Application of Multiscale Entropy in Mechanical Fault Diagnosis of High Voltage Circuit Breaker

Dou, Longjiang; Wan, Shuting; Zhan, Caimao

doi:10.3390/e20050325

Cited by 24 publications

(12 citation statements)

References 19 publications

(18 reference statements)

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“…The maximum sampling rate of 50 kS/s. LabVIEW [30,47,48] software to write the signal acquisition system. In addition, AFT-0931 signal conditioner, trigger circuit and DC voltage-stabilized power also play crucial roles in supplying to build HVCB vibration…”

Section: Related Work a Vibration Data Acquisitionmentioning

confidence: 99%

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Mechanical Fault Diagnosis of High Voltage Circuit Breakers Utilizing VMD Based on Improved Time Segment Energy Entropy and a New Hybrid Classifier

Cao

Liu

2020

IEEE Access

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In mechanical fault diagnosis of the high voltage circuit breakers (HVCBs), it is often expected that the fault type should be confirmed in time to avoid delaying the best time for mechanical fault diagnosis. The traditional diagnosis method of HVBC is not so profound for the identification of slight faults and does not consider the impact of the recall rate of fault samples on the fault diagnosis results. In this paper, we propose a method for HVCBs mechanical fault diagnosis utilizing variational mode decomposition (VMD) based on improved time segment energy entropy (ITSEE) and a new hybrid classifier. Firstly, the signal is decomposed into K intrinsic mode functions (IMFs) via VMD to establish a component matrix. Secondly, the ITSEE method is used to calculate the energy entropy of the matrix in time domain and frequency domain, so as to better extract the features of slight fault types. Finally, an optimal hybrid classifier model combined of one-class support vector machine (OCSVM) and probabilistic neural network (PNN) is used to identify four types of vibration signals of HVCBs. The experimental results show that the accuracy of unknown samples is 98.75%, and the recall of fault type is 100%. The experimental results show the effectiveness of the method and have important application value for the diagnosis of HVBCs. INDEX TERMS High voltage circuit breakers, mechanical fault diagnosis, variational mode decomposition, improved time segment energy entropy, one-class support vector machine, probabilistic neural network.

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Section: Related Work a Vibration Data Acquisitionmentioning

confidence: 99%

“…However, for non-stationary and non-linear signals, waveform distortion and poor adaptability are easy to occur. Motivated by the above considerations, VMD [28][29][30][31][32][33] method is applied in signal analysis of HVCBs in this paper. The VMD method has a solid theoretical foundation and good noise robustness [22].…”

Section: Introductionmentioning

confidence: 99%

Mechanical Fault Diagnosis of High Voltage Circuit Breakers Utilizing VMD Based on Improved Time Segment Energy Entropy and a New Hybrid Classifier

Cao

Liu

2020

IEEE Access

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“…The greater the uncertainty, the larger the entropy, and the greater the amount of information needs to clarify it. The Shannon entropy can be calculated as (12) In this study, the Shannon entropy is used as a characteristic attribute. The time-frequency entropy features are extracted in the energy matrix from the time-frequency domain direction.…”

Section: ) Time-frequency Entropymentioning

confidence: 99%

“…Therefore, the signal decomposition is always required to obtain the characteristics in the time-frequency domain before the feature extraction. General methods via time-frequency analyses mainly include the wavelet transform (WT) [18], the wavelet packet transform (WPT) [5]- [7], the empirical mode decomposition (EMD) [8], [19], the ensemble empirical mode decomposition (EEMD) [9], [10], [20], the variational mode decomposition (VMD) [11], [12], [21], etc. These algorithms have been widely used in the processing of vibration signals.…”

Section: Introductionmentioning

confidence: 99%

Mechanical Faults Diagnosis of High-Voltage Circuit Breaker via Hybrid Features and Integrated Extreme Learning Machine

et al. 2019

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As key electrical equipment in the power system, the normal operation of a high-voltage circuit breaker is related to the reliability and economy of the power supply. In this paper, a mechanical fault diagnostic method for a high-voltage circuit breaker via the hybrid feature extraction and the integrated extreme learning machine (IELM) is investigated. First, the complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) is used to decompose the vibration signal to obtain intrinsic mode functions (IMF). Then, the sub-band reconstruction of each order IMF component is performed by combining the Hilbert transform and the band-pass filter in order to obtain the time-frequency matrix. Moreover, mechanical fault feature vectors can be formed by the time-frequency entropy and the singular entropy, which are extracted by transforming the time-frequency matrix into the energy matrix and normalizing the frequency bands via the normal cumulative distribution function (NCDF). In addition, an IELM is built for the fault classification. The advantages of the proposed CEEMDAN scheme in combination with band-pass filtering can eliminate the modal aliasing, reduce the number of auxiliary noise additions, and improve the decomposition efficiency. Besides, the performance of the singular entropy normalized by the NCDF is more stable, and the IELM composed of multiple weak classifiers can solve the shortcomings of the traditional extreme learning machine. The experimental results based on measured data show the proposed method can effectively diagnose the mechanical failure via small samples of high-voltage circuit breakers. INDEX TERMS High-voltage circuit breaker, vibration signal, complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN), time-frequency entropy, singular entropy, integrated extreme learning machine (IELM).

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“…At present, the common methods of state recognition for HVCBs include Neural Network (NNs) [ 19 ], Random Forest (RF) [ 20 ], Support Vector Machine (SVM) [ 21 , 22 ], XGBoost [ 23 , 24 ], etc. NNs have a strong self-learning ability and nonlinear pattern recognition ability, but its training speed is slow and it is easy to fall into local optimal solution [ 25 ].…”

Section: Introductionmentioning

confidence: 99%

Mechanical Fault Diagnosis of a High Voltage Circuit Breaker Based on High-Efficiency Time-Domain Feature Extraction with Entropy Features

Huang

2020

Entropy

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The fault samples of high voltage circuit breakers are few, the vibration signals are complex, the existing research methods cannot extract the effective information in the features, and it is easy to overfit, slow training, and other problems. To improve the efficiency of feature extraction of a circuit breaker vibration signal and the accuracy of circuit breaker state recognition, a Light Gradient Boosting Machine (LightGBM) method based on time-domain feature extraction with multi-type entropy features for mechanical fault diagnosis of the high voltage circuit breaker is proposed. First, the original vibration signal of the high voltage circuit breaker is segmented in the time domain; then, 16 features including 5 kinds of entropy features are extracted directly from each part of the original signal after time-domain segmentation, and the original feature set is constructed. Second, the Split importance value of each feature is calculated, and the optimal feature subset is determined by the forward feature selection, taking the classification accuracy of LightGBM as the decision variable. After that, the LightGBM classifier is constructed based on the feature vector of the optimal feature subset, which can accurately distinguish the mechanical fault state of the high voltage circuit breaker. The experimental results show that the new method has the advantages of high efficiency of feature extraction and high accuracy of fault identification.

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Application of Multiscale Entropy in Mechanical Fault Diagnosis of High Voltage Circuit Breaker

Cited by 24 publications

References 19 publications

Mechanical Fault Diagnosis of High Voltage Circuit Breakers Utilizing VMD Based on Improved Time Segment Energy Entropy and a New Hybrid Classifier

Mechanical Fault Diagnosis of High Voltage Circuit Breakers Utilizing VMD Based on Improved Time Segment Energy Entropy and a New Hybrid Classifier

Mechanical Faults Diagnosis of High-Voltage Circuit Breaker via Hybrid Features and Integrated Extreme Learning Machine

Mechanical Fault Diagnosis of a High Voltage Circuit Breaker Based on High-Efficiency Time-Domain Feature Extraction with Entropy Features

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