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一种改进的DDTFA方法及其在齿轮箱故障诊断中的应用

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Abstract Aiming at initial phase function selection of the data-driven time-frequency analysis (DDTFA) method, an improved DDTFA method was proposed to optimize initial phase function accurately, quickly and adaptively.Introducing the idea of solving extreme values of a function in mathematics, a signal’s initial phase function selection problem was converted into a continuous optimization of an initial solution set.Through simplifying Gauss-Newton iterative algorithm in DDTFA, the slope variation of initial phase function in an initial solution set before and after first iteration was taken as the derivative to obtain the continuous derivative set of the initial solution set, and further get local extreme values.Then the energy of signal components corresponding to local extreme values being the maximum was taken as the criterion to optimally choose initial phase function of a signal, and further complete signal decomposition.Simulation analysis and actual examples of gearboxes’ fault diagnosis showed that the proposed method can correctly, rapidly and adaptively optimize initial phase function, and effectively extract fault features; it has a certain anti-noise ability.

Key words： fault diagnosis data-driven time-frequency analysis (DDTFA) method gearbox initial phase function

Received: 10 July 2018 Published: 28 February 2020

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	Articles by authors
	TIAN Lili
	LI Rong
	CHEN Lin
	LIU Jian

Cite this article:

TIAN Lili,LI Rong,CHEN Lin, et al. An improved DDTFA method and its application in gearboxes’ fault diagnosis[J]. JOURNAL OF VIBRATION AND SHOCK, 2020, 39(5): 81-88.

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http://jvs.sjtu.edu.cn/EN/ OR http://jvs.sjtu.edu.cn/EN/Y2020/V39/I5/81

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