An Improved Approach of Incomplete Information Fusion and Its Application in Sensor Data-Based Fault Diagnosis

Yutong Chen and Yongchuan Tang
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Yutong Chen: School of Computer and Information Science, Southwest University, Chongqing 400715, China
Yongchuan Tang: School of Big Data and Software Engineering, Chongqing University, Chongqing 401331, China

Mathematics, 2021, vol. 9, issue 11, 1-16

Abstract: The Dempster–Shafer evidence theory has been widely used in the field of data fusion. However, with further research, incomplete information under the open world assumption has been discovered as a new type of uncertain information. The classical Dempster’s combination rules are difficult to solve the related problems of incomplete information under the open world assumption. At the same time, partial information entropy, such as the Deng entropy is also not applicable to deal with problems under the open world assumption. Therefore, this paper proposes a new method framework to process uncertain information and fuse incomplete data. This method is based on an extension to the Deng entropy in the open world assumption, negation of basic probability assignment (BPA), and the generalized combination rule. The proposed method can solve the problem of incomplete information under the open world assumption, and obtain more uncertain information through the negative processing of BPA, which improves the accuracy of the results. The results of applying this method to fault diagnosis of electronic rotor examples show that, compared with the other uncertain information processing and fusion methods, the proposed method has wider adaptability and higher accuracy, and is more conducive to practical engineering applications.

Keywords: Dempster–Shafer evidence theory; sensor data fusion; fault diagnosis; generalized combination rule; incomplete information fusion (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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