A new correlation coefficient of mass function in evidence theory and its application in fault diagnosis

Qiang, Chenhui; Deng, Yong

doi:10.1007/s10489-021-02797-2

Cited by 33 publications

(11 citation statements)

References 42 publications

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“…In addition, another feature of decision-level fusion is that the fusion information has a relatively small amount of calculation. So it can be flexibly used in multi-sensor classification problems with its strong capacity for heterogeneous sensors, such as fault diagnosis [ 3 ], target recognition [ 4 , 5 ], environment grade evaluation [ 6 ], etc.…”

Section: Introductionmentioning

confidence: 99%

“…Yang, K [ 9 ] combined the Jousselme distance between pieces of evidence with the overall evidence decision to comprehensively measure the evidence conflict. Qiang, C [ 3 ] proposed a new correlation coefficient that has a better performance in measuring the relationship between quality functions. Si, L [ 10 ] introduced the correlation coefficient of evidence to measure the conflict between pieces of evidence in order to enhance the fusing effect and improve the applicability, as well as adopting the weighted average combination model to modify and preprocess the original evidence.…”

Section: Introductionmentioning

confidence: 99%

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A New Evidence Weight Combination and Probability Allocation Method in Multi-Sensor Data Fusion

Yao

Dai

et al. 2023

Sensors

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A single sensor is prone to decline recognition accuracy in the face of a complex environment, while the existing multi-sensor evidence theory fusion methods do not comprehensively consider the impact of evidence conflict and fuzziness. In this paper, a new evidence weight combination and probability allocation method is proposed, which calculated the degree of evidence fuzziness through the maximum entropy principle, and also considered the impact of evidence conflict on fusing results. The two impact factors were combined to calculate the trusted discount and reallocate the probability function. Finally, Dempster’s combination rule was used to fuse every piece of evidence. On this basis, experiments were first conducted to prove that the existing weight combination methods produce results contrary to common sense when handling high-conflicting and high-clarity evidence, and then comparative experiments were conducted to prove the effectiveness of the proposed evidence weight combination and probability allocation method. Moreover, it was verified, on the PAMAP2 data set, that the proposed method can obtain higher fusing accuracy and more reliable fusing results in all kinds of behavior recognition. Compared with the traditional methods and the existing improved methods, the weight allocation method proposed in this paper dynamically adjusts the weight of fuzziness and conflict in the fusing process and improves the fusing accuracy by about 3.3% and 1.7% respectively which solved the limitations of the existing weight combination methods.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A New Evidence Weight Combination and Probability Allocation Method in Multi-Sensor Data Fusion

Yao

Dai

et al. 2023

Sensors

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“…Multi-sensor information fusion technology [6][7][8] uses the advantages of each sensor to complement each other and fuses the multi-source information of each sensor, so as to obtain a more accurate and reliable description of the real environment. Therefore, it can be applied in many fields including pattern recognition [9,10], fault diagnosis [11][12][13][14][15], risk assessment [16,17]. In recent years, D-S evidence theory has received much attention as a promising theory for the handing of uncertain information in multi-sensor fusion system [18][19][20][21][22][23][24][25][26][27].…”

Section: Introductionmentioning

confidence: 99%

Conflicting data fusion method based on differences between fuzzy information of the single subset of focal elements

Wang

2023

Preprint

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The diversity of information, the complexity of environment and the limitations of sensors generally make information sources with strong uncertainty and high conflict. However, in Dempster-Shafer (D-S) evidence theory, the conflict coefficient k cannot effectively measure the degree of conflict between two bodies of evidence (BoEs). This paper quantifies the conflict in terms of the difference in ambiguity information between single subset focal elements. A novel fuzzy Chi-Square distance is proposed, in which the differences of elements in both the belief and plausibility are comprehensively considered to avoid the problem of missing information. The metric properties (boundedness, symmetry, nondegeneracy, and triangle inequality) of the fuzzy Chi-Square distance are proved in detail. And this paper investigates the resistance to disturbance of the new conflict metric Next, a new conflicting data fusion method is derived, which can obtain more accurate data fusion results and have strong robustness. Finally, the analysis of numerical examples and practical applications verifies the effectiveness and superiority of the method proposed in this paper.

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“…Multisensor data fusion is the technique which combines information offered via multiple sensors into consistent consequences [1]. In the past decades, multisensor information fusion has drew much attention and has been widely applied in many fields, like pattern recognition [2], decision making [3], fault diagnosis [4], supplier management [5], reliability evaluation [6], etc. [7][8][9].…”

Section: Introductionmentioning

confidence: 99%

A Weighted Evidence Combination Method for Multisensor Data Fusion

Liu¹,

Liu²

2022

Journal of Internet Technology

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<p>Multisensor information fusion exerts a key part in lots of practical usages. Dempster-shafer evidence theory has drawn extensive attention in many scopes of information fusion due to its flexibility and effectiveness in dealing with uncertain data without aforehand data. But when combining highly contradictory evidence with Dempster’s combinatorial principles, it can result in counterintuitive results. To solve the issue, the study proposes a multi-sensor data weighted evidence combination fusion method based on inter-evidence difference measure. Firstly, different measures including evidence distance and conflict are with the definition of characterizing distinctions between the two pieces of evidence. Then, according to the difference between each evidence and the average evidence, the weight coefficients of each evidence are calculated. In the end, initial evidence is discounted according to weighting factor, as well as Dempster’s combination principle is adopted to discount the evidence for fusion. Many instances show that this way can efficiently treat highly conflicting evidence and has good convergence performance.</p> <p> </p>

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A new correlation coefficient of mass function in evidence theory and its application in fault diagnosis

Cited by 33 publications

References 42 publications

A New Evidence Weight Combination and Probability Allocation Method in Multi-Sensor Data Fusion

A New Evidence Weight Combination and Probability Allocation Method in Multi-Sensor Data Fusion

Conflicting data fusion method based on differences between fuzzy information of the single subset of focal elements

A Weighted Evidence Combination Method for Multisensor Data Fusion

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