Fault Diagnosis Method for Anti-noise Bearings Based on Gaussian Filter and Multi-scale CNN
The problems that the fault diagnosis performance of rolling bearings under strong noise background of noisy environment is poor and the size of the existing fault diagnosis model based on deep learning and noise reduction has large scale and high complexity which leads to practical difficulty to deploy,are studied.A fault diagnosis method for rolling bearings based on Gaussian-filter and Multi-scale Convolution Neural Network(MSCNN)is proposed,which can realize the fault diagnosis for the bearings effectively and accurately in noisy environments,and has high robustness in different load conditions.In this method,firstly the optimal filter kernel that adapts to different signal-to-noise ratios is constructed;noise reduction processing is then performed on the noisy signal,and finally the MSCNN is used to adaptively extract the rich multi-scale features of the signal to realize multi-class fault diagnosis.The experimental results show that compared with the current state-of-the-art various fault diagnosis methods,this method has higher fault diagnosis accuracy under various intensity noises,and has lower complexity in time and space dimensions,which can be practically deployed in industrial production.