Abstract
According to the rolling bearing local fault vibration mechanism, a monopulse feature extraction and fault diagnosis method of rolling bearing under low-speed and heavy-load conditions based on phase scan and CNN is proposed. The synchronous collected speed signal is used to calculate bearing phase function and divide fault monopulse periods. The monopulse waveforms of multiple fault periods are scanned and ensemble averaged to suppress noise interference and detail feature loss at the same time of feature extraction. By iteratively calibrating phase function, the feature matrix containing bearing fault information can be obtained. Finally, CNN is used to recognize and classify different bearing states. The experimental and analysis results show that bearing fault diagnosis can be achieved. The total recognition rates of constant and variable speed samples are 99.67% and 99.89%, respectively. The trained network has fast convergence speed and good generalization ability for different fault sizes and working conditions. Further experiments show that the method can also accurately identify different bearing degradation states. The total recognition rates of constant and variable speed samples are 96.67% and 95.56%, respectively. The limited errors are concentrated between the degradation states with the same type weak fault. The experimental results using Case Western Reserve University bearing data show that feature extraction and network training are better, and the recognition rates of 5 bearing states are all 100%. Therefore, the proposed method is an effective rolling bearing feature extraction and fault diagnosis technology.
Highlights
Rolling bearing is an important part of rotating machinery, which is widely used in various industries
Li et al [7] improved the frequency band entropy method based on the maximum kurtosis principle and applied it with singular value decomposition and singular value kurtosis to extract bearing fault feature
Step 4: Pb is used as sample data to construct and train the deep learning network based on Convolutional neural network (CNN), so as to realize the fault diagnosis and degradation identification of rolling bearing under different working conditions
Summary
Rolling bearing is an important part of rotating machinery, which is widely used in various industries. It was used to extract rolling bearing weak fault combined with an improved basis pursuit algorithm with feature sign search. Chen et al [20] proposed a novel diagnosis model integrating CNN and Extreme Learning Machine to achieve higher classification accuracy with less computational time. Han et al [21] introduced the adversarial learning as a regularization into CNN and proposed a novel deep adversarial CNN to achieve the intelligent diagnosis of mechanical faults. The inner and outer ring faults of rolling bearings are studied, and a monopulse feature extraction method is proposed to obtain detail fault information. Combined with CNN, the feature extraction and fault diagnosis of rolling bearing under low-speed and heavyload conditions are realized.
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