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Image deep learning in fault diagnosis of mechanical equipment

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Abstract

With the development of industry, more and more crucial mechanical machinery generate wildness demand of effective fault diagnosis to ensure the safe operation. Over the past few decades, researchers have explored and developed a variety of approaches. In recent years, fault diagnosis based on deep learning has developed rapidly, which achieved satisfied results in the filed of mechanical equipment fault diagnosis. However, there is few review to systematically summarize and sort out these special image deep learning methods. In order to fill this gap, this paper concentrates on reviewing comprehensively the development of special image deep learning for mechanical equipment fault diagnosis in past 5 years. In general, a typical image fault diagnosis based on fault image deep learning generally consists of data acquisition, signal processing, model construction, feature learning and decision-making. Firstly, the method of signal preprocessing is introduced, and several common methods of converting signals into images are briefly compared and analyzed. Then, the principles and variants of deep learning models are expounded. Further more, the difficulties and challenges encountered at this stage are summarized. Last but not least, the future development and potential trends of the work are concluded, and it hoped that this work will facilitate and inspire further exploration for researchers in this area.

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  1. Chuanhao Wang and Yongjian Sun have contributed equally to this work and should be considered co-first authors.

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  1. School of Electrical Engineering, University of Jinan, Jinan, Shandong, China

    Chuanhao Wang, Yongjian Sun & Xiaohong Wang

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This work is supported by National Natural Science Foundation (NNSF) of China under Grant 61603149, by Shandong Key Research and Invention Program under Grant 2019GSF111046.

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Wang, C., Sun, Y. & Wang, X. Image deep learning in fault diagnosis of mechanical equipment. J Intell Manuf (2023). https://doi.org/10.1007/s10845-023-02176-3

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