Scalability, Explainability and Performance of Data-Driven Algorithms in Predicting the Remaining Useful Life: A Comprehensive Review

Abstract

<ul> <li>This work summarizes the state-of-the-art data-driven methods for prediction of the Remaining Useful Life<br> (RUL)<br> </li> <li>It discusses challenges and open problems faced in PdM<br> </li> <li>This study presents a discussion on the new problems that need to be considered towards the Industry 4.0 goals<br> </li> <li>We propose the future direction for each challenge discussed in this article</li> </ul>

Explainable Predictive Maintenance: A Survey of Current Methods, Challenges and Opportunities

“…To give a concrete example of XAI, a researcher may want to use a Long Short-Term Memory neural network for timeseries analysis due to its temporal modeling capabilities [1], [6]. Common deep learning models like this one are not commonly interpretable, so to make it explainable, the researcher might consider using a simpler model, i.e., linear regression, decision tree, etc., to serve as a surrogate for post-hoc explanations.…”

“…Predictive maintenance (PdM) is a subcategory of prognostics and health management (PHM) that has seen widespread attention in recent years [1], [22], [27], [28]. PdM utilizes AI and previous failure information from mechanical systems to predict a fault or downtime in the future [1], [6], [29]. PdM is implemented with a variety of tools, including anomaly detection, fault diagnosis and prognosis [22], [28].…”

“…These centuries and decades can be categorized into what is described as revolutions, i.e. Industrial Revolutions [1]. The most recent of these is agreed to be known as the fourth industrial revolution or Industry 4.0 [1]- [4].…”

“…Industrial Revolutions [1]. The most recent of these is agreed to be known as the fourth industrial revolution or Industry 4.0 [1]- [4]. Industry 4.0 is categorized by bridging the gap between machinery through hardware and software connectivity [5].…”

“…To tackle this level of optimization, researchers of Industry 4.0 have developed the field of predictive maintenance (PdM). PdM encompasses many different problems in the field of maintenance, but an overarching representation of PdM involves monitoring the system as it is in the present and alerting for any potential problems such as a specific anomaly or time until failure [1], [6]. While this problem that exists in the cyber-physical realm has been well studied from the perspective of deep learning models, statistical models, and more, the people that get impacted by these systems have had considerably less attention.…”