Research on the multi-sensor information fusion method based on factor graph

2016 ◽  
Author(s):  
Weina Chen ◽  
Qinghua Zeng ◽  
Jianye Liu ◽  
Leijiang Chen ◽  
Huizhe Wang
Keyword(s):  
Information Fusion ◽  
Factor Graph ◽  
Fusion Method ◽  
Sensor Information

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.

Application of Multi-Sensor Information Fusion Technology in the Fault Diagnosis of Transformer

2013 ◽  
Vol 385-386 ◽  
pp. 601-604
Author(s):  
Han Min Ye ◽  
Zun Ding Xiao
Keyword(s):  
Neural Networks ◽  
Fault Diagnosis ◽  
Information Fusion ◽  
Evidence Theory ◽  
State Parameter ◽  
Accurate Diagnosis ◽  
Fusion Method ◽  
Fusion Technology ◽  
Sensor Information ◽  
Transformer Fault

The information fusion method is introduced into the transformer fault diagnosis. Through the sensor acquire transformer in operation of each state parameter, using two parallel BP neural networks to local diagnosis, with D-S evidence theory to global fuse the local diagnostic results. It realized the accurate diagnosis when transformer comes out one or a variety of faults at the same time. The experiments demonstrate that the credibility of diagnosis results are improved significantly, uncertainties are obviously reduced, which fully shows that the method is effective.

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