Industrial fault detection and isolation using Dominant Feature Identification

In this paper, we show how to find a reduced feature subset which is optimal in both estimation and clustering least square errors using two new Dominant Feature Identification (DFI) methods. We apply DFI to to identify the important features in a given set of faults, and a Neural Network (NN) is used for online fault classification based on the determined reduced feature set in the proposed two-stage framework. Our experimental results on an industrial machine fault simulator show the effectiveness in fault diagnosis and classification. Accuracy of 99.4% for fault identification is observed when using proposed new DFI followed by NN classification, reducing the number of required features from 120 to 13 and the number of sensors from 8 to 4.