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ScienceDirect Procedia CIRP 11 (2013) 287 – 290

2nd International Through-life Engineering Services Conference

Review of condition monitoring and fault diagnosis technologies for wind turbine gearbox Mengyan Nie*, Ling Wang National Centre for Advanced Tribology at Southampton (nCATS), Engineering Sciences, Faculty of Engineering and the Environme nt, University of Southampton, Southampton SO17 1BJ, UK * Corresponding author. Tel.: +44-23-8059-3761; fax: +44-23-8059-3016. E-mail address:m.nie@soton.ac.uk

Abstract In order to develop robust condition monitoring and prognosis technologies and systems for wind turbine gearboxes, a comprehensive review of the state-of-art of condition monitoring and fault diagnosis techniques has been carried out. The challenges and opportunities are identified to guide future research in improving the accuracy and ability of condition monitoring and prognosis systems for wind turbine gearboxes. This review also focuses on the fault diagnosis technologies and application of novel sensors in wind turbine gearbox condition monitoring. © 2013 B.V. 2013 The TheAuthors. Authors.Published PublishedbybyElsevier Elsevier B.V. Open access under CC BY-NC-ND license. nd International Through-life Engineering Selection ofof thethe International Scientific Committee of the Selection and andpeer-review peer-reviewunder underresponsibility responsibility International Scientifi c Committee of "2 the “2nd International Through-life Services Conference" the Programme Chair – Ashutosh Tiwari. Engineering Servicesand Conference” and the Programme Chair – Ashutosh Tiwari Keywords: Condition monitoring; wind turbine; gearbox; oil analysis; signal processing; operation and maintenance

1. Introduction Since the early 1980s, wind power technology has undergone an immense growth with respect to both the turbine size and the capacity installed worldwide. Global Wind Energy Council (GW EC) reported that during the period of 1996 to 2011, the average cu mulat ive growth rate of wind power was over 20%, and the co mmercial wind power capacity installed in about 80 countries totalled about 240 GW at the end of 2011, as shown in Fig. 1 [1]. As a mainstream electricity generation source, wind power plays a central role in an increasing number of countries’ immed iate and longer term energy plans. For example, the US targets 20% wind based electricity generation, i.e. over 300 GW, by 2030 [ 2]. In 2009, EU Co mmun ication has also set a target share from wind energy of 20% for the EU electricity by 2020 and 33% by 2030 [3]. China aims for 15% renewable power generation by 2020 [4]. However, wind turbines (WT) are co mplex electro mechanical systems wh ich ext ract kinetic power of the wind and convert it into electrical power. The wind turbines often

work under very harsh environment, with rapidly changing environment temperature, air p ressure and alternating load operation conditions. Thus wind turbines are subjected to different sort of failures, and the wind energy industry has been affected by failu res of wind turbine co mponents, such as main bearings, gearbo xes, and generators. Moreover, wind turbines are hard-to-access structures, and often located in rural areas, such as a mountainous area or at sea with t raffic inconvenience. In addition to downtime loss of power generation, replacing failed components usually involves cranes and lift ing equip ment as well as service crews, which put huge impacts on the cost of wind energy. The relatively high operation and maintenance cost is a critical issue for making wind energy competitive to conventional sources and achieving the desirable renewable targets. Therefore, research in condition monitoring and fault diagnosis of wind turbines has gained dramatically increasing attention, as illustrated in Fig. 2. It clearly shows that the research effort in the areas of condition monitoring (CM) for wind turbine has dramatically increased since 2005. In part icular, in the area of condition monitoring for wind turbine gearbox (CM_ GB), there were

2212-8271 © 2013 The Authors. Published by Elsevier B.V. Open access under CC BY-NC-ND license.

Selection and peer-review under responsibility of the International Scientific Committee of the “2nd International Through-life Engineering Services Conference” and the Programme Chair – Ashutosh Tiwari doi:10.1016/j.procir.2013.07.018

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Mengyan Nie and Ling Wang / Procedia CIRP 11 (2013) 287 – 290

only two papers reported pre -2004, and about 3 papers per year in the period of 2004 to 2009. But the nu mber of the publications in this area dramatically increased to 15 in 2010, and then rapidly grew in the follo wing years. This reflects on the fact that the failure of gearbo x contributes 21% to the total downtime of wind turbines, with the highest rate of downtimes per co mponent among all the WT co mponents, although it only accounts for 4% of the faults [5,6,7]. Several rev iews on wind turbine gearbox condition monitoring have already been published in the last few years. Becker et al reviewed a nu mber of commercial available condition monitoring solutions for wind turbine gearbo x pre2006 [8]. Also in 2006, Hyers et al reviewed the state of the art condition monitoring and prognosis technologies used in aerospace applications and evaluated their applicability to wind turbine components (e.g. gearbox) [9]. In 2009, Lu et al reviewed the advancement made in wind turbine condition monitoring and fault diagnosis between 2006 and 2009 focusing on the monitoring of gearbo x and bearing, rotor and blades, generator and power electronics [ 10]. They also discussed that it is more appropriate to use time -frequency analysis techniques such as wavelet as a key signal processing tool for vibrat ion signals for wind turbine condition monitoring and fault diagnosis due to the transient nature.

2. Failure modes of wind turbine gearbox

250 Annual Installed Wind Capacity Cumulative Installed Wind Capacity

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The review also revealed there was an increasing trend in using acoustic emission (AE) for condition monitoring and in developing model based reasoning algorithms for fault detection. Recently, Hamilton and Quail published a detailed review on the oil monitoring techniques for wind turbine gearboxes and recommended that online ferrography, selective fluorescence spectroscopy, scattering measurements, Fourier Transform Infrared, photoacoustic spectroscopy and solid state viscometers are all suitable for gearbo x CM based on their cost, size, accuracy and develop ment. They also recommended that a combination of a number of sensors that analyse different characteristics of the oil would provide a greater accuracy than an individual one [ 11]. Zhong et al briefly discussed the common failures occurring in wind turbine gearboxes and their root causes. They also reviewed a number of mon itoring techniques, including vibrat ion analysis, oil analysis and AE techniques for gearbox condition monitoring, and outlined the challenges and future research directions for wind turbine gearbox condition monitoring [12]. Co mpared to the previous reviews, this paper aims to review the most recent advances in condition monitoring and fault diagnosis techniques for wind turbine gearbo x, with the focus on current and novel sensing techniques as well as new signal processing methods.

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Fig. 1. Global annual/cumulative installed wind capacities up to end of 2011

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Wind turbines generally operate under extremely harsh environment conditions including dustiness, humidity, temperature, air pressure and unpredictable loads due to gust wind. Wind turbine drive trains are thus sus ceptible to severe tribological conditions such as wear, fatigue and corrosion, leading to increased component damages and machine malfunctions. According to a number of surveys of wind farms cross Europe and A merica, gearbo x is by far the most liab le subsystem that is responsible for wind turbine downtime and maintenance cost. Wind turbine gearbo x components are found to be subjected to a number of wear mechanis ms, such as abrasive wear, pitting, scuffing, erosion, crack, breakage, and ch ipping etc. The co mmon faults observed in wind turbine gearboxes are gear damage, bearing damage, b roken shaft, leaking oil and high o il temperature, where bearing failures due to micro-p itting, scuffing and white structure flaking (WSF) are found to in itiate majority of the gearbox failures [10,12-15].

Papers in year

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3. Recent advances in wind turbine gearbox condition monitoring and fault diagnosis

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Fig. 2. Publications in research fields of condition monitoring for wind turbine (CM_WT) and wind turbine gearbox (CM_GB) in the period of 1991 to 2012.

SCA DA (Supervisory Control and Data Acquisition) is the most common practice in wind turbine condition monitoring. It monitors the conditions of wind turbines based on their historical data, including wind speed, temperature and power outputs. Some SCADA systems also include vibration and oil debris monitoring data. SCADA is used to relate wind turbine operating conditions (speed, temperature and power) with the condition of the system, e.g. the rate at which damage accumulated [15]. SCA DA systems have been imp lemented


in many wind farms as requested by insurance policy. Although a good and useful practice, SCADA systems are not effective especially in detecting incipient turbine fau lts to provide sufficient warn ings for operators to take maintenance actions. As more and larger wind farms are being developed, the amount data collected by SCADA also becomes difficult to manage using conventional data processing methods. Therefore advanced data fusion/min ing strategies and reliable theoretical models are desirable to improve the ability of SCA DA systems in detecting wind turbine failures early and accurately. This is in fact required by all other sensing systems too. Apart fro m SCADA systems, vibration-based condition monitoring systems (CMS) are also widely used in wind turbine condition monitoring, especially for wind turbine gearboxes. Majority of the existing commercially available vibration-based CMS use time -do main or frequency-domain analysis based on Fourier t ransform (FT) for fault detection and diagnosis. However, they are not part icularly suitable due to the high transient nature of wind turbine vibration signals as Fast Fourier transform (FFT) provides only the global frequency averaged over a relatively large time span, which means the time information is lost during the transformation. Many efforts have been made to develop advanced signal processing techniques for vibrat ion signals to extract useful informat ion in recent years. Wavelet analysis, as one of the time-frequency analysis methods, has been developed to provide the change of frequencies in the signal over a period of time. A number of wavelets have also been adopted to remove non-stationary noise fro m the recorded vibrat ion signals. Among them, continuous wavelet transform, discrete wavelet transform as well as harmonic wavelet transform have been accepted as key signal processing methods for wind turbine gearbox mon itoring [16-19]. Emp irical mode decomposition (EM D), intrinsic mode function (IMF) as well as local mean deco mposition (LM D), which are widely used for non-stationary and non-linear signal processing, have also been applied in wind turbine gearbo x failure detection and diagnosis, especially for slow speed WT components [20,21]. Other statistical analysis techniques, such as higher-order statistical analysis, cepstrum analysis and outlier analysis, have also been used to reduce the Gaussian noise in vibration signals and to reveal the fault-related non-Gaussian informat ion, thus identifying fault-related signal features [2224]. Apart fro m the above mentioned conventional statistics based data analysis, novel data mining approaches are also being developed for gearbox condition monitoring to effectively deal with the rapid ly growing data size and diversity of measurement datasets [25]. A method based on the monitoring of motor and stator current has recently developed for WT gearbo x mon itoring. The advantage of this method is that the motor and stator current are already available in machine control processes and the data is easy to be obtained [26-30]. Their results showed that changes in the current can be related to the wind turbine operation conditions and mach ine fau lts. Renaudin et al developed optical or magnetic encoders that measure angular speed fluctuations due to localised faults in bearings and thus were used for bearing fault detection [31].

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Apart from developing robust individual sensing techniques, it is becoming more and more recognised that mu ltip le-sensor integrated systems with mu lti-parameter approaches can achieve much better condition monitoring results. Each sensing technique has its own merits as well as limitat ions. For examp le, acoustic emission is found to be able to detect incipient failures in low-speed wind turbine components while vib ration monitoring is better in early fault diagnostics for the high-speed components. Crabtree et al [32] developed a mult i-parameter approach based on comparison of independent signals (conventional vibrat ion condition monitoring signals with operational signals, such as load or energy) to increase the confidence in fault signal interpretation and thresholds generated. Also, vibration and AE based multi-sensing technology together with model based reasoning algorithms were developed to enhance fault diagnosis for wind turbine gearbox [19,33,34]. 4. Conclusions Recent advances in sensing and signal processing technologies have significantly imp roved the condition monitoring and prognosis of wind turbine gearbo xes, and reduced the wind turbine operation and maintenance costs. However, accurate fau lt detection and reliable prognosis in wind turbine gearbo x remain challenging due to the inherited complexit ies in their mechanical systems and operation conditions. In order to achieve robust condition monitoring for WT gearbo xes, the following research areas need to be further strengthened: x Sensor Technologies: Most current condition monitoring systems are based on vibration and oil analysis. Accelerometers are often mounted on gearbox housings, which often leads to low signal-to-noise ratio that affecting the detection. This issue can be dealt with by using miniaturizing and/or embedding the sensors close to the fault sites. Apart from the existing sensing techniques, novel sensors such as Barkhausen noise inspection, in-situ hydrogen sensors, oil quality monitoring devices, as well as multi-sensor combination, should be further exploited for wind turbine gearbox monitoring. x Signal Processing and Data Management: As huge amount of data are being collected from wind farms, the fast growing databases require advanced signal processing techniques and data mining strategies to extract the most useful information for timely wind turbine condition monitoring and maintenance. Acknowledgements The authors would like to thank the EPSRC and TES Centre fo r their financial support to this feasibility study under grant number EP/1033246/ 1. References [1] Global wind report – Annual market update 2011. Global Wind Energy Council, March 2012.

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[2] 20% Wind energy by 2030: increasing wind energy’s contribution to US electricity supply. United States Department of Energy, Report DOE/GO102008-2567, July 2008. [3] Investing in the development of low carbon technologies (SET -Plan). European Commission Communication, Oct. 7, 2009. [4] Mid and long range plan for renewable energy development. Chinese Committee for National Development and Reform, Aug. 2007. [5] Hahn B, Durstewitz M, Rohrig K. Reliability of wind turbine experiences of 15 years with 1,500W. Wind Energy. Berlin: Springer; 2007, p. 329332. [6] MacMillan D, Ault GW. Quantification of condition monitoring benefit for offshore wind turbines. Wind Energy 2007; 31(4): 267-285. [7] Wilkinson MR, Spinato F, T avner PJ. Condition monitoring of generators and other subassemblies in wind turbine drive trains. 2007 IEEE International Symposium on Diagnostics for Electric Machines, Power Electronics and Drives, Sep. 2007, p. 388-392. [8] Becker E, Poste P. Keeping the blades turning: condition monitoring of wind turbine gears. Refocus 2006; 7(2): 26-32. [9] Hyers RW, McGowan JG, Sullivan KL, Manwell JF, Syrett BC. Condition monitoring and prognosis of utility scale wind turbines. Energy Materials 2006; 1(3): 187-203. [10] Lu B, Li Y, Wu X, Yang Z. A review of recent advances in wind turbine condition monitoring and fault diagnosis. 2009 IEEE Power Electronics and Machines in Wind Applications, Jun. 24-26, 2009, Lincoln, NE, USA. [11] Hamilton A, Quail F. Detailed state of the art review for the different on line / in-line oil analysis techniques in context of wind turbine gearboxes. Journal of Tribology-Transaction of ASME 2011; 133(4): 971-978. [12] Zhong X, Zeng L, Zhao C, Zhang J, Wan S. Research of condition monitoring and fault diagnosis techniques for wind turbine gearbox. Applied Mechanics and Materials 2012; 197: 206-210. [13] Bartelmus W. Gearbox damage process. 9 th International Conference on Damage Assessment of Structures, Jul 11-13, 2011, University of Oxford, Oxford, England. [14] Cibulka J, Ebbesen MK, Hovland G, Robbersmyr KG, Hansen MR. A review on approaches for condition based maintenance in application with induction machines located offshore. Modelling Identification and Control 2012; 33(2): 69-86. [15] Feng Y, Qiu Y, Crabtree CJ, Long H, Tavner PJ. Monitoring wind turbine gearboxes. Wind Energy 2012. DOI: 10.1002/we.1521. [16] Yang S, Li W, Wang C. The intelligent fault diagnosis of wind turbine gearbox based on artificial neural network. Proceedings of 2008 International Conference on Condition Monitoring and Diagnosis (CMD 2008), Apr 21-24, 2008, Beijing, China, p1327-1330. [17] Kou HJ, Yuan HQ, Zhao XY. Application of wavelet de-noising in fault diagnosis of wind turbine gearboxes. Advanced Materials Research 2012; 562-564: 1091-1094. [18] Zhou FC. Research on online monitoring of gear fault of wind power gearbox based on undecimated wavelet transformation. Manufacturing Science and Technology 2012; 383-390: 4928-4931. [19] Lu Y, Tang J, Luo H. Wind turbine gearbox fault detection using multiple sensors with features level data fusion. Journal of Engineering for Gas Turbines and Power 2012; 134(4): 042501. [20] Antoniadou I, Manson G, Staszewski WJ, Worden K. Damage detection

in gearboxes considering intermittent faults and time-varying loads. Key Engineering Materials 2012; 518: 76-86. [21] Liu WY, Zhang WH, Han JG, Wang GF. A new wind turbine fault diagnosis method based on the local mean decomposition. Renewable Energy 2012; 48: 411-415. [22]Liu Y, Zhou Y, Xin W, He Y, Fan P. Bispectrum analysis for feature extraction of pitting fault in wind turbine gearbox. 2010 IEEE International Conference on Mechatronics and Automation(ICMA 2010), Aug. 4-7, 2010, Xi'an, China, p. 1298-1301. [23] Teng W, Wu X, Gao Q, Liu Y. Cepstrum Analysis of Vibrat ion Signals of Wind T urbine Gearbox. Proceedings of the 2011 IEEE International Conference on Multimedia and Expo (ICME 2011) 2011, p. 4237-4240. [24] Antoniadou I, Manson G, Dervilis N, Staszewski W, Worden K. On damage detection in wind turbine gearboxes using outlier analysis. Proceedings of SPIE - The International Society for Optical Engineering, 2012, 8343. [25] Baqqar M, Ahmed M, Gu F. Data mining for gearbox condition monitoring. 17th International Conference on Automation and Computing, Sept. 10, 2011, Huddersfield, UK, p138-142. [26] Mohanty AR, Kar C. Monitoring gear vibrations through motor current signature analysis and wavelet transform. Mechanical Systems and Signal Processing 2006; 20(1): 158-187. [27] Kar C, Mohanty AR. Vibration and current transient monitoring for gearbox fault detection using multiresolution Fourier transform. Journal of Sound and Vibration 2008; 311(1-2): 109-132. [28] Amirat Y, Choqueuse V, Benbouzid M, Turi S. Hilbert transform-based bearing failure detection in DFIG-based wind turbines. International Review of Electrical Engineering 2011; 6(3): 1249-1256. [29] Baqqar M, Wang T, Ahmed M, Gu F, Lu J, Ball A. A general regression neural network model for gearbox fault detection using motor operating parameters. 2012 UKACC International Conference on Control (CONTROL 2012), Sept. 3-5, 2012, Cardiff, UK, p584-588. [30] Cibulka J, Ebbesen MK, Robbersmyr KG. Bearing Fault Detection in Induction Motor-Gearbox Drivetrain. 25th International Congress on Condition Monitoring and Diagnostic Engineering (COMADEM), Jun. 18-20, 2012, Huddersfield, UK. [31] Renaudin L, Bonnardot F, Musy O, Doray JB, Remond D. Natural roller bearing fault detection by angular measurement of true instantaneous angular speed. Mechanical Systems and Signal Processing 2010; 24(7): 1998-2011. [32] Crabtree CJ, Feng Y, T avner PJ. Detecting incipient wind turbine gearbox failure: a signal analysis method for on-line condition monitoring, European Wind Energy Conference and Exhibition 2010 (EWEC 2010), Apr. 20-23, 2010, Warsaw, Poland. [33] Lekou DJ, Mouzakis F, Anastasopoulos AA. Fused acoustic emission and vibration techniques for health monitoring of wind turbine gearboxes and bearings. European Wind Energy Conference and Exhibition 2009 (EWEC 2009), Mar. 16-19, 2009, Marseille, France. [34] Soua S, Lieshout PV, Perera A, Gan TH, Bridge B. Determination of the combined vibrational and acoustic emission signature of a wind turbine gearbox and generator shaft in service as a pre-requisite for effective condition monitoring. Renewable Energy 2013; 51: 175-181.