Abstract
The vibration signals of rolling bearing obtained under variable working conditions do not obey the same independent distribution so that the traditional method of bearing fault diagnosis has low accuracy, a fault diagnosis method about rolling bearing based on sparse denoising autoencoder (SDAE) for deep feature extraction combining transfer learning is proposed. First, the bearing vibration signal in the time domain is transformed for frequency domain signal via Fourier transform, which is input into the SDAE for adaptive deep feature extraction. Then, the joint geometrical and statistical alignment is introduced to deal with the deep feature samples for reducing the domain discrepancy both statistically and geometrically. Finally, the k-nearest neighbor classification algorithm is used for completing the fault diagnosis of rolling bearing under variable working conditions. The experimental results show that the method presented in the paper improves the accuracy rate of fault diagnosis about rolling bearing under variable working conditions, verifies its feasibility and effectiveness.
Similar content being viewed by others
References
Chen GH, Qie LF, Zhang AJ (2016) Improved CICA algorithm used for single channel compound fault diagnosis of Rolling Bearings. Chin J Mech Eng 29(1):204–211
Yang BY, Liu RN, Chen XF (2017) Fault diagnosis for a wind turbine generator bearing via sparse representation and shift-invariant K-SVD. IEEE Trans Ind Electron 13(3):1321–1331
Wang TY, Liang M, Li JY (2014) Rolling element bearing fault diagnosis via fault characteristic order(FCO) analysis. Mech Syst Signal Process 45(1):139–153
Xu J, Zhao J, Ma B (2013) Fault diagnosis of complex industrial process using KICA and sparse SVM. Math Probl Eng 3:87–118
Liu HH, Han MH (2014) A fault diagnosis method based on local mean decomposition and multi-scale entropy for roller bearings. Mech Mach Theory 18(75):67–78
Cocconcelli M, Bassi L, Secchi C (2012) An algorithm to diagnosis to diagnose ball bearing faults in servomotors running arbitrary motion profiles. Mech Syst Signal Process 27(1):667–682
Cerradam M, Zurita G, Cabrera D (2016) Fault diagnosis in spur gears based on genetic algorithm and random forest. Mech Syst Signal Process 70(1):87–103
Samanta B, Al-Balushi KR (2003) Artificial neural network based fault diagnostics of rolling element bearings using time-domain features. Mech Syst Signal Process 17(2):317–328
Zhou W, Habetler TG, Harley RG (2007) Stator current-based bearing fault detection techniques: a general review. In 2007 IEEE international symposium on diagnostics for electric machines power electronics and drives, pp 7–10
Duan RC, Wang FH (2016) Fault diagnosis of on-load tap-changer in converter transformer based on time-frequency vibration analysis. IEEE Trans Ind Electron 63(6):3815–3823
Miguel DP, Giansalvo C, Antonio GE (2013) Bearing fault detection by a novel condition-monitoring scheme based on statistical-time features and neural networks. IEEE Trans Ind Electron 60(8):3398–3407
Yann LC, Yoshua B, Geoffrey H (2015) Deep learning. Nature 521(7553):436–444
Yuan XF, Huang B, Wang YL (2018) Deep learning based feature representation and its application for soft sensor modeling with variable-wise weighted SAE. IEEE Trans Ind Electron 14(7):3235–3243
Wen L, Gao L, Li XY (2019) A new deep transfer learning based on sparse auto-encoder for fault diagnosis. IEEE Trans Syst Man Cybern Syst 49(1):136–144
Sun MD, Wang H, Liu P (2019) A sparse stacked denoising autoencoder with optimized transfer learning applied to the fault diagnosis of rolling bearings. Measurement 146:305–314
Shao HD, Jiang HK, Zhao HW (2017) A novel deep autoencoder feature learning method for rotating machinery fault diagnosis. Mech Syst Signal Process 95:187–204
Guo XJ, Chen L, Shen CQ (2016) Hierarchical adaptive deep convolution neural network and its application to bearing fault diagnosis. Measurement 93:490–502
Liu HM, Wang X, Chen L (2014) Rolling bearing fault diagnosis under variable conditions using Hilbert–Huang transform and singular value decomposition. Math Probl Eng 2014:765621. https://doi.org/10.1155/2014/765621
Yang Y, Wang HH, Cheng JS (2013) A fault diagnosis approach for bearings based on VPMCD under variable speed conditio. Measurement 46(8):2306–2312
Wu TY, Yu CL, Liu DC (2016) On multi-scale entropy analysis of order-tracking measurement for bearing fault diagnosis under variable speed. Entropy 18(8):292
Fei SW (2017) Fault diagnosis of bearing under varying load conditions by utilizing composite features self-adaptive reduction-based RVM classifier. J Vib Eng Technol 5(3):269–276
Pan SJ, Yang Q (2010) A survey on transfer learning. IEEE Trans Knowl Data Eng 20(10):1345–1359
Shell J, Coupland S (2015) Fuzzy transfer learning: methodology and application. Inf Sci 293:59–79
Shen F, Chen C, Yan RQ (2017) Application of singular value decomposition and transfer learning in motor fault diagnosis. J Vib Eng 30(1):118–126 (in Chinese)
Chen C, Shen F, Yan RQ (2017) Bearing Fault diagnosis based on improved LSSVM and transfer learning method. J Instrum 38(1):33–40 (In Chinese)
Pan SJ, Tsang IW, Kwork JT (2011) Domain adaptation via transfer component analysis. IEEE Trans Neural Netw 22(2):199–210
Long MS, Wang JM, Ding GG, Sun JG (2013) Transfer feature learning with joint distribution adaptation. In: IEEE international conference on computer vision, pp 2200–2207
Fernando B, Habrard A, Sebban M (2013) Unsupervised visual domain adaptation using subspace alignment. In: Proceedings of 2013 IEEE international conference on computer vision. IEEE, Sydney, NSW Australia, pp 2960–2967
Zhang J, Li WQ, Ogunbona PO (2017) Joint geometrical and statistical alignment for visual domain adaptation. https://www.researchgate.net/publication/314246097
Olshausen BA, Field DJ (1996) Emergence of simple-cell receptive field properties by learning a sparse code for natural images. Nature 381(6583):607–609
Vincent P, Larochelle H, Bengio Y (2008) Extracting and composing robust features with denoising autoencoders. In: Proceedings of the 25th international conference on machine learning Helsinki, Finland, pp 1096–1103
Gretton A, Borgwardt KM, Rasch MJ (2012) A kernal two-sample test. J Mach Learn Res 13:723–773
Yang B, Lei YG, Jia F (2020) A polynomial kernel induced distance metric to improve deep transfer learning for fault diagnosis of machines. IEEE Trans Ind Electron 67(11):9747–9757
Case Western Reserve University Bearing Data Center. http://csegroups.case.edu/bearingdatacenter/home
Acknowledgements
This research is supported by the National Natural Science Foundation of China (No. 51775072), Natural Science Foundation Project of CQ cstc2017jcyjAX0279.
Author information
Authors and Affiliations
Corresponding author
Additional information
Technical Editor: Marcelo Areias Trindade.
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
About this article
Cite this article
Dong, S., He, K. & Tang, B. The fault diagnosis method of rolling bearing under variable working conditions based on deep transfer learning. J Braz. Soc. Mech. Sci. Eng. 42, 585 (2020). https://doi.org/10.1007/s40430-020-02661-3
Received:
Accepted:
Published:
DOI: https://doi.org/10.1007/s40430-020-02661-3