Induction motor fault classification via entropy and column correlation features of 2D represented vibration data

• Autorzy: Basaran Murat , Fidan Mehmet

Identyfikatory

DOI

10.17531/ein.2021.1.14

• Języki publikacji: EN

Abstrakty

EN

Due to long-term use under challenging conditions, the sub-elements of induction motors may suffer certain defects over time. Such defects impair the vibration characteristics of the motors in different ways, depending on the type of defect. Therefore, the change in vibration characteristic provides indicators about the fault type and can be used in preventive maintenance strategies to ensure safe operation of the system. In this work, discrete-time vibration data were transformed into 2-dimensional grey-level images and decomposed into individual components by the Wavelet decomposition method. Features based on entropy and column correlation were extracted from these components and used to classify motor faults by using the Support Vector Machine method implemented by using the Sequential Minimal Optimisation algorithm. When the selected classifier is compared with other popular Machine Learning algorithms, it is observed that motor faults are more successfully classified, and these observations are presented in detail with comparative classification performance results.

Słowa kluczowe

EN

entropy; fault diagnosis; support vector machine; wavelet transforms

• Wydawca: Polskie Naukowo-Techniczne Towarzystwo Eksploatacyjne
• Czasopismo: Eksploatacja i Niezawodność
• Rocznik: 2021
• Tom: Vol. 23, no. 1
• Strony: 132--142
• Opis fizyczny: Bibliogr. 50 poz., rys., tab.

Twórcy

autor

Basaran Murat

• Vocational School of Transportation, Department of Motor Vehicles and Transportation Technologies, Eskisehir Technical University, 26140 Odunpazari, Eskisehir, Turkey

autor

Fidan Mehmet

• Vocational School of Transportation, Department of Motor Vehicles and Transportation Technologies, Eskisehir Technical University, 26140 Odunpazari, Eskisehir, Turkey

Bibliografia

• 1. Case Western Reserve University Bearing Data Center Website. https://csegroups.case.edu/bearingdatacenter/pages/welcome-case-westernreserve-university-bearing-data-center-website, (accessed 12-22-2019.
• 2. Akansu AN, Haddad R A. Multiresolution Signal Decomposition: Transforms, Subbands and Wavelets. Academic Press, 2001: 396-401.
• 3. Amar M., Gondal I., and Wilson C. Vibration Spectrum Imaging: A Novel Bearing Fault Classification Approach. IEEE Transactions on Industrial Electronics 2015; 62(1): 494-502, https://doi.org/10.1109/tie.2014.2327555.
• 4. Arivazhagan S, Ganesan L. Texture classification using wavelet transform. Pattern Recognition Letters 2003; 24(9-10): 1513-1521, https://doi.org/10.1016/S0167-8655(02)00390-2.
• 5. Banerjee TP, Das S. Multi-sensor data fusion using support vector machine for motor fault detection. Information Sciences 2012; 217: 96-107, https://doi.org/10.1016/j.ins.2012.06.016.
• 6. Do V, Chong UP. Signal Model-Based Fault Detection and Diagnosis for Induction Motors Using Features of Vibration Signal in Two-Dimension Domain. Stroj Vestn-J Mech E 2011; 57(9): 655-666, https://doi.org/10.5545/sv-jme.2010.162.
• 7. Duda RO, Hart PE. Pattern Classification and Scene Analysis. The Library Quarterly 1973; 44(3): 258-259, https://doi.org/10.1086/620282
• 8. Friedman N, Geiger D, Goldszmidt M. Bayesian Network Classifiers. Machine Learning 1997; 29(2-3): 131-163, https://doi.org/10.1023/A:1007465528199.
• 9. Gan Z, Zhao M-B, Chow T. W. Induction machine fault detection using clone selection programming. Expert Systems with Applications 2009; 36(4): 8000-8012, https://doi.org/10.1016/j.eswa.2008.10.058.
• 10. Gangsar P, Tiwari R. Comparative investigation of vibration and current monitoring for prediction of mechanical and electrical faults in induction motor based on multiclass-support vector machine algorithms. Mechanical Systems and Signal Processing 2017; 94: 464-481, https://doi.org/10.1016/j.ymssp.2017.03.016.
• 11. Gerek ON, Ece DG. 2-D analysis and compression of power-quality event data. IEEE Transactions on Power Delivery 2004; 19(2): 791-798, https://doi.org/10.1109/Tpwrd.2003.823197.
• 12. Germen E, Basaran M, Fidan M. Sound based induction motor fault diagnosis using Kohonen self-organizing map. Mechanical Systems and Signal Processing 2014; 46(1): 45-58, https://doi.org/10.1016/j.ymssp.2013.12.002.
• 13. Glowacz A. Recognition of acoustic signals of induction motor using FFT, SMOFS-10 and LSVM. Eksploatacja i Niezawodnosc - Maintenance and Reliability 2015; 17(4): 569-574, https://doi.org/10.17531/ein.2015.4.12.
• 14. Haar A. Zur Theorie der orthogonalen Funktionensysteme. Mathematische Annalen 1910; 69(3): 331-371, https://doi.org/10.1007/bf01456326.
• 15. Haralick RM, Shanmugam K, Dinstein I. Textural Features for Image Classification. IEEE Transactions on Systems, Man, and Cybernetics 1973; SMC-3(6): 610-621, https://doi.org/10.1109/tsmc.1973.4309314.
• 16. Holmes G, Donkin A, Witten IH. Weka: A machine learning workbench. Second Australia and New Zealand Conference on Intelligent Information Systems 1994, In: Proceedings of ANZIIS '94, https://doi.org/10.1109/ANZIIS.1994.396988.
• 17. Immovilli F, Bellini A, Rubini R, Tassoni C. Diagnosis of bearing faults in induction machines by vibration or current signals: A critical comparison. IEEE Transactions on Industry Applications 2010; 46(4): 1350-1359, https://doi.org/10.1109/TIA.2010.2049623.
• 18. Khan SA., Kim J-M. Automated Bearing Fault Diagnosis Using 2D Analysis of Vibration Acceleration Signals under Variable Speed Conditions. Shock and Vibration 2016; 2016: 1-11, https://doi.org/10.1155/2016/8729572.
• 19. Lei M, Meng G, Dong GM. Fault Detection for Vibration Signals on Rolling Bearings Based on the Symplectic Entropy Method. Entropy 2017; 19(11), https://doi.org/10.3390/e19110607.
• 20. Li Y, Wang X, Si S, Huang S. Entropy Based Fault Classification Using the Case Western Reserve University Data: A Benchmark Study. IEEE Transactions on Reliability 2019: 1-14, https://doi.org/10.1109/tr.2019.2896240.
• 21. Liu B, Yang Y, Webb GI, Boughton J. A Comparative Study of Bandwidth Choice in Kernel Density Estimation for Naive Bayesian Classification. Lecture Notes in Computer Science 2009; 5476: 302-313, https://doi.org/10.1007/978-3-642-01307-2_29.
• 22. Ma P, Zhang H, Fan W, Wang C, Wen G, Zhang X. A novel bearing fault diagnosis method based on 2D image representation and transfer learning-convolutional neural network. Measurement Science and Technology 2019; 30(5): 055402, https://doi.org/10.1088/1361-6501/ab0793.
• 23. Mao W, Wang L, Feng N. A New Fault Diagnosis Method of Bearings Based on Structural Feature Selection. Electronics 2019; 8(12), https://doi.org/10.3390/electronics8121406.
• 24. Mayoraz E, Alpaydın E. Support Vector Machines for Multi-Class Classification. IWANN99 1999, In: Engineering Applications of Bio-Inspired Artificial Neural Networks, Lecture Notes in Computer Science 1607, https://doi.org/10.1007/BFb0100551.
• 25. Nandi S, Toliyat HA, Li XD. Condition monitoring and fault diagnosis of electrical motors - A review. IEEE Transactions on Energy Conversion 2005; 20(4): 719-729, https://doi.org/10.1109/Tec.2005.847955.
• 26. Niu L, Cao H, He Z, Li Y. A systematic study of ball passing frequencies based on dynamic modeling of rolling ball bearings with localized surface defects. Journal of Sound and Vibration 2015; 357: 207-232, https://doi.org/10.1016/j.jsv.2015.08.002.
• 27. Ocak H, Loparo KA. Estimation of the running speed and bearing defect frequencies of an induction motor from vibration data. Mechanical Systems and Signal Processing 2004; 18(3): 515-533, https://doi.org/10.1016/S0888-3270(03)00052-9.
• 28. Ocak H, Loparo KA. HMM-Based Fault Detection and Diagnosis Scheme for Rolling Element Bearings. Journal of Vibration and Acoustics 2005; 127(4): 299-306, https://doi.org/10.1115/1.1924636.
• 29. Pearson K. Contributions to the Mathematical Theory of Evolution. II. Skew Variation in Homogeneous Material. Philosophical Transactions of the Royal Society of London. Series A, Containing Papers of a Mathematical or Physical Character 1895; 186: 343-414, https://doi.org/10.1098/rsta.1895.0010.
• 30. Pearson K. Mathematical Contributions to the Theory of Evolution. III. Regression, Heredity, and Panmixia. . Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences 1896; 187: 253-318, https://doi.org/10.1098/rsta.1896.0007
• 31. Pearson K. Mathematical Contributions to the Theory of Evolution. X. Supplement to a Memoir on Skew Variation. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences 1901; 197(287-299): 443-459, https://doi.org/10.1098/rsta.1901.0023.
• 32. Platt JC. Sequential Minimal Optimization: A Fast Algorithm for Training Support Vector Machines. Microsoft, MSR-TR-98-14, 1998.
• 33. Rish I. An Empirical Study of the Naive Bayes Classifier. IJCAI Workshop on Empirical Methods in AI. 2001.
• 34. Schoen RR, Lin BK, Habetler TG, Schlag JH, Farag S. An unsupervised, on-line system for induction motor fault detection using stator current monitoring. IEEE Transactions on Industry Applications 1995; 31(6): 1280-1286, https://doi.org/10.1109/28.475698.
• 35. Schölkopf B, Smola AJ. Learning with Kernels. The MIT Press, Cambridge, Massachusetts, London, England 2001, https://doi.org/10.7551/mitpress/4175.001.0001.
• 36. Shahriar MR, Ahsan T, Chong U. Fault diagnosis of induction motors utilizing local binary pattern-based texture analysis. Eurasip J Image Vide 2013, https://doi.org/10.1186/1687-5281-2013-29.
• 37. Stanković RS, Falkowski BJ. The Haar wavelet transform: its status and achievements. Computers & Electrical Engineering 2003; 29(1): 25-44, https://doi.org/10.1016/s0045-7906(01)00011-8.
• 38. Sun W, Cao X. Curvature enhanced bearing fault diagnosis method using 2D vibration signal. Journal of Mechanical Science and Technology 2020; 34(6): 2257-2266, https://doi.org/10.1007/s12206-020-0501-0.
• 39. Tabaszewski M, Szymański G. Engine valve clearance diagnostics based on vibration signals and machine learning methods. Eksploatacja i Niezawodnosc - Maintenance and Reliability 2020; 22(2): 331-339, https://doi.org/10.17531/ein.2020.2.16.
• 40. Trajin B, Régnier J, Faucher J. Comparison between vibration and stator current analysis for the detection of bearing faults in asynchronous drives. IET electric power applications 2010; 4(2): 90-100, https://doi.org/10.1049/iet-epa.2009.0040.
• 41. Tuceryan M, Jain AK. Texture Analysis. The Handbook of Pattern Recognition and Computer Vision. World Scientific Publishing Co., 1998: 207-248.
• 42. Üstün B, Melssen WJ, Buydens LMC. Facilitating the application of Support Vector Regression by using a universal Pearson VII function based kernel. Chemometrics and Intelligent Laboratory Systems 2006; 81(1): 29-40, https://doi.org/10.1016/j.chemolab.2005.09.003.
• 43. Vapnik V. The Support Vector method. ICANN’97 1997, In: Artificial Neural Networks, Lecture Notes in Computer Science 1327: 261-271, https://doi.org/10.1007/BFb0020166.
• 44. Weston J, Watkins C. Multi-class Support Vector Machines. Royal Holloway University of London, Egham, Surrey TW20 0EX, England, Technical Report CSD-TR-98-04, 1998.
• 45. Weszka JS, Rosenfeld A. An application of texture analysis to materials inspection. Pattern Recognition 1976; 8(4): 195-200, https://doi.org/10.1016/0031-3203(76)90039-x.
• 46. Wright S. Correlation and Causation. Journal of Agricultural Research 1921; 20(7): 557-585.
• 47. Zhang G. and Ge H. Support vector machine with a Pearson VII function kernel for discriminating halophilic and non-halophilic proteins. Comput Biol Chem 2013; 46: 16-22, https://doi.org/10.1016/j.compbiolchem.2013.05.001.
• 48. Zhang W, Peng G, Li C. Bearings Fault Diagnosis Based on Convolutional Neural Networks with 2-D Representation of Vibration Signals as Input. MATEC Web of Conferences 2017; 95, https://doi.org/10.1051/matecconf/20179513001.
• 49. Zhao D, Liu F, Meng H. Bearing fault diagnosis based on the switchable normalization SSGAN with 1-D representation of vibration signals as input. Sensors 2019; 19(9): 2000, https://doi.org/10.3390/s19092000.
• 50. Zimnickas T, Vanagas J, Dambrauskas K, Kalvaitis A. A Technique for Frequency Converter-Fed Asynchronous Motor Vibration Monitoring and Fault Classification, Applying Continuous Wavelet Transform and Convolutional Neural Networks. Energies 2020; 13(14): 3690, https://doi.org/0.3390/en13143690.

Uwagi

Opracowanie rekordu ze środków MNiSW, umowa Nr 461252 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2021).