Abstract
Data-based fault diagnosis is an important technology in modern manufacturing systems. However, most of these diagnosis methods assume that all the data should be identically distributed. In diagnosis tasks, this assumption means that these methods can only handle faults from the same working load. In real-world applications, the working load of the equipment varies for the different productions; if an unknown working load with no prior data available is given, then these traditional methods may be invalid. Zero-shot learning, using known data to diagnose the fault under unknown working loads, provides a transfer approach to solve this problem. In this paper, a zero-shot learning method based on contractive stacked autoencoders is proposed. The proposed method is only trained by the data from the known working load and can diagnose the fault from unknown but related working loads without prior data. The experimental results on the Case Western Reserve University dataset indicate that the proposed method performs better than the traditional methods under unknown working loads and has an accuracy of 97.82%. In addition, the analysis of the singular value and feature space also suggests that the proposed method is more robust and the feature representation is more contractive.
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References
Bøgh, S. (1995). Multiple hypothesis-testing approach to FDI for the industrial actuator benchmark. Control Engineering Practice,3(12), 1763–1768. https://doi.org/10.1016/0967-0661(95)00191-V.
Fedala, S., Rémond, D., Zegadi, R., & Felkaoui, A. (2018). Contribution of angular measurements to intelligent gear faults diagnosis. Journal of Intelligent Manufacturing,29(5), 1115–1131. https://doi.org/10.1007/s10845-015-1162-1.
Fengqi, W., & Meng, G. (2006). Compound rub malfunctions feature extraction based on full-spectrum cascade analysis and SVM. Mechanical Systems and Signal Processing,20(8), 2007–2021. https://doi.org/10.1016/j.ymssp.2005.10.004.
Fung, G. P. C., Yu, J. X., Lu, H., & Yu, P. S. (2006). Text classification without negative examples revisit. IEEE Transactions on Knowledge and Data Engineering,18, 6–20. https://doi.org/10.1109/TKDE.2006.16.
Glorot, X., Bordes, A., & Bengio, Y. (2011). Deep sparse rectifier neural networks. In Proceedings of the fourteenth international conference on artificial intelligence and statistics (pp. 315–323).
Hinton, G. E., & Salakhutdinov, R. R. (2006). Reducing the dimensionality of data with neural networks. Science,313(5786), 504–507. https://doi.org/10.1126/science.1127647.
Karakose, E., Gencoglu, M. T., Karakose, M., Yaman, O., Aydin, I., & Akin, E. (2018). A new arc detection method based on fuzzy logic using S-transform for pantograph–catenary systems. Journal of Intelligent Manufacturing,29(4), 839–856. https://doi.org/10.1007/s10845-015-1136-3.
Kingma, D. P., & Ba, J. (2014). Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980.
Krizhevsky, A., & Hinton, G. E. (2012). ImageNet classification with deep convolutional neural networks. In Neural information processing systems (pp. 1097–1105). http://dx.doi.org/10.1016/j.protcy.2014.09.007.
Lei, Y., Jia, F., Lin, J., Xing, S., & Ding, S. X. (2016). An intelligent fault diagnosis method using unsupervised feature learning towards mechanical big data. IEEE Transactions on Industrial Electronics,63(5), 3137–3147. https://doi.org/10.1109/TIE.2016.2519325.
Lei, Y., Zuo, M. J., He, Z., & Zi, Y. (2010). A multidimensional hybrid intelligent method for gear fault diagnosis. Expert Systems with Applications. https://doi.org/10.1016/j.eswa.2009.06.060.
Lu, C., Wang, Z. Y., Qin, W. L., & Ma, J. (2017). Fault diagnosis of rotary machinery components using a stacked denoising autoencoder-based health state identification. Signal Processing,130, 377–388. https://doi.org/10.1016/j.sigpro.2016.07.028.
Mehra, R. K., & Peschon, J. (1971). An innovations approach to fault detection and diagnosis in dynamic systems. Automatica,7(5), 637–640. https://doi.org/10.1016/0005-1098(71)90028-8.
Nikiforov, I., Varavva, V., & Kireichikov, V. (1993). Application of statistical fault detection algorithms to navigation systems monitoring. Automatica,29(5), 1275–1290. https://doi.org/10.1016/0005-1098(93)90050-4.
Pan, S. J., & Yang, Q. (2010). A survey on transfer learning. IEEE Transactions on Knowledge and Data Engineering,22(10), 1345–1359. https://doi.org/10.1109/TKDE.2009.191.
Peng, Z. K., Tse, P. W., & Chu, F. L. (2005). A comparison study of improved Hilbert–Huang transform and wavelet transform: Application to fault diagnosis for rolling bearing. Mechanical Systems and Signal Processing,19(5), 974–988. https://doi.org/10.1016/j.ymssp.2004.01.006.
Precup, R. E., Angelov, P., Costa, B. S. J., & Sayed-Mouchaweh, M. (2015). An overview on fault diagnosis and nature-inspired optimal control of industrial process applications. Computers in Industry,74, 75–94. https://doi.org/10.1016/j.compind.2015.03.001.
Rifai, S., Vincent, P., Muller, X., Glorot, X., & Bengio, Y. (2011). Contractive auto-encoders: Explicit invariance during feature extraction. In Proceedings of the 28th international conference on machine learning (ICML-11) (pp. 833–840). https://doi.org/10.1017/s0950268810000646.
Ripley, B. D. (2014). Pattern recognition and neural networks. Cambridge: Cambridge University Press. https://doi.org/10.1017/cbo9780511812651.
Santos, P., Maudes, J., & Bustillo, A. (2018). Identifying maximum imbalance in datasets for fault diagnosis of gearboxes. Journal of Intelligent Manufacturing,29(2), 333–351. https://doi.org/10.1007/s10845-015-1110-0.
Schmidhuber, J. (2015). Deep learning in neural networks: An overview. Neural Networks,61, 85–117. https://doi.org/10.1016/j.neunet.2014.09.003.
Shao, H., Jiang, H., Zhang, X., & Niu, M. (2015). Rolling bearing fault diagnosis using an optimization deep belief network. Measurement Science and Technology. https://doi.org/10.1088/0957-0233/26/11/115002.
Smith, W. A., & Randall, R. B. (2015). Rolling element bearing diagnostics using the Case Western Reserve University data: A benchmark study. Mechanical Systems and Signal Processing,64, 100–131. https://doi.org/10.1016/j.ymssp.2015.04.021.
Sun, W., Shao, S., Zhao, R., Yan, R., Zhang, X., & Chen, X. (2016). A sparse auto-encoder-based deep neural network approach for induction motor faults classification. Measurement,89, 171–178. https://doi.org/10.1016/j.measurement.2016.04.007.
Wang, C., Gan, M., & Zhu, C. (2018a). Fault feature extraction of rolling element bearings based on wavelet packet transform and sparse representation theory. Journal of Intelligent Manufacturing,29(4), 937–951. https://doi.org/10.1007/s10845-015-1153-2.
Wang, D., He, B., Liu, S., Liu, C., & Fei, L. (2016). Dimensional shrinkage prediction based on displacement field in investment casting. International Journal of Advanced Manufacturing Technology,85(1), 201–208. https://doi.org/10.1007/s00170-015-7836-1.
Wang, L., & Gao, R. X. (2006). Condition monitoring and control for intelligent manufacturing. Berlin: Springer. https://doi.org/10.1007/1-84628-269-1.
Wang, X., Chen, C., Cheng, Y., Chen, X., & Liu, Y. (2018b). Zero-shot learning based on deep weighted attribute prediction. IEEE Transactions on Systems, Man, and Cybernetics: Systems. https://doi.org/10.1109/tsmc.2018.2837670.
Wen, L., Gao, L., & Li, X. (2017). A new deep transfer learning based on sparse auto-encoder for fault diagnosis. IEEE Transactions on Systems, Man, and Cybernetics: Systems,49(1), 136–144. https://doi.org/10.1109/TSMC.2017.2754287.
Wen, L., Li, X., Gao, L., & Zhang, Y. (2018). A new convolutional neural network-based data-driven fault diagnosis method. IEEE Transactions on Industrial Electronics,65(7), 5990–5998. https://doi.org/10.1109/TIE.2017.2774777.
Zhou, Q., Yan, P., Liu, H., & Xin, Y. (2019). A hybrid fault diagnosis method for mechanical components based on ontology and signal analysis. Journal of Intelligent Manufacturing,30(4), 1693–1715. https://doi.org/10.1007/s10845-017-1351-1.
Acknowledgements
This work was supported by the Natural Science Foundation of China (NSFC) under Grants 51825502, 51775216 and 51711530038, the Natural Science Foundation of Hubei Province under Grant 2018CFA078, and the Program for HUST Academic Frontier Youth Team 2017QYTD04.
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Gao, Y., Gao, L., Li, X. et al. A zero-shot learning method for fault diagnosis under unknown working loads. J Intell Manuf 31, 899–909 (2020). https://doi.org/10.1007/s10845-019-01485-w
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DOI: https://doi.org/10.1007/s10845-019-01485-w