Explainable 1DCNN with demodulated frequency features method for fault diagnosis of rolling bearing under time-varying speed conditions. (1st September 2022)

• Record Type: Journal Article
• Authors: Lu, Feiyu; Tong, Qingbin; Feng, Ziwei; Wan, Qingzhu; an, Guoping; Li, Yilei; Wang, Meng; Cao, Junci; Guo, Tao
• Is Part Of: Measurement science & technology. Volume 33:Number 9(2022)
• Publication Date: 2022-09-01
• Subjects: fault diagnosis -- time-varying speed -- LIME -- rolling bearing -- 1DCNN; Physical measurements -- Periodicals; Scientific apparatus and instruments -- Periodicals; Equipment and Supplies -- Periodicals; Science -- instrumentation -- Periodicals; Technology -- instrumentation -- Periodicals; Mesures physiques -- Périodiques; Physical measurements; Scientific apparatus and instruments; Periodicals; 502.87
• DOI: 10.1088/1361-6501/ac78c5
• Languages: English
• ISSNs: 0957-0233

Abstract

Abstract: Intelligent fault diagnosis of rolling bearings under non-stationary and time-varying speed conditions is still a challenging task. At the same time, a reasonable explanation for an intelligent diagnosis model based on features is currently lacking. Therefore, we exploit an explainable one-dimensional convolutional neural network (1DCNN) model by combining with the demodulated frequency features of vibration signals and apply it to the fault classification of rolling bearings under time-varying speed conditions. First, the speed signals obtained by the speed encoder were transformed into generalized demodulation operator (GDO). Second, combined with the sensitive frequency band and GDO, the generalized demodulation algorithm was used to extract the frequency features from the amplitude envelope of the vibration signal. Subsequently, the proposed lightweight 1DCNN was trained to classify the frequency features and identify the health states of the rolling bearing. Finally, the local interpretable model-agnostic explanations model was utilized to explain the proposed model based on the features which own weight. It is found that the internal classification mechanism of the lightweight 1DCNN is realized according to the distribution of fault features, which is consistent with the process of human brain analysis. Two kinds of time-varying speed datasets which come from the University of Ottawa and XJTU are tested and verified. The results show that compared with other intelligent fault diagnosis methods, the identification error of the proposed method is lower and the diagnosis stability is better. The average diagnostic accuracy was 96.26% and 99.82%, respectively.