scholarly journals A Novel Evidence Conflict Measurement for Multi-Sensor Data Fusion Based on the Evidence Distance and Evidence Angle

Sensors ◽  
2020 ◽  
Vol 20 (2) ◽  
pp. 381 ◽  
Author(s):  
Zhan Deng ◽  
Jianyu Wang
Keyword(s):  
Measurement Method ◽  
Evidence Theory ◽  
Weighted Average ◽  
Uncertainty Modeling ◽  
New Method ◽  
Sensor Data ◽  
Mutual Support ◽  
Combination Rule ◽  
Practical Applications ◽  
Multi Sensor Data Fusion

As an important method for uncertainty modeling, Dempster–Shafer (DS) evidence theory has been widely used in practical applications. However, the results turned out to be almost counter-intuitive when fusing the different sources of highly conflicting evidence with Dempster’s combination rule. In previous researches, most of them were mainly dependent on the conflict measurement method between the evidence represented by the evidence distance. However, it is inaccurate to characterize the evidence conflict only through the evidence distance. To address this issue, we comprehensively consider the impacts of the evidence distance and evidence angle on conflicts in this paper, and propose a new method based on the mutual support degree between the evidence to characterize the evidence conflict. First, the Hellinger distance measurement method is proposed to measure the distance between the evidence, and the sine value of the Pignistic vector angle is used to characterize the angle between the evidence. The evidence distance indicates the dissimilarity between the evidence, and the evidence angle represents the inconsistency between the evidence. Next, two methods are combined to get a new method for measuring the mutual support degree between the evidence. Afterward, the weight of each evidence is determined by using the mutual support degree between the evidence. Then, the weights of each evidence are utilized to modify the original evidence to achieve the weighted average evidence. Finally, Dempster’s combination rule is used for fusion. Some numerical examples are given to illustrate the effectiveness and reasonability for the proposed method.

scholarly journals A novel divergence measure in Dempster–Shafer evidence theory based on pignistic probability transform and its application in multi-sensor data fusion

2021 ◽  
Vol 17 (7) ◽  
pp. 155014772110314
Author(s):  
Shijun Xu ◽  
Yi Hou ◽  
Xinpu Deng ◽  
Peibo Chen ◽  
Kewei Ouyang ◽  
...  
Keyword(s):  
Data Fusion ◽  
Evidence Theory ◽  
Weighted Average ◽  
Sensor Data ◽  
Single Element ◽  
Divergence Measure ◽  
Sensor Data Fusion ◽  
Multi Sensor Data Fusion ◽  
Probability Transformation ◽  
Jensen Shannon Divergence

Dempster–Shafer (D–S) evidence theory is more and more extensively applied in multi-sensor data fusion. However, it is still an open issue that how to effectively combine highly conflicting evidence in D–S evidence theory. In this article, a novel divergence measure, called pignistic probability transformation divergence, is proposed to measure the difference between evidences. The proposed pignistic probability transformation divergence can reflect the interaction between single-element and multi-element subsets by introducing the pignistic probability transformation, and satisfies the properties of boundedness, non-degeneracy, and symmetry. Moreover, the pignistic probability transformation divergence can degenerate as Jensen–Shannon divergence when mass function and the probability distribution are consistent. Based on the pignistic probability transformation divergence, a new multi-sensor data fusion method is presented. The proposed method takes advantage of pignistic probability transformation divergence to measure the discrepancy between evidences in order to obtain the credibility weights, and belief entropy to measure the uncertainty of the evidences in order to obtain the information volume weights, which can fully mine the potential information between evidences. Then, the credibility weights and the information volume weights are integrated to generate an appropriate weighted average evidence before using Dempster’s combination rule. The results of two application cases illustrate that the proposed method outperforms other related methods for combining highly conflicting evidences.

Application of Multi-Sensor Data Fusion Method in the Ammonia Modification System

2014 ◽  
Vol 615 ◽  
pp. 118-121 ◽  
Author(s):  
He Lei Zhu ◽  
Zhuo Meng ◽  
Yi Ze Sun ◽  
Wei Lu ◽  
Zi Na Zhu
Keyword(s):  
Data Fusion ◽  
Weighted Average ◽  
Process Variable ◽  
Sensor Data ◽  
Mutual Support ◽  
Modification System ◽  
Sensor Data Fusion ◽  
Data Measurement ◽  
Support Level ◽  
Multi Sensor Data Fusion

This paper provides a method of multi-sensor data detection and fusion, aims to resolve the data measurement inaccurate problem in the process variable control system of the ammonia modification equipment. First, it establishes the sensor data measurement model and calculates mutual support level of the multiple sensors, using boundary value judgment method to produce the multiple-sensor mutual support level matrix, the total number of mutual support level for one sensor in the matrix is the weight value of this sensor, and finally the normalized weighted average data fusion (NWADF) method is used for data process.Experimental results show that the proposed method for multi-sensor data fusion can effectively be used to identify the abnormal data in multi-sensor measurement system and improve the accuracy and stability of the process variable data measurement.

scholarly journals Health Monitoring and Diagnosis of Equipment Based on Multi-sensor Fusion

2018 ◽  
Vol 14 (04) ◽  
pp. 4
Author(s):  
Xuemei Yao ◽  
Shaobo Li ◽  
Yong Yao ◽  
Xiaoting Xie
Keyword(s):  
Evidence Theory ◽  
Sensor Data ◽  
Uncertain Information ◽  
Fusion Algorithm ◽  
Monitoring And Diagnosis ◽  
Multi Sensor Data Fusion ◽  
Basic Probability ◽  
Optimal Fusion ◽  
Single Sensor ◽  
Mechanical Devices

As the information measured by a single sensor cannot reflect the real situation of mechanical devices completely, a multi-sensor data fusion based on evidence theory is introduced. Evidence theory has the advantage of dealing with uncertain information. However, it produces unreasonable conclusions when the evidence conflicts. An improved fusion method is proposed to solve this problem. Basic probability assignment of evidence is corrected according to evidence and sensor weights, and an optimal fusion algorithm is selected by comparing an introduced threshold and a conflict factor. The effectiveness and practicability of the algorithm are tested by simulating the monitoring and diagnosis of rolling bearings. The result shows that the method has better robustness.

scholarly journals Multi-source data fusion method for structural safety assessment of water diversion structures

2021 ◽  
Author(s):  
Sherong Zhang ◽  
Ting Liu ◽  
Chao Wang
Keyword(s):  
Data Fusion ◽  
Safety Assessment ◽  
Evidence Theory ◽  
Weighted Average ◽  
Water Diversion ◽  
Sensor Data ◽  
Structural Safety ◽  
Fusion Method ◽  
Bhattacharyya Distance ◽  
Source Data

Abstract Building safety assessment based on single sensor data has the problems of low reliability and high uncertainty. Therefore, this paper proposes a novel multi-source sensor data fusion method based on Improved Dempster–Shafer (D-S) evidence theory and Back Propagation Neural Network (BPNN). Before data fusion, the improved self-support function is adopted to preprocess the original data. The process of data fusion is divided into three steps: Firstly, the feature of the same kind of sensor data is extracted by the adaptive weighted average method as the input source of BPNN. Then, BPNN is trained and its output is used as the basic probability assignment (BPA) of D-S evidence theory. Finally, Bhattacharyya Distance (BD) is introduced to improve D-S evidence theory from two aspects of evidence distance and conflict factors, and multi-source data fusion is realized by D-S synthesis rules. In practical application, a three-level information fusion framework of the data level, the feature level, and the decision level is proposed, and the safety status of buildings is evaluated by using multi-source sensor data. The results show that compared with the fusion result of the traditional D-S evidence theory, the algorithm improves the accuracy of the overall safety state assessment of the building and reduces the MSE from 0.18 to 0.01%.

Research of Dynamic Weighing System Based on Multi-Sensor Data Fusion Technology

2013 ◽  
Vol 721 ◽  
pp. 479-482 ◽  
Author(s):  
Yan Xia Wang ◽  
Chun Hui Bao ◽  
Chun Ling Fan
Keyword(s):  
Data Fusion ◽  
Electromagnetic Interference ◽  
New Method ◽  
Sensor Data ◽  
Fusion Technology ◽  
Sensor Data Fusion ◽  
Multi Sensor Data Fusion ◽  
Load Cells ◽  
Noise Vibration ◽  
Dynamic Weighing

The multi-sensor data fusion techniques is discussed in dynamic weighing system based on the data measured from ultrasonic sensors, speed sensors, capacitive sensors and load cells. This new method can greatly increase the measure precision of the dynamic weighing systems, at the same time it can effectively reduce noise, vibration, electromagnetic interference and other environmental factors on the influence of dynamic weighing system measurement. Judging from the simulation result, this new method proves to be more accurate and stable than ordinary processing methods.

Research on Equipment Fault Diagnosis Method Based on Multi-Sensor Data Fusion

2012 ◽  
Vol 466-467 ◽  
pp. 1222-1226
Author(s):  
Bin Ma ◽  
Lin Chong Hao ◽  
Wan Jiang Zhang ◽  
Jing Dai ◽  
Zhong Hua Han
Keyword(s):  
Fault Diagnosis ◽  
Data Fusion ◽  
Diagnostic Accuracy ◽  
Evidence Theory ◽  
Experimental Results ◽  
Sensor Data ◽  
Sensor Data Fusion ◽  
Multi Sensor Data Fusion ◽  
Diagnosis Method ◽  
Single Sensor

In this paper, we presented an equipment fault diagnosis method based on multi-sensor data fusion, in order to solve the problems such as uncertainty, imprecision and low reliability caused by using a single sensor to diagnose the equipment faults. We used a variety of sensors to collect the data for diagnosed objects and fused the data by using D-S evidence theory, according to the change of confidence and uncertainty, diagnosed whether the faults happened. Experimental results show that, the D-S evidence theory algorithm can reduce the uncertainty of the results of fault diagnosis, improved diagnostic accuracy and reliability, and compared with the fault diagnosis using a single sensor, this method has a better effect.

scholarly journals Combination of Evidential Sensor Reports with Distance Function and Belief Entropy in Fault Diagnosis

2019 ◽  
Vol 14 (3) ◽  
pp. 329-343 ◽  
Author(s):  
Yukun Dong ◽  
Jiantao Zhang ◽  
Zhen Li ◽  
Yong Hu ◽  
Yong Deng
Keyword(s):  
Fault Diagnosis ◽  
Data Fusion ◽  
Distance Function ◽  
Evidence Theory ◽  
Sensor Data ◽  
Combination Rule ◽  
Sensor Data Fusion ◽  
Real Application ◽  
Belief Entropy

Although evidence theory has been applied in sensor data fusion, it will have unreasonable results when handling highly conflicting sensor reports. To address the issue, an improved fusing method with evidence distance and belief entropy is proposed. Generally, the goal is to obtain the appropriate weights assigning to different reports. Specifically, the distribution difference between two sensor reports is measured by belief entropy. The diversity degree is presented by the combination of evidence distance and the distribution difference. Then, the weight of each sensor report is determined based on the proposed diversity degree. Finally, we can use Dempster combination rule to make the decision. A real application in fault diagnosis and an example show the efficiency of the proposed method. Compared with the existing methods, the method not only has a better performance of convergence, but also less uncertainty.

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