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Sensors 2017, 17(9), 2143; doi:10.3390/s17092143

An Improved Evidential-IOWA Sensor Data Fusion Approach in Fault Diagnosis

School of Electronics and Information, Northwestern Polytechnical University, Xi’an 710072, China
Authors to whom correspondence should be addressed.
Received: 30 July 2017 / Revised: 3 September 2017 / Accepted: 14 September 2017 / Published: 18 September 2017
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As an important tool of information fusion, Dempster–Shafer evidence theory is widely applied in handling the uncertain information in fault diagnosis. However, an incorrect result may be obtained if the combined evidence is highly conflicting, which may leads to failure in locating the fault. To deal with the problem, an improved evidential-Induced Ordered Weighted Averaging (IOWA) sensor data fusion approach is proposed in the frame of Dempster–Shafer evidence theory. In the new method, the IOWA operator is used to determine the weight of different sensor data source, while determining the parameter of the IOWA, both the distance of evidence and the belief entropy are taken into consideration. First, based on the global distance of evidence and the global belief entropy, the α value of IOWA is obtained. Simultaneously, a weight vector is given based on the maximum entropy method model. Then, according to IOWA operator, the evidence are modified before applying the Dempster’s combination rule. The proposed method has a better performance in conflict management and fault diagnosis due to the fact that the information volume of each evidence is taken into consideration. A numerical example and a case study in fault diagnosis are presented to show the rationality and efficiency of the proposed method. View Full-Text
Keywords: Dempster–Shafer evidence theory; belief entropy; distance of evidence; IOWA operator; fault diagnosis; sensor data fusion Dempster–Shafer evidence theory; belief entropy; distance of evidence; IOWA operator; fault diagnosis; sensor data fusion

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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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Tang, Y.; Zhou, D.; Zhuang, M.; Fang, X.; Xie, C. An Improved Evidential-IOWA Sensor Data Fusion Approach in Fault Diagnosis. Sensors 2017, 17, 2143.

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