Online Model-Based Remaining-Useful-Life Prognostics for Aircraft Cooling Units Using Time-Warping Degradation Clustering

• Published in: Aerospace 2021-06, Vol.8 (6), p.168
• Main Authors: Mitici, Mihaela, de Pater, Ingeborg
• Format: Article
• Language: English
• Subjects: Aging aircraft; Aircraft; Aircraft components; aircraft Cooling Unit; Aircraft maintenance; Airplane engines; Algorithms; Clustering; Cooling; Degradation; Kalman filters; Libraries; multi-model degradation; Neural networks; online remaining-useful-life prognostics; particle filtering; Predictive maintenance; Sensors; Standard deviation; Time measurement; Trends; Warping
• ISSN: 2226-4310; 2226-4310
• DOI: 10.3390/aerospace8060168

Remaining-useful-life prognostics for aircraft components are central for efficient and robust aircraft maintenance. In this paper, we propose an end-to-end approach to obtain online, model-based remaining-useful-life prognostics by learning from clusters of components with similar degradation trends. Time-series degradation measurements are first clustered using dynamic time-warping. For each cluster, a degradation model and a corresponding failure threshold are proposed. These cluster-specific degradation models, together with a particle filtering algorithm, are further used to obtain online remaining-useful-life prognostics. As a case study, we consider the operational data of several cooling units originating from a fleet of aircraft. The cooling units are clustered based on their degradation trends and remaining-useful-life prognostics are obtained in an online manner. In general, this approach provides support for intelligent aircraft maintenance where the analysis of cluster-specific component degradation models is integrated into the predictive maintenance process.