A Novel Model of one-class Bearing Fault Detection using RNCS Algorithm based on HOS

A novel model of bearing fault detection based on improved real-valued negative clone selection algorithm (RNCS) is presented In this paper. In many bearing fault detection application, only positive (normal) samples are available for training purposes, Then RNCS is used to generate probabilistically a set of fault detectors that can detect any abnormalities(including faults and damages) in the behavior pattern of bearings. Faults occurring in machine elements are often related to non-linear effects which may lead to non-linearity in the machine vibration signature. HOS make it possible to analyze the structure of the output signal and to provide information related to the non-linearity within the system. The extracted HOS features matrix from original signals are transformed to SVD features which are used as inputs to RNCS for one-class (normal) recognition to address the problem of difficultly collecting abnormal samples in bearing fault detection. Comparison of the performance of detection of RNCS with different detector´s numbers is experimented. This proposed approach is compared against other MTP detection techniques. The results show the relative effectiveness of the proposed classifiers in detection of the bearing condition with some concluding remarks.