In view of the low level testability of armored equipment,the important significance of armored equipment testability growth is discussed in this paper.The failure mode effects and criticality analysis( FMECA) method ...In view of the low level testability of armored equipment,the important significance of armored equipment testability growth is discussed in this paper.The failure mode effects and criticality analysis( FMECA) method to realize testability growth is introduced.Centering on the testability growth demands of new armored equipment,the deficiencies of traditional FMECA are analyzed.And an enhanced FMECA( EFMECA) method is proposed.The method increases the analysis contents,combines the information before the failure occurrence and impending failure modes together organically.Then the failure symptoms is analyzed,the failure modes and effects is determined,and the state development trend is predicted.Finally,the application of EFMECA method is illustrated by the example of the failure mode of typical armored equipment engine.展开更多
Rapid and precise location of the faults of on-board equipment of train control system is a significant factor to ensure reliable train operation.Text data of the fault tracking table of on-board equipment are taken a...Rapid and precise location of the faults of on-board equipment of train control system is a significant factor to ensure reliable train operation.Text data of the fault tracking table of on-board equipment are taken as samples,and an on-board equipment fault diagnosis model is designed based on the combination of convolutional neural network(CNN)and particle swarm optimization-support vector machines(PSO-SVM).Due to the characteristics of high dimensionality and sparseness of fault text data,CNN is used to achieve feature extraction.In order to decrease the influence of the imbalance of the fault sample data category on the classification accuracy,the PSO-SVM algorithm is introduced.The fully connected classification part of CNN is replaced by PSO-SVM,the extracted features are classified precisely,and the intelligent diagnosis of on-board equipment fault is implemented.According to the test analysis of the fault text data of on-board equipment recorded by a railway bureau and comparison with other models,the experimental results indicate that this model can obviously upgrade the evaluation indexes and can be used as an effective model for fault diagnosis for on-board equipment.展开更多
Aimed at the actuality of Peoplep's Liberation Army(PLA) army equipment maintenance, this paper develops equipment maintenance mode based on network,and focuses on the design of maintenance decision-making system....Aimed at the actuality of Peoplep's Liberation Army(PLA) army equipment maintenance, this paper develops equipment maintenance mode based on network,and focuses on the design of maintenance decision-making system. Analyzing maintenance weight is applied to making decision of repair level.The purpose of the research is introducing basic concept and setting up an equipment maintenance mode using military network.Maintenance mode based on network can reduce the costs,enhance the maintenance efficiency, and save the human resource and finance.展开更多
On-line measurement for dielectric loss angle can effectively monitor the insulation condition of capacitive equipment in power systems. Synthetic relative measuring methods not only markedly overcome the shortcomings...On-line measurement for dielectric loss angle can effectively monitor the insulation condition of capacitive equipment in power systems. Synthetic relative measuring methods not only markedly overcome the shortcomings of traditional absolute measuring methods but also greatly improve the accuracy of dielectric loss angle measurement. However, synthetic relative measuring methods based on two or three pieces of capacitive equipment do not have the characteristic of generality. In this paper, a principle of synthetic relative measuring method is presented. The example of application for synthetic relative methods based on three and four pieces of capacitive equipment running in the same phase is taken to present the failure judgment matrices for N pieces of equipment. According to these matrices, the fault condition of N pieces of capacitive equipment can be watched, which is more general. Then some problems needing to be concerned along with two diagnostic methods used in diagnostic system are introduced. Finally, two programmable flow charts for the two methods are given and corresponding examples demonstrate their feasibility in practice.展开更多
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 ...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.展开更多
文摘In view of the low level testability of armored equipment,the important significance of armored equipment testability growth is discussed in this paper.The failure mode effects and criticality analysis( FMECA) method to realize testability growth is introduced.Centering on the testability growth demands of new armored equipment,the deficiencies of traditional FMECA are analyzed.And an enhanced FMECA( EFMECA) method is proposed.The method increases the analysis contents,combines the information before the failure occurrence and impending failure modes together organically.Then the failure symptoms is analyzed,the failure modes and effects is determined,and the state development trend is predicted.Finally,the application of EFMECA method is illustrated by the example of the failure mode of typical armored equipment engine.
基金Gansu Province Higher Education Innovation Fund Project(No.2020B-104)“Innovation Star”Project for Outstanding Postgraduates of Gansu Province(No.2021CXZX-606)。
文摘Rapid and precise location of the faults of on-board equipment of train control system is a significant factor to ensure reliable train operation.Text data of the fault tracking table of on-board equipment are taken as samples,and an on-board equipment fault diagnosis model is designed based on the combination of convolutional neural network(CNN)and particle swarm optimization-support vector machines(PSO-SVM).Due to the characteristics of high dimensionality and sparseness of fault text data,CNN is used to achieve feature extraction.In order to decrease the influence of the imbalance of the fault sample data category on the classification accuracy,the PSO-SVM algorithm is introduced.The fully connected classification part of CNN is replaced by PSO-SVM,the extracted features are classified precisely,and the intelligent diagnosis of on-board equipment fault is implemented.According to the test analysis of the fault text data of on-board equipment recorded by a railway bureau and comparison with other models,the experimental results indicate that this model can obviously upgrade the evaluation indexes and can be used as an effective model for fault diagnosis for on-board equipment.
文摘Aimed at the actuality of Peoplep's Liberation Army(PLA) army equipment maintenance, this paper develops equipment maintenance mode based on network,and focuses on the design of maintenance decision-making system. Analyzing maintenance weight is applied to making decision of repair level.The purpose of the research is introducing basic concept and setting up an equipment maintenance mode using military network.Maintenance mode based on network can reduce the costs,enhance the maintenance efficiency, and save the human resource and finance.
文摘On-line measurement for dielectric loss angle can effectively monitor the insulation condition of capacitive equipment in power systems. Synthetic relative measuring methods not only markedly overcome the shortcomings of traditional absolute measuring methods but also greatly improve the accuracy of dielectric loss angle measurement. However, synthetic relative measuring methods based on two or three pieces of capacitive equipment do not have the characteristic of generality. In this paper, a principle of synthetic relative measuring method is presented. The example of application for synthetic relative methods based on three and four pieces of capacitive equipment running in the same phase is taken to present the failure judgment matrices for N pieces of equipment. According to these matrices, the fault condition of N pieces of capacitive equipment can be watched, which is more general. Then some problems needing to be concerned along with two diagnostic methods used in diagnostic system are introduced. Finally, two programmable flow charts for the two methods are given and corresponding examples demonstrate their feasibility in practice.
基金supported by National Natural Science Foundation of China(No.61763025)Gansu Science and Technology Program Project(No.18JR3RA104)+1 种基金Industrial support program for colleges and universities in Gansu Province(No.2020C-19)Lanzhou Science and Technology Project(No.2019-4-49)。
文摘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.
