Improved information fusion approach based on D-S evidence theory

Sun, Rui; Huang, Hong‐Zhong; Miao, Qiang

doi:10.1007/s12206-008-0801-2

Cited by 36 publications

(19 citation statements)

References 14 publications

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“…There are two kinds of improvement about the fusion rule of D-S evidence theory, one is the improvement of the fusion formula, and the other is to preprocess the evidence sources [7] . The first kind of improvement.The representatives of the first kind of improvement is Yager et al They think the reason resulting in counter-intuitive outcome is that Dempster abnegated all the conflict information.…”

Section: Representative Improvement Methods and Their Weight Processimentioning

confidence: 99%

A New Preprocessing Method on Evidence Weight of Dempster Fusion Rule

Li¹,

Xiong²,

Liu³

et al. 2017

Proceedings of the 2017 5th International Conference on Frontiers of Manufacturing Science and Measuring Technology (FMSMT 2017

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Abstract. In allusion to the three problems of conventional D-S (Dempster-Shafer) evidence theory, this paper proposes an improved Dempster fusion rule based on weight preprocessing. By comparing the fusion results with conventional method and other improved methods, we conclude that the method in this paper is more superior in dealing with uncertainty, and more reasonable in processing the evidence weight.

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Section: Representative Improvement Methods and Their Weight Processimentioning

confidence: 99%

A New Preprocessing Method on Evidence Weight of Dempster Fusion Rule

Li¹,

Xiong²,

Liu³

et al. 2017

Proceedings of the 2017 5th International Conference on Frontiers of Manufacturing Science and Measuring Technology (FMSMT 2017

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“…This theory is widely used in decision making [57][58][59][60][61] and information fusion [62][63][64][65]. In this part, a few concepts about D-S evidence theory are given.…”

Section: Dempster-shafer Evidence Theorymentioning

confidence: 99%

A New Engine Fault Diagnosis Method Based on Multi-Sensor Data Fusion

Jiang

Xie

2017

Applied Sciences

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Fault diagnosis is an important research direction in modern industry. In this paper, a new fault diagnosis method based on multi-sensor data fusion is proposed, in which the Dempster-Shafer (D-S) evidence theory is employed to model the uncertainty. Firstly, Gaussian types of fault models and test models are established by observations of sensors. After the models are determined, the intersection area between test model and fault models is transformed into a set of BPAs (basic probability assignments), and a weighted average combination method is used to combine the obtained BPAs. Finally, through some given decision making rules, diagnostic results can be obtained. The proposed method in this paper is tested by the Iris data set and actual measurement data of the motor rotor, which verifies the effectiveness of the proposed method.

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“…In the area of reliability engineering, reliability estimation and design are investigated by (Mourelatos and Zhou, 2004), Kozine and Filimonov (2000); Moller et al (1999;Huang (1995;; Huang et al (2006a;; Huang (2012); Li et al (2012); Pang et al (2012); Wang et al (2011;2012) and Xiao et al (2012). Modeling of reliability using a new data fusion rule is proposed by Delmotte and Borne (1998); Sun et al (2008) and Yang et al (2011a); Possibility-based design optimization is studied and developed by (Mourelatos and Zhou, 2004;Youn, 2005;Youn and Choi, 2004a;Youn et al, 2004;Choi et al, 2004;2012b;Zhang et al, 2010b). Fuzzy reliability theory in the context of possibility theory is proposed and developed by (Cai et al, 1991a;1991b;1993;Utkin and Gurov, 1996;Onisawa, 1988;Huang et al, 2004;.…”

Section: General Topics Of Applicationsmentioning

confidence: 99%

“…As a more general tool for uncertainty analysis, evidence theory has also been applied to many areas, including artificial intelligence (particularly in the development of expert systems) (Bae et al, 2004b;Nikolaidis and Haftka, 2001), object detection and approximate reasoning (Lowrance et al, 1986;Perrin et al, 2004;Xu and Smets, 1996;Borotschnig et al, 1999), design optimization (Mourelatos and Zhou, 2005), multidisciplinary design optimization (Agarwal et al, 2004), uncertainty quantification (Bae et al, 2004a;, risk and reliability evaluation (Yang et al, 2011b), remote sensing classification (Lee et al, 1987), pattern recognition and image analysis, decision making (Buckley, 1988;Limbourg, 2005), data fusion (Delmotte and Borne, 1998;Hall and Llinas, 1997;Sun et al, 2008;Yang et al, 2011a) and fault diagnosis (Fan and Zuo, 2006a;Wu et al, 1990). The popularity of evidence theory has risen, however, because evidence theory requires epistemological assumptions that are at odds with those underlying classical and Bayesian probability theories (Fioretti, 2004).…”

Section: General Topics Of Applicationsmentioning

confidence: 99%

“…One is theoretic development related to the fundamentals of reliability theory, e.g. imprecise reliability (Walley, 1991;Utkin and Coolen, 2007;Kozine and Filimonov, 2000) and fuzzy reliability (Cai et al, 1991a;1991b;1993;Huang et al, 2004;; the other is computational (or algorithmic) development in analysis and the design method, e.g., data fusion technology applied to reliability assessment (Hall and Llinas, 1997;Zhang et al, 2010a;Sun et al, 2008;Yang, 2011a; and optimum design methods (Youn and Choi, 2004b;Youn et al, 2004;Aughenbaugh and Paredis, 2005;Huang et al, 2005a;Limbourg, 2005;Mourelatos and Zhou, 2005;Huang et al, 2006a;2012a). These are illustrated in the sections that follow.…”

Section: General Topics Of Applicationsmentioning

confidence: 99%

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Possibility and Evidence-Based Reliability Analysis and Design Optimization

Huang

Liu

et al. 2013

American Journal of Engineering and Applied Sciences

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Engineering design under uncertainty has gained considerable attention in recent years. A great multitude of new design optimization methodologies and reliability analysis approaches are put forth with the aim of accommodating various uncertainties. Uncertainties in practical engineering applications are commonly classified into two categories, i.e., aleatory uncertainty and epistemic uncertainty. Aleatory uncertainty arises because of unpredictable variation in the performance and processes of systems, it is irreducible even adding more data or knowledge. On the other hand, epistemic uncertainty stems from lack of knowledge of the system due to limited data, measurement limitations, or simplified approximations in modeling system behavior and it can be reduced by obtaining more data or knowledge. More specifically, aleatory uncertainty is naturally represented by a statistical distribution and its associated parameters can be characterized by sufficient data. If, however, the data is limited and can be quantified in a statistical sense, epistemic uncertainty can be considered as an alternative tool in such a situation. Of the several optional treatments for epistemic uncertainty, possibility theory and evidence theory have proved to be the most computationally efficient and stable for reliability analysis and engineering design optimization. This study first attempts to provide a better understanding of uncertainty in engineering design by giving a comprehensive overview of its classifications, theories and design considerations. Then a review is conducted of general topics such as the foundations and applications of possibility theory and evidence theory. This overview includes the most recent results from theoretical research, computational developments and performance improvement of possibility theory and evidence theory with an emphasis on revealing the capability and characteristics of quantifying uncertainty from different perspectives. Possibility and evidence theory-based reliability methods have many advantages for practical engineering when compared with traditional probability-based reliability methods. They can work well under limited data while the latter need large amounts of information, more than possible in engineering practice due to aleatory and epistemic uncertainties. The possible directions for future work are summarized.

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Improved information fusion approach based on D-S evidence theory

Cited by 36 publications

References 14 publications

A New Preprocessing Method on Evidence Weight of Dempster Fusion Rule

A New Preprocessing Method on Evidence Weight of Dempster Fusion Rule

A New Engine Fault Diagnosis Method Based on Multi-Sensor Data Fusion

Possibility and Evidence-Based Reliability Analysis and Design Optimization

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