Hybrid data fusion model for restricted information using Dempster–Shafer and adaptive neuro-fuzzy inference (DSANFI) system

Brumancia, E.; Samuel, S. Justin; Gladence, L. Mary; Rathan, Karunya

doi:10.1007/s00500-018-03734-1

Cited by 11 publications

(7 citation statements)

References 21 publications

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“…Dempster–Shafer (D-S) evidence theory is a kind of uncertainty inference, which effectively solves the influence of incomplete and uncertain information and other factors on the inference results by describing the uncertainty of the state of each part of the system from different perspectives and generalizing and estimating its probability. However, when the conflict between the evidence is severe, the traditional D-S theory information fusion accuracy is not satisfactory [ 23 , 24 ]. To solve the fusion problem of high conflicting evidence, this paper uses the improved D-S evidence theory to complete the fusion of information to improve the identification accuracy of the fault model.…”

Section: Fault Diagnosis Of Industrial Robot Based On Dbnmentioning

confidence: 99%

Fault Diagnosis Method for Industrial Robots Based on DBN Joint Information Fusion Technology

Jiao¹,

Zheng²

2022

Computational Intelligence and Neuroscience

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Aiming at the problems of the traditional industrial robot fault diagnosis model, such as low accuracy, low efficiency, poor stability, and real-time performance in multi-fault state diagnosis, a fault diagnosis method based on DBN joint information fusion technology is proposed. By studying the information processing method and the deep learning theory, this paper takes the fault of the joint bearing of the industrial robot as the research object. It adopts the technique of combining the deep belief network (DBN) and wavelet energy entropy, and the fault diagnosis of industrial robot is studied. The wavelet transform is used to denoise, decompose, and reconstruct the vibration signal of the joint bearing of the industrial robot. The normalized eigenvector of the reconstructed energy entropy is established, and the normalized eigenvector is used as the input of the DBN. The improved D-S evidence theory is used to solve the problem of fusion of high conflict evidence to improve the fault model’s recognition accuracy. Finally, the feasibility of the model is verified by collecting the fault sample data and creating the category sample label. The experiment shows that the fault diagnosis method designed can complete the fault diagnosis of industrial robot well, and the accuracy of the test set is 97.96%. Compared with the traditional fault diagnosis model, the method is improved obviously, and the stability of the model is good; the utility model has the advantages of short time and high diagnosis efficiency and is suitable for the diagnosis work under the condition of coexisting multiple faults. The reliability of this method in the fault diagnosis of the joint bearing of industrial robot is verified.

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Section: Fault Diagnosis Of Industrial Robot Based On Dbnmentioning

confidence: 99%

Fault Diagnosis Method for Industrial Robots Based on DBN Joint Information Fusion Technology

Jiao¹,

Zheng²

2022

Computational Intelligence and Neuroscience

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“…Feature screening methods mainly include filtering method, packaging method and embedding method. Ultimately, the construction of the auxiliary system needs to update the image data continuously to maintain the leading position of the system [26]. Machine learning is to teach the machine to learn or solidify some knowledge and patterns to help improve the decisionmaking performance of users.…”

Section: Intelligent Diagnosis Technology Based On Deep Learning Algo...mentioning

confidence: 99%

Intelligent Imaging Technology in Diagnosis of Colorectal Cancer Using Deep Learning

Yang

Liang

et al. 2019

IEEE Access

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In order to explore the application of deep learning based intelligent imaging technology in the diagnosis of colorectal cancer, Tangdu Hospital patients are selected as the research object in this study. By scanning the cancer sites, then distinguishing and extracting the features of the tumors, the collected data are input into the designed in-depth learning intelligent assistant diagnosis system for comparison. The results show that in the analysis of image prediction accuracy, the best prediction accuracy of T1-weighted image method is matrix GLCM (gray level co-occurrence matrix) algorithm, the best prediction accuracy of adding T1-weighted image method is matrix MGLSZM (multi-gray area size matrix) algorithm, and the best prediction accuracy of T2-weighted image method is ALL combination of all texture features, and the best prediction accuracy of three imaging sequences is not more than 0.8. In the AUC analysis of the area under the curve of different texture features, it is found that T2-weighted imaging method has obvious advantages in differentiating colorectal cancer from other methods. Therefore, through this study, it is found that in the use of deep learning intelligent assistant diagnosis system for the diagnosis of colorectal cancer, it can provide useful information for the clinical diagnosis of colorectal cancer to a certain extent. Although there are some deficiencies in the research process, it still provides experimental basis for the diagnosis and treatment of colorectal cancer in later clinical stage.INDEX TERMS Deep learning, colorectal cancer, weighted images, accuracy, texture features.

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“…The simulation result showed that the proposed scheme had stronger performance in terms of uncertainty, reason, and decision accuracy in an intelligent environment. Brumancia et al [ 39 ] proposed an information fusion algorithm for decision making under different information conditions, which is based on D-S theory and adaptive neuro-fuzzy reasoning (DSANFI) system, it has widely used in robotics, statistics, control, and other fields.…”

Section: Introductionmentioning

confidence: 99%

Weighted assignment fusion algorithm of evidence conflict based on Euclidean distance and weighting strategy, and application in the wind turbine system

Gou

Zhang

et al. 2022

PLoS ONE

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In the process of intelligent system operation fault diagnosis and decision making, the multi-source, heterogeneous, complex, and fuzzy characteristics of information make the conflict, uncertainty, and validity problems appear in the process of information fusion, which has not been solved. In this study, we analyze the credibility and variation of conflict among evidence from the perspective of conflict credibility weight and propose an improved model of multi-source information fusion based on Dempster-Shafer theory (DST). From the perspectives of the weighting strategy and Euclidean distance strategy, we process the basic probability assignment (BPA) of evidence and assign the credible weight of conflict between evidence to achieve the extraction of credible conflicts and the adoption of credible conflicts in the process of evidence fusion. The improved algorithm weakens the problem of uncertainty and ambiguity caused by conflicts in the information fusion process, and reduces the impact of information complexity on analysis results. And it carries a practical application out with the fault diagnosis of wind turbine system to analyze the operation status of wind turbines in a wind farm to verify the effectiveness of the proposed algorithm. The result shows that under the conditions of improved distance metric evidence discrepancy and credible conflict quantification, the algorithm better shows the conflict and correlation among the evidence. It improves the accuracy of system operation reliability analysis, improves the utilization rate of wind energy resources, and has practical implication value.

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Hybrid data fusion model for restricted information using Dempster–Shafer and adaptive neuro-fuzzy inference (DSANFI) system

Cited by 11 publications

References 21 publications

Fault Diagnosis Method for Industrial Robots Based on DBN Joint Information Fusion Technology

Fault Diagnosis Method for Industrial Robots Based on DBN Joint Information Fusion Technology

Intelligent Imaging Technology in Diagnosis of Colorectal Cancer Using Deep Learning

Weighted assignment fusion algorithm of evidence conflict based on Euclidean distance and weighting strategy, and application in the wind turbine system

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