scholarly journals Based On Intrinsic Mode Function Energy Tracking Method of Circuit Breaker Vibration Signal Feature Extraction Studies

2017 ◽  
Vol 12 ◽  
pp. 03023
Author(s):  
Yi-Hang Sun
Keyword(s):  
Feature Extraction ◽  
Circuit Breaker ◽  
Vibration Signal ◽  
Intrinsic Mode Function ◽  
Tracking Method ◽  
Mode Function

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

Entropy ◽  
2020 ◽  
Vol 22 (4) ◽  
pp. 478 ◽  
Author(s):  
Jiajin Qi ◽  
Xu Gao ◽  
Nantian Huang
Keyword(s):  
Feature Extraction ◽  
High Voltage ◽  
Time Domain ◽  
High Efficiency ◽  
Circuit Breaker ◽  
Vibration Signal ◽  
Feature Subset ◽  
High Voltage Circuit Breaker ◽  
Optimal Feature Subset ◽  
Optimal Feature

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.

scholarly journals Feature Extraction of Ship Radiated Noise Based on Permutation Entropy of the Intrinsic Mode Function with the Highest Energy

2016 ◽  
Author(s):  
Yu-Xing Li ◽  
Ya-An Li ◽  
Zhe Chen ◽  
Xiao Chen
Keyword(s):  
Feature Extraction ◽  
Empirical Mode Decomposition ◽  
Characteristic Parameter ◽  
Intrinsic Mode Function ◽  
Permutation Entropy ◽  
Intrinsic Mode Functions ◽  
Characteristic Parameters ◽  
Radiated Noise ◽  
Mode Function ◽  
Mode Decomposition

In order to solve the problem of feature extraction of underwater acoustic signals in complex ocean environment, a new method for feature extraction from ship radiated noise is presented based on empirical mode decomposition theory and permutation entropy. It analyzes the separability for permutation entropies of the intrinsic mode functions of three types of ship radiated noise signals, and discusses the permutation entropy of the intrinsic mode function with the highest energy. In this study, ship radiated noise signals measured from three types of ships are decomposed into a set of intrinsic mode functions with empirical mode decomposition method. Then, the permutation entropies of all intrinsic mode functions are calculated with appropriate parameters. The permutation entropies are obviously different in the intrinsic mode functions with the highest energy, thus, the permutation entropy of the intrinsic mode function with the highest energy is regarded as a new characteristic parameter to extract the feature of ship radiated noise. After that, the characteristic parameters, namely, the energy difference between high and low frequency, permutation entropy, and multi-scale permutation entropy, are compared with the permutation entropy of the intrinsic mode function with the highest energy. It is discovered that the four characteristic parameters are at the same level for similar ships, however, there are differences in the parameters for different types of ships. The results demonstrate that the permutation entropy of the intrinsic mode function with the highest energy is better in separability as the characteristic parameter than the other three parameters by comparing their fluctuation ranges and the average values of the four characteristic parameters. Hence, the feature of ship radiated noise can be extracted efficiently with the method.

scholarly journals Online monitoring and diagnosis of high voltage circuit breaker faults: feature extraction analysis of vibration signals

2019 ◽  
Vol 10 ◽  
pp. 13 ◽  
Author(s):  
Long Li ◽  
Jianfeng Xiao ◽  
Bin Wu ◽  
Mengge Zhou ◽  
Qian Wang
Keyword(s):  
Feature Extraction ◽  
High Voltage ◽  
Extraction Method ◽  
Wavelet Packet ◽  
Circuit Breaker ◽  
Vibration Signal ◽  
Normal Operation ◽  
Support Vector ◽  
Feature Extraction Method ◽  
High Voltage Circuit Breaker

The development of power grid system not only increases voltage and capacity, but also increases power risk. This paper briefly introduces the feature extraction method of the vibration signal of high voltage circuit breaker and support vector machine (SVM) algorithm and then analyzed the high voltage circuit breaker in three states: normal operation, fixed screw loosening and falling of opening spring, using the SVM based on the above feature extraction method. The results showed that the accuracy and precision rates of fault identification of circuit breaker were the highest by using the wavelet packet energy entropy extraction features, the false alarm rate was the lowest, and the detection time was the shortest.

Gearbox Fault Diagnosis Based on EMD and Coefficient-Energy Value

2012 ◽  
Vol 542-543 ◽  
pp. 234-237
Author(s):  
Ping Wang ◽  
De Xiang Zhang ◽  
Yan Li Liu
Keyword(s):  
Fault Diagnosis ◽  
Vibration Signal ◽  
Intrinsic Mode Function ◽  
Vibration Signals ◽  
Energy Value ◽  
Mode Function ◽  
Mode Decomposition ◽  
Gearbox Vibration ◽  
Vibration Signal Analysis ◽  
Fault Information

This paper applies the empirical mode decomposition (EMD) methods to gearbox vibration signal analysis capture from vibrating acceleration sensor for gearbox fault diagnosis. The original modulation fault vibration signals are firstly decomposed into a number of intrinsic mode function (IMF) by the EMD method. Then the fault information diagnosis of the gearbox vibration signals can be extracted from the coefficient-energy value of intrinsic mode function. Experiment result has shown the feasibility and efficiency of the EMD algorithms and energy characteristic method in fault diagnosis and fault message abstraction. It is significant for the monitor operating state of gearbox and detects incipient faults as soon as possible.

scholarly journals A Method for Determining Intrinsic Mode Function Number in Variational Mode Decomposition and Its Application to Bearing Vibration Signal Processing

2020 ◽  
Vol 2020 ◽  
pp. 1-16
Author(s):  
Shoujun Wu ◽  
Fuzhou Feng ◽  
Junzhen Zhu ◽  
Chunzhi Wu ◽  
Guang Zhang
Keyword(s):  
Signal Processing ◽  
Great Influence ◽  
Vibration Signal ◽  
Intrinsic Mode Function ◽  
Center Frequency ◽  
Noise Robustness ◽  
Variational Mode Decomposition ◽  
Normalized Mutual Information ◽  
Mode Function ◽  
Mode Decomposition

Variational mode decomposition (VMD) method has been widely used in the field of signal processing with significant advantages over other decomposition methods in eliminating modal aliasing and noise robustness. The number (usually denoted by K) of intrinsic mode function (IMF) has a great influence on decomposition results. When dealing with signals including complex components, it is usually impossible for the existing methods to obtain correct results and also effective methods for determining K value are lacking. A method called center frequency statistical analysis (CFSA) is proposed in this paper to determine K value. CFSA method can obtain K value accurately based on center frequency histogram. To shed further light on its performance, we analyze the behavior of CFSA method with simulation signal in the presence of variable components amplitude, components frequency, and components number as well as noise amplitude. The normal and fault vibration signals obtained from a bearing experimental setup are used to verify the method. Compared with maximum center frequency observation (MCFO), correlation coefficient (CC), and normalized mutual information (NMI) methods, CFSA is more robust and accurate, and the center frequencies results are consistent with the main frequencies in FFT spectrum.

scholarly journals Gear Fault Diagnosis Based on Empirical Mode Decomposition and 1.5 Dimension Spectrum

2016 ◽  
Vol 2016 ◽  
pp. 1-10 ◽  
Author(s):  
Jianhua Cai ◽  
Xiaoqin Li
Keyword(s):  
Fault Diagnosis ◽  
Power Spectrum ◽  
Correlation Coefficient ◽  
Empirical Mode Decomposition ◽  
Vibration Signal ◽  
Intrinsic Mode Function ◽  
Dimension Spectrum ◽  
Mode Function ◽  
Mode Decomposition ◽  
Gear Fault

Aiming at the nonlinear and nonstationary feature of mechanical fault vibration signal, a new fault diagnosis method, which is based on a combination of empirical mode decomposition (EMD) and 1.5 dimension spectrum, is proposed. Firstly, the vibration signal is decomposed by EMD and the correlation coefficient between each intrinsic mode function and original signal is calculated. Then these intrinsic mode function components, which have a big correlation coefficient, are selected to estimate its 1.5 dimension spectrum. And this method uses 1.5 dimension spectrum of each intrinsic mode function to reconstruct its power spectrum. And these power spectrums are summed to obtain the primary power spectrum of gear fault signal. Finally, the information feature of fault is extracted from the reconstructed 1.5 dimension spectrum. A model to reconstruct 1.5 dimension spectrum is established, and the principle and steps of the method are presented. Some simulated and measured gear fault signals have been processed to demonstrate the effectiveness of new method. The result shows that this method can greatly inhibit the interference of Gauss noise to raise the SNR and recognize the secondary phase coupling feature of the signal. The proposed method has a good real-time performance and provides an effective method to determine the early crack fault of gear root.

scholarly journals Coupling Fault Feature Extraction Method Based on Bivariate Empirical Mode Decomposition and Full Spectrum for Rotating Machinery

2018 ◽  
Vol 2018 ◽  
pp. 1-10 ◽  
Author(s):  
Rong Jia ◽  
Fuqi Ma ◽  
Hua Wu ◽  
Xingqi Luo ◽  
Xiping Ma
Keyword(s):  
Feature Extraction ◽  
Empirical Mode Decomposition ◽  
Rotating Machinery ◽  
Intrinsic Mode Function ◽  
Full Spectrum ◽  
Feature Extraction Method ◽  
Mode Function ◽  
Mode Decomposition ◽  
Coupling Fault ◽  
Bivariate Empirical Mode Decomposition

To accurately extract the fault characteristics of vibration signals of rotating machinery is of great significance to the unit online monitoring and evaluation. However, because the current feature extraction methods are mainly for single channel, the results of feature extraction are often inaccurate. To this end, a coupling fault feature extraction method based on bivariate empirical mode decomposition (BEMD) and full spectrum is proposed for rotating machinery. Firstly, the two-dimensional orthogonal signal obtained by orthogonal sampling technique is decomposed by bivariate empirical mode decomposition to obtain the intrinsic mode function with phase information. In order to obtain the sensitive modal components, the sensitivity coefficients are constructed on the basis of mutual information. Then, the sensitivity coefficient of each intrinsic mode function is calculated, and the intrinsic mode function with the larger sensitive coefficient is selected as the sensitive component. Finally, the full spectrum of the sensitive component is obtained using the full vector envelope technique, so as to get a comprehensive and accurate characteristic component. The results of simulations experiment and an application example show that this method can extract the fault characteristic component of the rotating machinery comprehensively and accurately. It is of great significance to realize the accurate diagnosis of coupling faults of rotating machinery.

Application of Hilbert-Huang Transform to Vibration Signal Analysis of Coal and Gangue

2010 ◽  
Vol 40-41 ◽  
pp. 995-999 ◽  
Author(s):  
Wei Liu
Keyword(s):  
Signal Analysis ◽  
Vibration Signal ◽  
Intrinsic Mode Function ◽  
Frequency Interval ◽  
Hilbert Huang Transform ◽  
Mode Function ◽  
Mode Decomposition ◽  
Marginal Spectrum ◽  
The Difference ◽  
Vibration Signal Analysis

In this paper, a new method of vibration signal analysis of coal and gangue based on Hilbert-Huang transform is presented. Empirical mode decomposition algorithm was used to decompose the original vibration signal of coal and gangue into the intrinsic modes for further extract useful information contained in response signals under complicated environment. By analyzing local Hilbert marginal spectrum and local energy spectrum of the first four intrinsic mode function components, we found the difference of coal and rock in specific frequency interval that the amplitude and energy mainly distributed at frequency interval between 100Hz and 600Hz when coal was drawn, while the amplitude and energy were more concentrated at 1000Hz or so when gangue was drawn. Furthermore, the further analysis result from marginal spectrum of each intrinsic mode function component agreed well with the conclusion above. So the extracted features with the propose approach can be served as coal and gangue interface recognition.

scholarly journals Application of Adaptive Local Iterative Filtering and Permutation Entropy in Gear Fault Recognition

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Wenbin Zhang ◽  
Yun Wang ◽  
Yushuo Tan ◽  
Dewei Guo ◽  
Yasong Pu
Keyword(s):  
Vibration Signal ◽  
Intrinsic Mode Function ◽  
Gear System ◽  
Permutation Entropy ◽  
Intrinsic Mode Functions ◽  
Mode Function ◽  
Gray Correlation Degree ◽  
Correlation Degree ◽  
Gear Fault ◽  
Iterative Filtering

In this paper, a fault identification method combining adaptive local iterative filtering and permutation entropy is proposed. The adaptive local iterative filtering can decompose the nonstationary signal into a finite number of stationary intrinsic mode functions. And the experiment gear fault data are decomposed into several intrinsic mode functions by this method. Then, using the permutation entropy to calculate each intrinsic mode function, it is found that the permutation entropy of the first several intrinsic mode functions can represent the characteristics of different fault types, and the permutation entropy of the intrinsic mode function corresponding to the rotating frequency signal of the gear system could be the boundary. Finally, the fault type of gear is identified by calculating the gray correlation degree of permutation entropy of essential mode function of vibration signal decomposition under different working conditions. The example analysis results show that the proposed method can be effectively applied to the fault diagnosis of the gear system.

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