IDCR: Improved Dempster Combination Rule for multisensor fault diagnosis. (September 2021)

• Record Type: Journal Article
• Authors: Ghosh, Nimisha; Saha, Sayantan; Paul, Rourab
• Is Part Of: Engineering applications of artificial intelligence. Volume 104(2021)
• Publication Date: 2021-09
• Subjects: Conflict evidence -- Dempster–Shafer theory of evidence (DSTE) -- Dempster's Combination Rule (DCR) -- Fault diagnosis -- Weighted Deng entropy; Engineering -- Data processing -- Periodicals; Artificial intelligence -- Periodicals; Expert systems (Computer science) -- Periodicals; Ingénierie -- Informatique -- Périodiques; Intelligence artificielle -- Périodiques; Systèmes experts (Informatique) -- Périodiques; Artificial intelligence; Engineering -- Data processing; Expert systems (Computer science); Periodicals; 620.00285
• DOI: 10.1016/j.engappai.2021.104369
• Languages: English
• ISSNs: 0952-1976

Abstract

Abstract: Data gathered from multiple sensors can be effectively fused for accurate monitoring of many engineering applications. In the last few years, one of the most sought after applications for multisensor fusion has been fault diagnosis. Dempster–Shafer Theory of Evidence along with Dempster's Combination Rule is a very popular method for multisensor fusion which can be successfully applied to fault diagnosis. But if the information obtained from the different sensors shows high conflict, the classical Dempster's Combination Rule may produce counter-intuitive result. To overcome this shortcoming, this paper proposes an improved combination rule for multisensor data fusion. Numerical examples have been put forward to show the effectiveness of the proposed method. Comparative analysis has also been carried out with existing methods to show the superiority of the proposed method in multisensor fault diagnosis.