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In the field of Prognostics and Health Management (PHM), recent years havewitnessed a significant surge in the application of machine learning (ML).Despite this growth, the field grapples with a lack of unified guidelines andsystematic approaches for effectively implementing these ML techniques andcomprehensive analysis regarding industrial open-source data across variedscenarios. To address these gaps, this paper provides a comprehensive review ofmachine learning approaches for diagnostics and prognostics of industrialsystems using open-source datasets from PHM Data Challenge Competitions heldbetween 2018 and 2023 by PHM Society and IEEE Reliability Society andsummarizes a unified ML framework. This review systematically categorizes andscrutinizes the problems, challenges, methodologies, and advancementsdemonstrated in these competitions, highlighting the evolving role of bothconventional machine learning and deep learning in tackling complex industrialtasks related to detection, diagnosis, assessment, and prognosis. Moreover,this paper delves into the common challenges in PHM data challenge competitionsby emphasizing both data-related and model-related issues and summarizes thesolutions that have been employed to address these challenges. Finally, weidentify key themes and potential directions for future research, providingopportunities and prospects for ML further development in PHM.

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