One-class Bearings Fault Detection Model Based on Lyapunov Exponent Spectrum

In order to avoid the practical application problems, abnormal data insufficiency and unavailability, and solve the difficulty of calculating embed dimension and time tag before calculating Lyapunov exponent, an one-class Bearings fault detection model based on genetic algorithm and Lyapunov exponent spectrum is proposed in this paper. The normal training samples are used to decide reconstructed phase space. Then the signals with different conditions will be projected into RPS, the Lyapunov exponent is calculated which is classified as features. The optimum decision threshold values are determined by Genetic Algorithm. The results show that Lyapunov exponent for fault detection is more efficient than for fault diagnosis. In experiment, the performance of detector with largest Lyapunov exponent and Lyapunov exponent spectrum entropy is compared .The results evaluates the effectiveness of the proposed approach. This proposed approach is compared against MLP and other detection techniques. The results show the relative effectiveness of the proposed classifiers in detection of the bearing condition with some concluding remarks.