Skip to main content

Advertisement

SpringerLink
Log in

Enhanced bearing fault detection using multichannel, multilevel 1D CNN classifier

  • Original Paper
  • Published:
Electrical Engineering Aims and scope Submit manuscript

Abstract

Electric motors are widely used in many industrial applications on account of stability, solidity and ease of use. Mechanical bearing faults have the highest statistical occurrence percentage among all of the motor fault types. Accurate and advance detection of the bearing faults is critical to avoid unpredicted breakdowns of electric motors. Through early detection of bearing faults, it would be possible to solve the problem at a lower cost by repairing and/or replacing relevant parts. Most of the fault detection works in the literature attempted to detect binary {healthy, faulty} motor fault case based on a single input. In this study, we propose an enhanced performance bearing fault diagnosis system based on multichannel, multilevel 1D-CNN classifier processing vibration data collected from multiple accelerometers mounted on bearings in a test bed. Effectiveness and feasibility of the proposed method are validated by applying it to the benchmark IMS bearing vibration dataset for inner race and rolling element faults and comparing the results with the conventional single-axis data-based fault detection.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8

Similar content being viewed by others

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

    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, Mar. 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

    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

    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

    Google Scholar 

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

    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

    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

    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

    Google Scholar 

  11. Zhang R, Peng Z, Wu L, Yao B, Guan Y (2017) Fault diagnosis from raw sensor data using deep neural networks considering temporal coherence. Sensors. https://doi.org/10.3390/s17030549

    Article  Google Scholar 

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

    Google Scholar 

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

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

  15. 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

    Google Scholar 

  16. 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

    Google Scholar 

  17. Lou X, Loparo KA (2004) Bearing fault diagnosis based on wavelet transform and fuzzy inference. Mech Syst Signal Process 18(5):1077–1095. https://doi.org/10.1016/S0888-3270(03)00077-3

    Article  Google Scholar 

  18. Matić D, Kulić F, Pineda-Sánchez M, Kamenko I (2012) Support vector machine classifier for diagnosis in electrical machines: application to broken bar. Exp Syst Appl 39(10):8681–8689

    Google Scholar 

  19. Yu X, Dong F, Ding E, Wu S, Fan C (2017) Rolling bearing fault diagnosis using modified LFDA and EMD with sensitive feature selection. IEEE Access 6:3715–3730

    Google Scholar 

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

    MathSciNet  MATH  Google Scholar 

  21. 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

    Google Scholar 

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

    Google Scholar 

  23. Zheng J, Pan H, Cheng J (2017) Rolling bearing fault detection and diagnosis based on composite multiscale fuzzy entropy and ensemble support vector machines. Mech Syst Signal Process 85:746–759

    Google Scholar 

  24. Liu R, Meng G, Yang B, Sun C, Chen X (2017) Dislocated time series convolutional neural architecture: an intelligent fault diagnosis approach for electric machine. IEEE Trans Ind Inform 13(3):1310–1320

    Google Scholar 

  25. Dai X, Gao Z (2013) From model, signal to knowledge: a data-driven perspective of fault detection and diagnosis. IEEE Trans Ind Inf 9:2226–2238

    Google Scholar 

  26. Shen C, Wang D, Kong F, Tse PW (2013) Fault diagnosis of rotating machinery based on the statistical parameters of wavelet packet paving and a generic support vector regressive classifier. Meas J Int Meas Confed 46(4):1551–1564

    Google Scholar 

  27. Yaqub MF, Gondal I, Kamruzzaman J (2012) Inchoate fault detection framework: Adaptive selection of wavelet nodes and cumulant orders. IEEE Trans Instrum Meas 61:685–695

    Google Scholar 

  28. Konar P, Chattopadhyay P (2011) Bearing fault detection of induction motor using wavelet and support vector machines (SVMs). Appl Soft Comput 11:4203–4211

    Google Scholar 

  29. 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

    Google Scholar 

  30. Shuai J, Shen C, Zhu Z (2017) Adaptive morphological feature extraction and support vector regressive classification for bearing fault diagnosis. Int J Rotat Mach 2017:1–10

    Google Scholar 

  31. Vakharia V, Gupta VK, Kankar PK (2014) A multiscale permutation entropy based approach to select wavelet for fault diagnosis of ball bearings. J Vib Control 21:3123–3131

    Google Scholar 

  32. 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

    Google Scholar 

  33. Wang X, Zheng Y, Zhao Z, Wang J (2015) Bearing fault diagnosis based on statistical locally, linear embedding. Sensors 15:16225–16247

    Google Scholar 

  34. Ye Z, Wu B, Sadeghian A (2003) Current signature analysis of induction motor mechanical faults by wavelet packet decomposition. IEEE Trans Ind Electron 50:1217–1227

    Google Scholar 

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

    Google Scholar 

  36. 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

    Google Scholar 

  37. 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

    Google Scholar 

  38. Eren L, Ince T, Kiranyaz S (2019) A generic intelligent bearing fault diagnosis system using compact adaptive 1D CNN classifier. J Signal Process Syst 91(2):179–189

    Google Scholar 

  39. Ince T (2019) Real-time broken rotor bar fault detection and classification by shallow 1D convolutional neural networks. Electr Eng 101(2):599–608

    Google Scholar 

  40. Eren L (2017) Bearing fault detection by one-dimensional convolutional neural networks. Math Prob Eng 2017:1–9

    Google Scholar 

  41. Ahishali M, Kiranyaz S, Ince T, Gabbouj M (2019) Dual and single polarized SAR image classification using compact convolutional neural networks. Remote Sens 11(11):1340. https://doi.org/10.3390/rs11111340

    Article  Google Scholar 

  42. Grubic S, Aller JM, Lu B, Habetler TG (2008) A survey on testing and monitoring methods for stator insulation systems of low-voltage induction machines focusing on turn insulation problems. IEEE T Ind Electron 55:4127–4136

    Google Scholar 

  43. (1997) IEEE recommended practice for the design of reliable industrial and commercial power systems, IEEE Std. 493, IEEE Gold Book, Appendix H.

  44. Allbrecht PF, Appiarius JC, McCoy RM (1986) Assessment of the reliability of motors in utility applications-updated. IEEE Trans Energy Convers 1(1):39–46

    Google Scholar 

  45. Zhang R, Peng Z, Wu L, Yao B, Guan Y (2017) Fault diagnosis from raw sensor data using deep neural networks considering temporal coherence. Sensors 17:549–565. https://doi.org/10.3390/s17030549

    Article  Google Scholar 

  46. Chauvin Y, Rumelhart DE (1995) Back propagation: theory, architectures, and applications. Lawrence Erlbaum Associates Publishers, UK

    Google Scholar 

  47. Keras deep learning library web site: https://keras.io/

  48. Wowk V (1991) Machinery vibration, measurement and analysis. McGraw-Hill

    Google Scholar 

  49. Lee J, Qiu H, Yu G, Lin J (2017) Rexnord technical services, IMS, University of Cincinnati. bearing data set, NASA Ames prognostics data repository. NASA Ames Research Center: Moffett Field, CA, USA. Available online: https://ti.arc.nasa.gov/tech/dash/pcoe/prognostic-data-repository/#bearing. Accessed 15 Mar 2017

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

  51. Khorram A, Khalooei M (2019) Intelligent bearing fault diagnosis with convolutional long-short-term-memory recurrent neural network

  52. Mao W, Wang L, Feng N (2019) A new fault diagnosis method of bearings based on structural feature selection. Electronics 8:1406

    Google Scholar 

  53. 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

    Google Scholar 

  54. 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 

  55. Qian N (1999) On the momentum term in gradient descent learning algorithms. Neural Netw 12:145–151. https://doi.org/10.1016/S0893-6080(98)00116-6

    Article  Google Scholar 

Download references

Acknowledgements

The authors would like to thank to Center for Intelligent Maintenance Systems (IMS), University of Cincinnati, for making the bearing datasets publicly available and giving the permission to use it.

Author information

Authors and Affiliations

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

    Ibrahim Halil Ozcan, Ozer Can Devecioglu, Turker Ince, Levent Eren & Murat Askar

Authors
  1. Ibrahim Halil Ozcan
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Ozer Can Devecioglu
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Turker Ince
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Levent Eren
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Murat Askar
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Ibrahim Halil Ozcan.

Ethics declarations

Conflict of interest

The author declares no conflict of interest.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Ozcan, I.H., Devecioglu, O.C., Ince, T. et al. Enhanced bearing fault detection using multichannel, multilevel 1D CNN classifier. Electr Eng 104, 435–447 (2022). https://doi.org/10.1007/s00202-021-01309-2

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00202-021-01309-2

Keywords

Search

Navigation