Research on Pattern Recognition Method of Blockage Signal in Pipeline Based on LMD Information Entropy and ELM

Aiming at the nonstationary and nonlinear characteristics of acoustic impulse response signal in pipeline blockage and the difficulty in identifying the different degrees of blockage, this paper proposed a pattern recognition method based on local mean decomposition (LMD), information entropy theory, and extreme learning machine (ELM). Firstly, the impulse response signals of pipeline extracted in different operating conditions were decomposed with LMD method into a series of product functions (PFs). Secondly, based on the information entropy theory, the appropriate energy entropy, singular spectrum entropy, power spectrum entropy, and Hilbert spectrum entropy were extracted as the input feature vectors. Finally, ELM was introduced for classification of pipeline blockage. Through the analysis of acoustic impulse response signal collected under the condition of health and different degrees of blockages in pipeline, the results show that the proposed method can well characterize the state information. Also, it has a great advantage in terms of accuracy and it is time consuming when compared with the support vector machine (SVM) and BP (backpropagation) model.

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Statistical Feature Extraction Combined with Generalized Discriminant Component Analysis Driven SVM for Fault Diagnosis of HVDC GIS

Energies ◽

10.3390/en14227674 ◽

2021 ◽

Vol 14 (22) ◽

pp. 7674

Author(s):

Ruixu Zhou ◽

Wensheng Gao ◽

Weidong Liu ◽

Dengwei Ding ◽

Bowen Zhang

Keyword(s):

Pattern Recognition ◽

Fault Diagnosis ◽

Pulse Current ◽

State Of The Art ◽

Selection Criterion ◽

Component Analysis ◽

Support Vector ◽

Stable Operation ◽

Pattern Recognition Method ◽

Recognition Method

Accurately identifying the types of insulation defects inside a gas-insulated switchgear (GIS) is of great significance for guiding maintenance work as well as ensuring the safe and stable operation of GIS. By building a set of 220 kV high-voltage direct current (HVDC) GIS experiment platforms and manufacturing four different types of insulation defects (including multiple sizes and positions), 180,828 pulse current signals under multiple voltage levels are successfully measured. Then, the apparent discharge quantity and the discharge time, two inherent physical quantities unaffected by the experimental platform and measurement system, are obtained after the pulse current signal is denoised, according to which 70 statistical features are extracted. In this paper, a pattern recognition method based on generalized discriminant component analysis driven support vector machine (SVM) is detailed and the corresponding selection criterion of involved parameters is established. The results show that the newly proposed pattern recognition method greatly improves the recognition accuracy of fault diagnosis in comparison with 36 kinds of state-of-the-art dimensionality reduction algorithms and 44 kinds of state-of-the-art classifiers. This newly proposed method not only solves the difficulty that phase-resolved partial discharge (PRPD) cannot be applied under DC condition but also immensely facilitates the fault diagnosis of HVDC GIS.

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A NEW FAULT PATTERN RECOGNITION METHOD BASED ON WPT AND DAGSVM

International Journal of Computational Intelligence and Applications ◽

10.1142/s1469026809002631 ◽

2009 ◽

Vol 08 (03) ◽

pp. 345-353 ◽

Cited By ~ 1

Author(s):

GUANG-MING XIAN ◽

BI-QING ZENG

Keyword(s):

Pattern Recognition ◽

Fault Diagnosis ◽

Wavelet Packet ◽

Roller Bearing ◽

Support Vector ◽

Second Phase ◽

Pattern Recognition Method ◽

Recognition Method ◽

Fault Pattern ◽

Model Setup

A new pattern recognition method based on wavelet packet transform (WPT) and directed acyclic graph support vector machine (DAGSVM) is put forward for fault diagnosis of roller bearing. The fault pattern recognition model setup has two phases. The first phase is to extract the feature of faulty vibration signals from roller bearing by WPT via a db3 wavelet. The second phase is to use DAGSVM to recognize fault pattern of roller bearing. The testing results illustrates that WPT is more effective to diagnose fault types than the WT method. It is observed that among the strategy of multi-class SVM, DAGSVM acquires the highest accuracy, and therefore, this demonstrates the fact that suitable fault pattern recognition strategy can improve the overall performance of fault diagnosis. The present research illustrated that the features extracted by WPT represent the fault pattern of roller bearing, and the DAGSVM trained on these features achieved high recognition accuracies.

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Comparison of random forest, artificial neural networks and support vector machine for intelligent diagnosis of rotating machinery

Transactions of the Institute of Measurement and Control ◽

10.1177/0142331217708242 ◽

2017 ◽

Vol 40 (8) ◽

pp. 2681-2693 ◽

Cited By ~ 59

Author(s):

Te Han ◽

Dongxiang Jiang ◽

Qi Zhao ◽

Lei Wang ◽

Kai Yin

Keyword(s):

Neural Networks ◽

Pattern Recognition ◽

Support Vector Machine ◽

Artificial Neural Networks ◽

Random Forest ◽

Rotating Machinery ◽

Support Vector ◽

Pattern Recognition Method ◽

Recognition Method ◽

Intelligent Diagnosis

Nowadays, the data-driven diagnosis method, exploiting pattern recognition method to diagnose the fault patterns automatically, achieves much success for rotating machinery. Some popular classification algorithms such as artificial neural networks and support vector machine have been extensively studied and tested with many application cases, while the random forest, one of the present state-of-the-art classifiers based on ensemble learning strategy, is relatively unknown in this field. In this paper, the behavior of random forest for the intelligent diagnosis of rotating machinery is investigated with various features on two datasets. A framework for the comparison of different methods, that is, random forest, extreme learning machine, probabilistic neural network and support vector machine, is presented to find the most efficient one. Random forest has been proven to outperform the comparative classifiers in terms of recognition accuracy, stability and robustness to features, especially with a small training set. Additionally, compared with traditional methods, random forest is not easily influenced by environmental noise. Furthermore, the user-friendly parameters in random forest offer great convenience for practical engineering. These results suggest that random forest is a promising pattern recognition method for the intelligent diagnosis of rotating machinery.

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An integrated fault pattern recognition method of satellite control system using kernel principal component analysis and support vector machine

Proceedings of 2014 IEEE Chinese Guidance, Navigation and Control Conference ◽

10.1109/cgncc.2014.7007462 ◽

2014 ◽

Cited By ~ 1

Author(s):

Keqiang Xia ◽

Baohua Wang ◽

Ganhua Li

Keyword(s):

Principal Component Analysis ◽

Pattern Recognition ◽

Support Vector Machine ◽

Control System ◽

Principal Component ◽

Kernel Principal Component Analysis ◽

Support Vector ◽

Pattern Recognition Method ◽

Recognition Method ◽

Fault Pattern

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A Novel Motion Recognition Method Based on Force Myography of Dynamic Muscle Contractions

Frontiers in Neuroscience ◽

10.3389/fnins.2021.783539 ◽

2022 ◽

Vol 15 ◽

Author(s):

Xiangxin Li ◽

Yue Zheng ◽

Yan Liu ◽

Lan Tian ◽

Peng Fang ◽

...

Keyword(s):

Pattern Recognition ◽

Nearest Neighbor ◽

Support Vector ◽

Pattern Recognition Method ◽

Window Length ◽

K Nearest Neighbor ◽

Recognition Method ◽

Linear Discriminant ◽

Analysis Window ◽

Force Myography

Surface electromyogram-based pattern recognition (sEMG-PR) has been considered as the most promising method to control multifunctional prostheses for decades. However, the commercial applications of sEMG-PR in prosthetic control is still limited due to the ambient noise and impedance variation between electrodes and skin surface. In order to reduce these issues, a force-myography-based pattern recognition method was proposed. In this method, a type of polymer-based flexible film sensors, the piezoelectrets, were used to record the rate of stress change (RSC) signals on the muscle surface of eight able-bodied subjects for six hand motions. Thirteen time domain features and four classification algorithms of linear discriminant analysis (LDA), K-nearest neighbor (KNN), artificial neural network (ANN), and support vector machine (SVM) were adopted to decode the RSC signals of different motion classes. In addition, the optimal feature set, classifier, and analysis window length were investigated systematically. Results showed that the average classification accuracy was 95.5 ± 2.2% by using the feature combination of root mean square (RMS) and waveform length (WL) for the classifier of KNN, and the analysis window length of 300 ms was found to obtain the best classification performance. Moreover, the robustness of the proposed method was investigated, and the classification accuracies were observed above 90% even when the white noise ratio increased to 50%. The work of this study demonstrated the effectiveness of RSC-based pattern recognition method for motion classification, and it would provide an alternative approach for the control of multifunctional prostheses.

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