scholarly journals Multisensor Data Fusion in Testability Evaluation of Equipment

2020 ◽  
Vol 2020 ◽  
pp. 1-16
Author(s):  
Peng Di ◽  
Xuan Wang ◽  
Tong Chen ◽  
Bin Hu
Keyword(s):  
Data Fusion ◽  
Test Data ◽  
Evaluation Method ◽  
Evidence Theory ◽  
Uncertain Information ◽  
Fusion Method ◽  
Practical Applications ◽  
Multisensor Data Fusion ◽  
Basic Probability ◽  
Multisensor Data

The multisensor data fusion method has been extensively utilized in many practical applications involving testability evaluation. Due to the flexibility and effectiveness of Dempster–Shafer evidence theory in modeling and processing uncertain information, this theory has been widely used in various fields of multisensor data fusion method. However, it may lead to wrong results when fusing conflicting multisensor data. In order to deal with this problem, a testability evaluation method of equipment based on multisensor data fusion method is proposed. First, a novel multisensor data fusion method, based on the improvement of Dempster–Shafer evidence theory via the Lance distance and the belief entropy, is proposed. Next, based on the analysis of testability multisensor data, such as testability virtual test data, testability test data of replaceable unit, and testability growth test data, the corresponding prior distribution conversion schemes of testability multisensor data are formulated according to their different characteristics. Finally, the testability evaluation method of equipment based on the multisensor data fusion method is proposed. The result of experiment illustrated that the proposed method is feasible and effective in handling the conflicting evidence; besides, the accuracy of fusion of the proposed method is higher and the result of evaluation is more reliable than other testability evaluation methods, which shows that the basic probability assignment of the true target is 94.71%.

scholarly journals An Improved Total Uncertainty Measure in the Evidence Theory and Its Application in Decision Making

Entropy ◽  
2020 ◽  
Vol 22 (4) ◽  
pp. 487 ◽  
Author(s):  
Miao Qin ◽  
Yongchuan Tang ◽  
Junhao Wen
Keyword(s):  
Decision Making ◽  
Evidence Theory ◽  
Uncertain Information ◽  
Uncertainty Measure ◽  
Total Uncertainty ◽  
Practical Applications ◽  
Basic Probability ◽  
Belief Entropy ◽  
Element Number ◽  
Theory Framework

Dempster–Shafer evidence theory (DS theory) has some superiorities in uncertain information processing for a large variety of applications. However, the problem of how to quantify the uncertainty of basic probability assignment (BPA) in DS theory framework remain unresolved. The goal of this paper is to define a new belief entropy for measuring uncertainty of BPA with desirable properties. The new entropy can be helpful for uncertainty management in practical applications such as decision making. The proposed uncertainty measure has two components. The first component is an improved version of Dubois–Prade entropy, which aims to capture the non-specificity portion of uncertainty with a consideration of the element number in frame of discernment (FOD). The second component is adopted from Nguyen entropy, which captures conflict in BPA. We prove that the proposed entropy satisfies some desired properties proposed in the literature. In addition, the proposed entropy can be reduced to Shannon entropy if the BPA is a probability distribution. Numerical examples are presented to show the efficiency and superiority of the proposed measure as well as an application in decision making.

Study on Multisensor Data Fusion of Ultrasonic Sensor

2013 ◽  
Vol 722 ◽  
pp. 44-48
Author(s):  
Bao Jun Liu
Keyword(s):  
Embedded Systems ◽  
Data Fusion ◽  
Fuzzy Set ◽  
Measured Data ◽  
Fusion Method ◽  
Multisensor Data Fusion ◽  
Mean And Variance ◽  
Outlier Data ◽  
Final Expression ◽  
Multisensor Data

Aiming at data fusion of Autonomous car multisensors experiment at many times on distances, a novel fusion method is proposed based on the approach degree and weights. The method calculate mean and variance based on the measured sensors data, Using the maximum and minimum approach degree of this fuzzy set , the approach degree of the measured data from various sensors is processed quantitatively ,eliminating outlier data by Grubbs method, assigned the weights of data measured in the fusion process reasonably , so that the final expression of the data fusion is obtained, thus the data fusion of multisensor is realized.Test results demonstrate that this method can bring higher fusion precision, and more suitable for microcontroller and embedded systems applications.

scholarly journals A Multisensor Data Fusion Method Based on Gaussian Process Model for Precision Measurement of Complex Surfaces

Sensors ◽  
2020 ◽  
Vol 20 (1) ◽  
pp. 278
Author(s):  
Ji Ding ◽  
Qiang Liu ◽  
Mingxuan Bai ◽  
Pengpeng Sun
Keyword(s):  
Data Fusion ◽  
Gaussian Process ◽  
Process Model ◽  
Surface Registration ◽  
Measurement Technology ◽  
Fusion Method ◽  
Registration Method ◽  
Gaussian Process Model ◽  
Multisensor Data Fusion ◽  
Multisensor Data

As multisensor measurement technology is rapidly applied in industrial production, one key issue is the data fusion procedure by combining several datasets from multiple sensors to obtain the overall geometric measurement. In this paper, a multisensor data fusion method based on a Gaussian process model is proposed for complex surface measurements. A robust surface registration method based on the adaptive distance function is firstly used to unify the coordinate systems of different measurement datasets. By introducing an adjustment model, the residuals between several independent datasets from different sensors are then approximated to construct a Gaussian process model-based data fusion system. The proposed method is verified through both simulation verification and actual experiments, indicating that the proposed method can fuse multisensor measurement datasets with better fusion accuracy and faster computational efficiency compared to the existing method.

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