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计算机工程 ›› 2012, Vol. 38 ›› Issue (9): 162-165.doi: 10.3969/j.issn.1000-3428.2012.09.049

• 人工智能及识别技术 • 上一篇    下一篇

基于混合粒子滤波的温控传感器故障诊断方法

田梦楚，陈志敏，魏秀明，周 清，王振丽   

  1. (南京理工大学自动化学院，南京 210094)
  • 收稿日期:2011-07-18 出版日期:2012-05-05 发布日期:2012-05-05
  • 作者简介:田梦楚(1987－)，女，硕士研究生，主研方向：智能优化算法，人工智能；陈志敏，博士研究生；魏秀明、周 清、王振丽，硕士研究生
  • 基金项目:
    高等学校博士学科点专项科研基金资助项目(200802881017)

Temperature Control Sensor Fault Diagnosis Method Based on Hybrid Particle Filtering

TIAN Meng-chu, CHEN Zhi-min, WEI Xiu-ming, ZHOU Qing, WANG Zhen-li   

  1. (School of Automation, Nanjing University of Science and Technology, Nanjing 210094, China)
  • Received:2011-07-18 Online:2012-05-05 Published:2012-05-05

PDF

813

被引次数

18 | 3

摘要： 标准粒子滤波算法的精度不高、鲁棒性差，难以满足电厂温度传感器故障诊断的要求。针对该问题，提出一种新的适用于温度传感器故障检测的智能粒子滤波算法。该算法采用人工鱼群的全局收敛性找到满意的解域，利用粒子群算法引导粒子向高斯然区域移动，提高滤波精度。实验结果证明，该算法精度高、鲁棒性强，可以有效地应用于电厂温控系统故障的诊断。

关键词: 粒子滤波, 人工鱼群算法, 微粒群优化, 收敛性, 温度传感器, 故障诊断

Abstract: Particle filtering is not precise and has weak robustnest, and it is not able to meet the requirement of fault diagnosis of temperature control system in power plant. To solve these problems, a new particle filtering algorithm based on Hybrid algorithm is proposed. The algorithm looks for satisfactory solution space with artificial fish swarm algorithm, later the particles move to the high likelihood region with Particle Swarm Optimization(PSO) algorithm. It raises the accuracy. Simulation results show that this algorithm has the high precision, strong robustness and it is suitable for fault diagnosis of temperature control system.

Key words: Particle Filtering(PF), Artificial Fish Swarm Algorithm(AFSA), Particle Swarm Optimization(PSO), convergence, temperature sensor, fault diagnosis

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