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 detectors numbers is experimented. This proposed approach is compared against other MLP detection techniques. The results show the relative effectiveness of the proposed classifiers in detection of the bearing condition with some concluding remarks.
real-valued negative selection clone SVD High Order Statistics Multi-Layer Perception
Xin-min TAO Wan-hai CHEN Bao-xiang DU Yong XU Han-guang DONG
Harbin Engineering University, China PLA, China
国际会议
2nd IEEE Conference on Industrial Electronics and Applications(ICIEA 2007)(第二届IEEE工业电子与应用国际会议)
哈尔滨
英文
2007-05-23（万方平台首次上网日期，不代表论文的发表时间）