According to a recent trend study gathering interviews with more than 230 senior European business 3, roughly 93% of them deem their maintenance policy inefficient.Īs discussed later, the current most popular approaches to maintenance are divided into two categories, namely reactive maintenance and scheduled maintenance. If, on one hand, the above considerations highlight the fundamental impact of maintenance operations on manufacturers’ balances, on the other hand a large number of companies are still not satisfied with their maintenance strategies. Furthermore, according to the International Society of Automation (ISA) 2, the overall burden of unplanned downtime on industrial manufacturers across all industry segments is estimated to touch the impressive figure of $647 billion per year. Recent studies 1 show that, for the average factory, inefficient maintenance policies are responsible for costs ranging from 5 to 20% of the plant’s entire productive capacity. A substantial part of these costs often derives from the maintenance of industrial assets. Supporting the constant growth of modern industrial markets makes the optimization of operational efficiency and the minimization of superfluous costs essential. Our goal is to highlight potentially fruitful research directions along with characterizing the main challenges that need to be addressed in order to realize the promises of AI-based PHM systems. We present a thorough review of existing works both in the contexts of fault diagnosis and fault prognosis, highlighting the benefits and the drawbacks introduced by the adoption of AI techniques. In this work, we explore the intersection of Artificial Intelligence (AI) methods and PHM applications. On the other hand, the black-box nature of DL models significantly hinders their level of interpretability, de facto limiting their application to real-world scenarios. The increasing availability of data and the stunning progress of Machine Learning (ML) and Deep Learning (DL) techniques over the last decade represent two strong motivating factors for the development of data-driven PHM systems. Furthermore, they could potentially drastically reduce the often conspicuous costs associated with scheduled maintenance operations. Efficient PHM methods promise to decrease the probability of extreme failure events, thus improving the safety level of industrial machines. Efficiently detecting whether an industrial component has deviated from its normal operating condition or predicting when a fault will occur are the main challenges these systems aim at addressing. ![]() Prognostic and Health Management (PHM) systems are some of the main protagonists of the Industry 4.0 revolution. 2Robotics and Automation, CSEM SA: Swiss Center for Electronics and Microtechnology S.A., Alpnach, Switzerland.1Data Analytics Lab, Institute of Machine Learning, Department of Computer Science, ETHZ: Eidgenössische Technische Hochschule Zürich, Zurich, Switzerland.
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