scholarly journals Fuzzy Risk Evaluation in Failure Mode and Effects Analysis Using a D Numbers Based Multi-Sensor Information Fusion Method

Sensors ◽  
2017 ◽  
Vol 17 (9) ◽  
pp. 2086 ◽  
Author(s):  
Xinyang Deng ◽  
Wen Jiang
Keyword(s):  
Failure Mode ◽  
Information Fusion ◽  
Risk Evaluation ◽  
Fusion Method ◽  
Sensor Information ◽  
D Numbers

scholarly journals Research on the Relationship between Information Fusion Method and Information Failure Mode in Integrated Navigation System

2018 ◽  
Vol 2 (2) ◽  
pp. 53
Author(s):  
Binzi Han ◽  
Baiqing Hu
Keyword(s):  
Failure Mode ◽  
Information Fusion ◽  
Navigation Systems ◽  
Fusion Method ◽  
Integrated Navigation ◽  
Basic Principles ◽  
Weighted Fusion ◽  
Fusion Methods ◽  
Fault Mode ◽  
The Relationship

Abstract：On the basis of the basic principles of weighted fusion, Kalman filtering and BP neural networks, the basic principles of information fusion methods used in integrated navigation systems are expounded. Through the analysis of the basic principles, the association of information fusion methods commonly used in integrated navigation systems and information failure modes is obtained: the information fault mode of weighted fusion method The model is closely related to the specific weight allocation method, which depends on the fault mode of the sensor or sub-system in which the weight is dominant; the information fault mode of the Kalman filtering information fusion method is a continuous mutation fault corresponding to the nonlinear time interval of the system; the information fault mode of the BP neural network method is gradual with time. The information failure mode of the BP neural network method is a slowly varying fault that gradually accumulates over time. Starting from the complexity associated with the information fusion method and the information failure mode, it is pointed out that in order to systematically express the relationship between the information fusion method and the information failure mode, further research can be carried out.

A Smart Recognition Method for Otological Drill Milling through a Bone Tissue Wall

2014 ◽  
Vol 571-572 ◽  
pp. 331-338
Author(s):  
Xi Sheng Li ◽  
Yong Ming Xie ◽  
Zhi Qiang Gao ◽  
Guo Dong Feng
Keyword(s):  
Bone Tissue ◽  
Information Fusion ◽  
Recognition Rate ◽  
Fusion Method ◽  
Recognition Method ◽  
Ear Surgery ◽  
Minimally Invasive Operation ◽  
Average Recognition Rate ◽  
Sensor Information ◽  
Medical Equipments

Surgeons are striving to achieve higher quality results in minimally invasive operation. Intelligent medical equipments are able to improve operation safety. Otological drill milling through a bone tissue wall is a common milling fault in ear surgery. In this paper a multi-sensor information fusion method for identifying milling faults is presented. Five surgeons experimented on calvarian bones using the intelligent otological drill. The average recognition rate of milling faults was 91%, and only 0.8% of normal millings were identified as milling faults.

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