scholarly journals Mechanical Fault Diagnosis of HVCBs Based on Multi-Feature Entropy Fusion and Hybrid Classifier

Entropy ◽  
2018 ◽  
Vol 20 (11) ◽  
pp. 847 ◽  
Author(s):  
Shuting Wan ◽  
Lei Chen ◽  
Longjiang Dou ◽  
Jianping Zhou
Keyword(s):  
Support Vector Machine ◽  
Fault Diagnosis ◽  
Support Vector ◽  
Support Vector Data Description ◽  
Intrinsic Mode Functions ◽  
Hybrid Classifier ◽  
State Classification ◽  
Mode Decomposition ◽  
Spring Force ◽  
The Stability

As high-voltage circuit breakers (HVCBs) are directly related to the safety and the stability of a power grid, it is of great significance to carry out fault diagnoses of HVCBs. To accurately identify operating states of HVCBs, a novel mechanical fault diagnosis method of HVCBs based on multi-feature entropy fusion (MFEF) and a hybrid classifier is proposed. MFEF involves the decomposition of vibration signals of HVCBs into several intrinsic mode functions using variational mode decomposition (VMD) and the calculation of multi-feature entropy by the integration of three Shannon entropies. Principle component analysis (PCA) is then used to reduce the dimension of the multi-feature entropy to achieve an effective fusion of features for selecting the feature vector. The detection of an unknown fault in HVCBs is achieved using support vector data description (SVDD) trained by normal-state samples and specific fault samples. On this basis, the identification and classification of the known states are realized by the support vector machine (SVM). Three faults (i.e., closing spring force decrease fault, buffer spring invalid fault, opening spring force decrease fault) are simulated on a real SF6 HVCB to test the feasibility of the proposed method. The detection accuracies of the unknown fault are 100%, 87.5%, and 100% respectively when each of the three faults is assumed to be the unknown fault. The comparative experiments show that SVM has no ability to detect the unknown fault, and that one-class support vector machine (OCSVM) has a weaker ability to detect the unknown fault than SVDD. For known-state classification, the adoption of the MFEF method achieved an accuracy of 100%, while the use of a single-feature method only achieved an accuracy of 75%. These results indicate that the proposed method combining MFEF with hybrid classifier is thus more efficient and robust than traditional methods.

A gearbox fault diagnosis method based on frequency-modulated empirical mode decomposition and support vector machine

2016 ◽  
Vol 232 (2) ◽  
pp. 369-380 ◽  
Author(s):  
Chao Zhang ◽  
Zhongxiao Peng ◽  
Shuai Chen ◽  
Zhixiong Li ◽  
Jianguo Wang
Keyword(s):  
Support Vector Machine ◽  
Fault Diagnosis ◽  
Empirical Mode Decomposition ◽  
Vibration Signal ◽  
Dynamic State ◽  
Support Vector ◽  
Intrinsic Mode Functions ◽  
Vibration Signals ◽  
Mode Decomposition ◽  
Diagnosis Method

During the operation process of a gearbox, the vibration signals can reflect the dynamic states of the gearbox. The feature extraction of the vibration signal will directly influence the accuracy and effectiveness of fault diagnosis. One major challenge associated with the extraction process is the mode mixing, especially under such circumstance of intensive frequency. A novel fault diagnosis method based on frequency-modulated empirical mode decomposition is proposed in this paper. Firstly, several stationary intrinsic mode functions can be obtained after the initial vibration signal is processed using frequency-modulated empirical mode decomposition method. Using the method, the vibration signal feature can be extracted in unworkable region of the empirical mode decomposition. The method has the ability to separate such close frequency components, which overcomes the major drawback of the conventional methods. Numerical simulation results showed the validity of the developed signal processing method. Secondly, energy entropy was calculated to reflect the changes in vibration signals in relation to faults. At last, the energy distribution could serve as eigenvector of support vector machine to recognize the dynamic state and fault type of the gearbox. The analysis results from the gearbox signals demonstrate the effectiveness and veracity of the diagnosis approach.

Study on Mechanical Fault Diagnosis Based on IMF Complexity Feature and Support Vector Machine

2012 ◽  
Vol 246-247 ◽  
pp. 37-42
Author(s):  
Wei Dong Liu ◽  
Hu Sheng Wu
Keyword(s):  
Support Vector Machine ◽  
Fault Diagnosis ◽  
Support Vector ◽  
Svm Classifier ◽  
Intrinsic Mode Functions ◽  
Mutual Correlation ◽  
Vibration Signals ◽  
Comparative Tests ◽  
Mode Decomposition ◽  
Diagnosis Method

According to the non-stationarity characteristics of the vibration signals from reciprocating machinery,a fault diagnosis method based on empirical mode decomposition,Lempel-Ziv complexity and support vector machine(SVM) is proposed.Firstly,the vibration signals were decomposed into a finite number of intrinsic mode functions(IMF), then choosed some IMF components with the criteria of mutual correlation coefficient between IMF components and denoised signal.Thirdly the complexity feature of each IMF component was calculated as faulty eigenvector and served as input of SVM classifier so that the faults of machine are classified.Practical experimental data is used to verify this method,and the diagnosis results and comparative tests fully validate its effectiveness and generalization abilities.

scholarly journals Gearbox Fault Diagnosis Using Complementary Ensemble Empirical Mode Decomposition and Permutation Entropy

2016 ◽  
Vol 2016 ◽  
pp. 1-8 ◽  
Author(s):  
Liye Zhao ◽  
Wei Yu ◽  
Ruqiang Yan
Keyword(s):  
Fault Diagnosis ◽  
Empirical Mode Decomposition ◽  
Correlation Coefficients ◽  
Ensemble Empirical Mode Decomposition ◽  
Permutation Entropy ◽  
Support Vector ◽  
Svm Classifier ◽  
Intrinsic Mode Functions ◽  
Vibration Signals ◽  
Mode Decomposition

This paper presents an improved gearbox fault diagnosis approach by integrating complementary ensemble empirical mode decomposition (CEEMD) with permutation entropy (PE). The presented approach identifies faults appearing in a gearbox system based on PE values calculated from selected intrinsic mode functions (IMFs) of vibration signals decomposed by CEEMD. Specifically, CEEMD is first used to decompose vibration signals characterizing various defect severities into a series of IMFs. Then, filtered vibration signals are obtained from appropriate selection of IMFs, and correlation coefficients between the filtered signal and each IMF are used as the basis for useful IMFs selection. Subsequently, PE values of those selected IMFs are utilized as input features to a support vector machine (SVM) classifier for characterizing the defect severity of a gearbox. Case study conducted on a gearbox system indicates the effectiveness of the proposed approach for identifying the gearbox faults.

scholarly journals Mechanical Fault Diagnosis for HV Circuit Breakers Based on Ensemble Empirical Mode Decomposition Energy Entropy and Support Vector Machine

2015 ◽  
Vol 2015 ◽  
pp. 1-6 ◽  
Author(s):  
Jianfeng Zhang ◽  
Mingliang Liu ◽  
Keqi Wang ◽  
Laijun Sun
Keyword(s):  
Support Vector Machine ◽  
Fault Diagnosis ◽  
Circuit Breaker ◽  
Vibration Signal ◽  
Ensemble Empirical Mode Decomposition ◽  
Support Vector ◽  
Time Segment ◽  
Vibration Signals ◽  
Equal Time ◽  
Mode Decomposition

During the operation process of the high voltage circuit breaker, the changes of vibration signals can 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 (EEMD). Firstly, the original vibration signals are decomposed into a finite number of stationary intrinsic mode functions (IMFs). Secondly, calculating the envelope of each IMF and separating the envelope by equal-time segment and then forming equal-time segment energy entropy to reflect the change of vibration signal are performed. At last, the energy entropies could serve as input vectors of support vector machine (SVM) to identify the working state and fault pattern of the circuit breaker. Practical examples show that this diagnosis approach can identify effectively fault patterns of HV circuit breaker.

Ensemble empirical mode decomposition-entropy and feature selection for pantograph fault diagnosis

2020 ◽  
Vol 26 (23-24) ◽  
pp. 2230-2242
Author(s):  
Ying Shi ◽  
Cai Yi ◽  
Jianhui Lin ◽  
Zhe Zhuang ◽  
Senhua Lai
Keyword(s):  
Support Vector Machine ◽  
Fault Diagnosis ◽  
Empirical Mode Decomposition ◽  
Approximate Entropy ◽  
Ensemble Empirical Mode Decomposition ◽  
Support Vector ◽  
Effective Measure ◽  
Vibration Data ◽  
Ranking Criteria ◽  
Mode Decomposition

In this article, a fault diagnosis approach for a pantograph is developed with collected vibration data from a test rig. Ensemble empirical mode decomposition is used to decompose the signals to get intrinsic mode function, and four kinds of entropies (permu1tation entropy, approximate entropy, sample entropy, and fuzzy entropy) reflecting the working state are extracted as the inputs of the support vector machine based on particle swarm optimization algorithm support vector machine. The effect of data length, embedded dimension, and other parameters on calculation of the entropy value has also been studied. Multiple feature ranking criteria are used to select the useful features and improve the fault diagnosis accuracy of certain measurement points. Experimental results on pantograph vibration analysis have then confirmed that the proposed method provides an effective measure for pantograph diagnosis.

Predicts Based on Multi Modality Support Vector of the Settlement of Composite Foundation

2014 ◽  
Vol 919-921 ◽  
pp. 716-722
Author(s):  
Li Hua Zhang ◽  
Hai Bo Liu ◽  
Feng Ping An
Keyword(s):  
Support Vector Machine ◽  
Composite Foundation ◽  
Good Effect ◽  
Support Vector ◽  
Foundation Settlement ◽  
Modal Synthesis ◽  
Mode Decomposition ◽  
Calculation Results ◽  
The Stability ◽  
High Degree

Composite foundation settlement is affected by many factors, and settlement data is a non-linear changing process with complexity, suddenness and progressive nature and so on. So we must analyze and predict the stability of the foundation settlement. Because empirical mode decomposition (EMD) provides a new way for foundation settlement prediction, we can extract modal signals associated with the foundation settlement mechanisms by decomposing the monitoring data of settlement by EMD and use the support vector machine ( Support Vector Machine, SVM) modal to predict the obtained signal, Calculation results of the modal synthesis and accumulation of foundation settlement, get the evolution of Change with the time of foundation settlement. Combined with the engineering example for the application ,shows that the prediction model has good effect in multi modality support vector, a high degree of agreement with the monitoring values, indicating that this method has a promotional value.

Circuit Fault Diagnosis Based on OEMD and SVDD Classifier of KFPCM Optimal Algorithm

2015 ◽  
Vol 738-739 ◽  
pp. 366-372
Author(s):  
Jian Feng Shan ◽  
Liang Wei Wang
Keyword(s):  
Fault Diagnosis ◽  
Hilbert Transform ◽  
Optimal Algorithm ◽  
Support Vector ◽  
Support Vector Data Description ◽  
Hilbert Huang Transform ◽  
Mode Decomposition ◽  
Circuit Fault Diagnosis ◽  
The Hilbert Transform ◽  
Non Stationary Signal

Hilbert-Huang transform (HHT) has some problems such as insufficient characteristic, modal aliasing, illusive component in circuit fault feature extraction, a new method is proposed to obtain the transient characteristic which is especially suitable to process non-stationary signal. The method consists of orthogonal empirical mode decomposition (OEMD) and Hilbert transform. Use the OEMD algorithm to gain strict orthogonal intrinsic mode function (IMF) and obtain the characteristics such as time, amplitude and frequency after the Hilbert transform. Support vector data description (SVDD) is sensitive to noise and outliers. It needs to classify the data in advance, reduce noise and traversal data to the specific sample which has good similarity by using Kernelized Fuzzy Possibilistic C-Means clustering (KFPCM). Then put the sample into SVDD classifier for training and diagnosis. The results of experiments show that the SVDD improved by KFPCM has higher accuracy of fault diagnosis than original SVDD.

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