Fault prediction in aircraft engines using Self-Organizing Maps

Aircraft engines are designed to be used during several tens of years. Their maintenance is a challenging and costly task, for obvious security reasons. The goal is to ensure a proper operation of the engines, in all conditions, with a zero probability of failure, while taking into account aging. The fact that the same engine is sometimes used on several aircrafts has to be taken into account too. The maintenance can be improved if an efficient procedure for the prediction of failures is implemented. The primary source of information on the health of the engines comes from measurement during flights. Several variables such as the core speed, the oil pressure and quantity, the fan speed, etc. are measured, together with environmental variables such as the outside temperature, altitude, aircraft speed, etc. In this paper, we describe the design of a procedure aiming at visualizing successive data measured on aircraft engines. The data are multi-dimensional measurements on the engines, which are projected on a self-organizing map in order to allow us to follow the trajectories of these data over time. The trajectories consist in a succession of points on the map, each of them corresponding to the two-dimensional projection of the multi-dimensional vector of engine measurements. Analyzing the trajectories aims at visualizing any deviation from a normal behavior, making it possible to anticipate an operation failure.Comment: Communication pr\'esent\'ee au 7th International Workshop WSOM 09, St Augustine, Floride, USA, June 200

Aircraft engine fleet monitoring using Self-Organizing Maps and Edit Distance

International audienceAircraft 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

Visual Mining and Statistics for a Turbofan Engine Fleet

International audienceSnecma, as a turbofan manufacturer, needs to deal with a wide eet of more than thousands of engines. Every day, data from aircraft engines are broadcas- ted to the ground. Some airlines companies rely on their engine manufacturer to control the engines' behavior and help prepare for maintenance scheduling. The goal of the manufacturer is to detect abnormalities to help schedule main- tenance operations. The advantage of the manufacturer as MRO operator is the registered memory of all past events that appears on its eet of engines. If one opens the possibility to look in this huge amount of data for corresponding similar behaviors, which may have append in the past (for all engines of all customer companies), it becomes possible to make some targeted statistics of the future

Sudden change detection in turbofan engine behavior

International audienceSnecma, as a turbofan manufacturer, needs to deal with a wide eet of more than thousands of engines. Every day, data from aircraft engines are broadcas- ted to the ground. Some airlines companies rely on their engine manufacturer to control the engines' behavior and help prepare for maintenance scheduling. The goal of the manufacturer is to detect abnormalities to help schedule main- tenance operations. The advantage of the manufacturer as MRO operator is the registered memory of all past events that appears on its eet of engines. If one opens the possibility to look in this huge amount of data for corresponding similar behaviors, which may have append in the past (for all engines of all customer companies), it becomes possible to make some targeted statistics of the future

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

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.Comment: Proceedings of the 14th Industrial Conference, ICDM 2014, St. Petersburg : Russian Federation (2014

Anomaly Detection Based on Aggregation of Indicators

Automatic anomaly detection is a major issue in various areas. Beyond mere detection, the identification of the origin of the problem that produced the anomaly is also essential. This paper introduces a general methodology that can assist human operators who aim at classifying monitoring signals. The main idea is to leverage expert knowledge by generating a very large number of indicators. A feature selection method is used to keep only the most discriminant indicators which are used as inputs of a Naive Bayes classifier. The parameters of the classifier have been optimized indirectly by the selection process. Simulated data designed to reproduce some of the anomaly types observed in real world engines.Comment: 23rd annual Belgian-Dutch Conference on Machine Learning (Benelearn 2014), Bruxelles : Belgium (2014

Anomaly Detection Based on Indicators Aggregation

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.Comment: International Joint Conference on Neural Networks (IJCNN 2014), Beijing : China (2014). arXiv admin note: substantial text overlap with arXiv:1407.088

Performance-based health monitoring, diagnostics and prognostics for condition-based maintenance of gas turbines: A review

With the privatization and intense competition that characterize the volatile energy sector, the gas turbine industry currently faces new challenges of increasing operational flexibility, reducing operating costs, improving reliability and availability while mitigating the environmental impact. In this complex, changing sector, the gas turbine community could address a set of these challenges by further development of high fidelity, more accurate and computationally efficient engine health assessment, diagnostic and prognostic systems. Recent studies have shown that engine gas-path performance monitoring still remains the cornerstone for making informed decisions in operation and maintenance of gas turbines. This paper offers a systematic review of recently developed engine performance monitoring, diagnostic and prognostic techniques. The inception of performance monitoring and its evolution over time, techniques used to establish a high-quality dataset using engine model performance adaptation, and effects of computationally intelligent techniques on promoting the implementation of engine fault diagnosis are reviewed. Moreover, recent developments in prognostics techniques designed to enhance the maintenance decision-making scheme and main causes of gas turbine performance deterioration are discussed to facilitate the fault identification module. The article aims to organize, evaluate and identify patterns and trends in the literature as well as recognize research gaps and recommend new research areas in the field of gas turbine performance-based monitoring. The presented insightful concepts provide experts, students or novice researchers and decision-makers working in the area of gas turbine engines with the state of the art for performance-based condition monitoring

Computational framework for real-time diagnostics and prognostics of aircraft actuation systems

Prognostics and health management (PHM) are emerging approaches to product life cycle that will maintain system safety and improve reliability, while reducing operating and maintenance costs. This is particularly relevant for aerospace systems, where high levels of integrity and high performances are required at the same time. We propose a novel strategy for the nearly real-time fault detection and identification (FDI) of a dynamical assembly, and for the estimation of remaining useful life (RUL) of the system. The availability of a timely estimate of the health status of the system will allow for an informed adaptive planning of maintenance and a dynamical reconfiguration of the mission profile, reducing operating costs and improving reliability. This work addresses the three phases of the prognostic flow – namely (1) signal acquisition, (2) fault detection and identification, and (3) remaining useful life estimation – and introduces a computationally efficient procedure suitable for real-time, on-board execution. To achieve this goal, we propose to combine information from physical models of different fidelity with machine learning techniques to obtain efficient representations (surrogate models) suitable for nearly real-time applications. Additionally, we propose an importance sampling strategy and a novel approach to model damage propagation for dynamical systems. The methodology is assessed for the FDI and RUL estimation of an aircraft electromechanical actuator (EMA) for secondary flight controls. The results show that the proposed method allows for a high precision in the evaluation of the system RUL, while outperforming common model-based techniques in terms of computational time