Abstract
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.
Highlights
Globally, aircraft maintenance, repair and overhaul (MRO) costs account for 9% of the total airline operational costs [1]. To reduce these maintenance costs and, in particular, to reduce the costs of the maintenance needed in the case of an unexpected failure, MROs benefit from predictive maintenance
We propose an end-to-end approach to obtain online, model-based RUL prognostics for aircraft components by exploiting the knowledge obtained from clusters of component degradation trends
Model-based RUL estimation approach for aircraft components using a clustering of component degradation trends
Summary
Aircraft maintenance, repair and overhaul (MRO) costs account for 9% of the total airline operational costs [1]. We propose an end-to-end approach to obtain online, model-based RUL prognostics for aircraft components by exploiting the knowledge obtained from clusters of component degradation trends. As soon as a component is diagnosed as unhealthy, a cluster-specific degradation model is selected for this component based on a dynamic time-warping clustering of a library of health indicators. These degradation models, together with a particle filtering algorithm, are further used to obtain RUL prognostics. Once a component is diagnosed as being unhealthy, a degradation model and corresponding failure threshold are selected for this component using dynamic timewarping data clustering of a library of series of health indicators (steps 2 and 3).
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