Information Fusion Algorithm for Electromechanical Equipment Based on DS Evidence Theory

2013 ◽  
Vol 380-384 ◽  
pp. 1125-1128 ◽  
Author(s):  
Yao Hui Zhang ◽  
Jun Xu ◽  
Kang Du
Keyword(s):  
Information Fusion ◽  
Evidence Theory ◽  
Prediction Method ◽  
Electrical Equipment ◽  
Fusion Algorithm ◽  
State Prediction ◽  
Level Information ◽  
The Difference ◽  
Equipment State ◽  
Electromechanical Equipment

According to the problem that the difference of test mode, mixed quantitative and qualitative information of electromechanical equipment state prediction, a state prediction method based on information fusion was proposed in this paper. It was used DS evidence theory to fuse decision level information of electromechanical equipments at this method. Simulation results showed that it is feasible and effective that information fusion technology is applied on the state prediction for mechanical and electrical equipment. Information for decision-making integrated repeatedly by different forecasting methods, can greatly reduce the blindness of judgment and improve the accuracy of state prediction.

Research on Evaluation of Vehicle Dangerous Traveling State Based on Information Fusion Method

2013 ◽  
Vol 791-793 ◽  
pp. 1018-1022
Author(s):  
Peng Wang ◽  
Zhi Qiang Liu
Keyword(s):  
Information Fusion ◽  
Evaluation System ◽  
Evidence Theory ◽  
Recognition System ◽  
Fusion Method ◽  
Source Information ◽  
State Recognition ◽  
Research On Evaluation ◽  
Lane Departure ◽  
Level Information

An evaluation system of vehicle traveling state was proposed,and an unsafe vehicle traveling state recognition system was established using multi-level information fusion method. In view of the effects of the complexity of the driving environment, a variety of working conditions and the diversity of vehicle traveling characteristics, combing BP neural network with Dempster-Shafer evidence theory technique, the multi-information decision-level fusion was proposed to estimate the different kind model of the vehicle status. To verify the proposed strategies,the vehicle traveling posture evaluation system was established. The lane departure parameters and the relative distance parameters were studied in order to get the characterization of the vehicle traveling status information. The simulation results indicate that the adaptability and accuracy and the intelligence level of driving characterization estimation are significantly improved by using the pattern classification and decision technology of multi-source information fusion.

scholarly journals Research on Information Fusion for Machine Potential Fault Operation and Maintenance

Symmetry ◽  
2020 ◽  
Vol 12 (3) ◽  
pp. 375
Author(s):  
Wei Xu ◽  
Yi Wan ◽  
Tian-Yu Zuo ◽  
Xin-Mei Sha
Keyword(s):  
Decision Making ◽  
Information Fusion ◽  
Decision Model ◽  
Subjective Experience ◽  
Evidence Theory ◽  
Sensor Technology ◽  
Operation And Maintenance ◽  
Symmetric Structure ◽  
Asymmetric Data ◽  
The Difference

In recent years, the development of sensor technology in industry has profoundly changed the way of operation and management in manufacturing enterprises. Due to the popularization and promotion of sensors, the maintenance of machines on the production line are also changing from the subjective experience-based machine maintenance to objective data-driven maintenance decision-making. Therefore, more and more data decision model has been developed through AI technology and intelligence algorithms. Equally important, the information fusion between decision results, which got by data decision model, has also received widespread attention. Information fusion is performed on symmetric data structures. The asymmetric data under the symmetric structure leads to the difference in information fusion results. Therefore, fully considering the potential differences of asymmetric data under a symmetric structure is an important content of information fusion. In view of the above, this paper studies how to make information fusion between different decision results through the framework of D-S evidence theory and discusses the deficiency of D-S evidence theory in detail. Based on D-S evidence theory, then a comprehensive evidence method for information fusion is proposed in this paper. We illustrate the rationality and effectiveness of our method through analysis of experiment case. And, this method is applied to a real case from industry. Finally, the irrationality of the traditional D-S method in the comprehensive decision-making results of machine operation and maintenance was solved by our novel method.

A Research on the Improved Information Fusion Algorithm

2014 ◽  
Vol 543-547 ◽  
pp. 1909-1912
Author(s):  
Guo Dong Zhang ◽  
Rui Min Qi
Keyword(s):  
Decision Making ◽  
Information Fusion ◽  
Evidence Theory ◽  
Source Information ◽  
Fusion Algorithm ◽  
Mutually Independent

In the field of information confusion, evidence theory takes advantage of its uncertainties. But in practical evidences are always mutually independent, However, multi-source information fusion is a comprehensive integration and then obtaining decision-making, sometimes the result of information fusion give us wrong conclusion. So an improved information fusion algorithm is proposed in this paper. It can heighten the information confusion reliability and accuracy in a practical example.

Aided Navigation System Research Based on Multi-Information Fusion

2012 ◽  
Vol 479-481 ◽  
pp. 207-212
Author(s):  
Xiao Hui Zhang ◽  
Liu Qing ◽  
Mu Li
Keyword(s):  
Information Fusion ◽  
Navigation System ◽  
Evidence Theory ◽  
Final Decision ◽  
Test Results ◽  
System Research ◽  
Fusion Algorithm ◽  
Distance Information ◽  
Radar Sensors ◽  
Vision Sensors

This paper designed a multi-information fusion algorithm after analysis information from vision sensors and radar sensors. This algorithm used D-S evidence theory to fuse the information of vision sensors and radar sensors to judge the front obstacles, and a final decision is made by the distance information provided by radar to decide whether give the driver corresponding warning. It also designed a critical vehicle distance, which can change according to relative distance and relative velocity. The test results show that this algorithm can give warning information correctly and greatly decrease the uncertainty, thus satisfying the requirement of car aided navigation system. At a resolution of 320×480, the identifying speed of this algorithm can reach 62.5ms/F which satisfied the requirement of real-time of car navigation.

scholarly journals Full-Vector Signal Acquisition and Information Fusion for the Fault Prediction

2016 ◽  
Vol 2016 ◽  
pp. 1-7 ◽  
Author(s):  
Lei Chen ◽  
Jie Han ◽  
Wenping Lei ◽  
Yongxiang Cui ◽  
Zhenhong Guan
Keyword(s):  
Data Acquisition ◽  
Information Fusion ◽  
Prediction Method ◽  
Fault Prediction ◽  
Vibration Measurement ◽  
Signal Acquisition ◽  
Vector Data ◽  
Fusion Model ◽  
Fusion Algorithm ◽  
Full Vector

Fault prediction is the key technology of the predictive maintenance. Currently, researches on fault prediction are mainly focused on the evaluation of the intensities of the failure and the remaining life of the machine. There is lack of methods on the prediction of fault locations and fault characters. To satisfy the requirement of the prediction of the fault characters, the data acquisition and fusion strategies were studied. Firstly, the traditional vibration measurement mechanism and its disadvantages were presented. Then, the full-vector data acquisition and fusion model were proposed. After that, the sampling procedure and information fusion algorithm were analyzed. At last, the fault prediction method based on full-vector spectrum was proposed. The methodology is that of Dr. Bently and Dr. Muszynska. On the basis of this methodology, the application study has been carried out. The uncertainty of the spectrum structure can be eliminated by the designed data acquisition and fusion method. The reliability of the diagnosis on fault character was improved. The study on full-vector data acquisition system laid the technical foundation for the prediction and diagnosis research of the fault characters.

scholarly journals Complex Pignistic Transformation-Based Evidential Distance for Multisource Information Fusion of Medical Diagnosis in the IoT

Sensors ◽  
2021 ◽  
Vol 21 (3) ◽  
pp. 840
Author(s):  
Fuyuan Xiao
Keyword(s):  
Decision Making ◽  
Information Fusion ◽  
Medical Diagnosis ◽  
Evidence Theory ◽  
Mass Function ◽  
Communication Problems ◽  
Multi Attribute Decision Making ◽  
Open Issue ◽  
The Difference ◽  
Complex Basis

Multisource information fusion has received much attention in the past few decades, especially for the smart Internet of Things (IoT). Because of the impacts of devices, the external environment, and communication problems, the collected information may be uncertain, imprecise, or even conflicting. How to handle such kinds of uncertainty is still an open issue. Complex evidence theory (CET) is effective at disposing of uncertainty problems in the multisource information fusion of the IoT. In CET, however, how to measure the distance among complex basis belief assignments (CBBAs) to manage conflict is still an open issue, which is a benefit for improving the performance in the fusion process of the IoT. In this paper, therefore, a complex Pignistic transformation function is first proposed to transform the complex mass function; then, a generalized betting commitment-based distance (BCD) is proposed to measure the difference among CBBAs in CET. The proposed BCD is a generalized model to offer more capacity for measuring the difference among CBBAs. Additionally, other properties of the BCD are analyzed, including the non-negativeness, nondegeneracy, symmetry, and triangle inequality. Besides, a basis algorithm and its weighted extension for multi-attribute decision-making are designed based on the newly defined BCD. Finally, these decision-making algorithms are applied to cope with the medical diagnosis problem under the smart IoT environment to reveal their effectiveness.

The Application of Fusion Algorithm Based on Matrix Analysis in Evidence Theory

2014 ◽  
Vol 687-691 ◽  
pp. 1412-1415
Author(s):  
You Zhi Zhang ◽  
Yu Dong Qi ◽  
Han Li Wang
Keyword(s):  
Time Delay ◽  
Information Fusion ◽  
Evidence Theory ◽  
Matrix Analysis ◽  
Fusion Algorithm ◽  
C Language ◽  
Evidence Combination ◽  
Delay Increase ◽  
Recursive Calculation ◽  
Sensor Information

This paper directly adopts evidence reasoning formula to calculate sensor information fusion result. The amount of calculation and calculation time delay increase with the increasing number of target found, uses two recursive calculation ways of evidence combination to calculate results, and proposes a fusion algorithm based on matrix analysis, using matlab software and C language programming to realize the method and calculate by an example. The results prove that the fusion result calculated by the method gets the same result as that of evidence reasoning synthesis formula, but the time needed for calculation will be reduced.

Sign in / Sign up

Export Citation Format

Share Document