Research on bearing fault diagnosis based on a multimodal method.

As an essential component of mechanical systems, bearing fault diagnosis is crucial to ensure the safe operation of the equipment. However, vibration data from bearings often exhibit non-stationary and nonlinear features, which complicates fault diagnosis. To address this challenge, this paper introduces a novel multi-scale time-frequency and statistical features fusion model (MTSF-FM). Specifically, the method first employs continuous wavelet transform to generate time-frequency images, capturing local and global features of the signal at different scales. Contrast enhancement techniques are then used to improve the visual quality of these images. Next, features are extracted from the time-frequency images using a visual geometry group network to obtain deep features of image modalities. In parallel, 13 key features are extracted from the original vibration data in the time-frequency domain. Convolutional neural networks are then employed for deep feature extraction. Experimental results demonstrate that MTSF-FM achieves accuracies of 98.5% and 95.1% on two public datasets. These findings highlight the effectiveness of MTSF-FM in analyzing complex vibration data and propose a novel method for bearing fault diagnosis.