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Review on Machine Learning Algorithm Based Fault Detection in Induction Motors

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Abstract

Fault detection prior to their occurrence or complete shut-down in induction motor is essential for the industries. The fault detection based on condition monitoring techniques and application of machine learning have tremendous potential. The power of machine learning can be harnessed and optimally used for fault detection. The faults especially in induction motor needs to be addressed at a proper time for avoiding losses. Machine learning algorithm applications in the domain of fault detection provides a reliable and effective solution for preventive maintenance. This paper presents a review of the machine learning algorithm applications in fault detection in induction motors. This paper also presents the future prospects and challenges for an efficient machine learning based fault detection systems.

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References

  1. Acosta G, Verucchi C, Gelso E (2006) A current monitoring system for diagnosing electrical failures in induction motors. Mech Syst Signal Process 20(4):953–965

    Google Scholar 

  2. Banerjee TP, Das S, Roychoudhury J, Abraham A (2010) Implementation of a new hybrid methodology for fault signal classification using short-time fourier transform and support vector machines. In: Soft computing models in industrial and environmental applications, 5th international workshop (SOCO 2010), Springer, pp 219–225

  3. Baraldi P, Podofillini L, Mkrtchyan L, Zio E, Dang VN (2015) Comparing the treatment of uncertainty in Bayesian networks and fuzzy expert systems used for a human reliability analysis application. Reliab Eng Syst Saf 138:176–193

    Google Scholar 

  4. Benbouzid MEH (2000) A review of induction motors signature analysis as a medium for faults detection. IEEE Trans Ind Electron 47(5):984–993

    Google Scholar 

  5. Bengio Y et al (2009) Learning deep architectures for AI. Found Trends® Mach Learn 2(1):1–127

    MathSciNet  MATH  Google Scholar 

  6. Bin G, Gao J, Li X, Dhillon B (2012) Early fault diagnosis of rotating machinery based on wavelet packets-empirical mode decomposition feature extraction and neural network. Mech Syst Signal Process 27:696–711

    Google Scholar 

  7. Bonnett AH, Soukup GC (1986) Rotor failures in squirrel cage induction motors. IEEE Trans Ind Appl 6:1165–1173

    Google Scholar 

  8. Cai B, Zhao Y, Liu H, Xie M (2016) A data-driven fault diagnosis methodology in three-phase inverters for PMSM drive systems. IEEE Trans Power Electron 32(7):5590–5600

    Google Scholar 

  9. Cameron J, Thomson W, Dow A (1986) Vibration and current monitoring for detecting airgap eccentricity in large induction motors. In: IEE Proceedings B (electric power applications), IET, vol 133, pp 155–163

  10. Cao L, Chua KS, Chong W, Lee H, Gu Q (2003) A comparison of PCA, KPCA and ICA for dimensionality reduction in support vector machine. Neurocomputing 55(1–2):321–336

    Google Scholar 

  11. Casimir R, Boutleux E, Clerc G, Yahoui A (2006) The use of features selection and nearest neighbors rule for faults diagnostic in induction motors. Eng Appl Artif Intell 19(2):169–177

    Google Scholar 

  12. Castejón C, García-Prada J, Gómez M, Meneses J (2015) Automatic detection of cracked rotors combining multiresolution analysis and artificial neural networks. J Vib Control 21(15):3047–3060

    MathSciNet  Google Scholar 

  13. Chandrashekar G, Sahin F (2014) A survey on feature selection methods. Comput Electr Eng 40(1):16–28

    Google Scholar 

  14. Cover TM, Hart P et al (1967) Nearest neighbor pattern classification. IEEE Trans Inf Theory 13(1):21–27

    MATH  Google Scholar 

  15. Cusido J, Romeral L, Espinosa AG, Ortega JA, Riba Ruiz JR (2011) On-line fault detection method for induction machines based on signal convolution. Eur Trans Electr Power 21(1):475–488

    Google Scholar 

  16. Deng W, Yao R, Zhao H, Yang X, Li G (2019) A novel intelligent diagnosis method using optimal LS-SVM with improved PSO algorithm. Soft Comput 23(7):2445–2462

    Google Scholar 

  17. Doorsamy W, Cronje W (2014) Optimisation of shaft voltage based condition monitoring in generators using a Bayesian approach

  18. Elkasabgy NM, Eastham AR, Dawson GE (1992) Detection of broken bars in the cage rotor on an induction machine. IEEE Trans Ind Appl 28(1):165–171

    Google Scholar 

  19. Farajzadeh-Zanjani M, Razavi-Far R, Saif M, Rueda L (2016) Efficient feature extraction of vibration signals for diagnosing bearing defects in induction motors. In: 2016 international joint conference on neural networks (IJCNN), IEEE, pp 4504–4511

  20. Fengqi W, Meng G (2006) Compound rub malfunctions feature extraction based on full-spectrum cascade analysis and SVM. Mech Syst Signal Process 20(8):2007–2021

    Google Scholar 

  21. Finley WR, Hodowanec MM, Holter WG (1999) An analytical approach to solving motor vibration problems. In: Industry applications society 46th annual petroleum and chemical technical conference (Cat. No. 99CH37000), IEEE, pp 217–232

  22. Gan M, Wang C et al (2016) Construction of hierarchical diagnosis network based on deep learning and its application in the fault pattern recognition of rolling element bearings. Mech Syst Signal Process 72:92–104

    Google Scholar 

  23. Glowacz A (2019) Fault diagnosis of single-phase induction motor based on acoustic signals. Mech Syst Signal Process 117:65–80

    Google Scholar 

  24. Gyftakis KN, Antonino-Daviu JA, Garcia-Hernandez R, McCulloch MD, Howey DA, Cardoso AJM (2015) Comparative experimental investigation of broken bar fault detectability in induction motors. IEEE Trans Ind Appl 52(2):1452–1459

    Google Scholar 

  25. Han T, Jiang D, Zhao Q, Wang L, Yin K (2018) Comparison of random forest, artificial neural networks and support vector machine for intelligent diagnosis of rotating machinery. Trans Inst Meas Control 40(8):2681–2693

    Google Scholar 

  26. Han T, Yang BS, Choi WH, Kim JS (2006) Fault diagnosis system of induction motors based on neural network and genetic algorithm using stator current signals. Int J Rotat Mach. https://doi.org/10.1155/IJRM/2006/61690

    Article  Google Scholar 

  27. Han Y, Song Y (2003) Condition monitoring techniques for electrical equipment-a literature survey. IEEE Trans Power Deliv 18(1):4–13

    Google Scholar 

  28. Heller B, Hamata V (1977) Harmonic field effects in induction machines. Elsevier, Amsterdam

    Google Scholar 

  29. Ho TK (1995) Random decision forests. In: Proceedings of 3rd international conference on document analysis and recognition, IEEE, vol 1, pp 278–282

  30. Hoang DT, Kang HJ (2017) Convolutional neural network based bearing fault diagnosis. In: International Conference on Intelligent Computing, Springer, pp 105–111

  31. Hwang JN, Hu YH (2001) Handbook of neural network signal processing. CRC Press, Boca Raton

    Google Scholar 

  32. Ince T, Kiranyaz S, Eren L, Askar M, Gabbouj M (2016) Real-time motor fault detection by 1-d convolutional neural networks. IEEE Trans Ind Electron 63(11):7067–7075

    Google Scholar 

  33. Janssens O, Slavkovikj V, Vervisch B, Stockman K, Loccufier M, Verstockt S, Van de Walle R, Van Hoecke S (2016) Convolutional neural network based fault detection for rotating machinery. J Sound Vib 377:331–345

    Google Scholar 

  34. Jardine AK, Lin D, Banjevic D (2006) A review on machinery diagnostics and prognostics implementing condition-based maintenance. Mech Syst Signal Process 20(7):1483–1510

    Google Scholar 

  35. Jia F, Lei Y, Lin J, Zhou X, Lu N (2016) Deep neural networks: a promising tool for fault characteristic mining and intelligent diagnosis of rotating machinery with massive data. Mech Syst Signal Process 72:303–315

    Google Scholar 

  36. Jung U, Koh BH (2015) Wavelet energy-based visualization and classification of high-dimensional signal for bearing fault detection. Knowl Inf Syst 44(1):197–215

    Google Scholar 

  37. Kankar PK, Sharma SC, Harsha SP (2011) Fault diagnosis of ball bearings using machine learning methods. Expert Syst Appl 38(3):1876–1886

    Google Scholar 

  38. Kankar PK, Sharma SC, Harsha SP (2012) Vibration-based fault diagnosis of a rotor bearing system using artificial neural network and support vector machine. Int J Modell Identif Control 15(3):185–198

    Google Scholar 

  39. Karvelis P, Georgoulas G, Stylios CD, Tsoumas IP, Antonino-Daviu JA, Ródenas MJP, Climente-Alarcón V (2014) An automated thermographic image segmentation method for induction motor fault diagnosis. In: IECON 2014-40th annual conference of the IEEE industrial electronics society, IEEE, pp 3396–3402

  40. Kecman V (2001) Learning and soft computing: support vector machines, neural networks, and fuzzy logic models. MIT press, Cambridge

    MATH  Google Scholar 

  41. Keskes H, Braham A (2015) Recursive undecimated wavelet packet transform and dag SVM for induction motor diagnosis. IEEE Trans Ind Inf 11(5):1059–1066

    Google Scholar 

  42. Kia SH, Mabwe AM, Henao H, Capolino GA (2006) Wavelet based instantaneous power analysis for induction machine fault diagnosis. In: IECON 2006-32nd annual conference on IEEE industrial electronics, IEEE, pp 1229–1234

  43. Kim K, Parlos AG (2002) Induction motor fault diagnosis based on neuropredictors and wavelet signal processing. IEEE/ASME Trans Mech 7(2):201–219

    Google Scholar 

  44. Kliman G, Koegl R, Stein J, Endicott R, Madden M (1988) Noninvasive detection of broken rotor bars in operating induction motors. IEEE Trans Energy Convers 3(4):873–879

    Google Scholar 

  45. Kliman G, Premerlani W, Koegl R, Hoeweler D (1996) A new approach to on-line turn fault detection in AC motors. In: IAS’96. conference record of the 1996 IEEE industry applications conference thirty-first IAS annual meeting, IEEE, vol 1, pp. 687–693

  46. Kliman G, Stein J (1990) Induction motor fault detection via passive current monitoring-a brief survey. In: Proceedings of the 44th meeting of the mechanical failures prevention group, pp 49–65

  47. Kotsiantis SB, Zaharakis ID, Pintelas PE (2006) Machine learning: a review of classification and combining techniques. Artif Intell Rev 26(3):159–190

    Google Scholar 

  48. LeCun Y, Bottou L, Bengio Y, Haffner P et al (1998) Gradient-based learning applied to document recognition. Proc IEEE 86(11):2278–2324

    Google Scholar 

  49. Lei Y, Jia F, Lin J, Xing S, Ding SX (2016) An intelligent fault diagnosis method using unsupervised feature learning towards mechanical big data. IEEE Trans Ind Electron 63(5):3137–3147

    Google Scholar 

  50. Lei Y, Zuo MJ (2009) Gear crack level identification based on weighted k nearest neighbor classification algorithm. Mech Syst Signal Process 23(5):1535–1547

    MathSciNet  Google Scholar 

  51. Li X, Wang K, Jiang L (2011) The application of AE signal in early cracked rotor fault diagnosis with PWVD and SVM. JSW 6(10):1969–1976

    Google Scholar 

  52. Li Y, Yang Y, Wang X, Liu B, Liang X (2018) Early fault diagnosis of rolling bearings based on hierarchical symbol dynamic entropy and binary tree support vector machine. J Sound Vib 428:72–86

    Google Scholar 

  53. Lin SW, Ying KC, Chen SC, Lee ZJ (2008) Particle swarm optimization for parameter determination and feature selection of support vector machines. Expert Syst Appl 35(4):1817–1824

    Google Scholar 

  54. Liu H, Zhou J, Zheng Y, Jiang W, Zhang Y (2018) Fault diagnosis of rolling bearings with recurrent neural network-based autoencoders. ISA Trans 77:167–178

    Google Scholar 

  55. Liu R, Meng G, Yang B, Sun C, Chen X (2016) Dislocated time series convolutional neural architecture: an intelligent fault diagnosis approach for electric machine. IEEE Trans Ind Inf 13(3):1310–1320

    Google Scholar 

  56. Liu R, Yang B, Zio E, Chen X (2018) Artificial intelligence for fault diagnosis of rotating machinery: a review. Mech Syst Signal Process 108:33–47

    Google Scholar 

  57. Liu X, Bo L, Luo H (2015) Bearing faults diagnostics based on hybrid LS-SVM and EMD method. Measurement 59:145–166

    Google Scholar 

  58. Liu Z, Guo W, Hu J, Ma W (2017) A hybrid intelligent multi-fault detection method for rotating machinery based on RSGWPT, KPCA and twin SVM. ISA Trans 66:249–261

    Google Scholar 

  59. Martínez-Morales JD, Palacios-Hernández ER, Campos-Delgado D (2018) Multiple-fault diagnosis in induction motors through support vector machine classification at variable operating conditions. Electr Eng 100(1):59–73

    Google Scholar 

  60. Mehrjou MR, Mariun N, Marhaban MH, Misron N (2011) Rotor fault condition monitoring techniques for squirrel-cage induction machine-a review. Mech Syst Signal Process 25(8):2827–2848

    Google Scholar 

  61. Moosavian A, Ahmadi H, Sakhaei B, Labbafi R (2014) Support vector machine and k-nearest neighbour for unbalanced fault detection. J Quality Maint Eng 20(1):65–75

    Google Scholar 

  62. Mrugalski M, Witczak M, Korbicz J (2008) Confidence estimation of the multi-layer perceptron and its application in fault detection systems. Eng Appl Artif Intell 21(6):895–906

    Google Scholar 

  63. Nandi S, Toliyat HA, Li X (2005) Condition monitoring and fault diagnosis of electrical motors-a review. IEEE Trans Energy Convers 20(4):719–729

    Google Scholar 

  64. Palácios RHC, da Silva IN, Goedtel A, Godoy WF (2015) A comprehensive evaluation of intelligent classifiers for fault identification in three-phase induction motors. Electric Power Syst Res 127:249–258

    Google Scholar 

  65. Pandya D, Upadhyay S, Harsha SP (2013) Fault diagnosis of rolling element bearing with intrinsic mode function of acoustic emission data using APF-KNN. Expert Syst Appl 40(10):4137–4145

    Google Scholar 

  66. Peng Z, Peter WT, Chu F (2005) A comparison study of improved hilbert-huang transform and wavelet transform: application to fault diagnosis for rolling bearing. Mech Syst Signal Process 19(5):974–988

    Google Scholar 

  67. Pires VF, Kadivonga M, Martins J, Pires A (2013) Motor square current signature analysis for induction motor rotor diagnosis. Measurement 46(2):942–948

    Google Scholar 

  68. Quiroz JC, Mariun N, Mehrjou MR, Izadi M, Misron N, Radzi MAM (2018) Fault detection of broken rotor bar in ls-pmsm using random forests. Measurement 116:273–280

    Google Scholar 

  69. Rafiee J, Arvani F, Harifi A, Sadeghi M (2007) Intelligent condition monitoring of a gearbox using artificial neural network. Mech Syst Signal Process 21(4):1746–1754

    Google Scholar 

  70. Rodriguez PJ, Belahcen A, Arkkio A (2006) Signatures of electrical faults in the force distribution and vibration pattern of induction motors. IEE Proc Electr Power Appl 153(4):523–529

    Google Scholar 

  71. Sadeghian A, Ye Z, Wu B (2009) Online detection of broken rotor bars in induction motors by wavelet packet decomposition and artificial neural networks. IEEE Trans Instrum Meas 58(7):2253–2263

    Google Scholar 

  72. Safizadeh M, Latifi S (2014) Using multi-sensor data fusion for vibration fault diagnosis of rolling element bearings by accelerometer and load cell. Inf Fus 18:1–8

    Google Scholar 

  73. Saidi L, Ali JB, Fnaiech F (2015) Application of higher order spectral features and support vector machines for bearing faults classification. ISA Trans 54:193–206

    Google Scholar 

  74. Saimurugan M, Ramachandran K, Sugumaran V, Sakthivel N (2011) Multi component fault diagnosis of rotational mechanical system based on decision tree and support vector machine. Expert Syst Appl 38(4):3819–3826

    Google Scholar 

  75. Samanta B (2004) Gear fault detection using artificial neural networks and support vector machines with genetic algorithms. Mech Syst Signal Process 18(3):625–644

    Google Scholar 

  76. Samanta B, Al-Balushi KR, Al-Araimi SA (2006) Artificial neural networks and genetic algorithm for bearing fault detection. Soft Comput 10(3):264–271

    Google Scholar 

  77. Sanz J, Perera R, Huerta C (2012) Gear dynamics monitoring using discrete wavelet transformation and multi-layer perceptron neural networks. Appl Soft Comput 12(9):2867–2878

    Google Scholar 

  78. Saravanan N, Ramachandran K (2009) Fault diagnosis of spur bevel gear box using discrete wavelet features and decision tree classification. Expert Syst Appl 36(5):9564–9573

    Google Scholar 

  79. Saruhan H, Saridemir S, Qicek A, Uygur I (2014) Vibration analysis of rolling element bearings defects. J Appl Res Technol 12(3):384–395

    Google Scholar 

  80. Schmidhuber J (2015) Deep learning in neural networks: an overview. Neural Netw 61:85–117

    Google Scholar 

  81. Schoen RR, Habetler TG, Kamran F, Bartfield R (1995) Motor bearing damage detection using stator current monitoring. IEEE Trans Ind Appl 31(6):1274–1279

    Google Scholar 

  82. Seshadrinath J, Singh B, Panigrahi BK (2013) Vibration analysis based interturn fault diagnosis in induction machines. IEEE Trans Ind Inf 10(1):340–350

    Google Scholar 

  83. Shao H, Jiang H, Zhang X, Niu M (2015) Rolling bearing fault diagnosis using an optimization deep belief network. Meas Sci Technol 26(11):115002

    Google Scholar 

  84. Shen C, Wang D, Kong F, Peter WT (2013) Fault diagnosis of rotating machinery based on the statistical parameters of wavelet packet paving and a generic support vector regressive classifier. Measurement 46(4):1551–1564

    Google Scholar 

  85. Shen Z, Chen X, Zhang X, He Z (2012) A novel intelligent gear fault diagnosis model based on EMD and multi-class TSVM. Measurement 45(1):30–40

    Google Scholar 

  86. Siedlecki W, Sklansky J (1993) A note on genetic algorithms for large-scale feature selection. In: Handbook of pattern recognition and computer vision, World Scientific, pp 88–107

  87. Su H, Chong KT (2007) Induction machine condition monitoring using neural network modeling. IEEE Trans Ind Electron 54(1):241–249

    Google Scholar 

  88. Sun W, Chen J, Li J (2007) Decision tree and PCA-based fault diagnosis of rotating machinery. Mech Syst Signal Process 21(3):1300–1317

    Google Scholar 

  89. Sun W, Shao S, Zhao R, Yan R, Zhang X, Chen X (2016) A sparse auto-encoder-based deep neural network approach for induction motor faults classification. Measurement 89:171–178

    Google Scholar 

  90. Tian J, Morillo C, Azarian MH, Pecht M (2015) Motor bearing fault detection using spectral kurtosis-based feature extraction coupled with k-nearest neighbor distance analysis. IEEE Trans Ind Electron 63(3):1793–1803

    Google Scholar 

  91. Trutt FC, Sottile J, Kohler JL (2002) Online condition monitoring of induction motors. IEEE Trans Ind Appl 38(6):1627–1632

    Google Scholar 

  92. Vapnik V (2013) The nature of statistical learning theory. Springer, New York

    MATH  Google Scholar 

  93. Vas P (1993) Parameter estimation, condition monitoring, and diagnosis of electrical machines, vol 27. Oxford University Press, Oxford

    Google Scholar 

  94. Wang D (2016) K-nearest neighbors based methods for identification of different gear crack levels under different motor speeds and loads: revisited. Mech Syst Signal Process 70:201–208

    Google Scholar 

  95. Wang H, Chen P (2011) Intelligent diagnosis method for rolling element bearing faults using possibility theory and neural network. Comput Ind Eng 60(4):511–518

    Google Scholar 

  96. Wang H, Li S, Song L, Cui L (2019) A novel convolutional neural network based fault recognition method via image fusion of multi-vibration-signals. Comput Ind 105:182–190

    Google Scholar 

  97. Wen L, Li X, Gao L, Zhang Y (2017) A new convolutional neural network-based data-driven fault diagnosis method. IEEE Trans Ind Electron 65(7):5990–5998

    Google Scholar 

  98. Widodo A, Yang BS (2007) Support vector machine in machine condition monitoring and fault diagnosis. Mech Syst Signal Process 21(6):2560–2574

    Google Scholar 

  99. Witten IH, Frank E, Hall MA, Pal CJ (2016) Data mining: practical machine learning tools and techniques. Morgan Kaufmann, Burlington

    Google Scholar 

  100. Wu S, Chow TW (2004) Induction machine fault detection using som-based RBF neural networks. IEEE Trans Ind Electron 51(1):183–194

    Google Scholar 

  101. Yan R, Gao RX, Chen X (2014) Wavelets for fault diagnosis of rotary machines: a review with applications. Signal Process 96:1–15

    Google Scholar 

  102. Yang B, Liu R, Chen X (2017) Fault diagnosis for a wind turbine generator bearing via sparse representation and shift-invariant K-SVD. IEEE Trans Ind Inf 13(3):1321–1331

    Google Scholar 

  103. Yang H, Mathew J, Ma L (2005) Fault diagnosis of rolling element bearings using basis pursuit. Mech Syst Signal Process 19(2):341–356

    Google Scholar 

  104. You L, Fan W, Li Z, Liang Y, Fang M, Wang J (2019) A fault diagnosis model for rotating machinery using VWC and MSFLA-SVM based on vibration signal analysis. Shock Vib 2019:1–16

    Google Scholar 

  105. Yunusa-Kaltungo A, Sinha JK, Elbhbah K (2014) An improved data fusion technique for faults diagnosis in rotating machines. Measurement 58:27–32

    Google Scholar 

  106. Zhang K, Li Y, Scarf P, Ball A (2011) Feature selection for high-dimensional machinery fault diagnosis data using multiple models and radial basis function networks. Neurocomputing 74(17):2941–2952

    Google Scholar 

  107. Zhang P, Du Y, Habetler TG, Lu B (2010) A survey of condition monitoring and protection methods for medium-voltage induction motors. IEEE Trans Ind Appl 47(1):34–46

    Google Scholar 

  108. Zhao W, Zhang Y, Zhu Y (2009) Diagnosis for transformer faults based on combinatorial Bayes network. In: 2009 2nd international congress on image and signal processing, IEEE, pp. 1–3

  109. Zhou Z, Wen C, Yang C (2016) Fault isolation based on k-nearest neighbor rule for industrial processes. IEEE Trans Ind Electron 63(4):2578–2586

    Google Scholar 

  110. Ziani R, Felkaoui A, Zegadi R (2017) Bearing fault diagnosis using multiclass support vector machines with binary particle swarm optimization and regularized fisher’s criterion. J Intell Manuf 28(2):405–417

    Google Scholar 

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Kumar, P., Hati, A.S. Review on Machine Learning Algorithm Based Fault Detection in Induction Motors. Arch Computat Methods Eng 28, 1929–1940 (2021). https://doi.org/10.1007/s11831-020-09446-w

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