Fault identification technology for gear tooth surface wear based on MPE method by MI and improved FNN algorithm

Abstract

Multiscale Permutation Entropy (MPE) is a presented nonlinear dynamic technology for measuring the randomness and detecting the nonlinear dynamic change of time sequences and can be used effectively to extract the nonlinear dynamic wear fault feature of gear tooth surface from vibration signals of gear set. To solve the subjectivity drawback of threshold parameter selection process in MPE method, a joint calculation method based on the Mutual Information (MI) and improved False Nearest Neighbor (FNN) principle for calculating threshold parameters for MPE method was presented in this article. Then, the influence of threshold parameters on the identification accuracy of fault features with the MPE was studied by analyzing simulation data. Through the simulation analysis, the effectiveness of the proposed MPE method is validated. Finally, the wear failure test of spur gear was carried out, and the proposed method was applied to analyze the experimental data of fault signal. Meanwhile, the vibration characteristics of the fault signal are acquired. The analysis results show that the proposed method can effectively realize the fault diagnosis of gear box and has higher fault identification accuracy than the existing methods.Keywords: multiscale permutation entropy, mutual information, improved false nearest neighbor, delay time, embedded dimension, scale factor and fault feature.

Signal de-noising in gear pitting fault identification by an improved singular value decomposition method

“…Empirically, while the decay of autocorrelation function is approximated to 1/e, the optimal delay time is obtained [35]. For the determination of m, Cao's algorithm which is based on the False Nearest Neighbor (FNN) method, makes the calculation of m only related to τ, and overcomes the influence of various thresholds on m [27,28].…”