Skip to main content
SpringerLink
Log in

Ensemble deep transfer learning driven by multisensor signals for the fault diagnosis of bevel-gear cross-operation conditions

  • Article
  • Published:
Science China Technological Sciences Aims and scope Submit manuscript

Abstract

The existing intelligent fault diagnosis techniques of bevel gear focus on single-sensor signal analysis under the steady operation condition. In this study, a new method is proposed based on ensemble deep transfer learning and multisensor signals to enhance the fault diagnosis adaptability and reliability of bevel gear under various operation conditions. First, a novel stacked autoencoder (NSAE) is constructed using a denoising autoencoder, batch normalization, and the Swish activation function. Second, a series of source-domain NSAEs with multisensor vibration signals is pretrained. Third, the good model parameters provided by the source-domain NSAEs are transferred to initialize the corresponding target-domain NSAEs. Finally, a modified voting fusion strategy is designed to obtain a comprehensive result. The multisensor signals collected under the different operation conditions of bevel gear are used to verify the proposed method. The comparison results show that the proposed method can diagnose different faults in an accurate and stable manner using only one target-domain sample, thereby outperforming the existing methods.

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

Similar content being viewed by others

References

  1. Saravanan N, Cholairajan S, Ramachandran K I. Vibration-based fault diagnosis of spur bevel gear box using fuzzy technique. Expert Syst Appl, 2009, 36: 3119–3135

    Google Scholar 

  2. Buzzoni M, D’Elia G, Mucchi E, et al. A vibration-based method for contact pattern assessment in straight bevel gears. Mech Syst Signal Pr, 2019, 120: 693–707

    Google Scholar 

  3. Jedliński Ł, Jonak J. A disassembly-free method for evaluation of spiral bevel gear assembly. Mech Syst Signal Pr, 2017, 88: 399–412

    Google Scholar 

  4. Lafi W, Djemal F, Tounsi D, et al. Dynamic modelling of differential bevel gear system in the presence of a defect. Mech Mach Theory, 2019, 139: 81–108

    Google Scholar 

  5. Lei Y, Yang B, Jiang X, et al. Applications of machine learning to machine fault diagnosis: A review and roadmap. Mech Syst Signal Pr, 2020, 138: 106587

    Google Scholar 

  6. Saravanan N, Siddabattuni V N S K, Ramachandran K I. Fault diagnosis of spur bevel gear box using artificial neural network (ANN), and proximal support vector machine (PSVM). Appl Soft Comput, 2010, 10: 344–360

    Google Scholar 

  7. Yan X, Jia M. A novel optimized SVM classification algorithm with multi-domain feature and its application to fault diagnosis of rolling bearing. Neurocomputing, 2018, 313: 47–64

    Google Scholar 

  8. Shao H, Jiang H, Zhang H, et al. Electric locomotive bearing fault diagnosis using novel convolutional deep belief network. IEEE Trans Ind Electron, 2018, 65: 2727–2736

    Google Scholar 

  9. Guo L, Lei Y, Xing S, et al. Deep convolutional transfer learning network: A new method for intelligent fault diagnosis of machines with unlabeled data. IEEE Trans Ind Electron, 2019, 66: 7316–7325

    Google Scholar 

  10. Ahmed H O A, Wong M L D, Nandi A K. Intelligent condition monitoring method for bearing faults from highly compressed measurements using sparse over-complete features. Mech Syst Signal Pr, 2018, 99: 459–477

    Google Scholar 

  11. Jiang W, Zhou J, Liu H, et al. A multi-step progressive fault diagnosis method for rolling element bearing based on energy entropy theory and hybrid ensemble auto-encoder. ISA Trans, 2019, 87: 235–250

    Google Scholar 

  12. Khan S, Yairi T. A review on the application of deep learning in system health management. Mech Syst Signal Pr, 2018, 107: 241–265

    Google Scholar 

  13. Zhao X, Jia M, Lin M. Deep Laplacian Auto-encoder and its application into imbalanced fault diagnosis of rotating machinery. Measurement, 2020, 152: 107320

    Google Scholar 

  14. Zhang Y, Li X, Gao L, et al. Ensemble deep contractive auto-encoders for intelligent fault diagnosis of machines under noisy environment. Knowledge-based Syst, 2020, 196: 105764

    Google Scholar 

  15. Kong X, Mao G, Wang Q, et al. A multi-ensemble method based on deep auto-encoders for fault diagnosis of rolling bearings. Measurement, 2020, 151: 107132

    Google Scholar 

  16. Shao H D, Jiang H K, Zhao K, et al. A novel tracking deep wavelet auto-encoder method for intelligent fault diagnosis of electric locomotive bearings. Mech Syst Signal Pr, 2018, 110: 193–209

    Google Scholar 

  17. Xu F, Huang Z, Yang F, et al. Constructing a health indicator for roller bearings by using a stacked auto-encoder with an exponential function to eliminate concussion. Appl Soft Comput, 2020, 89: 106119

    Google Scholar 

  18. He Z Y, Shao H D, Lin J, et al. Transfer fault diagnosis of bearing installed in different machines using enhanced deep auto-encoder. Measurement, 2020, 152: 107393

    Google Scholar 

  19. Yang B, Lei Y, Jia F, et al. An intelligent fault diagnosis approach based on transfer learning from laboratory bearings to locomotive bearings. Mech Syst Signal Pr, 2019, 122: 692–706

    Google Scholar 

  20. Kang Z, Yang B, Yang S, et al. Online transfer learning with multiple source domains for multi-class classification. Knowledge-based Syst, 2020, 190: 105149

    Google Scholar 

  21. Shen S, Sadoughi M, Li M, et al. Deep convolutional neural networks with ensemble learning and transfer learning for capacity estimation of lithium-ion batteries. Appl Energy, 2020, 260: 114296

    Google Scholar 

  22. Raghu S, Sriraam N, Temel Y, et al. EEG based multi-class seizure type classification using convolutional neural network and transfer learning. Neural Networks, 2020, 124: 202–212

    Google Scholar 

  23. Han T, Liu C, Yang W, et al. Deep transfer network with joint distribution adaptation: A new intelligent fault diagnosis framework for industry application. ISA Trans, 2020, 97: 269–281

    Google Scholar 

  24. Wu Z, Jiang H, Zhao K, et al. An adaptive deep transfer learning method for bearing fault diagnosis. Measurement, 2020, 151: 107227

    Google Scholar 

  25. Shao S, McAleer S, Yan R, et al. Highly accurate machine fault diagnosis using deep transfer learning. IEEE Trans Ind Inf, 2019, 15: 2446–2455

    Google Scholar 

  26. Li X, Jia X D, Zhang W, et al. Intelligent cross-machine fault diagnosis approach with deep auto-encoder and domain adaptation. Neurocomputing, 2020, 383: 235–247

    Google Scholar 

  27. Li X, Jiang H, Zhao K, et al. A deep transfer nonnegativity-constraint sparse autoencoder for rolling bearing fault diagnosis with few labeled data. IEEE Access, 2019, 7: 91216–91224

    Google Scholar 

  28. He Z, Shao H, Wang P, et al. Deep transfer multi-wavelet auto-encoder for intelligent fault diagnosis of gearbox with few target training samples. Knowledge-based Syst, 2020, 191: 105313

    Google Scholar 

  29. Jiang H, Li C, Li H. An improved EEMD with multiwavelet packet for rotating machinery multi-fault diagnosis. Mech Syst Signal Pr, 2013, 36: 225–239

    Google Scholar 

  30. Shan P, Lv H, Yu L, et al. A multisensor data fusion method for ball screw fault diagnosis based on convolutional neural network with selected channels. IEEE Sens J, 2020, 20: 7896–7905

    Google Scholar 

  31. Azamfar M, Singh J, Bravo-Imaz I, et al. Multisensor data fusion for gearbox fault diagnosis using 2-D convolutional neural network and motor current signature analysis. Mech Syst Signal Pr, 2020, 144: 106861

    Google Scholar 

  32. Qu L, Liao Y, Lin J, et al. Investigation on the subsynchronous pseudo-vibration of rotating machinery. J Sound Vib, 2018, 423: 340–354

    Google Scholar 

  33. Ming T F, Zhang X H. 2-dimensional holospectrum based fault detection of rotor. Adv Mat Res, 2011, 346: 797–803

    Google Scholar 

  34. Cao Z, Chen L. Security in application layer of radar sensor networks: Detect friends or foe. Secur Commun Netw, 2015, 8: 2712–2722

    Google Scholar 

  35. Khaleghi B, Khamis A, Karray F O, et al. Multisensor data fusion: A review of the state-of-the-art. Inf Fusion, 2013, 14: 28–44

    Google Scholar 

  36. Serdio F, Lughofer E, Pichler K, et al. Fault detection in multi-sensor networks based on multivariate time-series models and orthogonal transformations. Inf Fusion, 2014, 20: 272–291

    Google Scholar 

  37. Huang M, Liu Z, Tao Y. Mechanical fault diagnosis and prediction in IoT based on multi-source sensing data fusion. Simul Model Practice Theor, 2020, 102: 101981

    Google Scholar 

  38. Georgoulas G, Loutas T, Stylios C D, et al. Bearing fault detection based on hybrid ensemble detector and empirical mode decomposition. Mech Syst Signal Pr, 2013, 41: 510–525

    Google Scholar 

  39. Wang Z Y, Lu C, Zhou B. Fault diagnosis for rotary machinery with selective ensemble neural networks. Mech Syst Signal Pr, 2018, 113: 112–130

    Google Scholar 

  40. Yu J. A selective deep stacked denoising autoencoders ensemble with negative correlation learning for gearbox fault diagnosis. Comput Ind, 2019, 108: 62–72

    Google Scholar 

  41. Zhang Z, Han H, Cui X, et al. Novel application of multi-model ensemble learning for fault diagnosis in refrigeration systems. Appl Thermal Eng, 2020, 164: 114516

    Google Scholar 

  42. Shao H, Jiang H, Wang F, et al. An enhancement deep feature fusion method for rotating machinery fault diagnosis. Knowledge-based Syst, 2017, 119: 200–220

    Google Scholar 

  43. Wang J, Li S, An Z, et al. Batch-normalized deep neural networks for achieving fast intelligent fault diagnosis of machines. Neurocomputing, 2019, 329: 53–65

    Google Scholar 

  44. Hayou S, Arnaud D, Judith R. On the impact of the activation function on deep neural networks training. Inter Confer Mach Learn (ICML), 2019, 97: 2672–2680

    Google Scholar 

  45. Ohn I, Kim Y. Smooth function approximation by deep neural networks with general activation functions. Entropy, 2019, 21: 627

    MathSciNet  Google Scholar 

  46. Tanaka M. Weighted sigmoid gate unit for an activation function of deep neural network. Pattern Recogn Lett, 2020, 135: 354–359

    Google Scholar 

  47. Ramachandran P, Zoph B, Le Q. Swish: A Self-Gated Activation Function. 2017, 1–12, arXiv: 1710.05941v1

  48. Shao H, Jiang H, Lin Y, et al. A novel method for intelligent fault diagnosis of rolling bearings using ensemble deep auto-encoders. Mech Syst Signal Pr, 2018, 102: 278–297

    Google Scholar 

  49. Li X, Jiang H, Niu M, et al. An enhanced selective ensemble deep learning method for rolling bearing fault diagnosis with beetle antennae search algorithm. Mech Syst Signal Pr, 2020, 142: 106752

    Google Scholar 

  50. Deng W, Liu H, Xu J, et al. An improved quantum-inspired differential evolution algorithm for deep belief network. IEEE Trans Instrum Meas, 2020, doi: https://doi.org/10.1109/TIM.2020.2983233

Download references

Author information

Authors and Affiliations

  1. State Key Laboratory of Advanced Design and Manufacturing for Vehicle Body, College of Mechanical and Vehicle Engineering, Hunan University, Changsha, 410082, China

    ZiYang Di & HaiDong Shao

  2. College of Mechanical and Electrical Engineering, Wenzhou University, Wenzhou, 325035, China

    JiaWei Xiang

Authors
  1. ZiYang Di
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. HaiDong Shao
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. JiaWei Xiang
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to HaiDong Shao.

Additional information

This work was supported by the National Natural Science Foundation of China (Grant No. 51905160), the Natural Science Foundation of Hunan Province (Grant No. 2020JJ5072), and the Fundamental Research Funds for the Central Universities (Grant No. 531118010335).

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Di, Z., Shao, H. & Xiang, J. Ensemble deep transfer learning driven by multisensor signals for the fault diagnosis of bevel-gear cross-operation conditions. Sci. China Technol. Sci. 64, 481–492 (2021). https://doi.org/10.1007/s11431-020-1679-x

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11431-020-1679-x

Keywords

Search

Navigation