scholarly journals Mechanical Fault Diagnosis of High Voltage Circuit Breaker based on Improved GSO-SVM Algorithm

2021 ◽  
Vol 2087 (1) ◽  
pp. 012033
Author(s):  
Xiaofei Xia ◽  
Xiajin Rao ◽  
Yi Su ◽  
Yufeng Lu ◽  
Bo Feng
Keyword(s):  
Phase Space ◽  
Dimensional Space ◽  
Signal Reconstruction ◽  
Circuit Breaker ◽  
Vibration Signal ◽  
Phase Space Reconstruction ◽  
Permutation Entropy ◽  
Support Vector ◽  
Energy Leakage ◽  
High Recognition Accuracy

Abstract The vibration signal generated by the transmission and impact of mechanical components of circuit breaker has chaotic performances, which is difficult to be analysed by conventional signal processing methods. The phase space reconstruction of vibration signal is worked on, and the signal reconstruction parameters are calculated by mutual information method and Cao algorithm respectively. The vibration signal is reconstructed into a high-dimensional space, and its permutation entropy is calculated as a feature vector. Support vector machine (SVM) is used to identify the failure type of circuit breaker, and PSO improved GSA hybrid algorithm is used to optimize the parameters of SVM so as to obtain high recognition accuracy. The experiment is carried out with the measured vibration signal of the typical operation state of the circuit breaker. The results show that the characteristics of circuit breaker vibration signals can be extracted accurately with the combination of phase space reconstruction and permutation entropy. By using PSO-GSA-SVM, the fault types of circuit breakers can be identified quickly and effectively, and the problems of path distortion, energy leakage and mode overlap of existing diagnosis methods can be solved.

Research on gearbox impact feature extraction method based on improved ESMD

2022 ◽  
Vol 64 (1) ◽  
pp. 20-27
Author(s):  
Fengfeng Bie ◽  
Sheng Gu ◽  
Yue Guo ◽  
Gang Yang ◽  
Jian Peng
Keyword(s):  
Feature Vector ◽  
Vibration Signal ◽  
Permutation Entropy ◽  
Significant Feature ◽  
Support Vector ◽  
Feature Extraction Method ◽  
Mode Decomposition ◽  
Envelope Spectrum ◽  
Impact Characteristics ◽  
The Impact

A gearbox vibration signal contains non-linear impact characteristics and the significant feature information tends to be overwhelmed by other interference components, which make it difficult to extract the typical fault features fully and effectively. Aiming at the key issue of how to effectively extract the impact characteristics, a fault diagnosis method based on improved extreme symmetric mode decomposition (ESMD) and a support vector machine (SVM) is proposed in this paper. The vibration signal is adaptively decomposed into multiple intrinsic mode function (IMF) components by the improved ESMD and then a certain number of components are selected with the maximum kurtosis-envelope spectrum index. The singular spectral entropy, energy entropy and permutation entropy of each component are applied to construct the feature vector set, in which the dimensionality of the set is reduced with the distance separability criterion. Finally, the dimension-reduced feature vector set is input into the SVM for pattern recognition. Dynamic simulation and experimental gearbox research show that the improved ESMD method can extract and identify gearbox fault information effectively.

Phase Space Reconstruction of EEG Signals for Classification of ADHD and Control Adults

2019 ◽  
Vol 51 (2) ◽  
pp. 102-113 ◽  
Author(s):  
Simranjit Kaur ◽  
Sukhwinder Singh ◽  
Priti Arun ◽  
Damanjeet Kaur ◽  
Manoj Bajaj
Keyword(s):  
Feature Selection ◽  
Phase Space ◽  
Performance Test ◽  
Phase Space Reconstruction ◽  
Behavioral Disorder ◽  
Support Vector ◽  
Reconstruction Technique ◽  
Eyes Closed ◽  
Eyes Open ◽  
And Control

Attention deficit hyperactivity disorder (ADHD) is a childhood behavioral disorder that can persist into adulthood. Electroencephalography (EEG) plays a significant role in assessing the neurophysiology of ADHD because of its ability to reveal complex brain activity. The present study proposes an EEG-based diagnosis system using the phase space reconstruction technique to classify ADHD and control adults. Electric activity is recorded for 47 ADHD and 50 control adults during the eyes-open, eyes-closed, and Continuous Performance Test (CPT) condition. Various statistical features are extracted from Euclidean distances based on phase space reconstruction of signals. The proposed system is evaluated with 2 feature selection methods (correlation-based feature selection and particle swarm optimization) and 5 machine learning methods (neural dynamic classifier, support vector machine, enhanced probabilistic neural network, k-nearest neighbor, and naive-Bayes classifier). Experimental results showed the highest testing accuracy of 93.3% under the eyes-open, 90% under the eyes-closed, and 100% under the CPT condition. This study focused on the utility of phase space reconstruction of brain signals to discriminate between ADHD and control adults.

ROTATING SYNCHRONIZATION IN THREE-DIMENSIONAL CHAOTIC SYSTEMS

2012 ◽  
Vol 26 (20) ◽  
pp. 1250120 ◽  
Author(s):  
FUZHONG NIAN ◽  
XINGYUAN WANG
Keyword(s):  
Phase Space ◽  
Chaotic Systems ◽  
Dimensional Space ◽  
Three Dimensional ◽  
Phase Space Reconstruction ◽  
Projective Synchronization ◽  
Reconstruction Method ◽  
Self Similarity ◽  
Application Fields ◽  
Three Dimensional Space

Projective synchronization investigates the synchronization of systems evolve in same orientation, however, in practice, the situation of same orientation is only minority, and the majority is different orientation. This paper investigates the latter, proposes the concept of rotating synchronization, and verifies its necessity and feasibility via theoretical analysis and numerical simulations. Three conclusions were elicited: first, in three-dimensional space, two arbitrary nonlinear chaotic systems who evolve in different orientation can realize synchronization at end; second, projective synchronization is a special case of rotating synchronization, so, the application fields of rotating synchronization is more broadly than that of the former; third, the overall evolving information can be reflected by single state variable's evolving, it has self-similarity, this is the same as the basic idea of phase space reconstruction method, it indicates that we got the same result from different approach, so, our method and the phase space reconstruction method are verified each other.

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.

scholarly journals Fault Diagnosis for Rolling Element Bearings Based on Feature Space Reconstruction and Multiscale Permutation Entropy

Entropy ◽  
2019 ◽  
Vol 21 (5) ◽  
pp. 519 ◽  
Author(s):  
Weibo Zhang ◽  
Jianzhong Zhou
Keyword(s):  
Diagnostic Algorithm ◽  
Feature Space ◽  
Vibration Signal ◽  
Rolling Bearing ◽  
Ensemble Empirical Mode Decomposition ◽  
Classification Performance ◽  
Fault Classification ◽  
Permutation Entropy ◽  
Support Vector ◽  
Intrinsic Mode Functions

Aimed at distinguishing different fault categories of severity of rolling bearings, a novel method based on feature space reconstruction and multiscale permutation entropy is proposed in the study. Firstly, the ensemble empirical mode decomposition algorithm (EEMD) was employed to adaptively decompose the vibration signal into multiple intrinsic mode functions (IMFs), and the representative IMFs which contained rich fault information were selected to reconstruct a feature vector space. Secondly, the multiscale permutation entropy (MPE) was used to calculate the complexity of reconstructed feature space. Finally, the value of multiscale permutation entropy was presented to a support vector machine for fault classification. The proposed diagnostic algorithm was applied to three groups of rolling bearing experiments. The experimental results indicate that the proposed method has better classification performance and robustness than other traditional methods.

scholarly journals Application of Variational Mode Decomposition and Permutation Entropy for Rolling Bearing Fault Diagnosis

2019 ◽  
Vol 24 (2) ◽  
pp. 303-311 ◽  
Author(s):  
Xiaoxia Zheng ◽  
Guowang Zhou ◽  
Dongdong Li ◽  
Haohan Ren
Keyword(s):  
Vibration Signal ◽  
Rolling Bearing ◽  
Permutation Entropy ◽  
Support Vector ◽  
Variational Mode Decomposition ◽  
Intrinsic Mode Functions ◽  
Bearing Fault Diagnosis ◽  
Multi Scale ◽  
Mode Decomposition ◽  
Bearing Vibration

Rolling bearings are the key components of rotating machinery. However, the incipient fault characteristics of a rolling bearing vibration signal are weak and difficult to extract. To solve this problem, this paper presents a novel rolling bearing vibration signal fault feature extraction and fault pattern recognition method based on variational mode decomposition (VMD), permutation entropy (PE) and support vector machines (SVM). In the proposed method, the bearing vibration signal is decomposed by VMD, and the intrinsic mode functions (IMFs) are obtained in different scales. Then, the PE values of each IMF are calculated to uncover the multi-scale intrinsic characteristics of the vibration signal. Finally, PE values of IMFs are fed into SVM to automatically accomplish the bearing condition identifications. The proposed method is evaluated by rolling bearing vibration signals. The results indicate that the proposed method is superior and can diagnose rolling bearing faults accurately.

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