Unscheduled aircraft maintenance causes a lot problems and costs for aircraft operators. This is due to the fact that aircraft cause significant costs if flights have to be delayed or canceled and because spares are not always available at any place and sometimes have to be shipped across the world. Reducing the number of unscheduled maintenance is thus a great costs factor for aircraft operators. This thesis describes three methods for aircraft health monitoring and prediction; one method for system monitoring, one method for forecasting of time series and one method that combines the two other methods for one complete monitoring and prediction process. Together the three methods allow the forecasting of possible failures. The two base methods use decision trees for decision making in the processes and genetic optimization to improve the performance of the decision trees and to reduce the need for human interaction. Decision trees have the advantage that the generated code can be fast and easily processed, they can be altered by human experts without much work and they are readable by humans. The human readability and modification of the results is especially important to include special knowledge and to remove errors, which the automated code generation produced.

La limite des connaissances techniques ou fondamentale, est une réalité dont l’industrie fait face. Le besoin de mettre à jour cette connaissance acquise est essentiel pour une compétitivité économique, mais aussi pour une meilleure maniabilité des systèmes et machines. Aujourd’hui grâce à ces systèmes et machine, l’expansion de données en quantité, en fréquence de génération est un véritable phénomène. À présent par exemple, les avions Airbus génèrent des centaines de mégas de données par jour, et intègrent des centaines voire des milliers de capteurs dans les nouvelles générations d’avions. Ces données générées par ces capteurs, sont exploitées au sol ou pendant le vol, pour surveiller l’état et la santé de l’avion, et pour détecter des pannes, des incidents ou des changements. En théorie, ces pannes, ces incidents ou ces changements sont connus sous le terme d’anomalie. Une anomalie connue comme un comportement qui ne correspond pas au comportement normal des données. Certains la définissent comme une déviation d’un modèle normal, d’autres la définissent comme un changement. Quelques soit la définition, le besoin de détecter cette anomalie est important pour le bon fonctionnement de l'avion. Actuellement, la détection des anomalies à bord des avions est assuré par plusieurs équipements de surveillance aéronautiques, l’un de ces équipements est le « Aircraft condition monitoring System –ACMS », enregistre les données générées par les capteurs en continu, il surveille aussi l’avion en temps réel grâce à des triggers et des seuils programmés par des Airlines ou autres mais à partir d’une connaissance a priori du système. Cependant, plusieurs contraintes limitent le bon fonctionnement de cet équipement, on peut citer par exemple, la limitation des connaissances humaines un problème classique que nous rencontrons dans plusieurs domaines. Cela veut dire qu’un trigger ne détecte que les anomalies et les incidents dont il est désigné, et si une nouvelle condition surgit suite à une maintenance, changement de pièce, etc. Le trigger est incapable s’adapter à cette nouvelle condition, et il va labéliser toute cette nouvelle condition comme étant une anomalie. D’autres problèmes et contraintes seront cités progressivement dans les chapitres qui suivent. L’objectif principal de notre travail est de détecter les anomalies et les changements dans les données de capteurs, afin d’améliorer le system de surveillance de santé d’avion connu sous le nom Aircraft Health Monitoring(AHM). Ce travail est basé principalement sur une analyse à deux étapes, Une analyse unie varie dans un contexte non supervisé, qui nous permettra de se focaliser sur le comportement de chaque capteur indépendamment, et de détecter les différentes anomalies et changements pour chaque capteur. Puis une analyse multi-variée qui nous permettra de filtrer certaines anomalies détectées (fausses alarmes) dans la première analyse et de détecter des groupes de comportement suspects. La méthode est testée sur des données réelles et synthétiques, où les résultats, l’identification et la validation des anomalies sont discutées dans cette thèse. / The limitation of the knowledge, technical, fundamental is a daily challenge for industries. The need to updates these knowledge are important for a competitive industry and also for an efficient reliability and maintainability of the systems. Actually, thanks to these machines and systems, the expansion of the data on quantity and frequency of generation is a real phenomenon. Within Airbus for example, and thanks to thousands of sensors, the aircrafts generate hundreds of megabytes of data per flight. These data are today exploited on the ground to improve safety and health monitoring system as a failure, incident and change detection. In theory, these changes, incident and failure are known as anomalies. An anomaly is known as deviation form a normal behavior of the data. Others define it as a behavior that do not conform the normal behavior. Whatever the definition, the anomaly detection process is very important for good functioning of the aircraft. Currently, the anomaly detection process is provided by several health monitoring equipments, one of these equipment is the Aircraft Health Monitoring System (ACMS), it records continuously the date of each sensor, and also monitor these sensors to detect anomalies and incident using triggers and predefined condition (exeedance approach). These predefined conditions are programmed by airlines and system designed according to a prior knowledge (physical, mechanical, etc.). However, several constraints limit the ACMS anomaly detection potential. We can mention, for example, the limitation the expert knowledge which is a classic problem in many domains, since the triggers are designed only to the targeted anomalies. Otherwise, the triggers do not cover all the system conditions. In other words, if a new behavior appears (new condition) in the sensor, after a maintenance action, parts changing, etc. the predefined conditions won't detect any thing and may be in many cases generated false alarms. Another constraint is that the triggers (predefined conditions) are static, they are unable to adapt their proprieties to each new condition. Another limitation is discussed gradually in the future chapters. The principle of objective of this thesis is to detect anomalies and changes in the ACMS data. In order to improve the health monitoring function of the ACMS. The work is based principally on two stages, the univariate anomaly detection stage, where we use the unsupervised learning to process the univariate sensors, since we don’t have any a prior knowledge of the system, and no documentation or labeled classes are available. The univariate analysis focuses on each sensor independently. The second stage of the analysis is the multivariate anomaly detection, which is based on density clustering, where the objective is to filter the anomalies detected in the first stage (false alarms) and to detect suspected behaviours (group of anomalies). The anomalies detected in both univariate and multivariate can be potential triggers or can be used to update the existing triggers. Otherwise, we propose also a generic concept of anomaly detection based on univariate and multivariate anomaly detection. And finally a new concept of validation anomalies within airbus.

Reducing unscheduled maintenance is important for aircraft operators. There are significant costs if flights must be delayed or cancelled, for example, if spares are not available and have to be shipped across the world. This thesis describes three methods of aircraft health condition monitoring and prediction; one for system monitoring, one for forecasting and one combining the two other methods for a complete monitoring and prediction process. Together, the three methods allow organizations to forecast possible failures. The first two use decision trees for decision-making and genetic optimization to improve the performance of the decision trees and to reduce the need for human interaction. Decision trees have several advantages: the generated code is quickly and easily processed, it can be altered by human experts without much work, it is readable by humans, and it requires few resources for learning and evaluation. The readability and the ability to modify the results are especially important; special knowledge can be gained and errors produced by the automated code generation can be removed. A large number of data sets is needed for meaningful predictions. This thesis uses two data sources: first, data from existing aircraft sensors, and second, sound and vibration data from additionally installed sensors. It draws on methods from the field of big data and machine learning to analyse and prepare the data sets for the prediction process.