Study On Nonlinear Feature Of Vibration Signal In Automobile Main Reducer

With the development of industry and science technology, higher reliability, usability and maintainability of machinery are expected. Modern non-linear signal processing methods, such as wavelet, fractal and chaos etc, have provided more advanced and reliable theory for vehicle test and satisfy the requirement of industry development to some extent. It is a promising subject in signal processing field to seek better approach for nonlinear time series analysis. Based on the projects of automobile parts performance test bed (such as drive axis, main reducer and differential), this article applies wavelet de-noising, fractal and chaos to product test and signal processing of automobile transmission, studies the nonlinear features of automobile deeply and opens up a new route to signal processing of complex vehicle parts vibration accurately. Main contents as follows:Thinking of nonlinear factors in gear pairs system: gear backlash, meshing stiffness, gear resultant error, 8 degree of freedom nonlinear kinetics model of main reducer is built, Differential equation is computed by 4-order Runge-Kutta numerical integration method. Three typical vibrations of main reducer are simulated, and the influence of different system parameters on nonlinear dynamic characteristics and the ability of correlation dimension and largest Lyapunov exponent reflecting dynamic performance of gears transmission system are analyzed.Due to the fact that vibration characteristics of faulty machinery are complex and defect-related vibration signal is normally buried in the wideband noise, de-noising method based on analytic Wavelet Transform Modulus Maximum (WTMM) is introduced into the noise reduction of automobile vibration signals. Analytic wavelet transform (AWT) only reflects positive frequencies of signals and its modulus oscillation is weaker than real wavelet transform (RWT), so signal noise reduction and singularity detect based AWT can be more accurate than RWT for the signal with additive white noise. Analytic wavelet based constructed by Hilbert transform is applied to the noise reduction based on WTMM. Experiment with automobile main reducer results show that noise reduction using modulus maximum of analytic wavelet is better than that of real wavelet.The theory of correlation dimension computation based on GP is concise, but the computation burden is heavy, and scaling region recognition automatically is hard. Analyzing the influencing factor of correlation dimension, a method to scaling region recognition and correlation dimension computation automatically based on second local slope of correlation integral is presented. The effectiveness of this method was verified by the analysis of Lorenz attractor. Data, which are sampled in an automobile main reducer performance test bed, is analyzed by this method. Experiments results show correlation dimension and largest lyapunov exponent of different main reducers are different, they can be used as the quantification factor of recognizing signal features and level. Moreover, nonlinear features coming from several sensors are constructed compound signal feature by GP. Compound signal feature based nonlinear features can distinguish various working state more...