A novel method for feature extraction using crossover characteristics of nonlinear data and its application to fault diagnosis of rotary machinery
Crossover characteristics of scaling-law curves of rotary machinery are exhibited. Multiple Hurst exponents can be obtained using crossover characteristics. A novel method for feature extraction is proposed using these exponents. The proposed method can distinguish types and severity of mechanical faults.
feature extraction nonlinear data rotary machinery detrended fluctuation analysis(dfa) crossover phenomena