Review of Machine Learning Approaches for Diagnostics and Prognostics of Industrial Systems Using Industrial Open Source Data

• URL: http://arxiv.org/abs/2312.16810v2
• Date: Fri, 24 May 2024 21:09:34 GMT
• Title: Review of Machine Learning Approaches for Diagnostics and Prognostics of Industrial Systems Using Industrial Open Source Data
• Authors: Hanqi Su, Jay Lee,
• Abstract summary: This paper provides a review of machine learning approaches for diagnostics and prognostics of industrial systems using open-source datasets. It highlights the evolving role of both conventional machine learning and deep learning in tackling complex industrial tasks.
• Score: 0.32885740436059047
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: In the field of Prognostics and Health Management (PHM), recent years have witnessed a significant surge in the application of machine learning (ML). Despite this growth, the field grapples with a lack of unified guidelines and systematic approaches for effectively implementing these ML techniques and comprehensive analysis regarding industrial open-source data across varied scenarios. To address these gaps, this paper provides a comprehensive review of machine learning approaches for diagnostics and prognostics of industrial systems using open-source datasets from PHM Data Challenge Competitions held between 2018 and 2023 by PHM Society and IEEE Reliability Society and summarizes a unified ML framework. This review systematically categorizes and scrutinizes the problems, challenges, methodologies, and advancements demonstrated in these competitions, highlighting the evolving role of both conventional machine learning and deep learning in tackling complex industrial tasks related to detection, diagnosis, assessment, and prognosis. Moreover, this paper delves into the common challenges in PHM data challenge competitions by emphasizing both data-related and model-related issues and summarizes the solutions that have been employed to address these challenges. Finally, we identify key themes and potential directions for future research, providing opportunities and prospects for ML further development in PHM.

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