基于自适应直接快速迭代滤波的滚动轴承故障诊断方法

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Abstract Direct fast iterative filtering (DFIF) is a recently proposed nonlinear and non-stationary signal analysis method. Aiming at the problems that the DFIF method needs to preset the adjustment parameter of filtering interval, which lacks adaptability in the process of iterative calculation. In this paper, adaptive direct fast iterative filtering method is proposed based on the instantaneous frequency fluctuation energy difference criterion, which can adaptively determine the optimal filter interval adjustment parameters in the iterative screening process of each layer in the outer loop of DFIF algorithm. The ADFIF method can adaptively decompose a given nonlinear and non-stationary signal into the sum of several approximately narrow band signals whose instantaneous frequency has physical significance and a trend term. Through the simulated and measured signal analysis of rolling bearings, the proposed ADFIF method is compared with original DFIF, the adaptive local iterative filtering, variational mode decomposition, and empirical mode decomposition and the results show that the proposed ADFF method has certain advantages in suppressing mode mixing and noise resistance, and can extract more fault characteristic information of rolling bearing.

Key words： fast iterative filtering adaptive local iterative filtering rolling bearing fault diagnosis

Received: 19 July 2022 Published: 28 July 2023

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	Articles by authors
	DING Wenhai1
	2
	ZHENG Jinde1
	2
	PAN Haiyang1
	2
	MENG Rui1
	2
	NIU Limin1
	2

Cite this article:

DING Wenhai1,2,ZHENG Jinde1, et al. Adaptive direct fast iterative filtering based rolling bearing fault diagnosis[J]. JOURNAL OF VIBRATION AND SHOCK, 2023, 42(14): 20-29.

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http://jvs.sjtu.edu.cn/EN/ OR http://jvs.sjtu.edu.cn/EN/Y2023/V42/I14/20

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