With the improvement of equipment intricacy and automation, it is more important for equipment failure diagnosis. In this paper, we propose a diagnostic model to automatically detect and identify faults in manufacturing processes by using a wavelet-based method. The idea behind our method is to use an image processing system that performs the following phases: image capturing, image preprocessing, determination of region of interest, object segmentation, computations of object features and decision-making. Moreover, the frequency spectrum analysis method and the gear, automatic monitoring system are introduced. Afterwards, the wavelet transform is used to decompose the vibration acceleration signals of ball bearing fualts to different scales, and the resonance frequency band is extracted. Finally, the analysis and validation have been done by using the gear box fault data. The results show that the method is very effective.
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