מאיר קלך

The sustainability of many critical systems, such as water transmission networks or electrical grid, requires predictive maintenance strategies to prevent malfunction of components. These strategies typically use a troubleshooting model to suggest the components that are most beneficial to replace. This paper suggests a new dimension, which considers not only replacement costs and failure probabilities of components, but also adjacency of the components being replaced. We propose a model in which replacing adjacent components is often beneficial, because they can be replaced in a single replacement action. This helps minimizing costs known as overhead costs, which include the cost of sending a team to perform the replacement, the disruption to service during the replacement, and more. We propose several algorithms and AI techniques to suggest economical replacement methods. Evaluation on a real-world water transmission network shows that near-optimal solutions return a solution very fast, which is very close in terms of expected cost to the optimal solution.