System-Level Predictive Maintenance: Review of Research Literature and Gap Analysis
It addresses the gap in predictive maintenance for complex systems, which is incremental as it reviews and identifies needs rather than proposing a new solution.
This paper reviews predictive maintenance literature, highlighting that current methods for condition estimation and failure risk forecasting work for simple components but do not scale to complex systems due to issues like latent degradation states and maintenance coupling, and calls for a novel holistic modeling approach.
This paper reviews current literature in the field of predictive maintenance from the system point of view. We differentiate the existing capabilities of condition estimation and failure risk forecasting as currently applied to simple components, from the capabilities needed to solve the same tasks for complex assets. System-level analysis faces more complex latent degradation states, it has to comprehensively account for active maintenance programs at each component level and consider coupling between different maintenance actions, while reflecting increased monetary and safety costs for system failures. As a result, methods that are effective for forecasting risk and informing maintenance decisions regarding individual components do not readily scale to provide reliable sub-system or system level insights. A novel holistic modeling approach is needed to incorporate available structural and physical knowledge and naturally handle the complexities of actively fielded and maintained assets.