Fault Diagnosis of Bearings Based on SSWT, Bayes Optimisation and CNN

Abstract Bearings are important components of rotating machinery and transmission systems, and are often damaged by wear, overload and shocks. Due to the low resolution of traditional time-frequency analysis for the diagnosis of bearing faults, a synchrosqueezed wavelet transform (SSWT) is proposed to improve the resolution. An improved convolutional neural network fault diagnosis model is proposed in this paper, and a Bayesian optimisation method is applied to automatically adjust the structure and hyperparameters of the model to improve the accuracy of bearing fault diagnosis. Experimental results from the accelerated life testing of bearings show that the proposed method is able to accurately identify various types of bearing fault and the different status of these faults under complex running conditions, while achieving very good generalisation ability.