Intelligent Detection Algorithm of Mechanical Vibration Fault Based on Multi-sensor Fusion

In this paper, firstly, the multi-sensor fusion technology is deeply analyzed, and combined with Deep learning (DL) technology, an intelligent fault detection model suitable for multi-sensor fusion data is constructed, and it is described in detail. According to the characteristics of mechanical vibration signal, the lifting Wavelet transformation (WT) is applied to the purification and denoising of axis trajectory on the basis of studying the principle of wavelet threshold denoising. The existing wavelet filter is decomposed into basic building blocks, the integer transform of wavelet is completed step by step, the wavelet coefficients are extracted more efficiently, the signal and noise are effectively separated, and the accurate reconstruction is realized. Then, the effectiveness of the proposed algorithm is verified by simulation research, and compared with the existing methods, which proves the superiority of the proposed algorithm in fault diagnosis accuracy and robustness. Simulation results show that the fault detection algorithm proposed in this paper performs well in accuracy, recall and F1 score. Compared with the traditional wavelet, the wavelet in this paper can suppress the noise and retain the signal characteristics to the maximum extent in signal denoising. It is expected that the work in this paper can contribute to solving practical problems in the field of mechanical vibration fault detection, and provide useful reference and enlightenment for researchers and engineers in related fields.