1 research outputs found
Feature selection and faultâseverity classificationâbased machine health assessment methodology for point machine slidingâchair degradation
In this paper, we propose an offline and online machine health assessment (MHA) methodology composed of feature extraction and selection, segmentationâbased fault severity evaluation, and classification steps. In the offline phase, the best representative feature of degradation is selected by a new filterâbased feature selection approach. The selected feature is further segmented by utilizing the bottomâup time series segmentation to discriminate machine health states, ie, degradation levels. Then, the health state fault severity is extracted by a proposed segment evaluation approach based on within segment rateâofâchange (RoC) and coefficient of variation (CV) statistics. To train supervised classifiers, a priori knowledge about the availability of the labeled data set is needed. To overcome this limitation, the health state faultâseverity information is used to label (eg, healthy, minor, medium, and severe) unlabeled raw condition monitoring (CM) data. In the online phase, the faultâseverity classification is carried out by kernelâbased support vector machine (SVM) classifier. Next to SVM, the kânearest neighbor (KNN) is also used in comparative analysis on the fault severity classification problem. Supervised classifiers are trained in the offline phase and tested in the online phase. Unlike to traditional supervised approaches, this proposed method does not require any a priori knowledge about the availability of the labeled data set. The proposed methodology is validated on infield point machine slidingâchair degradation data to illustrate its effectiveness and applicability. The results show that the time series segmentationâbased failure severity detection and SVMâbased classification are promising