Jixie chuandong (May 2021)
Application of Adaptive Local Iterative Filtering and Fuzzy Entropy in Gear Fault Identification
Abstract
Aiming at the problem that the measured signal of gear system can’t accurately reflect the fault characteristics due to noise interference， a fault identification method combining adaptive local iterative filtering and fuzzy entropy is proposed. By using adaptive local iterative filtering， the nonstationary signals of gears can be decomposed into finite stationary intrinsic mode functions. Since the adaptive local iterative filtering can effectively separate the rotating frequency signals of gear system， the fuzzy entropy of the first several intrinsic mode functions is calculated based on the intrinsic mode functions corresponding to the rotating frequency signals. Finally， grey relevance degree of the vibration signal’s fuzzy entropy under different working conditions is calculated to identify different fault types of gear system. The results show that the method can be effectively applied to the fault diagnosis of gear system.
