Data-Driven Prescriptive Maintenance: Failure Prediction Using Ensemble Support Vector Classification for Optimal Process and Maintenance Scheduling
Christopher Ampofo Kwadwo Gordon, Baris Burnak, Melis Onel, Efstratios N. Pistikopoulos
Abstract
Maintenance can improve the availability of aging production systems and prevent process safety incidents. However, because of system complexity, resource allocation is nontrivial. This research developed and applied a framework to obtain optimal future-failure aware and safety-conscious production and maintenance schedules. Ensembles of nonlinear support vector machine classification models were leveraged to predict the time and probability of future equipment failure from equipment condition data. Multiobjective optimization of expected profit and a safety metric was then used to determine optimal process and maintenance schedules. The results of this research were that the ensemble models had an average accuracy and an F1-score of 0.987, that the ensemble models were more accurate and sensitive than the individual classifiers by 3 percentage points, and that the Pareto-optimal process and maintenance schedules were obtained, providing alternative solutions to the decision maker. This research described optimal resource allocation to help improve safety and system effectiveness.