In this paper, comparative combined fault diagnosis schemes are studied including vibration analysis, acoustic signal analysis and thermal image analysis based on the Convolutional Neural Network (CNN). The advantage of the CNN structure is that it does not need manual feature extraction or selection, which requires prior knowledge of specific machinery dynamics. The vibration and acoustic signals were transformed into spectrograms, which are effective for the diagnostic analysis by using CNN. Comparatively, the thermal images were directly analyzed using CNN. The effectiveness of the CNN-based diagnosis methods was investigated through the analysis of different experimental data, i.e., vibration, acoustic signals and thermal images, which were collected from a test rig where different types of faults are induced on the roller bearing and shaft. The results show that the thermal image analysis and acoustic signal analysis could achieve relatively higher accuracy rate compared to vibration analysis. Moreover, the advantage is easy-deployment because of the non-contact way during signal acquisition. With the CNN-based fault diagnosis method for the three different signals collected, the accuracy of different signal predictions for combined faults can be compared, and the effective method can be applied to fault diagnosis of other industrial rotating machinery.
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机译:两岸四地累犯制度比较研究——兼论中国内地累犯制度一体化之构想 =Comparative Study on Recidivism System in Hong Kong, Macao, Taiwan and China: Concurrently Discuss the Conception of Recidivism System Integration in Mainland China