Abstract
In recent years, deep learning based diagnostic approaches have become more attractive. However, most of these methods are supervised diagnostic approaches. Developing a supervised diagnostic model requires a large number of labeled training data. And it is time consuming and labor intensive to obtain labeled data for a variety of systems and working conditions. Therefore, an unsupervised diagnostic model that does not require labeled training data is more desirable. This paper proposes an unsupervised diagnostic model by integrating a sparse autoencoder, a deep belief network, and a binary processor. In comparison with the existing unsupervised methods, the proposed method does not need to perform statistical features extraction, and directly uses the normalized frequency domain signals as the inputs. Moreover, in the proposed diagnostic model, the input data is passed through layer by layer without fine-tuning, which is completely unsupervised process. The proposed methods have been validated with bearing fault datasets and gear pitting fault datasets. The validation results show that the proposed method has a higher accuracy for both bearing and gear pitting fault diagnosis.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
References
Ben Ali, J., Saidi, L., Harrath, S., Bechhoefer, E., & Benbouzid, M. (2018). Online automatic diagnosis of wind turbine bearings progressive degradations under real experimental conditions based on unsupervised machine learning. Applied Acoustics, 132, 167–181.
Cao, Y., Ying, Y., Li, J., Li, S., & Guo, J. (2016). Study on rolling bearing fault diagnosis approach based on improved generalized fractal box-counting dimension and adaptive gray relation algorithm. Advances in Mechanical Engineering, 8(10), 168781401667558.
Chen, Z., & Li, W. (2017). Multisensor feature fusion for bearing fault diagnosis using sparse autoencoder and deep belief network. IEEE Transactions on Instrumentation and Measurement, 66(7), 1693–1702.
Cipollini, F., Oneto, L., Coraddu, A., & Savio, S. (2019). Unsupervised deep learning for induction motor bearings monitoring. Data-Enabled Discovery and Applications, 3(1), 1.
Dong, S., Zhang, Z., Wen, G., Dong, S., Zhang, Z., & Wen, G. (2017). Design and application of unsupervised convolutional neural networks integrated with deep belief networks for mechanical fault diagnosis. In 2017 Prognostics and system health management conference (PHM-Harbin) (pp. 1–7), Harbin, China, 2017.
Gao, Y., Gao, L., Li, X., & Zheng, Y. (2019). A zero-shot learning method for fault diagnosis under unknown working loads. Journal of Intelligent Manufacturing.
Gao, X., Wang, H., Gao, H., Wang, X., & Xu, Z. (2018). Fault diagnosis of batch process based on denoising sparse auto encoder. In 2018 33rd youth academic annual conference of Chinese Association of Automation (YAC) (pp. 764–769), Nanjing, 2018.
Geng, Z., Li, Z., & Han, Y. (2018). A new deep belief network based on RBM with glial chains. Information Sciences, 463–464, 294–306.
Guo, L., Lei, Y., Xing, S., Yan, T., & Li, N. (2019). Deep convolutional transfer learning network: A new method for intelligent fault diagnosis of machines with unlabeled data. IEEE Transactions on Industrial Electronics, 66(9), 7316–7325.
He, J., Yang, S., & Gan, C. (2017). Unsupervised fault diagnosis of a gear transmission chain using a deep belief network. Sensors, 17(7), 1564.
Hinton, G. E., Osindero, S., & Teh, Y.-W. (2006). A fast learning algorithm for deep belief nets. Neural Computation, 18(7), 1527–1554.
Hou, L., et al. (2019). Sparse autoencoder for unsupervised nucleus detection and representation in histopathology images. Pattern Recognition, 86, 188–200.
Hu, Y.-T., Qu, F.-H., & Wen, C.-J. (2016). Bearing fault diagnosis based on multi-scale possibilistic clustering algorithm. In 2016 13th International computer conference on wavelet active media technology and information processing (ICCWAMTIP) (pp. 354–357), Chengdu, China, 2016.
Jiang, G.-Q., Xie, P., Wang, X., Chen, M., & He, Q. (2017). Intelligent fault diagnosis of rotary machinery based on unsupervised multiscale representation learning. Chinese Journal of Mechanical Engineering, 30(6), 1314–1324.
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.
Li, J., Cao, Y., Ying, Y., & Li, S. (2016). A rolling element bearing fault diagnosis approach based on multifractal theory and gray relation theory. PLoS ONE, 11(12), e0167587.
Li, J., Li, X., He, D., & Qu, Y. (2019). A novel method for early gear pitting fault diagnosis using stacked SAE and GBRBM. Sensors, 19(4), 758.
Li, X., Zhang, W., Ding, Q., & Sun, J.-Q. (2018). Intelligent rotating machinery fault diagnosis based on deep learning using data augmentation. Journal of Intelligent Manufacturing.
Liu, R., Yang, B., Zio, E., & Chen, X. (2018a). Artificial intelligence for fault diagnosis of rotating machinery: A review. Mechanical Systems and Signal Processing, 108, 33–47.
Liu, H., Zhou, J., Xu, Y., Zheng, Y., Peng, X., & Jiang, W. (2018b). Unsupervised fault diagnosis of rolling bearings using a deep neural network based on generative adversarial networks. Neurocomputing, 315, 412–424.
Modi, S., Lin, Y., Cheng, L., Yang, G., Liu, L., & Zhang, W. J. (2011). A socially inspired framework for human state inference using expert opinion integration. IEEE/ASME Transactions on Mechatronics, 16(5), 874–878.
Piltan, F., & Kim, J.-M. (2018). Bearing fault diagnosis by a robust higher-order super-twisting sliding mode observer. Sensors, 18(4), 1128.
Qian, W., Li, S., Wang, J., & Wu, Q. (2018). A novel supervised sparse feature extraction method and its application on rotating machine fault diagnosis. Neurocomputing, 320, 129–140.
Qin, X., et al. (2018). A cable fault recognition method based on a deep belief network. Computers & Electrical Engineering, 71, 452–464.
Schmidhuber, J. (2015). Deep learning in neural networks: An overview. Neural Networks, 61, 85–117.
Shao, H., Jiang, H., Wang, F., & Wang, Y. (2017). Rolling bearing fault diagnosis using adaptive deep belief network with dual-tree complex wavelet packet. ISA Transactions, 69, 187–201.
Shao, H., Jiang, H., Zhang, X., & Niu, M. (2015). Rolling bearing fault diagnosis using an optimization deep belief network. Measurement Science & Technology, 26(11), 115002.
Sohaib, M., & Kim, J.-M. (2018). Reliable fault diagnosis of rotary machine bearings using a stacked sparse autoencoder-based deep neural network. Shock and Vibration, 2018, 1–11.
Tong, Z., Li, W., Zhang, B., & Zhang, M. (2018). Bearing fault diagnosis based on domain adaptation using transferable features under different working conditions. Shock and Vibration, 2018, 1–12.
Wang, C., Gan, M., & Zhu, C. (2017). Intelligent fault diagnosis of rolling element bearings using sparse wavelet energy based on overcomplete DWT and basis pursuit. Journal of Intelligent Manufacturing, 28(6), 1377–1391.
Wang, C., Gan, M., & Zhu, C. (2019). A supervised sparsity-based wavelet feature for bearing fault diagnosis. Journal of Intelligent Manufacturing, 30(1), 229–239.
Wang, S., Xiang, J., Zhong, Y., & Zhou, Y. (2018). Convolutional neural network-based hidden Markov models for rolling element bearing fault identification. Knowledge-Based Systems, 144, 65–76.
Xia, M., Li, T., Liu, L., Xu, L., & de Silva, C. W. (2017). Intelligent fault diagnosis approach with unsupervised feature learning by stacked denoising autoencoder. IET Science, Measurement and Technology, 11(6), 687–695.
Xiong, Q., Zhang, W., Lu, T., Mei, G., & Liang, S. (2016). A fault diagnosis method for rolling bearings based on feature fusion of multifractal detrended fluctuation analysis and alpha stable distribution. Shock and Vibration, 2016, 1–12.
C.W.R. University. http://csegroups.case.edu/bearingdatacenter/pages/download-data-file.
Yang, Y., & Fu, P. (2018). Rolling-element bearing fault data automatic clustering based on wavelet and deep neural network. Shock and Vibration, 2018, 1–11.
Yin, S., Ding, S. X., Xie, X., & Luo, H. (2014). A review on basic data-driven approaches for industrial process monitoring. IEEE Transactions on Industrial Electronics, 61(11), 6418–6428.
Ying, Y., Li, J., Chen, Z., & Guo, J. (2018). Study on rolling bearing on-line reliability analysis based on vibration information processing. Computers & Electrical Engineering, 69, 842–851.
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.
Yu, L., Qu, J., Gao, F., & Tian, Y. (2019). A novel hierarchical algorithm for bearing fault diagnosis based on stacked LSTM. Shock and Vibration, 2019, 1–10.
Zhang, W. J., Yang, G., Lin, Y., Ji, C., & Gupta, M. M. (2018). On definition of deep learning. In 2018 World automation congress (WAC) (pp. 1–5), Stevenson, WA, 2018.
Ziani, R., Felkaoui, A., & Zegadi, R. (2017). Bearing fault diagnosis using multiclass support vector machines with binary particle swarm optimization and regularized Fisher’s criterion. Journal of Intelligent Manufacturing, 28(2), 405–417.
Author information
Authors and Affiliations
Corresponding author
Ethics declarations
Conflict of interest
The authors declare 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
About this article
Cite this article
Li, J., Li, X., He, D. et al. Unsupervised rotating machinery fault diagnosis method based on integrated SAE–DBN and a binary processor. J Intell Manuf 31, 1899–1916 (2020). https://doi.org/10.1007/s10845-020-01543-8
Received:
Accepted:
Published:
Version of record:
Issue date:
DOI: https://doi.org/10.1007/s10845-020-01543-8