Classifier ensembles to improve the robustness to noise of bearing fault diagnosis

In this paper, we perform a noise analysis to assess the degree of robustness to noise of a neural classifier aimed at performing multi-class diagnosis of rolling element bearings. We work on vibration signals collected by means of two accelerometers and we consider ten levels of noise, each of which characterized by a different signal-to-noise ratio ranging from 40.55 to -11.35 db. We classify the noisy signals by means of a neural classifier initially trained on signals without noise and then we repeat the training process with signals affected by increasing levels of noise. We show that adding noisy signals to the training set we can significantly increase the classification accuracy of a single classifier. Finally, we apply the two most used strategies to combine classifiers: classifier fusion and classifier selection, and show that, in both cases, we can significantly increase the performance of the single best classifier. In particular, classifier selection achieves the best results for low and medium levels of noise, while classifier fusion is the most accurate for high levels of noise. The analysis presented in the paper can be profitably used to identify both the type of classifier (e.g., single classifier or classifier ensemble) and how many and which noise levels should be used in the training phase in order to achieve the desired classification accuracy in the application domain of interest.