Forecasting fault events for predictive maintenance using data-driven techniques and ARMA modeling

Presently, time-based airline maintenance scheduling does not take fault predictions into account, but happens at fixed time-intervals. This may result in unnecessary maintenance interventions and also in situations where components are not taken out of service despite exceeding their designed risk of failure. To address this issue we propose a framework that can predict when a component/system will be at risk of failure in the future, and therefore, advise when maintenance actions should be taken. In order to facilitate such prediction, we employ an auto-regressive moving average (ARMA) model along with data-driven techniques, and compare the performance of multiple data-driven techniques. The ARMA model adds a new feature that is used within the data-driven model to give the final prediction. The novelty of our work is the integration of the ARMA methodology with data-driven techniques to predict fault events. This study reports on a real industrial case of unscheduled removals of a critical valve of the aircraft engine. Our results suggest that the support vector regression model can outperform the life usage model on the evaluation measures of sample standard deviation, median error, median absolute error, and percentage error. The generalized linear model provides an effective approach for predictive maintenance with comparable results to the baseline. The remaining data-driven models have a lower overall performance.