Abstract:

Ideally, systems continue to operate reliably for a long time. But the reality is that systems fail, and the consequences can be serious. In the worst cases, lives may be lost and people may be injured; in all cases, people are adversely affected. The economic repercussions of critical system failure can be staggering. Current reliability methods cannot handle real-time changes in operational and environmental loads on a system, which can cause a system to fail in the field without warning, leading to unplanned downtime.
Prognostics and health management (PHM) is an enabling discipline consisting of technologies and methods to assess the reliability of a product in its actual life cycle conditions to determine the advent of failure and mitigate system risk. The approaches adopted for conducting prognostics for a product include physics-of-failure (PoF) based approaches, including use of canaries to provide advance warning of failure, and modeling of life cycle environment stress to compute accumulated damage; data-driven methods involving monitoring and analysis of product functional parameters; and the fusion approach, which combines the PoF and data-driven techniques to provide an accurate estimate of remaining useful life. This paper describes the various approaches to prognostics and presents case studies of the implementation of different prognostic approaches.

Key Words: Physics of Failure, Data-driven, Fusion, Prognostics

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