One-class bearing fault detection using negative clone selection algorithm

In order to solve the problems that in bearing fault detection application, only normal samples are available for training purposes, a one-class fault detection based on negative clone selection algorithm (NCSA) is investigated in this paper. NCSA with only normal samples for training is used to generate probabilistically a set of fault detectors that can detect any abnormalities in bearings. By incorporating the self-adaptive clone-mutation operator and the clone mature operator into conventional real-valued negative selection algorithm, the performance of convergence of the proposed approach is significantly improved and thus accuracy of detection is strongly enhanced. This paper analyzes the behavior of the classifier based on parameter selection and number of normal training samples. Furthermore, Comparison of the performance of detection of NCSA with different detector's numbers is also experimented. Finally, the proposed approach is compared against other detection techniques such as MLP (Multi-Layer Perception), etc. the experiments demonstrate that the proposed approach outperforms other methods with some concluding remarks.