Novelty detection for long-term diagnostic data with Gaussian and non-Gaussian disturbances using a support vector machine

Abstract In condition monitoring lack of properly balanced data sets with faulty and healthy cases makes proper condition recognition very challenging. In many cases, one may have good condition data only as the machine is unique and there is no other example. This issue is addressed by proposing a support vector machine for novelty detection applied to health index data. In this scheme, the moving window approach has been utilized in which the simple statistical parameterization of the data is carried out. Then the model in the multidimensional (mD) space is constructed whose shape is defined by an estimated hypersphere border. If the data lies inside the border then it can be used to re-train the model. Whereas if it is outside the border then it cannot be recognized as a healthy case. The size of the mD hypersphere (for m = 2) describes the location of the good-condition data cloud as a potential feature. If the size of the data cloud is growing, it means more dispersion of the data. The efficiency of the method is tested on simulated and well-known real data sets having Gaussian and non-Gaussian disturbances.