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Real-time broken rotor bar fault detection and classification by shallow 1D convolutional neural networks

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Abstract

Application of advanced fault diagnosis and monitoring techniques allows more efficient, reliable and safe operation of many complex industrial systems. Recently, there has been a significant increase in application of various data-driven deep learning models for motor fault detection and diagnosis problems. Due to high computational complexity and large training dataset requirements of deep learning models, in this study, shallow and adaptive 1D convolutional neural networks (CNNs) are applied to real-time detection and classification of broken rotor bars in induction motors. As opposed to traditional fault diagnosis systems with separately designed feature extraction and classification blocks, the proposed system takes directly raw stator current signals as input and it can automatically learn optimal features with the proper training. The other advantages of the proposed approach are (1) its compact architecture configuration performing only 1D convolutions with a set of filters and subsampling, making it suitable for implementing with real-time circuit monitoring, (2) its requirement for a limited size of training dataset for efficient training of the classifier and (3) its cost-effective implementation. Effectiveness and feasibility of the proposed method is validated by applying it to real motor current data from an induction motor under full load.

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References

  1. Gao Z, Cecati C, Ding SX (2015) A survey of fault diagnosis and fault-tolerant techniques—part I: fault diagnosis with model-based and signal-based approaches. IEEE Trans Ind Electron 62:3757–3767

    Article  Google Scholar 

  2. Filippetti F, Bellini A, Capolino GA (2013) Condition monitoring and diagnosis of rotor faults in induction machines: state of art and future perspectives. In: Proceedings of the IEEE WEMDCD, Paris, March 2013, pp 196–209

  3. Zhou W, Habetler T, Harley R (2008) Bearing fault detection via stator current noise cancellation and statistical control. IEEE Trans Ind Electron 55:4260–4269

    Article  Google Scholar 

  4. Kral C, Habetler TG, Harley RG (2004) Detection Of mechanical imbalances of induction machines without spectral analysis of time domain signals. IEEE Trans Ind Appl 40:1101–1106

    Article  Google Scholar 

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

    Article  Google Scholar 

  6. Kliman GB, Premerlani WJ, Yazici B, Koegl RA, Mazereeuw J (1997) Sensorless online motor diagnostics. IEEE Comput Appl Power 10:39–43

    Article  Google Scholar 

  7. Pons-Llinares J, Antonino-Daviu JA, Riera-Guasp M, Lee SB, Kang TJ, Yang C (2015) Advanced induction motor rotor fault diagnosis via continuous and discrete time–frequency tools. IEEE Trans Ind Electron 62:1791–1802

    Article  Google Scholar 

  8. Li DZ, Wang W, Ismail F (2015) An enhanced bispectrum technique with auxiliary frequency injection for induction motor health condition monitoring. IEEE Trans Instrum Meas 67:2279–2287

    Google Scholar 

  9. Eren L, Devaney MJ (2004) Bearing damage detection via wavelet packet decomposition of the stator current. IEEE Trans Instrum Meas 53:431–436

    Article  Google Scholar 

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

    Article  Google Scholar 

  11. Di Stefano R, Meo S, Scarano M (1994) Induction motor fault diagnostic via artificial neural network. In: Proceedings of the IEEE international symposium on industrial electronics (ISIE’ 94), Santiago, pp 220–225

  12. Li B, Chow M-Y, Tipsuwan Y, Hung JC (2000) Neural network based motor rolling bearing fault diagnosis. IEEE Trans Ind Electron 47:1060–1069

    Article  Google Scholar 

  13. Bin GF, Gao JJ, Li XJ, Dhillon BS (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

    Article  Google Scholar 

  14. Tung VT, Yang B-S, Oh M-S, Tan ACC (2009) Fault diagnosis of induction motor based on decision trees and adaptive neurofuzzy inference. Expert Syst Appl 36:1840–1849

    Article  Google Scholar 

  15. Wu S-D, Wu P-H, Wu C-W, Ding J-J, Wang C-C (2012) Bearing fault diagnosis based on multiscale permutation entropy and support vector machine. Entropy 14:1343–1356

    Article  MATH  Google Scholar 

  16. Kang M, Kim J, Kim J-M, Tan ACC, Kim EY, Choi B-K (2014) Reliable fault diagnosis for low-speed bearings using individually trained support vector machines with kernel discriminative feature analysis. IEEE Trans Power Electron 30:2786–2797

    Article  Google Scholar 

  17. Kowalski CT, Kowalska TO (2003) Neural network application for induction motor faults diagnosis. Math Comput Simul 63:435–448

    Article  MathSciNet  MATH  Google Scholar 

  18. Ballal MS, Khan ZJ, Suryawanshi HM, Sonolikar RL (2007) Adaptive neural fuzzy inference system for the detection of inter-turn insulation and bearing wear faults in induction motor. IEEE Trans Ind Electron 54:250–258

    Article  Google Scholar 

  19. Zidani F, Diallo D, Benbouzid MEH, Nait-Said R (2008) A fuzzy based approach for the diagnosis of fault modes in a voltage-fed PWM inverter induction motor drive. IEEE Trans Ind Electron 55(2):586–593

    Article  Google Scholar 

  20. Aydin I, Karakose M, Akin E (2014) An approach for automated fault diagnosis based on a fuzzy decision tree and boundary analysis of a reconstructed phase space. ISA Trans 53(2):220–229

    Article  Google Scholar 

  21. Van M, Kang H-J (2016) Bearing defect classification based on individual wavelet local fisher discriminant analysis with particle swarm optimization. IEEE Trans Ind Inf 12:124–135

    Article  Google Scholar 

  22. Liu R, Yang B, Zhang X, Wang S, Chen X (2016) Time-frequency atoms-driven support vector machine method for bearings incipient fault diagnosis. Mech Syst Signal Process 75:345–370

    Article  Google Scholar 

  23. Filippetti F, Franceschini G, Tassoni C, Vas P (2000) Recent developments of induction motor drives fault diagnosis using AI techniques. IEEE Trans Ind Electron 47(5):994–1004

    Article  Google Scholar 

  24. Thomson WT, Fenger M (2001) Current signature analysis to detect induction motor faults. IEEE Trans IAS Mag 7(4):26–34

    Google Scholar 

  25. Aydin I, Karakose M, Akin E (2011) A new method for early fault detection and diagnosis of broken rotor bars. Energy Convers Manag 52:1790–1799

    Article  Google Scholar 

  26. Martin-Diaz I, Morinigo-Sotelo D, Duque-Perez O, Arredondo-Delgado PA, Camarena-Martinez D, Romero-Troncoso RJ (2017) Analysis of various inverters feeding induction motors with incipient rotor fault using high-resolution spectral analysis. Electr Power Syst Res 152:18–26

    Article  Google Scholar 

  27. Ordaz-Moreno A, Romero-Troncoso RD, Rivera-Guillen JR, Vite-Frias JA, Garcia-Perez A (2008) Automatic online diagnosis algorithm for broken-bar detection on induction motors based on discrete wavelet transform for FPGA Implementation. IEEE Trans Ind Electron 55:2193–2202

    Article  Google Scholar 

  28. Eltabach M, Charara A, Zein I (2004) A comparison of external and internal methods of signal analysis for broken rotor bars detection in induction motors. IEEE Trans Ind Electron 51(1):107–121

    Article  Google Scholar 

  29. Bachir S, Tnani S, Trigeassou J, Champenois G (2006) Diagnosis by parameter estimation of stator and rotor faults occurring in induction machines. IEEE Trans Ind Electron 53(3):963–973

    Article  Google Scholar 

  30. Tan WW, Huo H (2005) A generic neurofuzzy model-based approach for detecting faults in induction motors. IEEE Trans Ind Electron 52(5):1420–1427

    Article  Google Scholar 

  31. Bellini A, Filippetti F, Franceshini G, Tassoni C (2001) Quantitative evaluation motor broken bars by means of electrical signature analysis. IEEE Trans Ind Appl 37:1248–1254

    Article  Google Scholar 

  32. Ayhan B, Chow M, Song M (2005) Multiple signature processing-based fault detection schemes for broken rotor bar in induction motors. IEEE Trans Energy Convers 20:336–343

    Article  Google Scholar 

  33. 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):2253–2262

    Article  Google Scholar 

  34. Keskes H, Braham A, Lachiri Z (2013) Broken rotor bar diagnosis in induction machines through stationary wavelet packet transform and multiclass wavelet SVM. Electric Power Syst Res 97:151–157

    Article  Google Scholar 

  35. Briz F, Degnert MW, Garcia P, Bragado D (2008) Broken rotor bar detection in line-fed induction machines using complex wavelet analysis of startup transients. IEEE Trans Ind Appl 44(3):760–768

    Article  Google Scholar 

  36. Zolfaghari S, Noor SBM, Mehrjou MR, Marhaban MH, Mariun N (2017) Broken rotor bar fault detection and classification using wavelet packet signature analysis based on fourier transform and multi-layer perceptron neural network. Appl Sci 8(25):1–21. https://doi.org/10.3390/app8010025

    Google Scholar 

  37. Kurek J, Osowski S (2010) Support vector machine for fault diagnosis of the broken rotor bars of squirrel-cage induction motor. Neural Comput Appl 19:557. https://doi.org/10.1007/s00521-009-0316-5

    Article  Google Scholar 

  38. Singh H, Seera M, Abdullah MZ (2013) Detection and diagnosis of broken rotor bars and eccentricity faults in induction motors using the fuzzy min-max neural network. In: 2013 international joint conference on neural networks (IJCNN), August 2013, pp 1–5

  39. Guo S, Yang T, Gao W, Zhang C (2018) A novel fault diagnosis method for rotating machinery based on a convolutional neural network. Sensors 18:1429

    Article  Google Scholar 

  40. Verstraete D, Ferrada A, López Droguett E, Meruane V, Modarres M (2017) Deep learning enabled fault diagnosis using time-frequency image analysis of rolling element bearings. Shock Vib. https://doi.org/10.1155/2017/5067651

    Google Scholar 

  41. Guo S, Yang T, Gao W, Zhang C, Zhang Y (2018) An intelligent fault diagnosis method for bearings with variable rotating speed based on pythagorean spatial pyramid pooling CNN. Sensors 18:3857

    Article  Google Scholar 

  42. 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. https://doi.org/10.1016/j.jsv.2016.05.027

    Article  Google Scholar 

  43. Wei Z, Gaoliang P, Chuanhao L (2016) Bearings fault diagnosis based on convolutional neural networks with 2-d representation of vibration signals as input. In: International conference on mechatronics and mechanical engineering (ICMME 2016), Feb 2017, pp 1–5

  44. Kiranyaz S, Ince T, Gabbouj M (2015) Real-time patient-specific ECG classification by 1D convolutional neural networks. IEEE Trans Biomed Eng 63:664–674

    Article  Google Scholar 

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

    Article  Google Scholar 

  46. Wiesel DH, Hubel TN (1959) Receptive fields of single neurones in the cat’s striate cortex. J Physiol 148:574–591

    Article  Google Scholar 

  47. Ciresan DC, Meier U, Gambardella LM, Schmidhuber J (2010) Deep big simple neural nets for handwritten digit recognition. Neural Comput 22:3207–3220

    Article  Google Scholar 

  48. Scherer D, Muller A, Behnke S (2010) Evaluation of pooling operations in convolutional architectures for object recognition. In: Proceedings of international conference on artificial neural networks, Thessaloniki, Greece, September 2010, pp 92–101

  49. Krizhevsky A, Sutskever I, Hinton G (2012) Imagenet classification with deep convolutional neural networks. In: Proceedings of advances in neural information processing systems (NIPS), Lake Tahoe, December 2012, pp 1097–1105

  50. Didier G, Ternisien E, Caspary O, Razik H (2007) A new approach to detect broken rotor bars in induction machines by current spectrum analysis. Mech Syst Signal Process Elsevier 21(2):1127–1142

    Article  Google Scholar 

  51. Eren L, Cekic Y, Devaney M (2009) Broken rotor bar detection via wavelet packet decomposition of motor current. Int Rev Electr Eng 4:844–850

    Google Scholar 

  52. Farabet C, Poulet C, Han J, LeCun Y (2009) CNP: an FPGA-based processor for convolutional networks. In: Proceedings of international conference on field programmable logic and applications, Prague, September 2009, pp 32–37

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Acknowledgements

The author would like to thank Prof. Levent Eren for providing the broken rotor bar dataset for the experiments and giving the permission to use it.

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  1. Department of Electrical and Electronics Engineering, Izmir University of Economics, Izmir, Turkey

    Turker Ince

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Correspondence to Turker Ince.

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Ince, T. Real-time broken rotor bar fault detection and classification by shallow 1D convolutional neural networks. Electr Eng 101, 599–608 (2019). https://doi.org/10.1007/s00202-019-00808-7

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