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

Search Strategies for Binary Feature Selection for a Naive Bayes Classifier

We compare in this paper several feature selection methods for the Naive Bayes Classifier (NBC) when the data under study are described by a large number of redundant binary indicators. Wrapper approaches guided by the NBC estimation of the classification error probability out-perform filter approaches while retaining a reasonable computational cost

Interpretable Aircraft Engine Diagnostic via Expert Indicator Aggregation

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.Comment: arXiv admin note: substantial text overlap with arXiv:1408.6214, arXiv:1409.4747, arXiv:1407.088

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

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

Probabilistic outlier detection in vibration spectra with small learning dataset

The issue of detecting abnormal vibrations from spectra is addressed in this article, when little is known both on the mechanical behavior of the system, and on the characteristic patterns of potential faults. With vibration measured from a bearing test rig and from an aircraft engine, we show that when only a small learning set is available, probabilistic approaches have several advantages, including modelling healthy vibrations, and thus ensuring fault detection. To do so, we compare two original algorithms: the first one relies on the statistics of the maximum of log-periodograms. The second one computes the probability density function (pdf) of the wavelet transform of log-periodograms, and a likelihood index when new periodograms are presented. A by-product of it is the ability to generate random log-periodograms according with respect to the learning dataset. Receiver Operator Characteristic (ROC) curves are built in several experimental settings, and show the superiority of one of our algorithms over state-of-the-art machine-learning-oriented fault detection methods; lastly we generate random samples of aircraft engine log-periodograms

Selecting the Number of Clusters KK with a Stability Trade-off: an Internal Validation Criterion

Model selection is a major challenge in non-parametric clustering. There is no universally admitted way to evaluate clustering results for the obvious reason that there is no ground truth against which results could be tested, as in supervised learning. The difficulty to find a universal evaluation criterion is a direct consequence of the fundamentally ill-defined objective of clustering. In this perspective, clustering stability has emerged as a natural and model-agnostic principle: an algorithm should find stable structures in the data. If data sets are repeatedly sampled from the same underlying distribution, an algorithm should find similar partitions. However, it turns out that stability alone is not a well-suited tool to determine the number of clusters. For instance, it is unable to detect if the number of clusters is too small. We propose a new principle for clustering validation: a good clustering should be stable, and within each cluster, there should exist no stable partition. This principle leads to a novel internal clustering validity criterion based on between-cluster and within-cluster stability, overcoming limitations of previous stability-based methods. We empirically show the superior ability of additive noise to discover structures, compared with sampling-based perturbation. We demonstrate the effectiveness of our method for selecting the number of clusters through a large number of experiments and compare it with existing evaluation methods.Comment: 43 page

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