• Corpus ID: 235765654

Explainable AI (XAI) for PHM of Industrial Asset: A State-of-The-Art, PRISMA-Compliant Systematic Review

@article{Nor2021ExplainableA,
  title={Explainable AI (XAI) for PHM of Industrial Asset: A State-of-The-Art, PRISMA-Compliant Systematic Review},
  author={Ahmad Nazrie Bin Mohd Nor and Srinivasa Rao Pedapati and Masdi Muhammad},
  journal={ArXiv},
  year={2021},
  volume={abs/2107.03869}
}

• Ahmad Nazrie Bin Mohd Nor, S. R. Pedapati, M. Muhammad
• Published 8 July 2021
• Computer Science
• ArXiv

A state-of-the-art systematic review on XAI applied to Prognostic and Health Management (PHM) of industrial asset is presented. The work attempts to provide an overview of the general trend of XAI in PHM, answers the question of accuracy versus explainability, investigates the extent of human role, explainability evaluation and uncertainty management in PHM XAI. Research articles linked to PHM XAI, in English language, from 2015 to 2021 are selected from IEEE Xplore, ScienceDirect, SpringerLink, ACM Digital Library and Scopus databases using PRISMA guidelines. Data was extracted from 35 selected articles and examined using MS. Excel. Several findings were synthesized. Firstly, while the discipline is still young, the analysis indicates the growing acceptance of XAI in PHM domain. Secondly, XAI functions as a double edge sword, where it is assimilated as a tool to execute PHM tasks as well as a mean of explanation, in particular in diagnostic and anomaly detection. There is thus a need for XAI in PHM. Thirdly, the review shows that PHM XAI papers produce either good or excellent results in general, suggesting that PHM performance is unaffected by XAI. Fourthly, human role, explainability metrics and uncertainty management are areas requiring further attention by the PHM community. Adequate explainability metrics to cater for PHM need are urgently needed. Finally, most case study featured on the accepted articles are based on real, industrial data, indicating that available AI and XAI approaches are equipped to solve complex real-world challenges, increasing the confidence of AI model’s adoption in the industry. This work is funded by the Universiti Teknologi Petronas Foundation.