Interpretable Aircraft Engine Diagnostic via Expert Indicator Aggregation

Abstract : Detecting early signs of failures (anomalies) in complex systems is one of the main goal of preventive maintenance. It allows in particular to avoid actual failures by (re)scheduling maintenance operations in a way that optimizes maintenance costs. Aircraft engine health monitoring is one representative example of a field in which anomaly detection is crucial. Manufacturers collect large amount of engine related data during flights which are used, among other applications, to detect anomalies. This article introduces and studies a generic methodology that allows one to build automatic early signs of anomaly detection in a way that builds upon human expertise and that remains understandable by human operators who make the final maintenance decision. The main idea of the method is to generate a very large number of binary indicators based on parametric anomaly scores designed by experts, complemented by simple aggregations of those scores. A feature selection method is used to keep only the most discriminant indicators which are used as inputs of a Naive Bayes classifier. This give an interpretable classifier based on interpretable anomaly detectors whose parameters have been optimized indirectly by the selection process. The proposed methodology is evaluated on simulated data designed to reproduce some of the anomaly types observed in real world engines.
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Transactions on Machine Learning and Data Mining, IBAI Publishing, 2014, 7 (2), pp.39-64. <http://www.ibai-publishing.org/journal/issue_mldm/2014_October/mldm_7_2_39_64.php>
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Soumis le : mercredi 18 mars 2015 - 16:41:32
Dernière modification le : jeudi 19 mars 2015 - 01:05:42
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Distributed under a Creative Commons Paternité - Partage selon les Conditions Initiales 4.0 International License

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  • HAL Id : hal-01133175, version 1
  • ARXIV : 1503.05526

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Tsirizo Rabenoro, Jérôme Lacaille, Marie Cottrell, Fabrice Rossi. Interpretable Aircraft Engine Diagnostic via Expert Indicator Aggregation. Transactions on Machine Learning and Data Mining, IBAI Publishing, 2014, 7 (2), pp.39-64. <http://www.ibai-publishing.org/journal/issue_mldm/2014_October/mldm_7_2_39_64.php>. <hal-01133175>

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