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Wear state identification of ball screw meta action unit based on parameter optimization VMD and improved Bilstm

Ge Hongyu, Wang Cangfu, Guo Anxiang, Zhang Chuanwei, Zhao Zhan, Yang Manzhi

Eksploatacja i Niezawodność

2024

Vol. 26, no. 4

art. no. 191461

Aiming at the problem that the wear feature information of the ball screw meta-action unit is easily disturbed by noise and the wear state recognition accuracy is not high, a wear state recognition method based on parameter optimization VMD and improved Bilstm was proposed.Firstly, Tent chaotic mapping and adaptive positive cosine algorithm are used to improve Northern Goshawk Optimisation Algorithm (INGO); Secondly, the INGO-VMD was used to decompose the collected vibration signals. The power spectrum entropy, alignment entropy and fuzzy entropy of the IMF components with large correlation after decomposition are calculated to construct the feature information matrix. Finally, the described feature information matrix with labels was input into the Bayesian Optimization Bilstm network model for training, and the Softmax classifier was used to classify the wear state categories. In order to verify the superiority of the proposed method, it is compared with VMD-Lstm, VMD-Bilstm and VMD-Bo-Bilstm models, and the results show that the designed method has higher recognition accuracy.