Data Fusion for Fault Diagnosis Using Dempster-Shafer Theory Based Multi-class SVMs

2005 ◽  
pp. 175-184
Author(s):  
Zhonghui Hu ◽  
Yunze Cai ◽  
Ye Li ◽  
Yuangui Li ◽  
Xiaoming Xu
Keyword(s):  
Fault Diagnosis ◽  
Data Fusion ◽  
Dempster Shafer Theory ◽  
Shafer Theory

Decision Support System for Advanced Traffic Management Through Data Fusion

2002 ◽  
Vol 1804 (1) ◽  
pp. 173-178 ◽  
Author(s):  
Lawrence A. Klein ◽  
Ping Yi ◽  
Hualiang Teng
Keyword(s):  
Data Fusion ◽  
Traffic Management ◽  
Information Sources ◽  
Partial Information ◽  
Weather Conditions ◽  
Field Testing ◽  
Traffic Operations ◽  
Dempster Shafer Theory ◽  
Classification Technique ◽  
Shafer Theory

The Dempster–Shafer theory for data fusion and mining in support of advanced traffic management is introduced and tested. Dempste–Shafer inference is a statistically based classification technique that can be applied to detect traffic events that affect normal traffic operations. It is useful when data or information sources contribute partial information about a scenario, and no single source provides a high probability of identifying the event responsible for the received information. The technique captures and combines whatever information is available from the data sources. Dempster’s rule is applied to determine the most probable event—as that with the largest probability based on the information obtained from all contributing sources. The Dempster–Shafer theory is explained and its implementation described through numerical examples. Field testing of the data fusion technique demonstrated its effectiveness when the probability masses, which quantify the likelihood of the postulated events for the scenario, reflect current traffic and weather conditions.

An intelligent fault diagnosis approach based on Dempster-Shafer theory for hydraulic valves

Measurement ◽  
2020 ◽  
Vol 165 ◽  
pp. 108129 ◽  
Author(s):  
Xiancheng Ji ◽  
Yan Ren ◽  
Hesheng Tang ◽  
Chong Shi ◽  
Jiawei Xiang
Keyword(s):  
Fault Diagnosis ◽  
Intelligent Fault Diagnosis ◽  
Dempster Shafer Theory ◽  
Shafer Theory ◽  
Hydraulic Valves ◽  
Diagnosis Approach

Research and Application of the Improved DST New Method Based on Fuzzy Consistent Matrix and the Weighted Average

2014 ◽  
Vol 1030-1032 ◽  
pp. 1764-1768 ◽  
Author(s):  
Wei Xiao Xu ◽  
Ji Wen Tan ◽  
Hong Zhan
Keyword(s):  
Fault Diagnosis ◽  
Evidence Synthesis ◽  
Weighted Average ◽  
Rolling Bearing ◽  
New Method ◽  
Cnc Machine ◽  
Bearing Fault Diagnosis ◽  
Dempster Shafer Theory ◽  
High Conflict ◽  
Shafer Theory

Aiming at the existing defects of evidence dempster-shafer theory (DST) in dealing with high conflict evidence, we proposed a new method to improve DST. By introducing concept of fuzzy consistent matrix, calculate the weights of factors, and put different sources of evidence into distinguish, and finally cast more than one vote to prevent the phenomenon, the average convergence of evidence. What’s more, the improved DST new method is applied to the rolling bearing fault diagnosis of CNC machine workbench .The test results show that the improved new synthetic formula increases the accuracy of fault diagnosis Ball, the conflict of evidence synthesis results better, to achieve better results.

scholarly journals Fault Matters: Sensor data fusion for detection of faults using Dempster–Shafer theory of evidence in IoT-based applications

2020 ◽  
Vol 162 ◽  
pp. 113887
Author(s):  
Nimisha Ghosh ◽  
Rourab Paul ◽  
Satyabrata Maity ◽  
Krishanu Maity ◽  
Sayantan Saha
Keyword(s):  
Data Fusion ◽  
Sensor Data ◽  
Sensor Data Fusion ◽  
Dempster Shafer Theory ◽  
Shafer Theory ◽  
Theory Of Evidence
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