Shunming Li, Mengqi Feng, Jinzhao Yang , Siqi Gong, and Jiangtao Lu
Time-varying speed, rolling bearing, deep learning, neural network,Nadam algorithm∗ School of Automotive Engineering, Nantong Institute ofTechnology, Nantong 226002, China; e-mail: [email protected];[email protected]; [email protected]∗∗ College of Energy and Power Engineering, Nanjing University ofAeronautics and Astronautics, Nanjing 210016, China; e-mail:[email protected]; [email protected] author: Li ShunmingRecommended by Xingxing Jiang
During the actual operation of rotating machinery, the external environment and internal structure will change the working conditions. The time-varying speed conditions will lead to the deviation, skew and amplitude change of the vibration signal characteristics of rolling bearing. The fault characteristics under this condition are difficult to fully extract and diagnose. To solve this problem, a diagnosis model based on multi-scale convolution bidirectional long short-term memory neural network is constructed. An intelligent diagnosis method for rolling bearing fault under time- varying speed is proposed. This method combines the more efficient Nadam optimisation method with two independent networks for parallel optimisation training to accurately extract fault features. The influence of Nadam optimisation algorithm on the training process and diagnosis results is analysed. The results of test data diagnosis and visual analysis show that the method can effectively achieve fault diagnosis of rolling bearings under time-varying speed conditions. The comparative analysis shows that the accuracy and robustness of the diagnosis are superior to other methods under the two time-varying speed conditions of speed increase and speed decrease.
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