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.

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.

scholarly journals Rope Tension Fault Diagnosis in Hoisting Systems Based on Vibration Signals Using EEMD, Improved Permutation Entropy, and PSO-SVM

Entropy ◽  
2020 ◽  
Vol 22 (2) ◽  
pp. 209 ◽  
Author(s):  
Shaohua Xue ◽  
Jianping Tan ◽  
Lixiang Shi ◽  
Jiwei Deng
Keyword(s):  
Fault Diagnosis ◽  
Vibration Signal ◽  
Ensemble Empirical Mode Decomposition ◽  
Permutation Entropy ◽  
Support Vector ◽  
Sensitivity Index ◽  
Intrinsic Mode Functions ◽  
Vibration Signals ◽  
Mode Decomposition ◽  
New Perspective

Fault diagnosis of rope tension is significantly important for hoisting safety, especially in mine hoists. Conventional diagnosis methods based on force sensors face some challenges regarding sensor installation, data transmission, safety, and reliability in harsh mine environments. In this paper, a novel fault diagnosis method for rope tension based on the vibration signals of head sheaves is proposed. First, the vibration signal is decomposed into some intrinsic mode functions (IMFs) by the ensemble empirical mode decomposition (EEMD) method. Second, a sensitivity index is proposed to extract the main IMFs, then the de-noised signal is obtained by the sum of the main IMFs. Third, the energy and the proposed improved permutation entropy (IPE) values of the main IMFs and the de-noised signal are calculated to create the feature vectors. The IPE is proposed to improve the PE by adding the amplitude information, and it proved to be more sensitive in simulations of impulse detecting and signal segmentation. Fourth, vibration samples in different tension states are used to train a particle swarm optimization–support vector machine (PSO-SVM) model. Lastly, the trained model is implemented to detect tension faults in practice. Two experimental results validated the effectiveness of the proposed method to detect tension faults, such as overload, underload, and imbalance, in both single-rope and multi-rope hoists. This study provides a new perspective for detecting tension faults in hoisting systems.

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 Rolling Bearings Fault Diagnosis Based on Improved Complete Ensemble Empirical Mode Decomposition with Adaptive Noise, Nonlinear Entropy, and Ensemble SVM

2020 ◽  
Vol 10 (16) ◽  
pp. 5542 ◽  
Author(s):  
Rui Li ◽  
Chao Ran ◽  
Bin Zhang ◽  
Leng Han ◽  
Song Feng
Keyword(s):  
Fault Diagnosis ◽  
Empirical Mode Decomposition ◽  
Economic Loss ◽  
Ensemble Empirical Mode Decomposition ◽  
Support Vector ◽  
Rolling Bearings ◽  
Vibration Signals ◽  
Operational Conditions ◽  
Mode Decomposition ◽  
Adaptive Noise

Rolling bearings are fundamental elements that play a crucial role in the functioning of rotating machines; thus, fault diagnosis of rolling bearings is of great significance to reduce catastrophic failures and heavy economic loss. However, the vibration signals of rolling bearings are often nonlinear and nonstationary, resulting in difficulty for feature extraction and fault recognition. In this paper, a hybrid method for multiple fault diagnosis of rolling bearings is presented. The bearing vibration signals are decomposed with the improved complete ensemble empirical mode decomposition with adaptive noise (ICEEMDAN) to denoise and extract nonlinear entropy features. The nonlinear entropy features are further processed to select the more discriminative fault features and to reduce feature dimension. Then a multi-class intelligent recognition model based on ensemble support vector machine (ESVM) is constructed to diagnose different bearing fault modes as well as fault severities. The effectiveness of the proposed method is assessed via experimental case studies of rolling bearings under multiple operational conditions (i.e., speeds and loads). The results show that our method gives better diagnosis results as compared to some existing approaches.

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.

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 Application of SVM and SVD Technique Based on EMD to the Fault Diagnosis of the Rotating Machinery

2009 ◽  
Vol 16 (1) ◽  
pp. 89-98 ◽  
Author(s):  
Junsheng Cheng ◽  
Dejie Yu ◽  
Jiashi Tang ◽  
Yu Yang
Keyword(s):  
Fault Diagnosis ◽  
Feature Vector ◽  
Roller Bearing ◽  
Vibration Signal ◽  
Singular Values ◽  
Rotating Machinery ◽  
Support Vector ◽  
Intrinsic Mode Functions ◽  
Vibration Signals ◽  
Mode Decomposition

Targeting the characteristics that periodic impulses usually occur whilst the rotating machinery exhibits local faults and the limitations of singular value decomposition (SVD) techniques, the SVD technique based on empirical mode decomposition (EMD) is applied to the fault feature extraction of the rotating machinery vibration signals. The EMD method is used to decompose the vibration signal into a number of intrinsic mode functions (IMFs) by which the initial feature vector matrices could be formed automatically. By applying the SVD technique to the initial feature vector matrices, the singular values of matrices could be obtained, which could be used as the fault feature vectors of support vector machines (SVMs) classifier. The analysis results from the gear and roller bearing vibration signals show that the fault diagnosis method based on EMD, SVD and SVM can extract fault features effectively and classify working conditions and fault patterns of gears and roller bearings accurately even when the number of samples is small.

scholarly journals A Hydraulic Pump Fault Diagnosis Method Based on the Modified Ensemble Empirical Mode Decomposition and Wavelet Kernel Extreme Learning Machine Methods

Sensors ◽  
2021 ◽  
Vol 21 (8) ◽  
pp. 2599
Author(s):  
Zhenbao Li ◽  
Wanlu Jiang ◽  
Sheng Zhang ◽  
Yu Sun ◽  
Shuqing Zhang
Keyword(s):  
Fault Diagnosis ◽  
Extreme Learning Machine ◽  
Recognition Accuracy ◽  
Ensemble Empirical Mode Decomposition ◽  
Hydraulic Pump ◽  
Vibration Signals ◽  
Mode Decomposition ◽  
Diagnosis Method ◽  
Kernel Extreme Learning Machine ◽  
Learning Machine

To address the problem that the faults in axial piston pumps are complex and difficult to effectively diagnose, an integrated hydraulic pump fault diagnosis method based on the modified ensemble empirical mode decomposition (MEEMD), autoregressive (AR) spectrum energy, and wavelet kernel extreme learning machine (WKELM) methods is presented in this paper. First, the non-linear and non-stationary hydraulic pump vibration signals are decomposed into several intrinsic mode function (IMF) components by the MEEMD method. Next, AR spectrum analysis is performed for each IMF component, in order to extract the AR spectrum energy of each component as fault characteristics. Then, a hydraulic pump fault diagnosis model based on WKELM is built, in order to extract the features and diagnose faults of hydraulic pump vibration signals, for which the recognition accuracy reached 100%. Finally, the fault diagnosis effect of the hydraulic pump fault diagnosis method proposed in this paper is compared with BP neural network, support vector machine (SVM), and extreme learning machine (ELM) methods. The hydraulic pump fault diagnosis method presented in this paper can diagnose faults of single slipper wear, single slipper loosing and center spring wear type with 100% accuracy, and the fault diagnosis time is only 0.002 s. The results demonstrate that the integrated hydraulic pump fault diagnosis method based on MEEMD, AR spectrum, and WKELM methods has higher fault recognition accuracy and faster speed than existing alternatives.

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