Application of adaptive local iterative filtering in gear fault identificatio

Abstract

In order to solve the problem that the measured signal of gears cannot accurately reflect the fault characteristics due to noise interference, adaptive local iterative filtering is proposed to be applied to gear fault identification, which is combined with sample entropy and grey correlation to realize gear fault identification. By using adaptive local iterative filtering to decompose the gear non-stationary signal into a finite number of stationary intrinsic mode functions, and calculating the sample entropy of each intrinsic mode function, it is found that the sample entropy of the first few intrinsic mode functions can represent different fault types, bounded by the sample entropy of the intrinsic mode function corresponding to the frequency conversion signal of the gear system. The average value of the sample entropy of multiple training samples under four working conditions of gear system, including normal, mild tooth surface wear, moderate tooth surface wear and broken tooth, is calculated and used as the reference value of the standard failure mode of the corresponding working condition. The grey correlation between the sample entropy of the sample to be detected and the average value of the sample entropy of the training samples under each condition is calculated, and the standard failure mode with the largest grey correlation with the sample to be identified is considered as the failure type of the sample to be identified. The results of the case analysis show that the adaptive iterative filtering can suppress the modal aliasing phenomenon effectively and find obvious gear frequency conversion signals. On the other hand, the modal aliasing phenomenon is more obvious after the signal decomposition by EEMD method, and the frequency conversion component of the gear is basically invisible in the decomposition results of EEMD method. The obvious differences in the shapes of the sample entropy curves of the four working conditions indicate that the sample entropy can represent the changes of gear fault characteristics effectively. The grey correlation method, which can classify and identify the four different fault types effectively, has better classification identification performance than that of BP neural network and has better classification identification ability for small sample data.

Keywords

• gear fault identification
• adaptive local iterative filtering
• sample entropy
• grey correlation degree
• frequency conversion signal