A Multi-Input and Multi-Task Convolutional Neural Network for Fault Diagnosis Based on Bearing Vibration Signal

Abstract

Bearing fault diagnosis is essential for the safe and stable operation of rotating machinery. Existing methods use signals from a single dimension, limiting diagnostic generality and accuracy. To address these limitations and make improved use of signal features from multiple dimensions, a novel convolutional neural network model with multi-dimensional signal inputs and multi-dimensional task outputs called MIMTNet is proposed. First, frequency domain signals and a time frequency graph are obtained by using the short-time Fourier transform and a wavelet transform to process original time domain signals simultaneously. Then, the time domain signals, the frequency domain signals, and the time frequency graph are fed into the model and a special aggregation is performed after extracting features from the three corresponding branches. Finally, the outputs of the three-dimensional tasks are acquired by different full connection layers to process the aggregated features of bearing position, damage location within the bearing, and the damage size. Two common bearing vibration signal datasets are used to verify the generalization ability of our proposed method. And experimental results show that the proposed method effectively improves the bearing diagnosis capability of the deep learning model.