Rolling Bearing Fault Diagnosis Based on Convolutional Neural Network and Support Vector Machine

Abstract

Rolling bearings are one of the essential components in rotating machinery. Efficient bearing fault diagnosis is necessary to ensure the regular operation of the mechanical system. Traditional fault diagnosis methods usually rely on a complex artificial feature extraction process, which requires a lot of human expertise. Emerging deep learning methods can reduce the dependence of the feature extraction process on manual intervention effectively. However, its training requires a large number of fault signals, which is difficult to obtain in actual engineering. In this paper, a rolling bearing fault diagnosis method based on Convolutional Neural Network and Support Vector Machine is proposed to solve the above problems. Firstly, the Continuous Wavelet Transform is used to convert one-dimensional original vibration signals into two-dimensional time-frequency images. Secondly, the obtained time-frequency images are input for training the constructed model. Finally, the diagnosis of the fault location and severity is completed. The method is verified on the CWRU data set and the MFPT data set. The results demonstrate that the proposed method achieves higher diagnostic accuracy and stability than other advanced techniques.