湖南大学学报（自然科学版）2018，Vol.45Issue(2)：87-94,8.DOI:10.16339/j.cnki.hdxbzkb.2018.02.11
基于粒子滤波的分布式智能故障诊断系统研究
Research on Distributed Intelligent Fault Diagnosis System Based on Particle Filter
摘要
Abstract
Due to the shortcomings of traditional fault diagnosis system,such as too complicated hard-ware system and fault recognition algorithm,a distributed intelligent fault diagnosis system based on parti-cle filter was proposed and studied.Real-time collection of distributed multi-variable parameters was real-ized by adopting ZigBee wireless sensor network,on-line processes variable data based on particle filter, and precise estimate about real system states were obtained based on simple pattern recognizing algorithm in order to realize the intelligent forecast and diagnose for system fault.The distributed fault diagnosis sys-tem includes ZigBee network,particle filter algorithm,system states model and malfunction mode recogni-tion.Particle filter can filter data collected by sensor,suppress and eliminate the interference or significant error that affects the estimate of system states based on sequential importance sampling and Monte-Carlo method.Finding a system state model that has the minimum sum of residuals with an estimate curve about system states from a particle filter is the process of the malfunction mode recognition.Realization of the distributed intelligent fault diagnosis system and the result of the experiment show that the system can re-alize the remote monitor,accurate state estimation and fault diagnose,and it has the advantage of low cost,high reliability and easy to realize.The work can expand the application range of distributed sensor network and improve the diagnosis level of the fault diagnosis system.关键词
故障诊断/ZigBee/粒子滤波/模式识别Key words
fault detection/ZigBee/particle filter/pattern recognition分类
信息技术与安全科学引用本文复制引用
孟志强,朱志亮,朱建波,张正江,戴瑜兴..基于粒子滤波的分布式智能故障诊断系统研究[J].湖南大学学报（自然科学版）,2018,45(2):87-94,8.基金项目
国家自然科学基金资助项目(61374167),National Natural Science Foundation of China(61374167) （61374167）
浙江省自然科学基金资助项目(LZ16E050002,LGG18F010016),Natural Science Foundation of Zhejiang Province(LZ16E050002,LGG18F010016) （LZ16E050002,LGG18F010016）
