ArXiv Preprint
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, indicating that
available AI and XAI approaches are equipped to solve complex real-world
challenges, increasing the confidence of AI model adoption in the industry.
This work is funded by the Universiti Teknologi Petronas Foundation.
Ahmad Kamal BIN MOHD NOR, Srinivasa Rao PEDAPATI, Masdi MUHAMMAD
2021-07-08
