Application of machine learning technique in wind turbine fault diagnosis

APPLICATION OF MACHINE LEARNING TECHNIQUE IN WIND TURBINE FAULT DIAGNOSIS Afrooz Purarjomandlangrudi B.Sc (Electrical Engineering) Principal supervisor: Dr Ghavameddin Nourbakhsh Submitted in fulfilment of the requirements for the degree of Master of Engineering (Research) Science and Engineering Faculty Queensland University of Technology 2014 KEYWORDS Wind turbine, renewable energy, fault detection, condition monitoring, fault diagnosis, rotating components, gearbox, bearing, machine learning, support vector machine, anomaly detection, acoustic emission technique, and data mining ABSTRACT With the increasing demand for electric power, environmental regulations are putting restrictions on the use of thermal power plants and renewable energy sources; in particular, wind farm energy turbines are becoming very popular around the world As a result, wind turbine availability and the ability to accurately predict faults in advance have become very critical in this industry Unpredicted failures of an element in a wind turbine, particularly in low speed rotating components such as gearboxes and bearings, can lead to major financial drawbacks One of the most efficient approaches to prevent catastrophic failures and unplanned outages is by using Condition Monitoring (CM) Although a variety of CM techniques have been used recently, their applications in the power industry are still relatively new In addition, most CMs require a large number of fault indicators to accurately diagnose the component faults Learning techniques can be employed to overcome such problems in CM, as the definition of machine learning is the ability of a program or system to learn, improve and develop its efficiency over time Machine learning techniques focus on creating a system that improves its performance based on previous results and historical data instead of understanding the process that generated the data In fact, the machine learning paradigm provides the ability I of changing execution strategy based on newly acquired information from a system Learning algorithms can be useful in different applications such as prediction of the future value, clustering and detection of anomaly behaviour in the data In this study, two learning algorithms called anomaly detection and Support Vector Machine (SVM) are employed to bearing fault diagnosis and CM Basically the anomaly detection algorithm is used to recognize the presence of unusual and potentially faulty data in a dataset, which contains two phases: a training phase and a testing phase In the former, the algorithm is trained with a training dataset and in the latter; the learned algorithm is applied to a set of new data Two bearing datasets were used to validate the proposed technique, fault-seeded bearing from a test rig located at Case Western Reserve University to validate the accuracy of the anomaly detection method Detecting faults and defects in their early stages is one of the most important aspects of machine CM The second dataset was a test to failure data of bearings from the NSF I/UCR Centre for Intelligent Maintenance Systems (IMS) which was used to compare anomaly detection with a previously applied method (SVM) for finding the time incipient faults II List of Publications Journal papers:  A Purarjomandlangrudi, G Nourbakhsh, A Tan, M Esmalifalak, “Fault Detection in Wind Turbine: A Systematic Literature Review” ” Wind Engineering Vol 37, NO 5, 2013, PP 535-546 ERA ranking C  A Purarjomandlangrudi, G Nourbakhsh, A Tan, H Ghaemaghami, “Wind Turbine Condition Monitoring Using Machine Learning Techniques” Expert systems and applications, submitted ERA ranking B Conference papers:  A Purarjomandlangrudi, G Nourbakhsh, A Tan, H Ghaemaghami, Y Mishra, “Application of Anomaly Technique in Wind Turbine Bearing Fault Detection” 2014 IEEE PES Innovative Smart Grid Technologies Conference Europe (ISGT-Europe), Submitted III STATEMENT OF ORIGINAL AUTHORSHIP The work contained in this thesis has not been previously submitted to meet requirements for an award at this or any other higher education institution To the best of my knowledge and belief, the thesis contains no material previously published or written by another person except where due reference is made Signature: Date: QUT Verified Signature 10/03/2014 IV ACKNOWLEDGEMENTS First and foremost, I would like to thank my father and mother, Mehdi Purarjomand and Azam Bodaghi, for their love and unwavering support throughout my education, from the 23rd of September 1992, when my mum walked me to the school where I started my primary education, to the present day in 2014, when I am finishing my master’s degree I attribute whatever achievement I have achieved or will achieve in my life to them I am also very thankful to my younger sister, Ema Purarjomand, for her love and kindness, and for being with my parents while I have been away I would like to gratefully and sincerely thank my supervisor, Dr Ghavameddin Nourbakhsh for believing in my work and for providing insightful advice and support during all stages of my master’s journey No words can justice to my appreciation of his nurturing support and attention His time, guidance and encouragement have made all the difference My sincere thanks also go to Professor Andy Tan, whose expertise, understanding, and patience have added considerably to my graduate experience I appreciate the vast knowledge and skill in many areas that he has shared with me V I would like to thank the editors and anonymous reviewers of the various journals in which I have published articles associated with this thesis for their precious time in reviewing my works and for their valuable comments and suggestions As well, professional editor, Ms Diane Kolomeitz, has provided copyediting and proofreading services, according to the guidelines laid out in the University-endorsed national policy guidelines, ‘The editing of research theseszby professional editors’ (available at http://iped- editors.org/About_editing/Editing_theses.aspx ) I would also like to thank my other good colleagues and co- authors for their support and insightful suggestions throughout this journey: Dr Amir Hossein Ghapanchi, Dr Mohhamad Esmalifalak and Dr Houman Ghaemmaghami VI TABLE OF CONTENTS CHAPTER 1: INTRODUCTION 1.1 1.2 1.3 1.4 1.5 1.6 1.7 GENERAL INTRODUCTION WIND TURBINE COMPONENTS AND FAILURES WIND TURBINE CONDITION MONITORING AND RESEARCH QUESTIONS RESEARCH PROBLEM OBJECTIVE OF RESEARCH OVERVIEW OF RESEARCH METHODOLOGY 11 THESIS PRESENTATION AND STRUCTURE 13 CHAPTER 2: PAPER 1- FAULT DETECTION IN WIND TURBINE: A SYSTEMATIC LITERATURE REVIEW 15 2.1 INTRODUCTION 17 2.2 LITERATURE REVIEW 21 2.2.1 Gearbox and Bearing 23 2.2.2 Power Electronics and Electrical Control Failures 24 2.2.3 Generators 25 2.3 RESEARCH METHODOLOGY 26 2.3.1 Resources Searched 27 2.3.2 Search terms 27 2.3.3 Inclusion/Exclusion Criteria 28 2.3.4 Data Analysis 29 2.4 LITERATURE REVIEW FINDINGS AND RESULTS 30 2.5 CONCLUSION 36 CHAPTER 3: PAPER 2- WIND TURBINE CONDITION MONITORING USING MACHINE LEARNING TECHNIQUES 38 3.1 INTRODUCTION 41 3.2 FEATURE EXTRACTION 43 3.2.1 Kurtosis 44 3.2.2 Non-Gaussianity Score (NGS) feature 45 3.3 MACHINE LEARNING APPROACHES 45 3.3.1 Support Vector Machine (SVM) 46 3.3.2 Anomaly detection 47 3.4 EXPERIMENTAL RESULTS 50 3.4.1 Model description 51 3.5 CONCLUSION 58 CHAPTER 4: PAPER 3- APPLICATION OF ANOMALY TECHNIQUE IN WIND TURBINE BEARING FAULT DETECTION 60 4.1 INDTRODUCTION 62 4.2 MACHINE LEARNING APPROACHES 66 4.2.1 One-class Support Vector Machine 66 4.2.2 Anomaly Detection (AD) 67 4.3 EXPERIMENTAL RESULTS 69 VII 4.3.1 Model description 70 4.4 CONCLUSIONS 74 CHAPTER 5: CONCLUSIONS 76 5.1 OVERVIEW 76 5.2 SUMMARY OF FINDINGS 76 5.3 ADDRESSING RESEARCH QUESTIONS AND CONCLUSION 79 5.4 IMPLICATIONS AND FUTURE WORKS 81 5.4.1 Implications for Industry Practitioners 82 5.4.2 Implications for Researchers 83 VIII 87 CONCLUSIONS 88 CONCLUSIONS 89 CONCLUSIONS 90 CONCLUSIONS 91 CONCLUSIONS 92 CONCLUSIONS 93 CONCLUSIONS 94 CONCLUSIONS 95 CONCLUSIONS 96 CONCLUSIONS REFERENCES [1]T E W E A (EWEA), "The European Wind Energy Association annual report." [2]A W E A (AWEA), "American Wind Energy Association annual report." [3]B B W e V G W E Association, "The Structure of a Modern Wind Turbine – An Overview." [4]B Hahn, M Durstewitz, and K Rohrig, "Reliability of wind turbines," in Wind Energy, ed: Springer, 2007, pp 329-332 [5]E Al Ahmar, V Choqueuse, M Benbouzid, Y Amirat, J El Assad, R Karam, et al., "Advanced signal processing techniques for fault detection and diagnosis in a wind turbine induction generator drive train: A comparative study," in Energy Conversion Congress and Exposition (ECCE), 2010 IEEE, 2010, pp 3576-3581 [6]S Fang and W Zijie, "Rolling bearing fault diagnosis based on wavelet packet and RBF neural network," in Control Conference, 2007 CCC 2007 Chinese, 2007, pp 451-455 [7]L Meng, W Miao, and W Chunguang, "Research on SVM Classification Performance in Rolling Bearing Diagnosis," in Intelligent Computation Technology and Automation (ICICTA), 2010 International Conference on, 2010, pp 132-135 [8]K F Al-Raheem and W Abdul-Karem, "Rolling bearing fault diagnostics using artificial neural networks based on Laplace wavelet analysis," International Journal of Engineering, Science and Technology, vol 2, 2010 [9]S.-T J Cristianini Nello, "An introduction to support vector machines and other kernel-based learning methods," 2004 [10]V VN., "The nature of statistical learning theory," New York: Springer Verlag, 1999 [11]J Cheng, D Yu, and Y Yang, "A fault diagnosis approach for gears based on IMF AR model and SVM," EURASIP Journal on Advances in Signal Processing, vol 2008, p 647135, 2008 [12]L Wenxiu and C Fulei, "Condition monitoring and fault diagnostics of wind turbines," in Prognostics and Health Management Conference, 2010 PHM '10., 2010, pp 1-11 [13]D Casadei, F Filippetti, C Rossi, A Stefani, A Yazidi, and G A Capolino, "Diagnostic technique based on rotor modulating signals signature analysis for doubly fed induction machines in wind generator systems," in Industry Applications Conference, 2006 41st IAS Annual Meeting Conference Record of the 2006 IEEE, 2006, pp 1525-1532 [14]D Casadei, F Filippetti, A Stefani, C Rossi, A Yazidi, and G Capolino, "Experimental fault characterization of doubly fed induction machines 97 CONCLUSIONS for wind power generation," in Power Electronics, Electrical Drives, Automation and Motion, 2006 SPEEDAM 2006 International Symposium on, 2006, pp 1281-1286 [15]H Douglas, P Pillay, and P Barendse, "The detection of interturn stator faults in doubly-fed induction generators," in Industry Applications Conference, 2005 Fourtieth IAS Annual Meeting Conference Record of the 2005, 2005, pp 1097-1102 [16]A Mohanty and C Kar, "Fault detection in a multistage gearbox by demodulation of motor current waveform," Industrial Electronics, IEEE Transactions on, vol 53, pp 1285-1297, 2006 [17]D Brown, G Georgoulas, H Bae, G Vachtsevanos, R Chen, Y Ho, et al., "Particle filter based anomaly detection for aircraft actuator systems," in Aerospace conference, 2009 IEEE, 2009, pp 1-13 [18]B C P Lau, E W M Ma, and M Pecht, "Review of offshore wind turbine failures and fault prognostic methods," in Prognostics and System Health Management (PHM), 2012 IEEE Conference on, 2012, pp 1-5 [19]B Lu, Y Li, X Wu, and Z Yang, "A review of recent advances in wind turbine condition monitoring and fault diagnosis," in Power Electronics and Machines in Wind Applications, 2009 PEMWA 2009 IEEE, 2009, pp 1-7 [20]"Literature survey guidelines," 2004 [21]A Zaher, S McArthur, D Infield, and Y Patel, "Online wind turbine fault detection through automated SCADA data analysis," Wind Energy, vol 12, pp 574-593, 2009 [22]A Kusiak and A Verma, "A data-driven approach for monitoring blade pitch faults in wind turbines," Sustainable Energy, IEEE Transactions on, vol 2, pp 87-96, 2011 [23]Z Zhang, A Verma, and A Kusiak, "Fault Analysis and Condition Monitoring of the Wind Turbine Gearbox," Energy Conversion, IEEE Transactions on, vol 27, pp 526-535, 2012 [24]P Guo, "Wind turbine generator bearing Condition Monitoring with NEST method," in Control and Decision Conference (CCDC), 2012 24th Chinese, 2012, pp 235-239 [25]E Siores and A Negro, "Condition monitoring of a gear box using acoustic emission testing," Materials evaluation, vol 55, 1997 [26]L Lin, W Lu, and F Chu, "Application of AE techniques for the detection of wind turbine using Hilbert-Huang transform," in Prognostics and Health Management Conference, 2010 PHM'10., 2010, pp 1-7 [27]Z Hameed, Y Hong, Y Cho, S Ahn, and C Song, "Condition monitoring and fault detection of wind turbines and related algorithms: A review," Renewable and Sustainable energy reviews, vol 13, pp 1-39, 2009 [28]V N Vapnik, "An overview of statistical learning theory," Neural Networks, IEEE Transactions on, vol 10, pp 988-999, 1999 98 CONCLUSIONS [29]S Marsland, Machine learning: an algorithmic perspective: Chapman & Hall/CRC, 2009 [30]R S Sutton and A G Barto, Reinforcement learning: An introduction vol 1: Cambridge Univ Press, 1998 [31]G N A.Purarjomandlangrudi, M Esmallifalak, A.C Tan, "Fault Detection in Wind Turbine: A systematic literature review," Wind engineering, 2013 [32]V Chandola, A Banerjee, and V Kumar, "Anomaly detection: A survey," ACM Computing Surveys (CSUR), vol 41, p 15, 2009 [33]V J Hodge and J Austin, "A survey of outlier detection methodologies," Artificial Intelligence Review, vol 22, pp 85-126, 2004 [34]D E Denning, "An intrusion-detection model," Software Engineering, IEEE Transactions on, pp 222-232, 1987 [35]N N M Esmalifalak, R Zheng, and Z Han, "Detecting Stealthy False Data Injection Using Machine Learning in Smart Grid," in the Proceedings of IEEE GLOBECOM 2013, Atlanta, 2013 [36]B v d s K A Loparo, Case Western Reserve University [37]Q J Lee, H., Yu, G., & Lin, J., "Bearing data set IMS, University of Cincinnati, NASA Ames Prognostics Data Repository, Rexnord Technical Services," 2007 [38]P Berberian, "Precision Turbine Shaft Alignment," wind systems magazine, pp 38-41, 2011 [39]I WIND, "2010 Annual Report," IEA, 2011 [40]A Kusiak and A Verma, "A Data-Mining Approach to Monitoring Wind Turbines," Sustainable Energy, IEEE Transactions on, vol 3, pp 150-157, 2012 [41]X Gong and W Qiao, "Simulation investigation of wind turbine imbalance faults," in Power System Technology (POWERCON), 2010 International Conference on, 2010, pp 1-7 [42]Y Amirat, M Benbouzid, B Bensaker, and R Wamkeue, "Condition monitoring and ault diagnosis in wind energy conversion systems: a review," in Electric Machines & Drives Conference, 2007 IEMDC'07 IEEE International, 2007, pp 1434-1439 [43]D Martinez-Rego, O Fontenla-Romero, and A Alonso-Betanzos, "Power wind mill fault detection via one-class ν-SVM vibration signal analysis," in Neural Networks (IJCNN), The 2011 International Joint Conference on, 2011, pp 511-518 [44]H Guolian, J Pan, W Zhentao, and Z Jianhua, "Research on fault diagnosis of wind turbine control system based on Artificial Neural Network," in Intelligent Control and Automation (WCICA), 2010 8th World Congress on, 2010, pp 4875-4879 [45]R F M Brandão, J A B Carvalho, and F P M Barbosa, "Neural networks for condition monitoring of wind turbines," in Modern Electric Power Systems (MEPS), 2010 Proceedings of the International Symposium, 2010, pp 1-4 99 CONCLUSIONS [46]S V J J Ramesh Babu, "Breakdown risks in wind energy turbines," Pravartak, the journal of Insurance and risk Management from National, vol III, p 20, April - June 2008 [47]The Confederation of Fire Protection Association CFP A Europe, "Wind turbines fire protection guideline," 2010 [48]B Lu and S K Sharma, "A literature review of IGBT fault diagnostic and protection methods for power inverters," Industry Applications, IEEE Transactions on, vol 45, pp 1770-1777, 2009 [49]C Tsai, C T Hsieh, and S J Huang, "Enhancement of damage-detection of wind turbine blades via CWT-based approaches," Energy Conversion, IEEE Transactions on, vol 21, pp 776-781, 2006 [50]S J Watson, B J Xiang, W Yang, P J Tavner, and C J Crabtree, "Condition monitoring of the power output of wind turbine generators using wavelets," Energy Conversion, IEEE Transactions on, vol 25, pp 715721, 2010 [51]X Yuan and L Cai, "Variable amplitude Fourier series with its application in gearbox diagnosis—Part I: Principle and simulation," Mechanical systems and signal processing, vol 19, pp 1055-1066, 2005 [52]P Brereton, B A Kitchenham, D Budgen, M Turner, and M Khalil, "Lessons from applying the systematic literature review process within the software engineering domain," Journal of Systems and Software, vol 80, pp 571-583, 2007 [53]A H Ghapanchi and A Aurum, "Antecedents to IT personnel's intentions to leave: A systematic literature review," Journal of Systems and Software, vol 84, pp 238-249, 2011 [54]H G Natke and C Cempel, Model-aided diagnosis of mechanical systems: fundamentals, detection, localization, and assessment: Springer Verlag Germany., 1997 [55]J Taylor, "The Gear Analysis Handbook, Vibration Consultants," Inc., USA, 2000 [56]T Barszcz and R B Randall, "Application of spectral kurtosis for detection of a tooth crack in the planetary gear of a wind turbine," Mechanical Systems and Signal Processing, vol 23, pp 1352-1365, 2009 [57]X Yao, C Guo, M Zhong, Y Li, G Shan, and Y Zhang, "Wind Turbine Gearbox Fault Diagnosis Using Adaptive Morlet Wavelet Spectrum," in Intelligent Computation Technology and Automation, 2009 ICICTA'09 Second International Conference on, 2009, pp 580-583 [58]C Changzheng, S Changcheng, Z Yu, and W Nan, "Fault diagnosis for large-scale wind turbine rolling bearing using stress wave and wavelet analysis," in Electrical Machines and Systems, 2005 ICEMS 2005 Proceedings of the Eighth International Conference on, 2005, pp 2239-2244 100 CONCLUSIONS [59]K Iyer, X Lu, K Mukherjee, and N C Kar, "Fault detection in copper-rotor SEIG system using artificial neural network for distributed wind power generation," in Electrical Machines (ICEM), 2012 XXth International Conference on, 2012, pp 1700-1705 [60]Z Chen, X Lian, H Yu, and Z Bao, "Algorithm of Data Mining and its Application in Fault Diagnosis for Wind Turbine," in Knowledge Acquisition and Modeling, 2009 KAM'09 Second International Symposium on, 2009, pp 240-243 [61]K Kim, G Parthasarathy, O Uluyol, W Foslien, S Sheng, and P Fleming, Use of SCADA Data for Failure Detection in Wind Turbines: National Renewable Energy Laboratory, 2011 [62]J Lee, Qiu, H., Yu, G., & Lin, J., "Bearing data set IMS, University of Cincinnati, NASA Ames Prognostics Data Repository, Rexnord Technical Services.," 2007 [63]A Y D M J Tax, and R P W Duin, "Pump failure determination using support vector data description," Lecture Notes in Computer Science, pp pp 415-425, 1999 [64]K A Loparo, "Bearings vibration data set, Case Western Reserve University http://csegroups.case.edu/bearingdatacenter/home." [65]Y Li, S Billington, C Zhang, T Kurfess, S Danyluk, and S Liang, "Adaptive prognostics for rolling element bearing condition," Mechanical systems and signal processing, vol 13, pp 103-113, 1999 [66]H Ghaemmaghami, D Dean, S Sridharan, and I McCowan, "Noise robust voice activity detection using normal probability testing and time-domain histogram analysis," in Acoustics Speech and Signal Processing (ICASSP), 2010 IEEE International Conference on, 2010, pp 4470-4473 [67]S Wisdom, "The Research of Data Mining in HHM Technology Based On Association Rule," American Journal of Computing and Computation, vol 2, pp 1-9, 2012 [68]N Pican and Y Lallement, "The OWE Artificial Neural Network and its Parallel Implementation." [69]L B J a A K Nandi, "Fault detection using support vector machine and artificial neural network, augmented by genetic algorithm," Mechanical System and Signal Processing, vol 16, pp pp 373-390, 2002 [70]S Pöyhönen, A Arkkio, P Jover, and H Hyötyniemi, "Coupling pairwise support vector machines for fault classification," Control Engineering Practice, vol 13, pp 759-769, 2005 [71]J Sun, G Hong, M Rahman, and Y Wong, "The application of nonstandard support vector machine in tool condition monitoring system," in Electronic Design, Test and Applications, 2004 DELTA 2004 Second IEEE International Workshop on, 2004, pp 295-300 [72]J Sun, M Rahman, Y Wong, and G Hong, "Multiclassification of tool wear with support vector machine by manufacturing loss consideration," 101 CONCLUSIONS International Journal of Machine Tools and Manufacture, vol 44, pp 1179-1187, 2004 [73]F He and W Shi, "WPT-SVMs based approach for fault detection of valves in reciprocating pumps," in American Control Conference, 2002 Proceedings of the 2002, 2002, pp 4566-4570 [74]S Marsland, "Novelty detection in learning systems," Neural computing surveys, vol 3, pp 157-195, 2003 [75]K.-R Muller, S Mika, G Ratsch, K Tsuda, and B Scholkopf, "An introduction to kernel-based learning algorithms," Neural Networks, IEEE Transactions on, vol 12, pp 181-201, 2001 [76]S Kumar and E H Spafford, "An application of pattern matching in intrusion detection," 1994 [77]A Davies, Handbook of condition monitoring: techniques and methodology: Springer, 1998 [78]R Upadhyay, L Kumaraswamidhas, and M S Azam, "Rolling Element Bearing Failure Analysis: A Case Study," Case Studies in Engineering Failure Analysis, 2012 [79]C James Li and S Li, "Acoustic emission analysis for bearing condition monitoring," Wear, vol 185, pp 67-74, 1995 [80]N Tandon and A Choudhury, "A review of vibration and acoustic measurement methods for the detection of defects in rolling element bearings," Tribology International, vol 32, pp 469-480, 1999 [81]L I Kim PY, "A review of rolling element bearing health monitoring " In: Proceedings of Machinery Vibration Monitoring and Analysis Meeting, Vibration Institute, Houston, TX, 19–21 p.145–54, 1983 [82]R Sehgal, O Gandhi, and S Angra, "Reliability evaluation and selection of rolling element bearings," Reliability Engineering & System Safety, vol 68, pp 39-52, 2000 [83]D Fernández-Francos, D Martínez-Rego, O Fontenla-Romero, and A Alonso-Betanzos, "Automatic Bearing Fault Diagnosis Based on OneClass ν-SVM," Computers & Industrial Engineering, 2012 [84]D Fernández-Francos, D Martínez-Rego, O Fontenla-Romero, and A Alonso-Betanzos, "Automatic Bearing Fault Diagnosis Based on OneClass< i> ν-SVM," Computers & Industrial Engineering, 2012 ... fault diagnostic in wind turbines Figure 2.2 Failure rate of wind turbine components 2.2.1 Gearbox and Bearing The majority of literatures regarding fault and fatigues in wind turbine have focused... ? ?Wind turbine? ?? - ? ?Fault diagnosis in wind turbine? ?? - “Data analysis techniques in fault detection” 28 Paper 1- Fault Detection Systematic Literature Review in Wind Turbine: A - “Condition monitoring... algorithm of data mining based on fuzzy clustering for fault diagnosis in wind turbine In [24] the author used a SCADA system to detect the incipient failure of wind turbine based on Nonlinear State