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

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.

Abnormality Detection and Failure Prediction Using Explainable Bayesian Deep Learning: Methodology and Case Study of Real-World Gas Turbine Anomalies

“…PHM-XAI is still a very young discipline. As testified by the recent systematic review on PHM-XAI presented in [39] and shown in Figure 2(a), several peer reviewed journal articles treating the subject is still small but steadily rising. Several explainability approaches have been explored by the PHM-XAI researchers.…”

“…While the explanation fulfils several demanded general qualities, the need to evaluate explanation based of PHM criteria such as security and safety, cost, and time are still present. This aspect is also echoed in [39]. Therefore, it is crucial for PHM-XAI researchers to develop explanation metrics satisfying PHM needs.…”

“…The review presented in [39] further lists several deficiencies plaguing the research in PHM-XAI that need to be remedied promptly, considering: 1. Lack of human involvement: Human engagement is crucial for assessing the generated explanation as the latter is meant for them.…”

Abnormality Detection and Failure Prediction Using Explainable Bayesian Deep Learning: Methodology and Case Study with Industrial Data

“…The review presented in [44] further lists several deficiencies plaguing the research in PHM-XAI that need to be remedied promptly, considering:…”

“…In recent years, AI has become a predominant tool in reliability-based research [43]. PHM-XAI is still a very young discipline as testified by the recent systematic review on PHM-XAI presented in [44] and shown in Figure 2a, where several peer-reviewed journal articles treating the subject demonstrate that the PHM-XAI is still scarcely studied but steadily rising. Several explainability approaches have been explored by the PHM-XAI researchers.…”