Permutation Entropy and K-ELM in Gear Fault Diagnosis

For Learning Machine Extreme the number of hidden layer nodes artificially set and the fault classification model of the gear is of low accuracy and poor stability, gear fault diagnosis method based on permutation entropy and nuclear kernel extreme learning machine. First, the measured signal by set of empirical mode decomposition treated by a series of IMF the intrinsic mode functions and extraction of various components of the permutation entropy PE value high dimensional feature vector set. Second, in the inner product by Gauss kernel function to express the ELM output function to adaptively determine the number of the hidden layer nodes; After that, the high dimension feature vector set is used as the input of the K-ELM algorithm to establish the kernel function limit learning machine gear fault classification model, and the classification and identification of different fault states of gears are carried out. The experimental results show that the K-ELM gear fault classification model is better than ELM, and the SVM fault classification model has higher accuracy and stronger stability.