Prognostics and Health Management of Industrial Assets: Current Progress and Road Ahead

Prognostic and Health Management (PHM) systems are some of the main protagonists of the Industry 4.0 revolution. 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. Efficient PHM methods promise to decrease the probability of extreme failure events, thus improving the safety level of industrial machines. Furthermore, they could potentially drastically reduce the often conspicuous costs associated with scheduled maintenance operations. The increasing availability of data and the stunning progress of Machine Learning (ML) techniques over the last decade represent two strong motivating factors for the development of data-driven PHM systems. In particular, Deep Learning (DL) models have been able to provide unprecedented results in several data analysis tasks ranging from Image Recognition (IR) to Natural Language Processing (NLP). In light of these surprising achievements, the development of PHM methods based on Artificial Intelligence (AI) techniques is extremely appealing. Nonetheless, the black-box nature of DL models significantly hinders their level of interpretability, de facto limiting their application to real-world scenarios. In this work, we explore the intersection of AI methods and PHM applications. 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. 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.