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Accompanying deep framework for faults in motor and gearbox with disproportion vibrational samples

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Abstract

An important demand in the machinery environment is the frequent handling of variant faults occurring during active performance of motors and gearbox and through its vibrational signals. Accordingly, effective intelligent approaches detecting and treating causes of low productivity, reliability and higher maintenance cost have been introduced. A huge attention in recent years recommended deep learning and reports higher capabilities in this context. However, one limitation of using the deep learning is that its performance (in terms of evaluation matrices and/or over-fitting) is highly connected to the availability of large number of training samples as it learns by examples. This paper tackles this problem, proposing novel generative adversarial networks based augmentation method to balance the dataset and hence increase generalizability of the machine learning model. The generator of the generative adversarial networks method provides more samples with main objective of class balancing. Its discriminator recognizes the real and fake samples and assigns classes to the inputs as well. Accompanying deep framework based on the pervious augmentation method and a two subsequent unsupervised sparse autoencoders is also proposed to develop accurate intelligent identification model for motor and gearbox multi-faults with 10 classes. Precision, recall, and F1 measures proved the validity of the proposed augmentation when evaluated on the dataset of gear and motor signals and enhanced the class balancing. In addition, it enhances results of deep learning and reports identification accuracy of 93.7%.

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References

  1. Choudhary A, Goyal D, Shimi SL, Akula A (2018) Condition monitoring and fault diagnosis of induction motors: a review. Arch Comput Methods Eng 26:1221–1238

    Google Scholar 

  2. Agrawal V, Panigrahi B, Subbarao P (2015) Review of control and fault diagnosis methods applied to coal mills. Process Control 32:138–153

    Google Scholar 

  3. Bolón-Canedo V, Sánchez-Maroño N, Alonso-Betanzos A (2013) A review of feature selection methods on synthetidata. Knowl Inf Syst 34:483–519

    Google Scholar 

  4. Lei Y, Yang B, Jiang X, Jia F, Li N, Nandi AK (2020) Applications of machine learning to machine fault diagnosis: a review and roadmap. Mech Syst Signal Process 138:106587

    Google Scholar 

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

    Google Scholar 

  6. Xue X, Zhou J, Xu Y, Zhu W, Li C (2015) An adaptively fast ensemble empirical mode decomposition method and its applications to rolling element bearing fault diagnosis. Mech Syst Signal Process 62:444–459

    Google Scholar 

  7. Pan L, Zhu D, She S, Song A, Shi X, Duan S (2018) Gear fault diagnosis method based on wavelet-packet independent component analysis and support vector machine with kernel function fusion. Adv Mech Eng 10(11):1–10

    Google Scholar 

  8. Zair M, Rahmoune C, Benazzouz D (2019) Multi-fault diagnosis of rolling bearing using fuzzy entropy of empirical mode decomposition, principal component analysis, and SOM neural network. Proc Inst Mech Eng Part C J Mech Eng Sci 233:3317–3328

    Google Scholar 

  9. Cerrada M, Sanchez R, Cabrera D, Zurita G, Li C (2015) Multi-stage feature selection by using genetic algorithms for fault diagnosis in gearboxes based on vibration signal. Sensors 15:23903–23926

    Google Scholar 

  10. Yun JX (2011) Fault diagnosis for rolling bearing on genetic-SVM classifier. Adv Mater Res 199:620–624

    Google Scholar 

  11. Huang R, Liao Y, Zhang S, Li W (2019) Deep decoupling convolutional neural network for intelligent compound fault diagnosis. IEEE Access 7:1848–1858

    Google Scholar 

  12. Zhu X, Hou D, Zhou P, Han Z, Yuan Y, Zhou W, Yin Q (2019) Rotor fault diagnosis using a convolutional neural network with symmetrized dot pattern images. Measurement 138:526–535

    Google Scholar 

  13. Guan Z, Liao Z, Li K, Chen P (2019) A precise diagnosis method of structural faults of rotating machinery based on combination of empirical mode decomposition, sample entropy, and deep belief network. Sensors 19(3):591

    Google Scholar 

  14. Du W, Zhou J, Wang Z, Li R, Wang J (2018) Application of improved singular spectrum decomposition method for composite fault diagnosis of gear boxes. Sensors 18:3804

    Google Scholar 

  15. Wang Z, Wang J, Du W (2018) Research on fault diagnosis of gearbox with improved vibrational mode decomposition. Sensors 18:3510

    Google Scholar 

  16. Chen X, Cheng G, Li H, Li Y (2017) Research of planetary gear fault diagnosis based on multi-scale fractal box dimension of ceemd and elm. Strojniski Vestnik J Mech Eng 63:45–55

    Google Scholar 

  17. Li X, Zhang W, Ding Q (2018) Cross-domain fault diagnosis of rolling element bearings using deep generative neural networks. IEEE Trans Ind Electron 66:5525–5534

    Google Scholar 

  18. Xiang Z, Zhang X, Zhang W, Xia X (2019) Fault diagnosis of rolling bearing under fluctuating speed and variable load based on tco spectrum and stacking autoencoder. Measurement 138:162–174

    Google Scholar 

  19. Ando S, Huang CY (2017) Deep oversampling framework for classifying imbalanced data. Proc Mach Learn Knowl Discov Databases 66:770–785

    Google Scholar 

  20. Garca S, Zhang ZL, Altalhi A, Alshomrani S, Herrera F (2018) Dynamic ensemble selection for multi-class imbalanced datasets. Inf Sci 445:22–37

    MathSciNet  Google Scholar 

  21. Radford A, Metz L, Chintala S (2016) Unsupervised representation learning with deep convolutional generative adversarial networks. Proc ICLR 66:1–16

    Google Scholar 

  22. Douzas G, Bacao F (2018) Effective data generation for imbalanced learning using conditional generative adversarial networks. Expert Syst Appl 91:464–471

    Google Scholar 

  23. Mullick SS, Datta S, Das S (2019) Generative adversarial minority oversampling. In: Processing of IEEE/CVF international conference on computer vision (ICCV), pp 1695–1704

  24. Eren L (2017) Bearing fault detection by one-dimensional convolutional neural networks. Math Probl Eng 2017:9

    Google Scholar 

  25. Jiang G, He H, Yan J, Xie P (2019) Multiscale convolutional neural networks for fault diagnosis of wind turbine gearbox. IEEE Trans Ind Electron 66:3196–3207

    Google Scholar 

  26. Gryllias KC, Antoniadis IA (2012) A support vector machine approach based on physical model training for rolling element bearing fault detection in industrial environments. Eng Appl Artif Intell 25(2):326–344

    Google Scholar 

  27. Li Z, Yan X, Yuan C, Peng Z, Li L (2011) Virtual prototype and experimental research on gear multi-fault diagnosis using wavelet-autoregressive model and principal component analysis method. Mech Syst Signal Process 25:2589–2607

    Google Scholar 

  28. Janakiraman V, Nguyen X, Assanis D (2013) Nonlinear identification of a gasoline HCCI engine using neural networks coupled with principal component analysis. Appl Soft Comput 13:2375–2389

    Google Scholar 

  29. Appana DK, Prosvirin A, Kim J-M (2018) Reliable fault diagnosis of bearings with varying rotational speeds using envelope spectrum and convolution neural networks. Soft Comput 22:6719–6729

    Google Scholar 

  30. 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

    Google Scholar 

  31. Pandhare V, Singh J, Lee J (2019) Convolutional neural network based rolling-element bearing fault diagnosis for naturally occurring and progressing defects using time-frequency domain features. In: Proceedings of the prognostics and system health management conference, pp 320–326

  32. Jiang G, He H, Xie P, Tang Y (2017) Stacked multilevel-denoising autoencoders: a new representation learning approach for wind turbine gearbox fault diagnosis. IEEE Trans Instrum Meas 66:2391–2402

    Google Scholar 

  33. Liu G, Bao H, Han B (2018) A stacked autoencoder-based deep neural network for achieving gearbox fault diagnosis. Math Problems Eng 2018:10

    Google Scholar 

  34. Goodfellow I, Pouget-Abadie J, Mirza M, Xu B, Warde-Farley D, Ozair S, Courville A, Bengio Y (2014) Generative adversarial networks. Adv Neural Inf Process Syst 27:2672–2680

    Google Scholar 

  35. Mao W, Liu Y, Ding L, Li Y (2019) Imbalanced fault diagnosis of rolling bearing based on generative adversarial network: a comparative study. IEEE Access 7:9515–9530

    Google Scholar 

  36. Lee YO, Jo J, Hwang J (2017) Application of deep neural network and generative adversarial network to industrial maintenance: a case study of induction motor fault detection. In: Proceedings of the IEEE international conference big data, pp 3248–3253

  37. Liu H, Zhou J, Xu Y, Zheng Y, Peng X, Jiang W (2018) Unsupervised fault diagnosis of rolling bearings using a deep neural network based on generative adversarial networks. Neurocomputing 315:412–424

    Google Scholar 

  38. Han T, Liu C, Yang W, Jiang D (2019) A novel adversarial learning framework in deep convolutional neural network for intelligent diagnosis of mechanical faults. Knowl Based Syst 165:474–487

    Google Scholar 

  39. Frini M, Soualhi A, Badaoui M, Marrakchi G (2017) Gear fault detection using the geometric properties of electrical currents in three-phase induction motor-based systems. Condition Monit 7(2):47–52

    Google Scholar 

  40. JF Lea Jr, L Rowlan (2019) Use of beam pumps to deliquefy gas wells, gas well deliquification, 3rd ed. sciencDirect, Elsevier

  41. Liang X, Zuo MJ, Feng Z (2018) Dynamic modeling of gearbox faults: a review. Mech Syst Signal Process 98:852–876

    Google Scholar 

  42. Schwack F, Stammler M, Poll G, Reuter A (2016) Comparison of life calculations for oscillating bearings considering individual pitch control in wind turbines. J Phys Conf Ser 753(11):112013

    Google Scholar 

  43. Seo B, Sung S, Kang K, Song J, Jang G (2016) Unbalanced magnetic force and cogging torque of PM motors due to the interaction between PM magnetization and stator eccentricity. Micro Syst Technol 22:1249–1255

    Google Scholar 

  44. Fan C, Syu J, Pan M, Tsao W (2011) Study of start-up vibration response for oil whirl, oil whip and dry whip. Mech Syst Signal Process 25(8):3103–31115

    Google Scholar 

  45. Singhal S, Mistry R (2009) Oil whirl rotor dynamic instability phenomenon-diagnosis and cure in large induction motor. Proc Ind Appl Soc 66:14–16

    Google Scholar 

  46. P Dang, L Do, N Vo, T Ngo, H Le (2019) Identification of unbalance in rotating machinery using vibration analyse solution. In: 4th International conference on mechatronics and electrical systems, p 841

  47. Jung J, Lee SB, Lim C, Cho C, Kim K (2016) Electrical monitoring of mechanical looseness for induction motors with sleeve bearings. IEEE Trans Energy Convers 31(4):1377–1386

    Google Scholar 

  48. Verma A, Sarangi S, Kolekar MH (2014) Experimental investigation of misalignment effects on rotor shaft vibration and on stator current signature. Fail Anal Prev 14:125–138

    Google Scholar 

  49. Patidar L, Rao KU (2012) Soft foot and motor problem solved through predictive maintenance approach. Proc Recent Trends Eng Sci 66:20–21

    Google Scholar 

  50. Majid S, Nikravesh Y, Goudarzi MD (2017) A review paper on looseness detection methods in bolted structures. Latin Am J Solids Struct 14:2153–2176

    Google Scholar 

  51. Qiao Z, Lei Y, Li N (2019) Applications of stochastic resonance to machinery fault detection: a review and tutorial. Mech Syst Signal Process 122:502–536

    Google Scholar 

  52. Plante T, Nejadpak A, Yang CX (2015) Faults detection and failures prediction using vibration analysis. Proc IEEE Autotestcon 66:227–231

    Google Scholar 

  53. Sohaib M, Kim J-M (2018) Reliable fault diagnosis of rotary machine bearings using a stacked sparse autoencoder-based deep neural network. Shock Vib 2018:66

    Google Scholar 

  54. Nath AG, Sharma A, Udmale SS, Singh SK (2021) An early classification approach for improving structural rotor fault diagnosis. IEEE Trans Instrum Meas 70:1–13

    Google Scholar 

  55. Sun N, Mo X, Wei T, Zhang D, Luo W (2020) The effectiveness of noise in data augmentation for fine-grained image classification. In: ACPR 2019. Lecture notes in computer science, vol 12046. Springer, Cham, pp 779–792

  56. Helwan A, Ozsahin DU (2017) Sliding window based machine learning system for the left ventricle localization in MR cardiac images. Appl Comput Intell Soft Comput 66:1–9

    Google Scholar 

  57. Lashgari E, Liang D, Maoz U (2020) Data augmentation for deep-learning-based electroencephalography. J Neurosci Methods 346:108885

    Google Scholar 

Download references

Acknowledgements

The authors extend their appreciation to the Deputyship for Research & Innovation, Ministry of Education in Saudi Arabia for funding this research work through the project number RI-44-0224.

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Authors and Affiliations

  1. Department of Computer Sciences, College of Computer and Information Sciences, Princess Nourah Bint Abdulrahman University, P.O. Box 84428, Riyadh, 11671, Saudi Arabia

    Hanen Karamti & Fadwa Alrowais

  2. College of Engineering, Princess Nourah Bint Abdulrahman University, P.O. Box 84428, Riyadh, 11671, Saudi Arabia

    Maha M. A. Lashin

  3. Department of Computer Science, Faculty of Computer and Information Sciences, Ain Shams University, Cairo, Egypt

    Abeer M. Mahmoud

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  1. Hanen Karamti
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Correspondence to Fadwa Alrowais.

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Karamti, H., Lashin, M.M.A., Alrowais, F. et al. Accompanying deep framework for faults in motor and gearbox with disproportion vibrational samples. Neural Comput & Applic 35, 7659–7676 (2023). https://doi.org/10.1007/s00521-022-08020-8

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