monitoring Experience on a wind turbine. Annual Conference of prognostics and health management society},  		 			Eric Bechoefer 		.  		2012. 2012.  	 
\bibitem[Saeed (2008)]{b23}\label{b23} 	 		\textit{Master's Thesis Department of electrical Engineering},  		 			Asif Saeed 		.  		2008. Oct. 2008.  		 			Blekinge Institute of Technology 		 	 	 (Online condition monitoring system for wind turbine) 
\bibitem[Mike Woebbekeng (2008) 30 years of experience for wind turbines and wind farms-Lesson learnt]{b21}\label{b21} 	 		\textit{Mike Woebbekeng (2008) 30 years of experience for wind turbines and wind farms-Lesson learnt},  		 	 
\bibitem[Kumar ()]{b28}\label{b28} 	 		\textit{Monitoring and fault diagnosis in wind turbine using Wavelet Transform},  		 			Deepak Kumar 		.  		2012.  	 
\bibitem[Zhang ()]{b26}\label{b26} 	 		‘Monitoring wind turbine vibration based on SCADA data’.  		 			Zijun Zhang 		.  	 	 		\textit{Journal of Solar Energy Engineering}  		2012. 2012.  (134) .  	 
\bibitem[Bartelmus ()]{b37}\label{b37} 	 		‘Object and operation oriented diagnostics’.  		 			Walter Bartelmus 		.  	 	 		\textit{Proceedings of the 2 nd International Conference, cmmno's 2012 isbn},  				 (the 2 nd International Conference, cmmno's 2012 isbn)  		2012. p. .  	 
\bibitem[Verma ()]{b32}\label{b32} 	 		\textit{Performance monitoring of wind turbines: A data mining approach. Thesis submitted in partial fulfillment of the requirements for the Doctor of Philosphy degree in industrial Engineering in Graduate College of the university of Iowa},  		 			Anoop Prakash Verma 		.  		2012. 2012.  	 
\bibitem[Ishihara ()]{b34}\label{b34} 	 		\textit{Prediction of dynamic response of wind turbines using SCADA data and updated Aeroelastic model},  		 			Takeshi Ishihara 		.  		2012.  	 
\bibitem[Unknown (2007)]{b17}\label{b17} 	 		\textit{Principles for condition based maintenance of wind turbines. Adopted by expert consulting committee of the German Wind Energy Association on 21 st},  		 			Unknown 		.  		2007. Sep. 2007.  	 
\bibitem[Proceedings of ASME Turb Expo ()]{b36}\label{b36} 	 		 GT2012-68011.  	 	 		\textit{Proceedings of ASME Turb Expo},  				 (ASME Turb Expo)  		2012.  	 
\bibitem[Mc and Millan ()]{b13}\label{b13} 	 		‘Quantification of condition monitoring benefit for offshore wind turbines’.  		 			David Mc 		,  		 			Millan 		.  	 	 		\textit{Wind Engineering}  		2007. 2007. 31  (4)  p. .  	 
\bibitem[Boskoski ()]{b39}\label{b39} 	 		‘Renyi Entropy based statistical Complexity analyses for gear fault Prognostics under variable load’.  		 			Pavle Boskoski 		.  	 	 		\textit{Proceedings of the 2 nd International Conference},  				 (the 2 nd International Conference)  		2012. 2012. p. .  	 	 (Condition monitoring of machinery operations) 
\bibitem[Dinwoodie ()]{b33}\label{b33} 	 		\textit{Sensitivity of offshore wind turbine operation \& maintenance costs to operational parameters. Risk and reliability for wind energy and other renewable sources, 42 nd ESReDA Seminar},  		 			Lain Dinwoodie 		.  		2012. 2012. Glasgow, UK.  	 
\bibitem[Randall ()]{b38}\label{b38} 	 		‘Separation of gear and bearing fault signals from a wind transmission under varying speed and load’.  		 			Robert B Randall 		.  	 	 		\textit{Proceedings of 2 nd International Conference},  				 (2 nd International Conference)  		2012. 2012. p. .  	 	 (Condition monitoring of machinery in non stationary operations) 
\bibitem[Tullah ()]{b14}\label{b14} 	 		\textit{Statistical and semantic multi sensor data evolution for fluid condition monitoring in wind turbines},  		 			Aye Tullah 		,  		 			Gunel 		.  		2007. Germany.  		 			Saarland University, Lab for measurement technology, saarbruecken 		 	 
\bibitem[Smarsly ()]{b31}\label{b31} 	 		\textit{Structural health monitoring of wind turbines observed by autonomous software components-2 nd level monitoring},  		 			K Smarsly 		.  		2012.  	 
\bibitem[Swartz ()]{b19}\label{b19} 	 		\textit{Structural monitoring of wind turbines using wireless sensor networks},  		 			R 		,  		 			Andrew Swartz 		.  		2008. 6.  	 
\bibitem[Douglas (2002)]{b8}\label{b8} 	 		‘The detection of interturn stator faults in Doubly Fed Induction Generator’.  		 			H Douglas 		.  	 	 		\textit{14 th IAS annual meeting},  				2005. 2005. 2-6 oct, 2005. 2 p. .  	 	 (Industry applications conference) 
\bibitem[Holroyd ()]{b1}\label{b1} 	 		\textit{The use of acoustic emission for bearing defect identification and estimation of defect size. Condition monitoring of very slowly rotating machinery.14 th international congress on condition monitoring and diagnostic engineering management},  		 			T Holroyd 		.  		2001. 2001.  	 
\bibitem[Boufenar ()]{b45}\label{b45} 	 		‘Time frequency analyses techniques: Review and their application on roller bearing prognostics’.  		 			Mohamed Boufenar 		.  	 	 		\textit{Proceedings of 2 nd International Conference},  				 (2 nd International Conference)  		2012. 2012. p. .  	 	 (Condition monitoring of machinery in non stationary operations) 
\bibitem[Michael and Lipsett ()]{b47}\label{b47} 	 		\textit{Using parametric dynamical models to determine observability requirements for condition monitoring systems},  		 			G Michael 		,  		 			Lipsett 		.  		2012.  	 
\bibitem[Lie-Ahmar ()]{b25}\label{b25} 	 		\textit{Wind energy conversion system fault diagnosis using wavelet analyses},  		 			E Lie-Ahmar 		.  		2008.  	 
\bibitem[Verbruggen ()]{b4}\label{b4} 	 		\textit{Wind turbine operation and maintenance based on condition monitoring. ECN-C-03-047},  		 			T W Verbruggen 		.  		2003. p. .  	 
\bibitem[Walfard ()]{b9}\label{b9} 	 		\textit{Wind turbine reliability: Understanding and minimizing wind turbine operation and maintenance costs},  		 			Christopher A Walfard 		.  		2006. 2006. p. .  		 			Sandia National Laboratories 		 	 
\end{bibitemlist}
 			 		 	 
\end{document}
