Modeling Sensor Reliability in Fault Diagnosis Based on Evidence Theory

Yuan, Kaijuan; Xiao, Fuyuan; Fei, Liguo; Kang, Bingyi; Deng, Yong

doi:10.3390/s16010113

Cited by 91 publications

(78 citation statements)

References 47 publications

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“…Deng entropy can deal with the uncertainty represented not only by BPA but also by probability distribution. In other words, Deng entropy is the generalization of Shannon entropy [34,48].…”

Section: Evidence Distancementioning

confidence: 99%

Weighted Evidence Combination Based on Distance of Evidence and Entropy Function

Wang

Xiao

Deng

et al. 2016

International Journal of Distributed Sensor Networks

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Conflict management in Dempster-Shafer theory (D-S theory) is a hot topic in information fusion. In this paper, a new weighted evidence combination on the basis of the distance between evidence and entropy function is presented. The proposed approach is identified as two procedures. First, the weight is determined based on the distance of evidence. Then, the obtained weight value in the first step is modified by making advantage of Deng entropy function. Our proposed method can efficiently cope with high conflicting evidences with better performance of convergence. A numerical example is provided to demonstrate that the proposed method is reasonable and efficient in the end.

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“…Deng entropy can deal with the uncertainty represented not only by BPA but also by probability distribution. In other words, Deng entropy is the generalization of Shannon entropy [34,48].…”

Section: Evidence Distancementioning

confidence: 99%

Weighted Evidence Combination Based on Distance of Evidence and Entropy Function

Wang

Xiao

Deng

et al. 2016

International Journal of Distributed Sensor Networks

Self Cite

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“…To characterize this learning process, we adopt a belief decision model which is developed based on the framework of Dempster-Shafer theory of evidence (DST). DST is widely studied in the field of Artificial Intelligence (AI) and Expert Systems, such as recognition38, classification39, fault diagnosis40, and also has a great potential for multi-criteria decision making4142 which often necessitates the decision maker to make judgments on decision alternatives over a range of criteria41. The belief decision model in this paper is proposed to describe people’s subjective preferences for different options after they receive various information from multiple sources.…”

Section: Methodsmentioning

confidence: 99%

A social contagious model of the obesity epidemic

Huang

Yan

Chen

et al. 2016

Sci Rep

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Obesity has been recognized as a global epidemic by WHO, followed by many empirical evidences to prove its infectiousness. However, the inter-person spreading dynamics of obesity are seldom studied. A distinguishing feature of the obesity epidemic is that it is driven by a social contagion process which cannot be perfectly described by the infectious disease models. In this paper, we propose a novel belief decision model based on the famous Dempster-Shafer theory of evidence to model obesity epidemic as the competing spread of two obesity-related behaviors: physical inactivity and physical activity. The transition of health states is described by an SIS model. Results reveal the existence of obesity epidemic threshold, above which obesity is quickly eradicated. When increasing the fading level of information spread, enlarging the clustering of initial obese seeds, or introducing small-world characteristics into the network topology, the threshold is easily met. Social discrimination against the obese people plays completely different roles in two cases: on one hand, when obesity cannot be eradicated, social discrimination can reduce the number of obese people; on the other hand, when obesity is eradicable, social discrimination may instead cause it breaking out.

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“…While performing the fault diagnosis method in [10], this paper changes Deng entropy into the new belief entropy.…”

Section: A Case Studymentioning

confidence: 99%

A modified belief entropy in Dempster-Shafer framework

2017

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How to quantify the uncertain information in the framework of Dempster-Shafer evidence theory is still an open issue. Quite a few uncertainty measures have been proposed in Dempster-Shafer framework, however, the existing studies mainly focus on the mass function itself, the available information represented by the scale of the frame of discernment (FOD) in the body of evidence is ignored. Without taking full advantage of the information in the body of evidence, the existing methods are somehow not that efficient. In this paper, a modified belief entropy is proposed by considering the scale of FOD and the relative scale of a focal element with respect to FOD. Inspired by Deng entropy, the new belief entropy is consistent with Shannon entropy in the sense of probability consistency. What’s more, with less information loss, the new measure can overcome the shortage of some other uncertainty measures. A few numerical examples and a case study are presented to show the efficiency and superiority of the proposed method.

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Modeling Sensor Reliability in Fault Diagnosis Based on Evidence Theory

Cited by 91 publications

References 47 publications

Weighted Evidence Combination Based on Distance of Evidence and Entropy Function

Weighted Evidence Combination Based on Distance of Evidence and Entropy Function

A social contagious model of the obesity epidemic

A modified belief entropy in Dempster-Shafer framework

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