Risk-based optimization of inspection using influence diagrams is investigated. To this end, a fatigue deterioration model using a Dynamic Bayesian Network (DBN) approach is presented. The DBN incorporates information from previous inspection campaigns. Decision and utility nodes are defined inside the network to represent inspection and repair activities. The optimal inspection strategy (subject to safety or utility constraints) is approximated using the Limited Memory Influence Diagram (LIMID) approach, and is solved using the single policy updating, a local optimization strategy. In a numerical investigation, this method is found to give solutions that are slightly better than those obtained with simple heuristics that were previously applied, such the reliability threshold or periodic inspection heuristic. Finally, the numerical example demonstrates the superiority of adaptive inspection strategies, whereby inspections are planned based on the results of previous inspections.     «

Risk-based optimization of inspection using influence diagrams is investigated. To this end, a fatigue deterioration model using a Dynamic Bayesian Network (DBN) approach is presented. The DBN incorporates information from previous inspection campaigns. Decision and utility nodes are defined inside the network to represent inspection and repair activities. The optimal inspection strategy (subject to safety or utility constraints) is approximated using the Limited Memory Influence Diagram (L...     »