In this paper, a novel neural network based on parameter optimization Variational Mode Decomposition (VMD) and improved bidirectional long short-term memory (BiLSTM) is proposed. Wear state recognition method of ball screw element action unit component based on Bilstm. Firstly, Tent chaotic map and adaptive sine cosine Algorithm were used to improve the improved Northern Goshawk Optimisation Algorithm (INGO) to verify the superiority of INGO algorithm and determine the optimal parameter combination of VMD. Secondly, INGO-VMD was used to decompose the collected vibration signals and calculate the correlation of IMF components, and the multi-feature information matrix that could characterize the wear state change of the lead screw was constructed after retaining the IMF components with large correlation. Finally, the divided feature information matrix and labels were input into the Bilstm network model of Bayesian optimization (BO) for training, and the Softmax classifier was used to classify and identify the wear state category.

To solve the problems of dynamic model parameter perturbation and external disturbances during the operation of the ball screw meta-action unit, an adaptive sliding mode control method based on disturbance compensation and inertia identification is proposed in this paper. First, a model of the ball screw meta-action unit is established and the dynamic equation is derived. Taking into account the uncertainty of the dynamic parameters, a least squares method with a forgetting factor is introduced to identify the moment of inertia in real time, and the identification results are utilized to adaptively adjust the sliding mode control law. Second, a nonlinear disturbance observer is designed to effectively observe the load disturbance, and the observed value is substituted into the sliding mode control as feedforward compensation to improve the anti-interference ability of the controller. Committed to the chattering of traditional sliding mode control, an improved adaptive exponential sliding mode reaching law is exploited to construct a novel sliding mode controller, thereby suppressing the slid mode chattering more thoroughly. Finally, the superiority of the proposed method is verified by MATLAB/Simulink simulation and compared with the other two control methods; the control method proposed in this paper can effectively improve the tracking performance of the system and has good robustness.