MCMC-Based Simulation Method for Efficient Risk- Based Maintenance Optimization

Mechanical and structural systems often implement a structural integrity program to monitor and sustain structural reliability throughout the service life. To properly consider uncertainty and variability, one commonly used decision tool is probabilistic risk assessment that incorporates probabilistic damage accumulations, damage detections, mitigation actions, and expected costs of maintenance and failure consequences. Given the wide spectrum of maintenance options and the increasing complexities in high-fidelity modeling, the implementation of RBMO can be very challenging and, computationally, only random simulations can provide flexible and robust simulation capabilities. This paper describes an efficient random simulation based RBMO computational approach built on a two-stage maintenance simulation framework and featuring (1) a MCMC-based failure sample generator, (2) an Adaptive Stratified Importance Sampling (ASIS) method for computing probability of failure with error control, and (3) an on-demand series-system failure samples generator that uses component samples. Several demonstration examples are included.