Expert Systems for Infrastructure Maintenance

Prognostics and Health Management (PHM) has become increasingly popular in recent years, with machine learning methods and big data emerging as dominant tools within the PHM field. This trend is mainly due to the increasing use of sensors and the ability of machine learning techniques to leverage real-time tracking. However, despite their utility and efficacy, these techniques are not without drawbacks. One major issue is that data-driven methods often lack transparency in their reasoning, which is crucial for understanding fault occurrences and diagnostics. Additionally, the availability of data can be a challenge. In some cases, data are scarce or hard to obtain, either due to the cost of installing necessary sensors or the rarity of the required information. Lastly, the insights derived from data can sometimes diverge from those obtained through expert analysis and established norms. This contrasts with knowledge-based approaches, which formally organize the knowledge acquired from norms and experts, and then deduce the desired conclusion. While research is increasingly exploring data-driven techniques, industry tends to more frequently employ knowledgebased methods. To address this gap, this paper, on one hand, provides a comprehensive survey of knowledge-based and expert systems in PHM. It examines various methodologies, assessing how these systems are integrated and their impact in PHM, specifically for fault detection, diagnosis, and prognosis. On the other hand, the report also includes an applied section, detailing the implementation of an expert system for addressing a railway maintenance use case. The study provides a thorough evaluation of current developments and contributes significant insights into the current capabilities and future directions of knowledge-based techniques in enhancing decision-making processes in PHM.