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Online fault recognition of electric power cable in coal mine based on the minimum risk neural network

Journal of Coal Science & Engineering(China) 2008年第3期14卷 492-496页

作者：汪梅 STATHAKI Tania School of Electric and Control Engineering Xi'an University of Science and TechnologyXi'an 710054China Department of Electrical and Electronic Engineering Imperial CollegeLondon SW7 2AZUnited Kingdom

Firstly,the concepts of the traveling wave entropy and the feature function of traveling wave entropy were *** the statistic characters of the traveling wave entropy feature function,mean value and variance were analyzed after the zero-order component of the traveling wave of online cable was selected to serve as the observed ***,the new recognition algorithm of minimum risk neural network was pre- *** simulation experiments show that the recognitions of the early fault states can be completed correctly by using the proposed recognition *** classes of cable faults include in 1-phase ground faults,and the 2-phase short circuit faults or ground faults and the 3-phase short circuit faults or ground faults,open *** fault resistance range is 1×10^(-1)～1×10~9Ω.

关键词： minimum risk neural network traveling wave entropy zero-order component online cable recognition algorithm early fault

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A prediction method of operation trend for large axial-flow fan based on vibration-electric information fusion

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Journal of Central South University 2021年第6期28卷 1786-1796页

作者： GU Zhen-yu ZHU Yao-yao XIANG Ji-lei ZENG Yuan College of Automation Chongqing UniversityChongqing 400044China

As the critical equipment,large axial-flow fan(LAF)is used widely in highway tunnels for *** that any malfunction of LAF can cause severe consequences for ***,fault deterioration is suppressed tremendously when an abnormal state is detected in the stage of early ***,the monitoring of the early fault characteristics is very difficult because of the low signal amplitude and system disturbance(or noise).In order to overcome this problem,a novel early fault judgment method to predict the operation trend is proposed in this *** vibration-electric information fusion,the support vector machine(SVM)with particle swarm optimization(PSO),and the cross-validation(CV)for predicting LAF operation states are proposed and ***,the results of the experimental study verify that the performance of the proposed method is superior to that of the contrast models.

关键词： large axial-flow fan early fault state prediction particle swarm optimization

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