摘要 :
In view of K-fault testability,the topological construction of a practical circuitis far from ideal.In order to improve the testability of a circuit,we may increase the numberof accessible nodes or use the multi-excitation method....
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In view of K-fault testability,the topological construction of a practical circuitis far from ideal.In order to improve the testability of a circuit,we may increase the numberof accessible nodes or use the multi-excitation method.Effectiveness of these methods and thefeasibility of choosing accessible nodes are discussed in detail.The conditions for multi-excitationtestability are presented.
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摘要 :
Increasing IC densities necessitate diagnosis methodologies with enhanced defect locating capabilities. Yet the computational effort expended in extracting diagnostic information and the stringent storage requirements constitute m...
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Increasing IC densities necessitate diagnosis methodologies with enhanced defect locating capabilities. Yet the computational effort expended in extracting diagnostic information and the stringent storage requirements constitute major concerns due to the tremendous number of faults in typical ICs. In this paper, we propose an RT-level diagnosis methodology capable of responding to these challenges. In the proposed scheme, diagnostic information is computed on a grouped fault effect basis, enhancing both the storage and the computational aspects. The fault effect grouping criteria are identified based on a module structure analysis, improving the propagation ability of the diagnostic information through RT modules. Experimental results show that the proposed methodology provides superior speed-ups and significant diagnostic information compression at no sacrifice in diagnostic resolution, compared to the existing gate-level diagnosis approaches.
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Dissolved Gas Analysis(DGA)is an important method for oil-immersed transformer fault diagnosis.However,collecting labelled DGA data is difficult because the determi-nation of the transformer fault is time-consuming and expensive i...
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Dissolved Gas Analysis(DGA)is an important method for oil-immersed transformer fault diagnosis.However,collecting labelled DGA data is difficult because the determi-nation of the transformer fault is time-consuming and expensive in the transformer substation,but DGA data without labels is easier to obtain.Therefore,the paper pro-posed a semi-supervised two-stage diagnostic system based DGA by using less labelled samples.The two-stage system includes a novel semi-supervised feature selection based Genetic Algorithm(GA)and Support Vector Machine(SVM)model(SSL-FS-GASVM)for selecting optimal features and a novel semi-supervised transformer fault diagnosis model based improved Artificial Fish Swarm Algorithm(AFSA)and SVM(SSL-IAFSA-SVM)for optimising the SVM parameter.Finally,the performances of SSL-FS-GASVM and SSL-IAFSA-SVM models are tested and compared with traditional supervised diagnostic models combined with other optimisation methods,respectively.The results show that the proposed two-stage system works in optimising features and parameters and has strong robustness in solving small sample classification problems.
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Online monitoring of gases dissolved in transformer oil is widely applied.Improving the performance of dissolved gas analysis(DGA)‐based fault diagnosis methods by exploring new features of time‐series data has become an appeali...
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Online monitoring of gases dissolved in transformer oil is widely applied.Improving the performance of dissolved gas analysis(DGA)‐based fault diagnosis methods by exploring new features of time‐series data has become an appealing topic.In this study,a new type of correlation features between characteristic gases was extracted from time‐series data based on the maximal information coefficient(MIC),and a fuzzy inference system was established.After the introduction of the principle of the MIC and a method for calculating the MIC‐based correlation features,the dominant symptom features that can be used to classify fault types were extracted through the receiver operating characteristic curve.Then,fuzzy rules were learnt,and a fuzzy inference system was designed.In addition,to improve the feasibility of the method,the Newton interpolation method was used for adaptation to the existing sampling cycle.The diagnostic results of the test data show that the proposed method has excellent per-formance and outperforms some prevailing traditional rule‐based methods as well as some artificial intelligent methods.The results also show that by exploring new cor-relation features from time‐series data based on the MIC,the performance of DGA‐based methods can be improved.
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摘要 :
Improving the accuracy of transformer dissolved gas analysis is always an important demand for power companies.However,the requirement for large numbers of fault samples becomes an obstacle to this demand.This article creatively u...
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Improving the accuracy of transformer dissolved gas analysis is always an important demand for power companies.However,the requirement for large numbers of fault samples becomes an obstacle to this demand.This article creatively uses a large number of health data,which is much easier to obtain by power companies,to improve diagnosis accuracy.Comprehensive investigations from the view of both data set and methodology to deal with this problem are presented.A data set consists of 9595 health samples and 993 fault samples is used for analysis.The characteristics of the data set and the influence of the health data on diagnostic accuracy are discussed.The performance of many state‐of‐art algorithms that handle the imbalanced prob-lem is evaluated.Meanwhile,an efficient fault diagnosis algorithm named self‐paced ensemble(SPE)is presented.In SPE,classification hardness is proposed to include the data characteristic in the classification.This method can guarantee the diversity of the data set and keep high performance.According to the experiment results,the superior of SPE is confirmed and also proves that involving more health samples can improve transformer diagnosis when fault data are limited.
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Despite the complicated fault mechanism of power equipment,with the increasing promotion and development of the Ubiquitous Power Internet of Things(UPIoT),the fault information of power equipment can be instantly saved,which makes...
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Despite the complicated fault mechanism of power equipment,with the increasing promotion and development of the Ubiquitous Power Internet of Things(UPIoT),the fault information of power equipment can be instantly saved,which makes possible intelligent diagnosis via fault samples.This study proposes a new method to implement comprehensive intelligent diagnosis by adopting the ShuffleNet light-weight convolution neural network(SLCNN).Considering the requirements of the UPIoT intelligent terminal,this study constructs six models,which are measured in terms of recognition accuracy,model storage,and calculation cost when applied to insulation and mechanical datasets.Compared with the existing models,the SLCNN outperforms them significantly in terms of recognition accuracy,with an accuracy of 95.77%and 99.9%in insulation and mechanical fault diagnoses,respectively.The SLCNN also demonstrates obvious advantages in other performance indicators,all of which contribute to its use in the accurate and reliable fault diagnosis of power equipment in the UPIoT context.Furthermore,through comparing feature maps,it is discovered that the aliasing degree and boundary of insulation defects are not as obvious as those of mechanical faults,which means that insulation fault diagnosis is much more difficult than mechanical fault diagnosis.
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The statistics of the Conference International des Grands Reseaux Electriques(CIGRE)indicate that the operational reliability of SF6 gas‐insulated equipment(GIE)is very high;however,the failure rate of the GIE in operation is muc...
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The statistics of the Conference International des Grands Reseaux Electriques(CIGRE)indicate that the operational reliability of SF6 gas‐insulated equipment(GIE)is very high;however,the failure rate of the GIE in operation is much higher than that of the IEC standard,and the fault occurs frequently in the GIE at a high voltage level.The reason is due to the complex and strong on‐site electromagnetic interference environment and fully enclosed structure of GIE.The key method and technology for effective on‐line monitoring and fault diagnosis of GIE are still lacking.Given the partial strong electromagnetic energy and high temperature induced by early latent insulation faults in the equipment,SF6 gas insulation presents different degrees of decomposition.The decomposition products mainly include SO_(2)F_(2),SOF_(2),SO_(2),HF,and H2S.The decomposition characteristics of SF6 are closely related to the property of insulation faults.At present,this area is attracting attention from the power industry and research institutes.This study summarises the current research on SF_(6)decomposition component analysis(DCA).The content mainly includes the latest progress of SF_(6)decomposition characteristics and mechanism under fault conditions,and fault diagnosis methods based on decomposition components.
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