Interpretable Aircraft Engine Diagnostic via Expert Indicator Aggregation

TL;DR

This paper presents an interpretable, expert-guided anomaly detection method for aircraft engines that combines parametric scores, feature selection, and Naive Bayes classification to improve early fault detection.

Contribution

It introduces a novel methodology that leverages expert-designed indicators and feature selection to create an understandable and effective anomaly detection system for aircraft engines.

Findings

Method effectively detects simulated engine anomalies

Indicators are selected for high discriminative power

Classifier remains interpretable and aligned with expert knowledge

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.