System-Level Predictive Maintenance: Review of Research Literature and Gap Analysis

TL;DR

This paper reviews the challenges of applying predictive maintenance at the system level, highlighting the need for holistic models that incorporate structural knowledge to handle complex assets and maintenance interactions.

Contribution

It identifies the gaps in current predictive maintenance methods for complex systems and proposes the development of a novel holistic modeling approach.

Findings

Current methods excel at component-level risk forecasting

System-level analysis involves complex latent degradation states

A new holistic modeling approach is needed for system-level predictive maintenance

Abstract

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.