Bearings Fault Diagnosis Based on HMM and Fractal Dimensions Spectrum

Fractional dimensions have wide applications in fault diagnosis fields as a nonlinear signal processing method. Especially, there are more correlation between the generalized fractal dimensions of bearings signals and fault activities. Generalized fractal dimensions spectrum is different with different bearings condition and faults. To solve the problems of detection rate decreasing due to the noise influence within certain fractal dimensions, factor of linear discrimination ability is employed as the indicator for optimizing fractal dimensions. The results indicate that the proposed approach can effectively remove the noise and improve the performance. Furthermore, In order to solve the problems of traditional classification´s overfitting due to data unbalanced, the model based on HMM is proposed in this paper. HMM-based single fault detection, HMM-based single fault diagnosis models are also presented. More especially, we focus on analysis of the HMM-based single bearings fault diagnosis model in this paper. This proposed approach is compared against other approaches such as MLP detection techniques. The results show the relative effectiveness of the investigated classifiers in detection and diagnosis of the bearing condition with some concluding remarks.