Research on gear fault diagnosis based on feature fusion optimization and improved two hidden layer extreme learning machine

Abstract

Aiming at gear fault diagnosis in rotating machinery, a novel state assessment method based on feature fusion optimization and improved two-hidden-layer extreme learning machine (ITELM) was proposed in order to improve the fault diagnosis accuracy. Firstly, variational mode decomposition (VMD) and wavelet packet (WP) are employed to decompose the vibration signal, and statistical parameter features are extracted. Then, ReliefF algorithm is adopted to manage the extracted features and obtain the optimal feature subset. Finally, in order to avoid the network structure limitation of the existing two-hidden layer extreme learning machine with same hidden-layer node number, ITELM with different hidden-layer node number is proposed to evaluate the gear running states. Meanwhile, the empirical formula and fuzzy logic reasoning methods are developed in order to effectively determine the number of nodes for first and second hidden layers, respectively. The experimental results from different perspectives verified the effectiveness of the proposed method.