Related references
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Summary: The recent advances in intelligent fault diagnosis demonstrate the strong capabilities of deep learning in automatic feature extraction and accurate identification of fault signals, yet challenges such as data scarcity and varying working conditions may impact model performance. The tool proposed to address these challenges is meta-learning, which quickly adapts to new tasks using small samples and has great potential in few-shot and cross-domain fault diagnosis. The lack of a survey to summarize existing work and look into the future is noted, with this paper comprehensively investigating deep meta-learning in fault diagnosis from three perspectives.
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Te Han et al.
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IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS
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MECHANICAL SYSTEMS AND SIGNAL PROCESSING
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Pengfei Zhu et al.
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IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS
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Wei Zhang et al.
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Zhaoyi Xu et al.
Summary: This paper provides a synthesis of existing literature on ML for reliability and safety applications, offering a roadmap and important guidelines. ML has the potential to provide novel and accurate insights, and its analysis of accident data presents distinct advantages, contributing to better accident prevention.
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INTERNATIONAL JOURNAL OF COMPUTER VISION
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