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In this article, we present a risk evaluation and maintenance strategy optimization approach for systems with parallel identical assets subject to continuous deterioration. System performance is defined by the number of functional assets, and the penalty cost is measured by the loss of performance. To overcome the practical challenges of information sparsity, we employ a Bayesian framework to dynamically update unknown parameters in a Wiener degradation model. Order statistics are utilized to describe the failure times of assets and the stepwise incurred performance penalty cost. Furthermore, based on the Bayesian parameter inferences, we propose a short-term value-based replacement policy to minimize the expected cost rate in the current planning horizon. The proposed strategy simultaneously considers the variability of parameter estimators and the inherent uncertainty of the stochastic degradation processes. A simulation study and a realistic example from the petrochemical industry are presented to demonstrate the proposed framework.

Performance-oriented risk evaluation and maintenance for multi-asset systems: A Bayesian perspective