Novelty detection in airframe strain data

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. The 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 produces measures of unusual counts of stress elements 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 eigenface algorithm. Novelty is measured using a mixture of Gaussian model of the data, fitted using the expectation-maximisation algorithm