基于信息融合实现的激光陀螺调腔检测

马立; 徐次雄; 欧阳航空; 荣伟彬; 孙立宁

doi:10.3788/OPE.20122005.1134

您当前的位置：

首页 >

文章列表页 >

基于信息融合实现的激光陀螺调腔检测

信息科学 | 更新时间：2020-08-12

- 基于信息融合实现的激光陀螺调腔检测
- Detection of laser gyro cavity adjustment using information fusion
- 光学精密工程 2012年20卷第5期页码：1134-1140
- 作者机构：
  
  1. 上海大学机电工程与自动化学院上海,200072
  2. 哈尔滨工业大学机器人技术与系统国家重点实验室, 黑龙江哈尔滨 150080
  3. 苏州大学机器人与微系统研究中心,江苏苏州,215021
- 作者简介：
- 基金信息：
  
  国家自然科学基金资助项目(No.50905105)();机器人技术与系统国家重点实验室开放课题资助项目(No. SKLRS-2010-MS-02)();上海大学创新基金
- DOI：10.3788/OPE.20122005.1134
  中图分类号： V241.5文献标识码A;TP391
- 收稿日期：2011-12-06，
  
  修回日期：2012-03-01，
  
  网络出版日期：2012-05-10，
  
  纸质出版日期：2012-05-10
- 稿件说明：
移动端阅览
马立, 徐次雄, 欧阳航空, 荣伟彬, 孙立宁. 基于信息融合实现的激光陀螺调腔检测[J]. 光学精密工程, 2012,20(5): 1134-1140

MA Li, XU Ci-xiong, OUYANG Hang-kong, RONG Wei-bin, SUN Li-ning. Detection of laser gyro cavity adjustment using information fusion[J]. Editorial Office of Optics and Precision Engineering, 2012,20(5): 1134-1140
马立, 徐次雄, 欧阳航空, 荣伟彬, 孙立宁. 基于信息融合实现的激光陀螺调腔检测[J]. 光学精密工程, 2012,20(5): 1134-1140 DOI： 10.3788/OPE.20122005.1134.

MA Li, XU Ci-xiong, OUYANG Hang-kong, RONG Wei-bin, SUN Li-ning. Detection of laser gyro cavity adjustment using information fusion[J]. Editorial Office of Optics and Precision Engineering, 2012,20(5): 1134-1140 DOI： 10.3788/OPE.20122005.1134.

摘要

考虑激光陀螺调腔人工检测耗时较长、易受干扰

本文建立了激光陀螺自动调腔系统。在分析激光陀螺调腔工艺的基础上

构建了一种由CCD相机和光电倍增管组成的多传感器信息融合体系结构

提出了基于D-S证据理论的激光陀螺调腔检测方法。通过分析计算CCD相机和光电倍增管检测到的信号得出光斑、光阑中心点坐标差值及陀螺损耗值

并由这些信息获得调腔质量的评价函数。然后

根据D-S证据理论对评价函数进行融合处理

分别获得陀螺调腔质量合格与不合格的信度函数

应用最大支持度规则对调腔质量进行综合判断。实验结果显示

基于D-S证据理论的激光陀螺调腔方法检测准确率为91.14%

有效提高了调腔质量

验证了该方法的可行性。

Abstract

An automatic cavity adjustment system was established to improve the quality of laser gyro cavity adjustment and to overcome the drawbacks of manual detection such as low efficiency and low anti-interference capacity. According to the process of the cavity adjustment

a multi-sensor information fusion architecture was established by a CCD camera and a photomultiplier. A detection method of laser gyro cavity adjustment was proposed based on the D-S evidence theory. By analysis and calculation of the signals detected by the CCD camera and the photomultiplier

the center coordinate difference between facula and diaphragm and the loss value of the laser gyro were obtained and the evaluation function for each cavity adjustment result was deduced by these data. Furthermore

the qualified and unqualified belief functions of the cavity adjustment were obtained

respectively

and the quality of the cavity adjustment was verified based on the maximum support rule. The experimental result indicates that the accuracy of the detection method based on D-S evidence theory is 91.14%

which improves the quality of cavity adjustment effectively and validates the feasibility of the proposed method.

关键词

Keywords

references

OTMAN B, YUAN X H. Engine fault diagnosis based on multi-sensor information fusion using Dempster-Shafer evidence theory[J]. Information Fusion, 2007,8(4):379-386.[2] GUO H Y, LI Z L. A two-stage method to identify structural damage sites and extents by using evidence theory and micro-search genetic algorithm [J]. Mechanical Systems and Signal Processing, 2009, 23(3):769-782.[3] MADHI T, REZA G, REZA E. Knitted fabric defect classification for uncertain labels based on Dempster-Shafer theory of evidence[J]. Expert Systems with Applications, 2011, 38(5):5259-5267.[4] 龚卫国,王林泓,贺莉芳. 基于特征子模式典型相关分析的热释电红外信号识别 [J]. 光学精密工程, 2011, 19(4):884-891. GONG W G, WANG L H, HE L F. Pyroelectric infrared signal recognition based on feature sub-pattern canonical correlation analysis [J]. Opt. Precision Eng., 2011, 19(4):884-891. (in Chinese)[5] ERVAS E, MPIMPOUDIS A, ANAGNOSTOPOULOS C, et al.. Multisensor data fusion for fire detection [J]. Information Fusion, 2011, 12(3):150-159.[6] GUO K H, LI W L. Combination rule of D-S evidence theory based on the strategy of cross merging between evidences [J]. Expert Systems with Applications, 2011, 38 (10):13360-13366.[7] LIN T CH. Decision-based fuzzy image restoration for noise reduction based on evidence theory [J]. Expert Systems with Applications, 2011, 38(7):8303-8310.[8] AYTUNC P, MEHMET G. Information fusion with dempster-shafer evidence theory for software defect prediction [J]. Procedia Computer Science, 2011, 3:600-605.[9] MOHAMED A B, YVES D S, AHMED F, et al.. A ranking model in uncertain, imprecise and multi-experts contexts: The application of evidence theory [J]. International Journal of Approximate Reasoning, 2011, 52(8):1171-1194.[10] AI L M, WANG J, WANG X L. Multi-features fusion diagnosis of tremor based on artificial neural network and D-S evidence theory [J]. Signal Processing, 2008, 88(12):2927-2935.[11] 马巍. 基于证据理论信息融合的电站锅炉故障诊断 . 北京:华北电力大学 , 2010. MA W.Fault diagnosis of the power plant boiler based on the evidence theory of information fusion . Beijing: North China Electric Power University, 2010. (in Chinese)[12] LAURENCE B, SOPHIE M. Pedestrian crossing detection based on evidential fusion of video-sensors [J]. Transportation Research Part C: Emerging Technologies, 2009, 17(5):484-497.[13] AHMAD O, VALéRIE K, UIF H. Improvement of X-ray castings inspection reliability by using Dempster-Shafer data fusion theory [J]. Pattern Recognition Letters, 2011,32(2):168-180.[14] 陈谋,谭晓宇,姜长生. 基于信息融合的空中红外小目标识别 [J]. 光学精密工程, 2009, 17(8):2032-2039. CHEN M, TAN X Y, JIANG C SH. IR small target recognition in sky background based on information fusion[J]. Opt. Precision Eng., 2009, 17(8):2032-2039. (in Chinese)[15] LATIFA O, ALEXANDRA D, THIERRY D, et al.. Fault diagnosis in railway track circuits using Dempster-Shafer classifier fusion [J]. Engineering Applications of Artificial Intelligence, 2010, 23(1):117-128.[16] 高晶,孙继银,刘婧,等. 基于区域模糊阈值的前视红外目标识别 [J]. 光学精密工程, 2011,19(12):3056-3063. GAO J, SUN J Y, LIU J, et al.. FLIR target recognition based on local fuzzy threshold[J]. Opt. Precision Eng., 2011, 19(12):3056-3063. (in Chinese)

浏览量

306

下载量

CSCD

文章被引用时，请邮件提醒。

提交

工具集

关联资源

多层次信息融合在铁谱图像磨粒识别中的应用

融合通用形变模型信息的面部三维重建

基于神经网络与遗传算法的刀具磨损检测与控制

基于改进径向基函数神经网络的激光陀螺温度补偿

神经网络辅助卡尔曼滤波在组合导航中的应用