Application of Adaptive Local Iterative Filtering and Permutation Entropy in Gear Fault Recognition

Abstract

In this paper, a fault identification method combining adaptive local iterative filtering and permutation entropy is proposed. The adaptive local iterative filtering can decompose the nonstationary signal into a finite number of stationary intrinsic mode functions. And the experiment gear fault data are decomposed into several intrinsic mode functions by this method. Then, using the permutation entropy to calculate each intrinsic mode function, it is found that the permutation entropy of the first several intrinsic mode functions can represent the characteristics of different fault types, and the permutation entropy of the intrinsic mode function corresponding to the rotating frequency signal of the gear system could be the boundary. Finally, the fault type of gear is identified by calculating the gray correlation degree of permutation entropy of essential mode function of vibration signal decomposition under different working conditions. The example analysis results show that the proposed method can be effectively applied to the fault diagnosis of the gear system.

Design of a Fault Diagnosis Method for Transmission System of Hydraulic Support Truck for Coal Mine

“…The execution time of system instructions is compensated with the transmission response time to eliminate the effect of fault diagnosis time difference. The system database is correlated and the information of data in the system is obtained, and the abnormal data is recorded by using the tagging function of the database [15][16]. At this point, the ratio between the correlation dimension and the abnormal data is greater than the preset criterion, indicating that the diagnostic region of the model is unstable and needs to be readjusted.…”

State of the art on vibration signal processing towards data‐driven gear fault diagnosis

“…Zhang et al. applied ALIF to decompose gear fault signals into several IMFs and calculated the permutation entropy for each IMF and their grey correlation degree to identify fault types of gears [41]. They showed that this method can effectively diagnose gear faults.…”