A survey of modeling for prognosis and health management of industrial equipment

•Survey of papers with historic perspective.•Discussion on implementation of analytics for prognosis and health management.•Implementation of machine learning beyond black-box models. Prognosis and health management plays an important role in the control of costs associated with operating large industrial equipment, such as wind turbines and aircraft. It is only fair that engineers and scientists have vastly researched modeling approaches to support decision making. Motivated by the growing availability of data and computational power as well as the advances in algorithms and methods, modeling frameworks often merge elements of physics, machine learning, and statistical learning. In this paper, we present a review on modeling in support of prognosis and health management of industrial equipment. This survey complements the existing prognosis and health management literature by discussing how modeling strategies are influenced by industry-specific aspects such as maintenance approaches (e.g., reactive, proactive, and predictive), implementation factors (e.g., industry, business model, purpose, development, and deployment), as well as supporting technologies (sensing, repair, and modeling itself). We use the onshore wind energy and civil aviation industries to illustrate how these aforementioned aspects can influence modeling and implementation of prognosis and health management. The literature review is broad and covers contributions over the past 40 years. We close the paper with few topics that can motive research going forward.