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fault classification for On-board Equipment of High-speed Railway Based on Attention Capsule Network

International Journal of Automation and computing 2021年第5期18卷 814-825页

作者： Lu-Jie Zhou Jian-Wu Dang Zhen-Hai Zhang School of Automation and Electrical Engineering Lanzhou Jiaotong UniversityLanzhou 730070China Gansu Provincial Engineering Research Center for Artificial Intelligence and Graphic&Image Processing Lanzhou 730070China

The conventional troubleshooting methods for high-speed railway on-board equipment, with over-reliance on personnel experience, is characterized by one-sidedness and low efficiency. In the process of high-speed train operation, numerous text-based onboard logs are recorded by on-board computers. Machine learning methods can help technicians make a correct judgment of fault types using the on-board log reasonably. Therefore, a fault classification model of on-board equipment based on attention capsule networks is proposed. This paper presents an empirical exploration of the application of a capsule network with dynamic routing in fault classification. A capsule network can encode the internal spatial part-whole relationship between various entities to identify the fault types. As the importance of each word in the on-board log and the dependencies between them have a significant impact on fault classification, an attention mechanism is incorporated into the capsule network to distill important information. Considering the imbalanced distribution of normal data and fault data in the on-board log, the focal loss function is introduced into the model to adjust the imbalanced data. The experiments are conducted on the on-board log of a railway bureau and compared with other baseline models. The experimental results demonstrate that our model outperforms the compared baseline methods, proving the superiority and competitiveness of our model.

关键词： On-board equipment fault classification capsule network attention mechanism focal loss

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fault classification and reconfiguration of distribution ystems using equivalent capacity margin method

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Frontiers in Energy 2012年第4期6卷 394-402页

作者： K.Sathish KUMAR T.JAYABARATHI School of Electrical Engineering VIT UniversityVellore 632014India

This paper investigates the capability of support vector machines （SVM） for prediction of fault classification and the use of the concept of equivalent capacity margin （ECM） for restoration of the power system. The SVM, as a novel type of machine learning based on statistical learning theory, achieves good generalization ability by adopting a structural risk minimization （SRM） induction principle aimed at minimizing a bound on the generalization error of a model rather than the minimization of the error on the training data only. Here, the SVM has been used as a classification. The inputs of the SVM model are power and voltage values. An equation has been developed for the prediction of the fault in the power system based on the developed SVM model. The next steps of this paper are the restoration and reconfiguration by using the ECM concept, the development of a code, and the testing of the results with various load outages, which have been executed for a 12 load system.

关键词： support vector machines (SVM) structuralrisk minimization (SRM) equivalent capacity margin(ECM) restoration fault classification

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fault classification for On-board Equipment of High-speed Railway Based on Attention Capsule Network

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Machine Intelligence Research 2021年

作者： Lu-JieZhou Jian-WuDang Zhen-HaiZhang SchoolofAutomationandElectricalEngineering LanzhouJiaotongUniversity GansuProvincialEngineeringResearchCenterforArtificialIntelligenceandGraphic&ImageProcessing

The conventional troubleshooting methods for high-speed railway on-board equipment, with over-reliance on personnel experience,***-speedtrainoperation,numeroustext-basedonboard logs are recorded by on-board computers. Machine learning methods can help technicians make a correct judgment of fault types using the on-board log reasonably. Therefore, a fault classification model of on-board equipment based on attention capsule networks is proposed. This paper presents an empirical exploration of the application of a capsule network with dynamic routing in fault classification. A capsule network can encode the internal spatial part-whole relationship between various entities to identify the fault types. As the importance of each word in the on-board log and the dependencies between them have a significant impact on fault classification, an attention mechanism is incorporated into the capsule network to distill important information. Considering the imbalanced distribution of normal data and fault data in the on-board log, the focal loss function is introduced into the model to adjust the imbalanced data. The experiments are conducted on the on-board log of a railway bureau and compared with other baseline models. The experimental results demonstrate that our model outperforms the compared baseline methods, proving the superiority and competitiveness of our model.

关键词： On-board equipment fault classification capsule network attention mechanism focal loss

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Ensemble enhanced active learning mixture discriminant analysis model and its application for semi-supervised fault classification

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Frontiers of Information Technology & Electronic Engineering 2022年第12期23卷 1814-1827页

作者： Weijun WANG Yun WANG Jun WANG Xinyun FANG Yuchen HE Key Laboratory of Intelligent Manufacturing Quality Big Data Tracing and Analysis of Zhejiang Province China Jiliang UniversityHangzhou 310018China Mechanical and Electrical Engineering Department Zhejiang Tongji Vocational College of Science and TechnologyHangzhou 311231China Suzhou Institute of Metrology Suzhou 215004China

As an indispensable part of process monitoring, the performance of fault classification relies heavily on the sufficiency of process knowledge. However, data labels are always difficult to acquire because of the limited sampling condition or expensive laboratory analysis, which may lead to deterioration of classification *** handle this dilemma, a new semi-supervised fault classification strategy is performed in which enhanced active learning is employed to evaluate the value of each unlabeled sample with respect to a specific labeled *** samples with large values will serve as supplementary information for the training dataset. In addition,we introduce several reasonable indexes and criteria, and thus human labeling interference is greatly reduced. Finally,the fault classification effectiveness of the proposed method is evaluated using a numerical example and the Tennessee Eastman process.

关键词： Semi-supervised Active learning Ensemble learning Mixture discriminant analysis fault classification

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A Transmission Line fault classification Approach by Support Vector Machines

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Journal of Energy and Power Engineering 2011年第3期5卷 268-274页

作者： A.M. Ibrahim A.Y. Abdelaziz S.F. Mekhamer M. Ramadan Electrical Power and Machines Department Faculty of Engineering Ain Shams University Cairo Egypt

This paper presents an approach for shunt faults detection and classification in transmission line using Support Vector Machine （SVM）. The paper compares between using three line post-fault current samples for one-half cycle and one-fourth cycle from the inception of the fault as inputs for SVM. Two SVMs are used, first SVMabc is used for faulty phase detection and second SVMg is used for ground detection. SVMs with polynomial kernel with different degrees are used to obtain the best classification score. The classification test results show that the proposed method is accurate and reliable.

关键词： Transmission line protection fault detection fault classification support vector machine.

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Nonlinear Manifold Learning Based fault classification Method

Nonlinear Manifold Learning Based Fault Classification Metho...

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第二十九届中国控制会议

作者： JIANG Quansheng,LU Jiayun,JIA Minping 1.Department of Physics and Electronics,Chaohu University,Chaohu 238000,P.R.China 2.School of Mechanical Engineering,Southeast University,Nanjing 211189,P.R.China

In this paper,nonlinear manifold learning is introduced into equipment fault diagnosis field and solving fault pattern classification problem,a novel method of fault classification based on manifold learning is *** nonlinear manifold learning algorithms based on LLE and Laplacian Eigenmaps are utilized to learn original fault signal directly and extract intrinsic manifold feature in data *** new approach can greatly hold the global geometry structure information embedded in the signal,and availably overcome the flaw of general feature extraction methods which only obtain data’s local linear structure,obviously improve classify performance of fault *** experimentations on simulation and engineering instance show that,the presented method excels compared to PCA,and is a feasibly and effective technique for fault classification.

关键词： fault classification Manifold Learning LLE Laplacian Eigenmaps

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Actor Critic-based Dynamic Cost-Sensitive Classifier for Imbalanced fault classification

Actor Critic-based Dynamic Cost-Sensitive Classifier for Imb...

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第31届中国过程控制会议（CPCC2020）

作者： Blind Review State Key Laboratory of Industrial Control Technology Institute of Industrial Process Control College of Control Science and Engineering Zhejiang University

fault classification plays a crucial role in industrial processes. Most of fault classification algorithms are designed in the context of balanced data. However, in the actual industrial process, the data collected is usually imbalanced, that is, there are large amounts of normal data(majority) and only a few fault data(minority). This situation is also known as the imbalanced classification problem, which is a major obstacle to traditional classifiers. To handle the imbalanced classification problem, a novel actor critic-based dynamic cost-sensitive classifier(AC-DCC) is proposed in this work. In AC-DCC, a novel cost matrix learning strategy that can learn the cost matrix adaptively and yield an optimal cost matrix is developed. Thus, the proposed AC-DCC is more flexible and efficient in handling the imbalanced fault classification problem. The effectiveness and practicability of the proposed AC-DCC method are verified through its application to an industrial benchmark process.

关键词： fault classification Actor-Critic Cost-Sensitive classifier Imbalanced classification Cost matrix

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Parameter optimization algorithm of SVM for fault classification in traction converter

Parameter optimization algorithm of SVM for fault classifica...

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第26届中国控制与决策会议

作者： Zhao Jin Wu Chaorong Huang Chengguang Wu Feng School of automation HuaZhong University of Science and Technology

The classification performance of Support Vector Machine（SVM） is heavily influenced by its kernel parameter g and penalty factor c. in this paper, Cross-validation（CV） based grid-search optimization, CV-based genetic algorithm（GA） and CV-based particle swarm optimization（PSO） are respectively used for parameters optimization in SVM for fault classification of inverters in traction converter. Simulation result shows that SVM can reach the highest classification accuracy by using CV-based grid-search optimization algorithm, and it has been proved to be practical to use CV-based grid-search as SVM’s parameters optimization algorithm for fault classification in traction converter.

关键词： SVM fault classification parameter optimization traction converter

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ADAPTIVE WAVELET-BASED NEURAL NETWORK FOR fault classification IN BEARING

ADAPTIVE WAVELET-BASED NEURAL NETWORK FOR FAULT CLASSIFICATI...

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第七届海内外青年设计与制造科学会议

作者： Chen Wenge HuJiao Zhao and Xuezhi Faculty of Electromechanical Engineering Guangdong Univ. of Tech. China Mechanical Engineering College South China Univ. of Tech. China

＜正＞One kind of adaptive wavelet-based neural network for fault classification is put forward. The first component of network is used to extract signal’s feature through a family of wavelet’s decomposition for signal in different channel, the second component of network is used to learn these information or judge fault type according to these feature. The learning algorithm is presented in detail and this network is applied to the bearings’ fault classification. The result demonstrates that this neural network can classify fault accurately and reliably.

关键词： Adaptive Wavelet Neural Networks Bearings fault classification

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An Audio-based Intelligent fault classification System for Belt Conveyor Rollers

An Audio-based Intelligent Fault Classification System for B...

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第40届中国控制会议

作者： Mingjin Yang Chen Peng Zhipeng Li Department of Automation School of Mechatronic Engineering and AutomationShanghai University

This paper researches how to realize the fault classification of the roller components in the belt conveyor of the coal preparation plant,which is selected as the actual industrial scene for the application of *** actual fault state classification system based on audio data and designed for rollers of belt conveyor is *** system is consists of hardware parts and software *** hardware part applies with wired communication to achieve on-line dynamically measuring in an awful spot *** software applied with machine learning algorithms is designed for fault classification to achieve the goal of more than 90% classification *** application demonstrates that the system has the characteristic of simple structure,higher interference-free capability,moving steadily,high precision,good expansibility,productivity-improving,and advanced performance-to-price ratio.

关键词： fault classification Audio data sensors Machine learning K-means algorithm Support vector machine Neural network

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