Skip to main content
SpringerLink
Log in

A High Generalizable Feature Extraction Method Using Ensemble Learning and Deep Auto-Encoders for Operational Reliability Assessment of Bearings

  • Published:
Neural Processing Letters Aims and scope Submit manuscript

Abstract

Feature extraction is a major challenge in operational reliability assessment, which requires techniques and prior knowledge. Deep auto-encoder (DAE) is a popular deep learning method and is widely used in feature extraction. However, low generalization ability and structure parameters design are still the major problems of DAE for operational reliability assessment. To overcome the two problems, an ensemble DAE is proposed for operational reliability assessment. Firstly, different structure parameters are employed to design a series of DAEs for feature learning from the measured data. Secondly, a feature ensemble strategy is designed to enhance the generalization ability of the DAE model, in which the features learned by different DAEs are clustered to remove the irrelevant DAEs and select the more general feature subset. Finally, the operational reliability indicator is defined by the Euclidean distance of the selected features and the operational reliability model is developed. The proposed method is utilized to analyze the experimental bearings and the results indicate that the proposed method is effective for operational reliability assessment.

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
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15
Fig. 16
Fig. 17

Similar content being viewed by others

References

  1. 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 72–73:303–315. https://doi.org/10.1016/j.ymssp.2015.10.025

    Google Scholar 

  2. Zhao R, Wang D, Yan R, Mao K, Shen F, Wang J (2018) Machine health monitoring using local feature-based gated recurrent unit networks. IEEE Trans Ind Electron 65(2):1539–1548. https://doi.org/10.1109/TIE.2017.2733438

    Google Scholar 

  3. Yu JB (2017) Adaptive hidden Markov model-based online learning framework for bearing faulty detection and performance degradation monitoring. Mech Syst Signal Process 83:149–162. https://doi.org/10.1016/j.ymssp.2016.06.004

    Google Scholar 

  4. Liu S, Hu Y, Li C, Lu H, Zhang H (2017) Machinery condition prediction based on wavelet and support vector machine. J Intell Manuf 28(4):1045–1055. https://doi.org/10.1007/s10845-015-1045-5

    Google Scholar 

  5. Liu Y, Zuo MJ, Li Y-F, Huang H-Z (2015) Dynamic reliability assessment for multi-state systems utilizing system-level inspection data. IEEE Trans Reliab 64(4):1287–1299. https://doi.org/10.1109/tr.2015.2418294

    Google Scholar 

  6. Guo L, Li NP, Jia F, Lei YG, Lin J (2017) A recurrent neural network based health indicator for remaining useful life prediction of bearings. Neurocomputing 240:98–109. https://doi.org/10.1016/j.neucom.2017.02.045

    Google Scholar 

  7. Zhang B, Zhang LJ, Xu JW (2016) Degradation feature selection for remaining useful life prediction of rolling element bearings. Qual Reliab Eng Int 32(2):547–554. https://doi.org/10.1002/qre.1771

    Google Scholar 

  8. Wang B, Wang F, Dun B, Chen X, Yan D, Zhu H (2016) Remaining life prediction of rolling bearing based on PCA and improved logistic regression model. J VibroEng 18(8):5192–5203. https://doi.org/10.21595/jve.2016.17449

    Google Scholar 

  9. Yu X, Dong F, Ding E, Wu S, Fan C (2018) Rolling bearing fault diagnosis using modified LFDA and EMD with sensitive feature selection. IEEE Access 6:3715–3730

    Google Scholar 

  10. Guo T, Deng ZM (2017) An improved EMD method based on the multi-objective optimization and its application to fault feature extraction of rolling bearing. Appl Acoust 127:46–62. https://doi.org/10.1016/j.apacoust.2017.05.018

    Google Scholar 

  11. Javed K, Gouriveau R, Zerhouni N, Nectoux P (2015) Enabling health monitoring approach based on vibration data for accurate prognostics. IEEE Trans Ind Electron 62(1):647–656. https://doi.org/10.1109/tie.2014.2327917

    Google Scholar 

  12. Liu J, Hu YM, Wu B, Jin C (2017) A hybrid health condition monitoring method in milling operations. Int J Adv Manuf Technol 92(5–8):2069–2080. https://doi.org/10.1007/s00170-017-0252-y

    Google Scholar 

  13. Fu L, Wei YD, Fang S, Zhou XJ, Lou JQ (2017) Condition monitoring for roller bearings of wind turbines based on health evaluation under variable operating states. Energies 10(10):21. https://doi.org/10.3390/en10101564

    Google Scholar 

  14. Chao G, Luo Y, Ding W (2019) Recent advances in supervised dimension reduction: a survey. Mach Learn Knowl Extr 1(1):341–358

    Google Scholar 

  15. Chao G, Mao C, Wang F, Zhao Y, Luo Y (2018) Supervised nonnegative matrix factorization to predict ICU mortality risk. In: 2018 IEEE international conference on bioinformatics and biomedicine (BIBM). IEEE, pp 1189–1194

  16. Yao L, Mao C, Luo Y (2018) Clinical text classification with rule-based features and knowledge-guided convolutional neural networks. In: 2018 IEEE international conference on healthcare informatics workshop (ICHI-W), 4–7 June 2018, pp 70–71. https://doi.org/10.1109/ichi-w.2018.00024

  17. Hinton GE, Salakhutdinov RR (2006) Reducing the dimensionality of data with neural networks. Science 313(5786):504–507

    Google Scholar 

  18. Yu J, Hong C, Rui Y, Tao D (2018) Multitask autoencoder model for recovering human poses. IEEE Trans Ind Electron 65(6):5060–5068. https://doi.org/10.1109/TIE.2017.2739691

    Google Scholar 

  19. LeCun Y, Bengio Y, Hinton G (2015) Deep learning. Nature 521(7553):436–444. https://doi.org/10.1038/nature14539

    Google Scholar 

  20. Wu YT, Yuan M, Dong SP, Lin L, Liu YQ (2018) Remaining useful life estimation of engineered systems using vanilla LSTM neural networks. Neurocomputing 275:167–179. https://doi.org/10.1016/j.neucom.2017.05.063

    Google Scholar 

  21. Wang F, Jiang H, Shao H, Duan W, Wu SP (2017) An adaptive deep convolutional neural network for rolling bearing fault diagnosis. Meas Sci Technol 28(9):095005. https://doi.org/10.1088/1361-6501/aa6e22

    Google Scholar 

  22. Jia F, Lei Y, Guo L, Lin J, Xing S (2018) A neural network constructed by deep learning technique and its application to intelligent fault diagnosis of machines. Neurocomputing 272:619–628. https://doi.org/10.1016/j.neucom.2017.07.032

    Google Scholar 

  23. Guo L, Lei YG, Li NP, Xing SB (2017) Deep convolution feature learning for health indicator construction of bearings. In: 2017 prognostics and system health management conference. IEEE, New York, pp 1–6

  24. Yin JT, Zhao WT (2016) Fault diagnosis network design for vehicle on-board equipments of high-speed railway: a deep learning approach. Eng Appl Artif Intell 56:250–259. https://doi.org/10.1016/j.engappai.2016.10.002

    Google Scholar 

  25. Yu J, Rui Y, Tao D (2014) Click prediction for web image reranking using multimodal sparse coding. IEEE Trans Image Process 23(5):2019–2032

    Google Scholar 

  26. Zabalza J, Ren JC, Zheng JB, Zhao HM, Qing CM, Yang ZJ, Du PJ, Marshall S (2016) Novel segmented stacked autoencoder for effective dimensionality reduction and feature extraction in hyperspectral imaging (vol 185, pg 1, 2016). Neurocomputing 214:1062. https://doi.org/10.1016/j.neucom.2016.09.065

    Google Scholar 

  27. Xu J, Xiang L, Liu QS, Gilmore H, Wu JZ, Tang JH, Madabhushi A (2016) Stacked sparse autoencoder (SSAE) for nuclei detection on breast cancer histopathology images. IEEE Trans Med Imaging 35(1):119–130. https://doi.org/10.1109/tmi.2015.2458702

    Google Scholar 

  28. Gehring J, Miao YJ, Metze F, Waibel A (2013) Extracting deep bottleneck features using stacked auto-encoders. In: 2013 IEEE international conference on acoustics, speech and signal processing (ICASSP). IEEE, New York, pp 3377–3381

  29. Wang P, Gao RX, Yan R (2017) A deep learning-based approach to material removal rate prediction in polishing. CIRP Ann 66(1):429–432. https://doi.org/10.1016/j.cirp.2017.04.013

    Google Scholar 

  30. Chen RX, Chen SY, He M, He D, Tang BP (2017) Rolling bearing fault severity identification using deep sparse auto-encoder network with noise added sample expansion. Proc Inst Mech Eng Part O-J Risk Reliab 231(6):666–679. https://doi.org/10.1177/1748006x17726452

    Google Scholar 

  31. Liu W, Ma T, Xie Q, Tao D, Cheng J (2017) LMAE: a large margin auto-encoders for classification. Sig Process 141:137–143

    Google Scholar 

  32. Shao H, Jiang H, Zhao H, Wang F (2017) A novel deep autoencoder feature learning method for rotating machinery fault diagnosis. Mech Syst Signal Process 95:187–204

    Google Scholar 

  33. Liu J, Hu Y, Wang Y, Wu B, Fan J, Hu Z (2018) An integrated multi-sensor fusion-based deep feature learning approach for rotating machinery diagnosis. Meas Sci Technol 29(5):055103. https://doi.org/10.1088/1361-6501/aaaca6

    Google Scholar 

  34. Liu W, Ma X, Zhou Y, Tao D, Cheng J (2018) p-Laplacian regularization for scene recognition. IEEE Trans Cybern 99:1–14

    Google Scholar 

  35. Ma X, Liu W, Li S, Tao D, Zhou Y (2019) Hypergraph p-laplacian regularization for remotely sensed image recognition. IEEE Trans Geosci Remote Sens 57(3):1585–1595

    Google Scholar 

  36. Ma X, Liu W, Tao D, Zhou Y (2019) Ensemble p-laplacian regularization for scene image recognition. Cogn Comput. https://doi.org/10.1007/s12559-019-09637-z

    Google Scholar 

  37. Hansen LK, Salamon P (1990) Neural network ensembles. IEEE Trans Pattern Anal Mach Intell 12(10):993–1001

    Google Scholar 

  38. Sui C, Bennamoun M, Togneri R (2017) Deep feature learning for dummies: a simple auto-encoder training method using particle swarm optimisation. Pattern Recogn Lett 94:75–80. https://doi.org/10.1016/j.patrec.2017.03.021

    Google Scholar 

  39. Bergstra J, Bengio Y (2012) Random search for hyper-parameter optimization. J Mach Learn Res 13:281–305

    Google Scholar 

  40. Li YY, Lu G, Zhou LH, Jiao LC (2017) Quantum inspired high dimensional hyper-parameter optimization of machine learning model. In: 2017 international smart cities conference. IEEE, New York, pp 1–6

  41. Kim JK, Han YS, Lee JS (2017) Particle swarm optimization-deep belief network-based rare class prediction model for highly class imbalance problem. Concurr Comput Pract Exp 29(11):e4128. https://doi.org/10.1002/cpe.4128

    Google Scholar 

  42. Shao H, Jiang H, Lin Y, Li X (2018) A novel method for intelligent fault diagnosis of rolling bearings using ensemble deep auto-encoders. Mech Syst Signal Process 102:278–297

    Google Scholar 

  43. Tang XS, Ding YS, Hao KR (2018) A novel method based on line-segment visualizations for hyper-parameter optimization in deep networks. Int J Pattern Recogn Artif Intell 32(3):15. https://doi.org/10.1142/s0218001418510023

    Google Scholar 

  44. Wang LM, Shao YM (2018) Crack fault classification for planetary gearbox based on feature selection technique and k-means clustering method. Chin J Mech Eng 31(1):11. https://doi.org/10.1186/s10033-018-0202-0

    Google Scholar 

  45. Nectoux P, Gouriveau R, Medjaher K, et al. (2012) PRONOSTIA: an experimental platform for bearings accelerated degradation tests. In: IEEE international conference on prognostics and health management (PHM’12), IEEE catalog number: CPF12PHM-CDR, pp 1–8

  46. Ng SSY, Tse PW, Tsui KL (2014) A one-versus-all class binarization strategy for bearing diagnostics of concurrent defects. Sensors 14(1):1295–1321. https://doi.org/10.3390/s140101295

    Google Scholar 

  47. Lei Y, Niu S, Guo L, Li N (2017) A distance metric learning based health indicator for health prognostics of bearings. In: 2017 international conference on sensing, diagnostics, prognostics, and control (SDPC), 16–18 Aug. 2017. pp 47–52. https://doi.org/10.1109/sdpc.2017.19

  48. Zhao Y, Li J, Yu L (2017) A deep learning ensemble approach for crude oil price forecasting. Energy Econ 66:9–16

    Google Scholar 

  49. Jiang G, He H, Xie P, Tang Y (2017) Stacked multilevel-denoising autoencoders: a new representation learning Aapproach for wind turbine gearbox fault diagnosis. IEEE Trans Instrum Meas 66(9):2391–2402. https://doi.org/10.1109/tim.2017.2698738

    Google Scholar 

  50. Wang X, Zheng Y, Zhao Z, Wang J (2015) Bearing fault diagnosis based on statistical locally linear embedding. Sensors 15(7):16225–16247

    Google Scholar 

Download references

Funding

This work was supported by the National Natural Science Foundation of China (Grant Nos. 51875432, 51605361); and the Natural Science Basic Research Plan in Shaanxi Province of China (Grant No. 2018JQ5034).

Author information

Authors and Affiliations

  1. School of Mechano-Electronic Engineering, Xidian University, Xi’an, 710071, People’s Republic of China

    Xianguang Kong, Qibin Wang, Hongbo Ma, Xiaodong Wu & Gang Mao

  2. State Key Laboratory for Manufacturing and Systems Engineering, School of Mechanical Engineering, Xi’an Jiaotong University, Xi’an, 710049, People’s Republic of China

    Yang Fu

Authors
  1. Xianguang Kong
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Yang Fu
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Qibin Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Hongbo Ma
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Xiaodong Wu
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Gang Mao
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Qibin Wang.

Ethics declarations

Conflicts of interest

The authors declare that they have 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

Kong, X., Fu, Y., Wang, Q. et al. A High Generalizable Feature Extraction Method Using Ensemble Learning and Deep Auto-Encoders for Operational Reliability Assessment of Bearings. Neural Process Lett 51, 383–406 (2020). https://doi.org/10.1007/s11063-019-10094-w

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11063-019-10094-w

Keywords

Search

Navigation