A quantitative framework for performance-based reliability prediction for a multi-component system subject to dynamic self-reconfiguration

In a multi-component system, the performance of all components might be restricted by the most degraded component. This dependency results in an undesirable performance loss of the system. To date, engineers have developed a performance-maximization-oriented technique that enables dynamic isolation and retrieval of the components from and back to the system to mitigate the dependency-induced negative impact. Despite its engineering application, the technique’s effectiveness in system performance enhancement still lacks systematic explorations. In this paper, we fill the gap by developing a quantitative framework for the system’s performance-based reliability metrics prediction, considering the technique (defined as dynamic self-reconfiguration mechanism in this paper) may function perfectly or imperfectly, and the real-time system information may be unavailable or partially available with biases. First, we analytically characterize the mechanism by modeling the probability distribution of the system configuration, building on which we proactively predict the system’s performance-based reliability metrics. Afterward, we develop a particle filtering algorithm to utilize the noisy multi-dimensional-multi-type real-time information for progressive system state estimation and reliability prediction. Based on the prediction models, we quantify the effectiveness of the dynamic self-reconfiguration mechanism, which assists operators in system reliability enhancement. A case study of a photovoltaic system is provided.