A condition monitoring method of deep-hole drilling based on multi-sensor information fusion is discussed. The signal of vibration and cutting force are collected when the condition of deep-hole drilling on stainless ...A condition monitoring method of deep-hole drilling based on multi-sensor information fusion is discussed. The signal of vibration and cutting force are collected when the condition of deep-hole drilling on stainless steel 0Cr17Ni4Cu4Nb is normal or abnormal. Four eigenvectors are extracted on time-domain and frequency-domain analysis of the signals. Then the four eigenvectors are combined and sent to neural networks to dispose. The fusion results indicate that multi-sensor information fusion is superior to single-sensor information, and that cutting force signal can reflect the condition of cutting tool better than vibration signal.展开更多
According to fault type diversity and fault information uncertainty problem of the hydraulic driven rocket launcher servo system（HDRLSS） , the fault diagnosis method based on the evidence theory and neural network e...According to fault type diversity and fault information uncertainty problem of the hydraulic driven rocket launcher servo system（HDRLSS） , the fault diagnosis method based on the evidence theory and neural network ensemble is proposed. In order to overcome the shortcomings of the single neural network, two improved neural network models are set up at the com-mon nodes to simplify the network structure. The initial fault diagnosis is based on the iron spectrum data and the pressure, flow and temperature（PFT） characteristic parameters as the input vectors of the two improved neural network models, and the diagnosis result is taken as the basic probability distribution of the evidence theory. Then the objectivity of assignment is real-ized. The initial diagnosis results of two improved neural networks are fused by D-S evidence theory. The experimental results show that this method can avoid the misdiagnosis of neural network recognition and improve the accuracy of the fault diagnosis of HDRLSS.展开更多
To solve the problem of information fusion from multiple sources in innovation alliances, an information fusion model based on the Bayesian network is presented. The multi-source information fusion process of innovati...To solve the problem of information fusion from multiple sources in innovation alliances, an information fusion model based on the Bayesian network is presented. The multi-source information fusion process of innovation alliances was classified into three layers, namely, the information perception layer, the feature clustering layer,and the decision fusion layer. The agencies in the alliance were defined as sensors through which information is perceived and obtained, and the features were clustered. Finally, various types of information were fused by the innovation alliance based on the fusion algorithm to achieve complete and comprehensive information. The model was applied to a study on economic information prediction, where the accuracy of the fusion results was higher than that from a single source and the errors obtained were also smaller with the MPE less than 3%, which demonstrates the proposed fusion method is more effective and reasonable. This study provides a reasonable basis for decision-making of innovation alliances.展开更多
Information networks provide a powerful representation of entities and the relationships between them.Information networks fusion is a technique for information fusion that jointly reasons about entities,links and rel...Information networks provide a powerful representation of entities and the relationships between them.Information networks fusion is a technique for information fusion that jointly reasons about entities,links and relations in the presence of various sources.However,existing methods for information networks fusion tend to rely on a single task which might not get enough evidence for reasoning.In order to solve this issue,in this paper,we present a novel model called MC-INFM(information networks fusion model based on multi-task coordination).Different from traditional models,MC-INFM casts the fusion problem as a probabilistic inference problem,and collectively performs multiple tasks(including entity resolution,link prediction and relation matching)to infer the final result of fusion.First,we define the intra-features and the inter-features respectively and model them as factor graphs,which can provide abundant evidence to infer.Then,we use conditional random field(CRF)to learn the weight of each feature and infer the results of these tasks simultaneously by performing the maximum probabilistic inference.Experiments demonstrate the effectiveness of our proposed model.展开更多
文摘A condition monitoring method of deep-hole drilling based on multi-sensor information fusion is discussed. The signal of vibration and cutting force are collected when the condition of deep-hole drilling on stainless steel 0Cr17Ni4Cu4Nb is normal or abnormal. Four eigenvectors are extracted on time-domain and frequency-domain analysis of the signals. Then the four eigenvectors are combined and sent to neural networks to dispose. The fusion results indicate that multi-sensor information fusion is superior to single-sensor information, and that cutting force signal can reflect the condition of cutting tool better than vibration signal.
文摘According to fault type diversity and fault information uncertainty problem of the hydraulic driven rocket launcher servo system（HDRLSS） , the fault diagnosis method based on the evidence theory and neural network ensemble is proposed. In order to overcome the shortcomings of the single neural network, two improved neural network models are set up at the com-mon nodes to simplify the network structure. The initial fault diagnosis is based on the iron spectrum data and the pressure, flow and temperature（PFT） characteristic parameters as the input vectors of the two improved neural network models, and the diagnosis result is taken as the basic probability distribution of the evidence theory. Then the objectivity of assignment is real-ized. The initial diagnosis results of two improved neural networks are fused by D-S evidence theory. The experimental results show that this method can avoid the misdiagnosis of neural network recognition and improve the accuracy of the fault diagnosis of HDRLSS.
基金supported by the National Natural Science Foundation of China(Nos.71472053,71429001,and91646105)
文摘To solve the problem of information fusion from multiple sources in innovation alliances, an information fusion model based on the Bayesian network is presented. The multi-source information fusion process of innovation alliances was classified into three layers, namely, the information perception layer, the feature clustering layer,and the decision fusion layer. The agencies in the alliance were defined as sensors through which information is perceived and obtained, and the features were clustered. Finally, various types of information were fused by the innovation alliance based on the fusion algorithm to achieve complete and comprehensive information. The model was applied to a study on economic information prediction, where the accuracy of the fusion results was higher than that from a single source and the errors obtained were also smaller with the MPE less than 3%, which demonstrates the proposed fusion method is more effective and reasonable. This study provides a reasonable basis for decision-making of innovation alliances.
基金This work was supported by the National Key R&D Program of China(2018YFB1003404)the National Natural Science Foundation of China(Grant Nos.61672142,U1435216,61602103).
文摘Information networks provide a powerful representation of entities and the relationships between them.Information networks fusion is a technique for information fusion that jointly reasons about entities,links and relations in the presence of various sources.However,existing methods for information networks fusion tend to rely on a single task which might not get enough evidence for reasoning.In order to solve this issue,in this paper,we present a novel model called MC-INFM(information networks fusion model based on multi-task coordination).Different from traditional models,MC-INFM casts the fusion problem as a probabilistic inference problem,and collectively performs multiple tasks(including entity resolution,link prediction and relation matching)to infer the final result of fusion.First,we define the intra-features and the inter-features respectively and model them as factor graphs,which can provide abundant evidence to infer.Then,we use conditional random field(CRF)to learn the weight of each feature and infer the results of these tasks simultaneously by performing the maximum probabilistic inference.Experiments demonstrate the effectiveness of our proposed model.
