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

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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)

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.

Suggested Citation

  • Yutong Chen & Yongchuan Tang, 2021. "An Improved Approach of Incomplete Information Fusion and Its Application in Sensor Data-Based Fault Diagnosis," Mathematics, MDPI, vol. 9(11), pages 1-16, June.
  • Handle: RePEc:gam:jmathe:v:9:y:2021:i:11:p:1292-:d:568867
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    References listed on IDEAS

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    1. Seyed Morteza Hatefi & Mohammad Ehsan Basiri & Jolanta Tamošaitienė, 2019. "An Evidential Model for Environmental Risk Assessment in Projects Using Dempster–Shafer Theory of Evidence," Sustainability, MDPI, vol. 11(22), pages 1-16, November.
    2. Xiao-Li Meng & Liu-Tang Gong & Jen-Chih Yao, 2020. "A fuzzy evaluation approach with the quasi-ordered set: evaluating the efficiency of decision making units," Fuzzy Optimization and Decision Making, Springer, vol. 19(3), pages 297-310, September.
    3. Fu, Chao & Yang, Shanlin, 2012. "An evidential reasoning based consensus model for multiple attribute group decision analysis problems with interval-valued group consensus requirements," European Journal of Operational Research, Elsevier, vol. 223(1), pages 167-176.
    4. Xiaoyan Su & Sankaran Mahadevan & Peida Xu & Yong Deng, 2015. "Dependence Assessment in Human Reliability Analysis Using Evidence Theory and AHP," Risk Analysis, John Wiley & Sons, vol. 35(7), pages 1296-1316, July.
    5. Wen Jiang & Jun Zhan & Deyun Zhou & Xin Li, 2016. "A Method to Determine Generalized Basic Probability Assignment in the Open World," Mathematical Problems in Engineering, Hindawi, vol. 2016, pages 1-11, May.
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    Cited by:

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