Aircraft Engine Health Monitoring Using Self-Organizing Maps

2014 International Joint Conference on Neural Networks (IJCNN)

et al. 2014

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Automatic anomaly detection is a major issue in various areas. Beyond mere detection, the identification of the source of the problem that produced the anomaly is also essential. This is particularly the case in aircraft engine health monitoring where detecting early signs of failure (anomalies) and helping the engine owner to implement efficiently the adapted maintenance operations (fixing the source of the anomaly) are of crucial importance to reduce the costs attached to unscheduled maintenance.This paper introduces a general methodology that aims at classifying monitoring signals into normal ones and several classes of abnormal ones. The main idea is to leverage expert knowledge by generating a very large number of binary indicators. Each indicator corresponds to a fully parametrized anomaly detector built from parametric anomaly scores designed by experts. A feature selection method is used to keep only the most discriminant indicators which are used at 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. ACKNOWLEDGMENTThis study is supported by grant from Snecma 1 .

Section: B Health Monitoringmentioning

confidence: 99%

Anomaly detection based on indicators aggregation

Rabenoro

Lacaille

2014 International Joint Conference on Neural Networks (IJCNN)

et al. 2014

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“…The normalized vector is a failure score signature that may be described easily by experts to identify the fault origin, in particular because the original indicators have some meaning for them. See [4], [5] and [9] for other examples.…”

Section: Detecting Faults and Abnormal Behaviorsmentioning

confidence: 99%

A Methodology for the Diagnostic of Aircraft Engine Based on Indicators Aggregation

Rabenoro

Lacaille

Advances in Data Mining. Applications and Theoretical Aspects

et al. 2014

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Abstract. Aircraft engine manufacturers collect large amount of engine related data during flights. These data are used to detect anomalies in the engines in order to help companies optimize their maintenance costs. This article introduces and studies a generic methodology that allows one to build automatic early signs of anomaly detection in a way that is 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. The best indicators are selected via a classical forward scheme, leading to a much reduced number of indicators that are tuned to a data set. We illustrate the interest of the method on simulated data which contain realistic early signs of anomalies.

“…The estimated signals are also shown on these two figures. For more information on this aspect of the analysis process see [2]. From now, the observations corresponding to each flight are …”

Section: Change Detection -Cdmentioning

confidence: 99%

Aircraft Engine Fleet Monitoring Using Self-Organizing Maps and Edit Distance

Côme¹,

Advances in Self-Organizing Maps

Verleysen

et al. 2011

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Abstract. Aircraft engines are designed to be used during several tens of years. Ensuring a proper operation of engines over their lifetime is therefore an important and difficult task. The maintenance can be improved if efficient procedures for the understanding of data flows produced by sensors for monitoring purposes are implemented. This paper details such a procedure aiming at visualizing in a meaningful way successive data measured on aircraft engines and finding for every possible request sequence of data measurement similar behaviour already observed in the past which may help to anticipate failures. The core of the procedure is based on Self-Organizing Maps (SOM) which are used to visualize the evolution of the data measured on the engines. Rough measurements can not be directly used as inputs, because they are influenced by external conditions. A preprocessing procedure is set up to extract meaningful information and remove uninteresting variations due to change of environmental conditions. The proposed procedure contains four main modules to tackle these difficulties: environmental conditions normalization (ECN), change detection and adaptive signal modeling (CD), visualization with Self-Organizing Maps (SOM) and finally minimal Edit Distance search (SEARCH). The architecture of the procedure and of its modules is described in this paper and results on real data are also supplied.