Self-Organizing Maps
Process Monitoring and Modeling Using the Self-Organizing Map
Integrated Computer-Aided Engineering
Min-max hyperellipsoidal clustering for anomaly detection in network security
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Condition monitoring of 3G cellular networks through competitive neural models
IEEE Transactions on Neural Networks
Aircraft engine health monitoring using self-organizing maps
ICDM'10 Proceedings of the 10th industrial conference on Advances in data mining: applications and theoretical aspects
Prediction interval on spacecraft telemetry data based on modified block bootstrap method
AICI'10 Proceedings of the 2010 international conference on Artificial intelligence and computational intelligence: Part II
Aircraft engine fleet monitoring using self-organizing maps and edit distance
WSOM'11 Proceedings of the 8th international conference on Advances in self-organizing maps
| Hi-index | 0.00 |
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. However rough engine measurements are inappropriate for such an analysis; they are indeed influenced by external conditions, and may in addition vary between engines. In this work, we first process the data by a General Linear Model (GLM), to eliminate the effect of engines and of measured environmental conditions. The residuals are then used as inputs to a Self-Organizing Map for the easy visualization of trajectories.