Research on Acoustic Mechanical Fault Diagnosis Method of High Voltage Circuit Breaker Based on Improved EEMD

Zhao, Xian Ping; Zhi, Cheng; Tan, Xiang Yu; Niu, Wei

doi:10.4028/www.scientific.net/amm.687-691.1054

Cited by 6 publications

(6 citation statements)

References 5 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…A unique hybrid method of electrical and chemical techniques for the fault diagnosis of gasinsulated switchgears has been analyzed by (Li, Tang, & Liu, 2015). An acoustic signal processing based on blindsource separation through fast independent component analysis has been discussed in (Zhao & Cheng, 2014) while acoustic signal processing based on wavelet transform has been presented by (CHARBKAEW, 2012;Mingliang & Keqi, 2014). A comprehensive survey of the testing methodologies used by power engineers for analyzing CBs and disconnectors in the last decade has been reviewed by (Westerlund & Hilber, 2014).…”

Section: Literature Review and Novelty Of Proposed Algorithmmentioning

confidence: 98%

An expert system for acoustic diagnosis of power circuit breakers and on-load tap changers

Hussain

Lee

Choi

et al. 2015

Expert Systems with Applications

View full text Add to dashboard Cite

Section: Literature Review and Novelty Of Proposed Algorithmmentioning

confidence: 98%

An expert system for acoustic diagnosis of power circuit breakers and on-load tap changers

Hussain

Lee

Choi

et al. 2015

Expert Systems with Applications

View full text Add to dashboard Cite

“…Wavelet transform (WT), empirical mode decomposition (EMD) and ensemble empirical mode decomposition (EEMD) are usually used to obtain vibration signal characteristics [6][7][8]. WT depends on fixed wavelet basis and number of layers, so it cannot be decomposed adaptively and has the problem of band energy leakage [9,10].…”

Section: Introductionmentioning

confidence: 99%

Fault Diagnosis Method of Circuit Breaker Based on CEEMDAN and PSO‐GSA‐SVM

Ма¹,

Wang

Zhang³

et al. 2022

IEEJ Transactions Elec Engng

View full text Add to dashboard Cite

Due to that the feature extraction of vibration signal to judge the mechanical fault process of circuit breaker affected by interference, a fault feature extraction method based on complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) and support vector machine (SVM) is proposed. Several intrinsic modal function (IMF) components reflecting the mechanical state information of the circuit breaker during operation are extracted from the vibration signal through CEEMDAN. According to the correlation coefficient and energy distribution of each IMF, the first seven IMF components are denoised by wavelet packet soft threshold, and the sample entropy is calculated as the feature quantity. Finally, the improved gravitational search algorithm (GSA) based on particle swarm optimization (PSO) is used to optimize the SVM classifier to classify and identify the different operating states of the circuit breaker. The experimental results show that the entropy characteristics of CEEMDAN samples are insensitive to signal interference, and the PSO-GSA-SVM diagnosis method has a good effect on fault identification in the opening operation process of high voltage circuit breakers.

show abstract

“…For the sound-vibration signal combination method, Zhao et al [12] process sound and vibration signals via improving the ensemble empirical mode decomposition (EEMD) to calculate the two-dimensional spectral entropy. e diagnostic effect is improved.…”

Section: Introductionmentioning

confidence: 99%

“…e diagnostic accuracy is improved. Although studies [12][13][14][15] have achieved some good results, the difference in sound and vibration signals is not considered.…”

Section: Introductionmentioning

confidence: 99%

Fault Diagnosis of Oil Pumping Machine Retarder Based on Sound Texture-Vibration Entropy Characteristics and Gray Wolf Optimization-Support Vector Machine

Zhao

Chang

Wang

et al. 2020

Shock and Vibration

View full text Add to dashboard Cite

In order to diagnose the retarder faults of oil pumping machine accurately in complex environments and improve the generalization of the algorithm, a GWO-SVM fault diagnosis algorithm based on the combination of sound texture and vibration entropy characteristics was proposed. Firstly, the acquired sound signal was purified by band-pass filter, then generalized S-transform was developed to extract the box dimension, directivity, and contrast ratio, which reflect the characteristics of time-frequency spectrum, to construct three-dimensional texture eigenvectors. Secondly, the K parameter of variational mode decomposition (VMD) was reasonably selected by the energy method, and then the vibration signal was decomposed to get modal components, and the permutation entropy was obtained from modal components. Finally, joint eigenvectors were constructed and fed into SVM for learning. The gray wolf optimization (GWO) algorithm was used to optimize the parameters of the SVM model based on mixed kernel function, which reduces the impact of sensor frequency response, environmental noise, and load fluctuation disturbance on the accuracy of retarder fault diagnosis. The results showed that the GWO-SVM fault diagnosis method, which is based on the combination of sound texture and vibration entropy characteristics, makes full use of the complementary advantages of signal frequency band. And the overall diagnostic accuracy for the experimental samples reaches 100%, which has good generalization ability.

show abstract

Research on Acoustic Mechanical Fault Diagnosis Method of High Voltage Circuit Breaker Based on Improved EEMD

Cited by 6 publications

References 5 publications

An expert system for acoustic diagnosis of power circuit breakers and on-load tap changers

An expert system for acoustic diagnosis of power circuit breakers and on-load tap changers

Fault Diagnosis Method of Circuit Breaker Based on CEEMDAN and PSO‐GSA‐SVM

Fault Diagnosis of Oil Pumping Machine Retarder Based on Sound Texture-Vibration Entropy Characteristics and Gray Wolf Optimization-Support Vector Machine

Contact Info

Product

Resources

About