Name
Abstract | ||
|---|---|---|
The structural health of airframes is often monitored by analysis of the frequency of occurrence matrix (FOOM) produced after each flight. Each cell in the matrix records a stress event of a particular severity. These matrices are used to determine how much of the aircraft's life has been used up in each flight. Unfortunately, the sensors that produce this data are subject to degradation themselves, resulting in corruption of FOOMs. This paper reports a method of automating detection of sensor faults. It is the only known method that is capable of detecting such faults. The method is in essence a dimensionality reduction algorithm coupled to a novelty detection algorithm that produce measures of unusual counts of stress events at the level of the individual cell and unusual distributions of counts over the entire FOOM. Cell-level error is detected using a probability threshold and a sum of standard deviations. FOOM-level error is detected using a novel application of the Eigen-face algorithm. Novelty is measured using Gaussian basis function neural network fitted using the Expectation-Maximization algorithm. |
Year | DOI | Venue |
|---|---|---|
2000 | 10.1109/IJCNN.2000.859424 | IJCNN (6) |
Keywords | Field | DocType |
entire foom,foom-level error,neural networks,stress event,airframe strain data,individual cell,dimensionality reduction algorithm,eigen-face algorithm,novelty detection,matrix record,cell-level error,novelty detection algorithm,expectation-maximization algorithm,sensors,multi layer perceptron,fault detection,degradation,probability,pattern recognition,neural network,standard deviations,noise,strain gauge,frequency,standard deviation,capacitive sensors,expectation maximization algorithm | Novelty detection,Dimensionality reduction,Eigenface,Pattern recognition,Fault detection and isolation,Computer science,Artificial intelligence,Basis function,Novelty,Artificial neural network,Standard deviation,Machine learning | Conference |
ISSN | ISBN | Citations |
2161-4393 | 0-7695-0619-4 | 6 |
PageRank | References | Authors |
1.04 | 0 | 2 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Simon Hickinbotham | 1 | 73 | 16.67 |
| jim austin | 2 | 6 | 1.04 |