Influence of characteristic parameters of signal on fault feature extraction of singular value method

The detection of mechanical fault signals by singular value decomposition is a commonly used method in fault diagnosis. The delay time of the fault signal time series and the rationality of the value of the phase space embedding dimension, as well as the fluctuation of the characteristic parameters of the fault signal, will cause the singular value decomposition method to have a greater impact on the accuracy of fault feature identification and diagnosis. In this article, the simulation model of the similarity signal is established by the combination of the autocorrelation function method and the Cao’s algorithm. Then, the delay time of the signal sequence and the optimal value of the embedded dimension are obtained through simulation. Next, using this method to study the fluctuation of the characteristic parameters such as the frequency, amplitude and initial phase of the signal, the relationship between the characteristic parameters of the signal and the singular value of the signal is obtained. Finally, through the experimental study of the pitting corrosion of the gear tooth surface, the vibration of the fault feature is obtained. The research shows that the combination of autocorrelation function method and Cao's algorithm can calculate the optimal characteristic parameters for the singular value decomposition method and improve the ability of the method to identify fault features.