Image-Based Remaining Useful Life Prediction in PHM Systems: A Review

Predictive maintenance enhances the reliability and safety of industrial components and systems, which are pivotal to prognostic and health management (PHM) systems. Specifically, a remaining useful life (RUL) prediction can support the decision-making process for predictive maintenance. With the development of vision sensors and deep learning technologies, image-based RUL prediction, including acquired images from machine vision and synthetic images from image-encoding approaches, has attracted considerable attention. Against this background, this article investigates the development trend, classification, characteristics, and typical applications of RUL prediction using image sensors and image encoding methods. First, a global view is summarized from the perspective of development trends, the usage phase of RUL prediction in typical components and systems, and primary motivations of image-based methods. Subsequently, related work on acquired and synthetic images is reviewed from the perspectives of computational mechanisms, characteristics, and related research. Then, image-only processing and multimodal fusion in RUL prediction are discussed. Finally, this article concludes with a summary of the key scientific issues and potential future research.