Fault diagnosis and prognosis (FDP) tries to recognize and locate the faults from the captured sensory data, and also predict their failures in advance, which can greatly help to take appropriate actions for maintenance and avoid serious consequences in industrial systems. In recent years, deep learning methods are being widely introduced into FDP due to the powerful feature representation ability, and its rapid development is bringing new opportunities to the promotion of FDP. In order to facilitate the related research, we give a summary of recent advances in deep learning techniques for industrial FDP in this paper. Related concepts and formulations of FDP are firstly given. Seven commonly used deep learning architectures, especially the emerging generative adversarial network, transformer, and graph neural network, are reviewed. Finally, we give insights into the challenges in current applications of deep learning-based methods from four different aspects of imbalanced data, compound fault types, multimodal data fusion, and edge device implementation, and provide possible solutions, respectively. This paper tries to give a comprehensive guideline for further research into the problem of intelligent industrial FDP for the community.

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SummaryAiming at current serious problems that the bolt looseness can cause heavy damages and the manual bolt looseness detection is ineffective, this study presents a bolt looseness detection method based on Canny edge detector. First, HSV (hue, saturation, value) color space is used to extract the area of mark bar. Then, after expanding, eroding and median filtering the image, Canny edge detector is used to segment the marker area to extract its edge. Last, the fitted slope of edge is used to judge whether the bolt is loose. The experimental results show that the proposed method has F1 score of 88.37%, contributing to the automated detection of bolt looseness.

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