Review of Machine Learning Approaches for Diagnostics and Prognostics of
  Industrial Systems Using Industrial Open Source Data
Hanqi Su Jay Lee
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.