Abstract: The nonlinear multi-parameters of the correlation dimension and Lyapunov exponent and so on are applied in the research of the fault diagnosis of rotating machines. By using theory of phase space reconstruction, simulating fault signal of rotating machine is reconstructed. In order to reconstruct the phase space which can be adequately reflect the movement characteristics of the system, the time delay and embedding dimension are discussed emphatically, the four nonlinear values of correlation dimension, Lyapunov exponent, complexity and approximate entropy are calculated. On this basis, four nonlinear parameters are fused, and the characteristic quantities of non-linearity is introduced for extraction and recognition of the fault signal characteristic. Because the nonlinearity is the syntheses of multi parameters of correlation dimension, Lyapunov exponent, complexity and the approximate entropy, it will be more conducive to recognize and analyze fault signal, to enhance the reliability. Studies shows that, the fault type is different, nonlinearity is significantly different, which verifies that the nonlinear feature quantities are effective parameters for fault information, and thus a more effective way is provided for studying the fault diagnosis of complexity rotating machinery.

Key words: Approximate entropy, Complexity, Correlation dimension, Fault diagnosis, Lyapunov exponent, Nonlinearity