Mechanical Fault Diagnosis of High Voltage Circuit Breakers Based on Wavelet Time-Frequency Entropy and One-Class Support Vector Machine

Huang, Nantian; Chen, Huaijin; Zhang, Shuxin; Cai, Guowei; Li, Weiguo; Xu, Dianguo; Fang, Lihua

doi:10.3390/e18010007

Cited by 53 publications

(65 citation statements)

References 26 publications

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“…It is widely used in the field of fault diagnosis and detection [25,26,27,28]. The classification boundary of OCSVM is closer to the object samples than that of SVM.…”

Section: Introductionmentioning

confidence: 99%

Mechanical Fault Diagnosis of High Voltage Circuit Breakers Based on Variational Mode Decomposition and Multi-Layer Classifier

Huang

Chen

Cai

et al. 2016

Sensors

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Mechanical fault diagnosis of high-voltage circuit breakers (HVCBs) based on vibration signal analysis is one of the most significant issues in improving the reliability and reducing the outage cost for power systems. The limitation of training samples and types of machine faults in HVCBs causes the existing mechanical fault diagnostic methods to recognize new types of machine faults easily without training samples as either a normal condition or a wrong fault type. A new mechanical fault diagnosis method for HVCBs based on variational mode decomposition (VMD) and multi-layer classifier (MLC) is proposed to improve the accuracy of fault diagnosis. First, HVCB vibration signals during operation are measured using an acceleration sensor. Second, a VMD algorithm is used to decompose the vibration signals into several intrinsic mode functions (IMFs). The IMF matrix is divided into submatrices to compute the local singular values (LSV). The maximum singular values of each submatrix are selected as the feature vectors for fault diagnosis. Finally, a MLC composed of two one-class support vector machines (OCSVMs) and a support vector machine (SVM) is constructed to identify the fault type. Two layers of independent OCSVM are adopted to distinguish normal or fault conditions with known or unknown fault types, respectively. On this basis, SVM recognizes the specific fault type. Real diagnostic experiments are conducted with a real SF6 HVCB with normal and fault states. Three different faults (i.e., jam fault of the iron core, looseness of the base screw, and poor lubrication of the connecting lever) are simulated in a field experiment on a real HVCB to test the feasibility of the proposed method. Results show that the classification accuracy of the new method is superior to other traditional methods.

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“…It is widely used in the field of fault diagnosis and detection [25,26,27,28]. The classification boundary of OCSVM is closer to the object samples than that of SVM.…”

Section: Introductionmentioning

confidence: 99%

Mechanical Fault Diagnosis of High Voltage Circuit Breakers Based on Variational Mode Decomposition and Multi-Layer Classifier

Huang

Chen

Cai

et al. 2016

Sensors

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“…To extract fault feature from vibration signal, scholars adopt different methods to achieve the goal. Some scholars converts the vibration signal from the time domain to the frequency domain [6,7], and the fault type and wear degree of the CB can be identified by using a frequency analysis, such as zoom spectrum analysis and modal analysis. On the other side, many scholars analyze the vibration signals of CBs by using data sequence method, the vibration data are used to establish a mathematical model, the essential characteristics of the signal is extracted from the mathematical model, such as integral parameter method [8], fractal method [9], information entropy [10] and phase-space reconstruction [11].…”

Section: Introductionmentioning

confidence: 99%

Fault diagnosis for high voltage circuit breaker based on timing parameters and FCM

Wan

Dou

et al. 2018

IEICE Electron. Express

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Abstract:The high voltage circuit breaker (CB) play an important role in the power systems, so fault diagnosis of CB has great significance. A new fault diagnosis method based on timing parameters and Fuzzy C-means clustering (FCM) is proposed in this paper. First, the cause of CB vibration signal is analyzed theoretically, it is found that the vibration events will be change in different states. Then the short time energy entropy ratio of vibration signal is calculated, which can enhance the impact feature of vibration event. The occurrence time and the end time of vibration events are extracted as feature vector by the double threshold method. Finally, FCM is used to calculate the cluster center of feature vector, depending on the approach degree, fault diagnosis of CB is completed. The experimental test shows that the proposed method can extract feature vector effectively and diagnose the fault type of CB accurately.

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“…Mechanical faults are the main reasons that affect the reliability of HVCBs [1][2][3][4][5]. It is therefore necessary to carry out the research on the diagnosis method of HVCB mechanical faults in order to ensure the safe and stable running of the power system.…”

Section: Introductionmentioning

confidence: 99%

“…One-class classifiers can complete the training only using the normal state samples which are relatively easy to obtain. It is very satisfied for the high reliability applications [5]. Support Vector Data Description (SVDD) was proposed by Tax [29].…”

Section: Introductionmentioning

confidence: 99%

Mechanical Fault Diagnosis of High Voltage Circuit Breakers with Unknown Fault Type Using Hybrid Classifier Based on LMD and Time Segmentation Energy Entropy

Huang

Fang

Cai

et al. 2016

Entropy

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Abstract:In order to improve the identification accuracy of the high voltage circuit breakers' (HVCBs) mechanical fault types without training samples, a novel mechanical fault diagnosis method of HVCBs using a hybrid classifier constructed with Support Vector Data Description (SVDD) and fuzzy c-means (FCM) clustering method based on Local Mean Decomposition (LMD) and time segmentation energy entropy (TSEE) is proposed. Firstly, LMD is used to decompose nonlinear and non-stationary vibration signals of HVCBs into a series of product functions (PFs). Secondly, TSEE is chosen as feature vectors with the superiority of energy entropy and characteristics of time-delay faults of HVCBs. Then, SVDD trained with normal samples is applied to judge mechanical faults of HVCBs. If the mechanical fault is confirmed, the new fault sample and all known fault samples are clustered by FCM with the cluster number of known fault types. Finally, another SVDD trained by the specific fault samples is used to judge whether the fault sample belongs to an unknown type or not. The results of experiments carried on a real SF 6 HVCB validate that the proposed fault-detection method is effective for the known faults with training samples and unknown faults without training samples.

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Mechanical Fault Diagnosis of High Voltage Circuit Breakers Based on Wavelet Time-Frequency Entropy and One-Class Support Vector Machine

Cited by 53 publications

References 26 publications

Mechanical Fault Diagnosis of High Voltage Circuit Breakers Based on Variational Mode Decomposition and Multi-Layer Classifier

Mechanical Fault Diagnosis of High Voltage Circuit Breakers Based on Variational Mode Decomposition and Multi-Layer Classifier

Fault diagnosis for high voltage circuit breaker based on timing parameters and FCM

Mechanical Fault Diagnosis of High Voltage Circuit Breakers with Unknown Fault Type Using Hybrid Classifier Based on LMD and Time Segmentation Energy Entropy

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