An automatic and robust features learning method for rotating machinery fault diagnosis based on contractive autoencoder. (November 2018)

• Record Type: Journal Article
• Authors: Shen, Changqing; Qi, Yumei; Wang, Jun; Cai, Gaigai; Zhu, Zhongkui
• Is Part Of: Engineering applications of artificial intelligence. Volume 76(2018)
• Page Start: 170
• Page End: 184
• Publication Date: 2018-11
• Subjects: Robust features -- Contractive autoencoder -- Correlation analysis -- Fault diagnosis; Engineering -- Data processing -- Periodicals; Artificial intelligence -- Periodicals; Expert systems (Computer science) -- Periodicals; Ingénierie -- Informatique -- Périodiques; Intelligence artificielle -- Périodiques; Systèmes experts (Informatique) -- Périodiques; Artificial intelligence; Engineering -- Data processing; Expert systems (Computer science); Periodicals; 620.00285
• DOI: 10.1016/j.engappai.2018.09.010
• Languages: English
• ISSNs: 0952-1976

Abstract

Abstract: Fault diagnosis of rotating machinery is vital to improve the security and reliability as well as avoid serious accidents. For instance, robust fault features are crucial to achieve a high diagnosis precision. However, traditional feature extraction methods rely on an abundant amount of expertise and human interference. As a breakthrough in fault diagnosis, deep learning holds the potential to automatically extract discriminative features without much prior knowledge and human interference. However, only a few deep learning models are designed to deal with noise and extract robust features. Contractive autoencoder (CAE) is a potential tool to grasp the internal factors and directly obtain the hidden robust features by penalizing the Frobenius norm of the Jacobian matrix of the hidden features with respect to the inputs. Thus, this paper proposes a method based on stacked CAE for automatic robust features extraction and fault diagnosis of rotating machinery. Gearbox and bearing fault diagnosis experiments are conducted, and the testing accuracy of the proposed method is approximately 100% for both two cases and higher than that of other methods, which fully validates the effectiveness and superiority of the proposed method. In addition, experiments and correlation analysis under different signal-to-noise ratios (SNRs) are conducted. Results show that the diagnosis accuracies of the proposed method are higher than those of the stacked autoencoder (AE) network under each SNR, especially when under 0 dB, the testing accuracies of the proposed method are 4.14% and 5.88% higher than those of the stacked AE network in two case studies, and the correlation coefficients of the CAE are higher than those of the AE, which demonstrate the capability of CAE in mining more robust features compared to the regular AE automatically and the superiority of the proposed method in fault diagnosis. Highlights: A fault diagnosis method is proposed for automatic and robust feature learning. Signals under six SNRs are analyzed to validate the superiority of the method. Correlation analysis is conducted to validate the robustness of the mined features. PCA is used for feature visualization to validate the features' representativeness.