Improving prescriptive maintenance by incorporating post-prognostic information through chance constraints

Abstract

Maintenance is one of the critical areas in operations in which a careful balance between preventive costs and the effect of failures is required. Thanks to the increasing data availability, decision-makers can now use models to better estimate, evaluate, and achieve this balance. This work presents a maintenance scheduling model which considers prognostic information provided by a predictive system. In particular, we developed a prescriptive maintenance system based on run-to-failure signal segmentation and a Long Short Term Memory (LSTM) neural network. The LSTM network returns the prediction of the remaining useful life when a fault is present in a component. We incorporate such predictions and their inherent errors in a decision support system based on a stochastic optimization model, incorporating them via chance constraints. These constraints control the number of failed components and consider the physical distance between them to reduce sparsity and minimize the total maintenance cost. We show that this approach can compute solutions for relatively large instances in reasonable computational time through experimental results. Furthermore, the decision-maker can identify the correct operating point depending on the balance between costs and failure probability.

Publication
IEEE Access
Anthony D. Cho Lo
Anthony D. Cho Lo
PhD Student, Industrial Engineering and Operations Research

My research interests are in machine learning, operation research, and image processing. I’m currently working in applications related to diagnostic and pattern detection related to astronomy instrument and maintenance management.

Rodrigo A. Carrasco
Rodrigo A. Carrasco
Associate Professor & Academic Director Master in Industrial Engineering
Gonzalo Ruz
Gonzalo Ruz
Universidad Adolfo Ibáñez

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