Diversity controlled rotating machinery fault detection

Classifier ensembles are more and more often applied for technical diagnostic problems. When dealing with vibration signals a lot of point features can be extracted. In this situation there is the problem of how to choose the best classifiers in the ensemble. One solution is the use of measures that quantify diversities amongst the classifier outputs. While there is no general diversity definition and method of calculation, the selection of the correct measure is a vital task. In this paper research is presented on the application of classifier ensembles built with Bagging for the detection of rotating machinery faults. It was found that there is a relationship between classification accuracy and the diversity measures