Bayesian filtering based prognostic framework incorporating varying loads

Unexpected system failures are costly and preventing them is crucial to guarantee availability and reliability of complex assets. Prognostics help to increase the availability and reliability. However, existing methods have their limitations: physics-based methods have limited adaptivity to specific applications, while data-driven methods heavily rely on (scarcely available) historical data, which reduces their prognostic performance. Especially when operational conditions change over time, existing methods do not always perform well. As a solution, this paper proposes a new framework in which loads are explicitly incorporated in a prognostic method based on Bayesian filtering. This is accomplished by zooming in on the failure mechanism on the material level, thus establishing a quantitative relation between usage and degradation rates. This relation is updated using a Bayesian filter and measured loads, but also allows accurate degradation predictions by considering future (changing) loads. This enables decision support on either operational use or maintenance activities. The performance of the proposed load-controlled prognostic method is demonstrated in an atmospheric corrosion use case, based on a public real data set constructed from annual corrosion measurements on carbon steel specimens. The developed load-controlled particle filter (LCPF) is demonstrated to outperform a method based on a regular particle filter, a regression model and an ARIMA model for this specific scenario with changing operating conditions. The generalization of the framework is demonstrated by two additional conceptual case studies on crack propagation and seal wear. • A framework is proposed for maintenance decision-making in varying usage conditions. • The method showed good performance in a corrosion case study. • If load conditions change for longer periods, loads are essential prognostic input. • If load conditions vary frequently, loads are no required prognostic input. • Framework generalization is demonstrated on a crack growth and a wear case study.