Affiliation:
1. School of Automotive and Rail Transit, Nanjing Institute of Technology, Nanjing 211167, China
2. State Key Laboratory of Automotive Simulation and Control, Jilin University, Changchun 130015, China
Abstract
Aiming at the difficult problem of extracting fault characteristics and the low accuracy of fault diagnosis throughout the full life cycle of rolling bearings, a fault diagnosis method for rolling bearings based on grey relation degree is proposed in this paper. Firstly, the subtraction-average-based optimizer is used to optimize the parameters of the variational mode decomposition algorithm. Secondly, the vibration signals of bearings are decomposed by using the optimized results, and the feature vector of the intrinsic mode function component corresponding to the minimum envelope entropy is extracted. Finally, the grey proximity and similarity relation degree based on standard distance entropy are weighted to calculate the grey comprehensive relation degree between the feature vector of vibration signals and each standard state. By comparing the results, the diagnosis of different fault states and degrees of rolling bearings is realized. The XJTU-SY dataset was used for experimentation, and the results show that the proposed method achieves a diagnostic accuracy of 95.24% and has better diagnosis performance compared to various algorithms. It provides a reference for the fault diagnosis of rolling bearings throughout the full life cycle.
Funder
National Natural Science Foundation of China
Key Research and Development Program of Jiangsu Province
Reference37 articles.
1. Autocorrelation aided random forest classifier-based bearing fault detection framework;Roy;IEEE Sens. J.,2020
2. Rostaghi, M., Khatibi, M.M., Ashory, M.R., and Azami, H. (2023). Refined composite multiscale fuzzy dispersion entropy and its applications to bearing fault diagnosis. Entropy, 25.
3. Prediction and analysis of bending fatigue life of hub bearing considering oil film lubrication;Lin;Lubr. Eng.,2022
4. Fault diagnosis of electric two-wheeler under pragmatic operating conditions using wavelet synchrosqueezing transform and CNN;Choudhary;IEEE Sens. J.,2023
5. Intelligent fault diagnosis via semisupervised generative adversarial nets and wavelet transform;Liang;IEEE Trans. Instrum. Meas.,2020