Prescriptive maintenance with causal machine learning
This addresses maintenance planning for industrial assets by providing individualized schedules, but it is incremental as it builds on existing causal inference methods.
The paper tackles the problem of imperfect machine maintenance by learning the effect of maintenance conditional on machine characteristics from observational data using causal inference, and validates it on real-life data from over 4,000 contracts, showing more accurate and cost-effective individualized schedules compared to supervised or non-individualized approaches.
Machine maintenance is a challenging operational problem, where the goal is to plan sufficient preventive maintenance to avoid machine failures and overhauls. Maintenance is often imperfect in reality and does not make the asset as good as new. Although a variety of imperfect maintenance policies have been proposed in the literature, these rely on strong assumptions regarding the effect of maintenance on the machine's condition, assuming the effect is (1) deterministic or governed by a known probability distribution, and (2) machine-independent. This work proposes to relax both assumptions by learning the effect of maintenance conditional on a machine's characteristics from observational data on similar machines using existing methodologies for causal inference. By predicting the maintenance effect, we can estimate the number of overhauls and failures for different levels of maintenance and, consequently, optimize the preventive maintenance frequency to minimize the total estimated cost. We validate our proposed approach using real-life data on more than 4,000 maintenance contracts from an industrial partner. Empirical results show that our novel, causal approach accurately predicts the maintenance effect and results in individualized maintenance schedules that are more accurate and cost-effective than supervised or non-individualized approaches.