Abstract
In the practice of deep learning-based rotating machinery fault diagnosis method, how to improve the accuracy of cross-working condition diagnosis under noise background is an urgent problem to be solved, which limits the application of deep learning method in engineering practice. Although domain adaptation methods have been widely researched to solve the problem, they often face the problem of domain shift. In this paper, two domain adaptation methods: domain adversarial learning and distribution matching methods, are used to train the deep network, to make the model realize the fault diagnosis across load conditions and signal-to-noise ratio. Moreover, in order to suppress the domain shift phenomenon which often occurred in domain adaptation tasks. A new network architecture based on the wide convolution kernel, multi-scale attention module, and self-adaptive soft threshold function is proposed so that the network is more suitable for feature extraction in cross-working condition fault diagnosis, and can avoid the influence of noise in vibration signals. Compared with other methods, the proposed method has good performance in faults diagnosis across load conditions and under noise. Feature visualization proved that the proposed network can effectively extract the conjoint fault features across different load conditions from the signal; hence, the fault diagnosis across load conditions and under the noise background can be realized and the domain shift can be suppressed effectively.
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References
Wang C, Sun H, Cao X (2021) Construction of the efficient attention prototypical net based on the time–frequency characterization of vibration signals under noisy small sample. Measurement 179:109412. https://doi.org/10.1016/j.measurement.2021.109412
Mao W, Tian S, Fan J et al (2020) Online detection of bearing incipient fault with semi-supervised architecture and deep feature representation. J Manuf Syst 55:179–198. https://doi.org/10.1016/j.jmsy.2020.03.005
Wu JD, Kuo JM (2009) An automotive generator fault diagnosis system using discrete wavelet transform and artificial neural network[J]. Expert Syst Appl 36(6):9776–9783. https://doi.org/10.1016/j.eswa.2009.02.027
Shi M, Cao Z, Liu Y et al (2021) Feature extraction method of rolling bearing based on adaptive divergence matrix LDA. Meas Sci Technol. https://doi.org/10.1088/1361-6501/abde72
Schmidt S, Heyns PS, Gryllias KC (2021) An informative frequency band identification framework for gearbox fault diagnosis under time-varying operating conditions. Mech Syst Signal Process 158:107771. https://doi.org/10.1016/j.ymssp.2021.107771
Zhao R, Yan R, Chen Z et al (2019) Deep learning and its applications to machine health monitoring. Mech Syst Signal Process 115:213–237. https://doi.org/10.1016/j.ymssp.2018.05.050
Shao H, Jiang H, Wang F et al (2017) Rolling bearing fault diagnosis using adaptive deep belief network with dual-tree complex wavelet packet. ISA Trans 69:187–201. https://doi.org/10.1016/j.isatra.2017.03.017
Guo S, Yang T, Gao W et al (2018) An intelligent fault diagnosis method for bearings with variable rotating speed based on pythagorean spatial pyramid pooling CNN. Sensors 18(11):3857. https://doi.org/10.3390/s18113857
Li W, Shang Z, Gao M et al (2021) A novel deep autoencoder and hyperparametric adaptive learning for imbalance intelligent fault diagnosis of rotating machinery. Eng Appl Artif Intell 102:104279. https://doi.org/10.1016/j.engappai.2021.104279
Zhang R, Tao H, Wu L et al (2017) Transfer learning with neural networks for bearing fault diagnosis in changing working conditions[J]. IEEE Access 5:14347–14357. https://doi.org/10.1109/ACCESS.2017.2720965
Zhou H, Wang S, Miao Z, et al. Review of the application of deep learning in fault diagnosis. In: 2019 Chinese control conference (CCC). IEEE, 2019: 4951–4955. https://doi.org/10.23919/ChiCC.2019.8865387
Lu N, Yin T (2021) Transferable common feature space mining for fault diagnosis with imbalanced data. Mech Syst Signal Process 156:107645. https://doi.org/10.1016/j.ymssp.2021.107645
Che C, Wang H, Ni X et al (2020) Domain adaptive deep belief network for rolling bearing fault diagnosis. Comput Ind Eng 143:106427. https://doi.org/10.1016/j.cie.2020.106427
Li X, Zhang W, Ding Q et al (2019) Multi-layer domain adaptation method for rolling bearing fault diagnosis. Signal Process 157:180–197. https://doi.org/10.1016/j.sigpro.2018.12.005
Li Y, Song Y, Jia L et al (2020) Intelligent fault diagnosis by fusing domain adversarial training and maximum mean discrepancy via ensemble learning[J]. IEEE Trans Ind Inform 17(4):2833–2841. https://doi.org/10.1109/TII.2020.3008010
Wang X, Liu F, Zhao D (2020) Cross-machine fault diagnosis with semi-supervised discriminative adversarial domain adaptation. Sensors 20(13):3753. https://doi.org/10.3390/s20133753
Kodirov E, Xiang T, Gong S. Semantic autoencoder for zero-shot learning. In: proceedings of the IEEE conference on computer vision and pattern recognition. 2017: 3174–3183. https://doi.org/10.1109/CVPR.2017.473
Zhu J, Chen N, Shen C (2020) A new multiple source domain adaptation fault diagnosis method between different rotating machines. IEEE Trans Ind Inform 17(7):4788–4797. https://doi.org/10.1109/TII.2020.3021406
Stacke K, Eilertsen G, Unger J et al (2020) Measuring domain shift for deep learning in histopathology. IEEE J Biomed Health Inform 25(2):325–336. https://doi.org/10.1109/JBHI.2020.3032060
Chang Y, Chen J, Qu C et al (2020) Intelligent fault diagnosis of wind turbines via a deep learning network using parallel convolution layers with multi-scale kernels. Renew Energy 153:205–213. https://doi.org/10.1016/j.renene.2020.02.004
Ma S, Cai W, Liu W et al (2019) A lighted deep convolutional neural network based fault diagnosis of rotating machinery. Sensors 19(10):2381. https://doi.org/10.3390/s19102381
Yu J, Zhang C, Wang S (2021) Multichannel one-dimensional convolutional neural network-based feature learning for fault diagnosis of industrial processes. Neural Comput Appl 33(8):3085–3104. https://doi.org/10.1007/s00521-020-05171-4
Yinghua Y, Doliang L, Xiaozhi L. Fault diagnosis based on one-dimensional deep convolution neural network. In: 2020 Chinese control and decision conference (CCDC). IEEE, 2020: 5630–5635. https://doi.org/10.1109/CCDC49329.2020.9164297
Wen L, Li X, Gao L et al (2017) A new convolutional neural network-based data-driven fault diagnosis method. IEEE Trans Ind Electron 65(7):5990–5998. https://doi.org/10.1109/TIE.2017.2774777
He F, He X (2019) A continuous differentiable wavelet shrinkage function for economic data denoising. Comput Econ 54(2):729–761. https://doi.org/10.1007/s10614-018-9849-y
Yang WX, Ren XM (2004) Detecting impulses in mechanical signals by wavelets. EURASIP J Adv Signal Process 2004(8):1–7. https://doi.org/10.1155/S1110865704311091
Donoho DL (1995) De-noising by soft-thresholding. IEEE Trans Inf Theory 41(3):613–627. https://doi.org/10.1109/18.382009
Lu T, Yu F, Han B et al (2020) A generic intelligent bearing fault diagnosis system using convolutional neural networks with transfer learning. IEEE Access 8:164807–164814. https://doi.org/10.1109/ACCESS.2020.3022840
Shi X, Qiu G, Yin C et al (2021) An Improved bearing fault diagnosis scheme based on hierarchical fuzzy entropy and alexnet network. IEEE Access 9:61710–61720. https://doi.org/10.1109/ACCESS.2021.3073708
Ghulanavar R, Dama KK, Jagadeesh A (2020) Diagnosis of faulty gears by modified alexnet and improved grasshopper optimization algorithm (IGOA). J Mech Sci Technol 34(10):4173–4182. https://doi.org/10.1007/s12206-020-0909-6
Huang H, Ouyang H, Gao H et al (2016) A feature extraction method for vibration signal of bearing incipient degradation. Meas Sci Rev 16(3):149. https://doi.org/10.1515/msr-2016-0018
Hao S, Ge FX, Li Y et al (2020) Multisensor bearing fault diagnosis based on one-dimensional convolutional long short-term memory networks. Measurement 159:107802. https://doi.org/10.1016/j.measurement.2020.107802
Zhao M, Zhong S, Fu X et al (2019) Deep residual shrinkage networks for fault diagnosis. IEEE Trans Ind Inform 16(7):4681–4690. https://doi.org/10.1109/TII.2019.2943898
Song X, Cong Y, Song Y et al (2021) A bearing fault diagnosis model based on CNN with wide convolution kernels. J Ambient Intell Humaniz Comput. https://doi.org/10.1007/s12652-021-03177-x
Ganin Y, Lempitsky V. Unsupervised domain adaptation by backpropagation. In: international conference on machine learning. PMLR, 2015: 1180–1189.
Song Y, Li Y, Jia L et al (2019) Retraining strategy-based domain adaption network for intelligent fault diagnosis. IEEE Trans Ind Inform 16(9):6163–6171. https://doi.org/10.1109/TII.2019.2950667
Zhang W, Peng G, Li C et al (2017) A new deep learning model for fault diagnosis with good anti-noise and domain adaptation ability on raw vibration signals. Sensors 17(2):425. https://doi.org/10.3390/s17020425
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Gao, S., Sun, H. & Ma, S. A fault diagnosis network based on domain adversarial learning and distribution matching for rotating machine vibration signal with noise and across-load conditions. J Braz. Soc. Mech. Sci. Eng. 45, 51 (2023). https://doi.org/10.1007/s40430-022-03974-1
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DOI: https://doi.org/10.1007/s40430-022-03974-1