Original scientific paper
https://doi.org/10.1080/00051144.2019.1578037
An application of ensemble empirical mode decomposition and correlation dimension for the HV circuit breaker diagnosis
Mingliang Liu
; HLJ Province Key Lab of Senior-Education for Electronic Engineering, Heilongjiang University, Harbin, China
Bing Li
; HLJ Province Key Lab of Senior-Education for Electronic Engineering, Heilongjiang University, Harbin, China
Jianfeng Zhang
; HLJ Province Key Lab of Senior-Education for Electronic Engineering, Heilongjiang University, Harbin, China
Keqi Wang
; College of Mechanical and Electrical Engineering, Northeast Forestry University, Harbin, China
Abstract
During the operation process of the high-voltage circuit breaker, the changes of vibration signals reflect the machinery states of the circuit breaker. The extraction of the vibration signal feature will directly influence the accuracy and practicability of fault diagnosis. This paper presents an extraction method based on ensemble empirical mode decomposition) and correlation dimension and a classification method with BP (back propagation) neural network. Firstly, original vibration signals are decomposed into a finite number of stationary intrinsic mode functions (IMFs). Secondly, correlation dimension of the top four IMFs by the G–P algorithm is calculated and the characteristic vector of the vibration signal of the circuit breaker is formed. At last, the classification of characteristic parameter is realized with a simple BP neural network for fault diagnosis. The experimentation without loads indicates that the method can easily and accurately diagnose breaker faults and exploit a new road for diagnosis of high-voltage circuit breakers.
Keywords
High-voltage circuit breaker; vibration signal; ensemble empirical mode decomposition; correlation dimension; BP neural network; fault diagnosis
Hrčak ID:
239768
URI
Publication date:
26.2.2019.
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