HMM-Based Bearings Fault Diagnosis using Fractal Dimensions Spectrum
Fractional dimensions have wide applications in fault diagnosis fields as a nonlinear signal processing method. 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, to cope with the problems of traditional classifications overfitting due to data imbalanced, the model based on HMM is proposed in this paper. HMM-based single fault detection, HMM-based single fault diagnosis models are also presented. 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.
Xinmin Tao Baoxiang Du Yong Xu
Department of Information and Communication University of HRBEU Harbin, 150001,China
国际会议
南宁
英文
2007-07-20（万方平台首次上网日期，不代表论文的发表时间）