Overview of Explainable Artificial Intelligence for Prognostic and Health Management of Industrial Assets Based on Preferred Reporting Items for Systematic Reviews and Meta-Analyses.

Ahmad Kamal Mohd Nor,Srinivasa Rao Pedapati,Masdi Muhammad,Víctor Leiva

doi:10.3390/s21238020

Abstract

Surveys on explainable artificial intelligence (XAI) are related to biology, clinical trials, fintech management, medicine, neurorobotics, and psychology, among others. Prognostics and health management (PHM) is the discipline that links the studies of failure mechanisms to system lifecycle management. There is a need, which is still absent, to produce an analytical compilation of PHM-XAI works. In this paper, we use preferred reporting items for systematic reviews and meta-analyses (PRISMA) to present a state of the art on XAI applied to PHM of industrial assets. This work provides an overview of the trend of XAI in PHM and answers the question of accuracy versus explainability, considering the extent of human involvement, explanation assessment, and uncertainty quantification in this topic. Research articles associated with the subject, since 2015 to 2021, were selected from five databases following the PRISMA methodology, several of them related to sensors. The data were extracted from selected articles and examined obtaining diverse findings that were synthesized as follows. First, while the discipline is still young, the analysis indicates a growing acceptance of XAI in PHM. Second, XAI offers dual advantages, where it is assimilated as a tool to execute PHM tasks and explain diagnostic and anomaly detection activities, implying a real need for XAI in PHM. Third, the review shows that PHM-XAI papers provide interesting results, suggesting that the PHM performance is unaffected by the XAI. Fourth, human role, evaluation metrics, and uncertainty management are areas requiring further attention by the PHM community. Adequate assessment metrics to cater to PHM needs are requested. Finally, most case studies featured in the considered articles are based on real industrial data, and some of them are related to sensors, showing that the available PHM-XAI blends solve real-world challenges, increasing the confidence in the artificial intelligence models’ adoption in the industry.

Highlights

A state-of-the-art systematic review on the applications of explainable artificial intelligence linked to prognostics and health management of industrial assets was compiled
35 peer-reviewed articles, in the English language, from 2015 to 2021, about explainable artificial intelligence related to prognostics and health management, to accomplish the review objectives
The interpretable model, rule- and knowledge-based methods, and attention mechanism are the most widely used explainable artificial intelligence techniques applied in the works of prognostics and health management

Summary

Introduction

Artificial intelligence (AI) continues its extensive penetration into emerging markets, driven by untapped opportunities of the 21st century and backed by steady and sizeable investments. In the last few years, AI-based research shows much concentration in areas such as large-scale machine learning (ML), deep learning (DL), reinforcement learning, robotic, computer vision, natural language processing, and internet of thing [1]. According to the first AI experts report in the “One-hundred-year study on artificial intelligence”, AI ability will be heavily embodied in education, healthcare, home robotics, 4.0/). Safety, security, and transportation, as well as entertainment, in North American cities by the 2030s [1]. The increasing data volume [2] and breakthrough in ML, coupled with the pressing need to be more efficient and innovatively democratize AI to the global scene, are currently relevant. A survey conducted by McKinsey [3]

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