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

Onur Sürücü; Connor Wilkinson; Uygar Yeprem; Waleed Hilal; S. Andrew Gadsden; John Yawney; Naseem Alsadi; Alessandro Giuliano

doi:10.1117/12.2618913

6 June 2022 PROGNOS: an automatic remaining useful life (RUL) prediction model for military systems using machine learning

Onur Sürücü, Connor Wilkinson, Uygar Yeprem, Waleed Hilal, S. Andrew Gadsden, John Yawney, Naseem Alsadi, Alessandro Giuliano

Author Affiliations +

Proceedings Volume 12113, Artificial Intelligence and Machine Learning for Multi-Domain Operations Applications IV; 121130R (2022) https://doi.org/10.1117/12.2618913
Event: SPIE Defense + Commercial Sensing, 2022, Orlando, Florida, United States

Abstract

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.

Conference Presentation

Citation Download Citation

Onur Sürücü, Connor Wilkinson, Uygar Yeprem, Waleed Hilal, S. Andrew Gadsden, John Yawney, Naseem Alsadi, and Alessandro Giuliano "PROGNOS: an automatic remaining useful life (RUL) prediction model for military systems using machine learning", Proc. SPIE 12113, Artificial Intelligence and Machine Learning for Multi-Domain Operations Applications IV, 121130R (6 June 2022); https://doi.org/10.1117/12.2618913

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