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.
Highlights
Since some industrial machines need to work continuously in harsh environments, failures of critical components such as bearings often occur
EXPERIMENTS To verify the flexibility and utility of the constructed model in bearing fault diagnosis, two open-source datasets are used for research in this paper, including Case Western Reserve University (CWRU) Bearing Data Center dataset [37] and the Society for Mechanical Failure Prevention Technology (MFPT) dataset [38]
3) RESULTS OF THE EXPERIMENT First, the proposed Convolutional Neural Network (CNN)-Support Vector Machine (SVM) model is trained by the constructed training samples of the 12 bearing fault states
Summary
Since some industrial machines need to work continuously in harsh environments, failures of critical components such as bearings often occur. As one of the basic elements of many industrial machinery, the working state of rolling bearings has a great influence on the operation of the entire equipment [1]. The research on the fault diagnosis technology of rolling bearings is significant for the safety of the production process and the reduction of economic losses. With the development of machine learning, many typical intelligent methods have been successfully used in fault diagnosis research, mainly including of two stages: signal feature extraction and fault classification [2]. The vibration signals of bearings usually contain sufficient fault information, but most of them are nonlinear and nonstationary.
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