PROGNOS: an automatic remaining useful life (RUL) prediction model for military systems using machine learning

In modern industrial settings, the quality of maintenance efforts directly influence equipment’s operational uptime and efficiency. Condition monitoring is a common process employed for predicting the health of a technical asset, whereby a predictive maintenance strategy can be adopted to minimize machine downtime and potential losses. Throughout the field, machine learning (ML) methods have become noteworthy for predicting failures before they occur, thereby preventing significant financial costs and providing a safer workplace environment. These benefits from predictive maintenance techniques, are particularly useful in the context of military equipment. Such equipment is often significantly expensive, and untimely machine failure could result in significant human endangerment. In this paper, a prognostic model (PROGNOS) is proposed to predict military equipment’s remaining useful life (RUL) based on their monitoring signals. The main considerations of PROGNOS are expectation maximization tuned Kalman Filter (EM-KF) for signal filtering, a recently introduced feature extraction algorithm (PCA-mRMR-VIF), and predictive LSTM model with an adaptive sliding window. The viability and performance of the proposed model were tested on a highly complex competition dataset: the NASA aircraft gas turbine engine degradation dataset, wherein readings from multiple sensor channels were recorded for degrading machines. According to testing results, we can confidently say that the proposed PROGNOS model was viable and robust overall, proving its general usefulness on all military equipment that emit signals.