基于粒子滤波的分布式智能故障诊断系统研究

针对传统故障诊断系统硬件结构以及故障识别算法过于复杂的问题,提出并研究了一种基于粒子滤波的分布式智能故障诊断系统.该系统采用ZigBee无线传感网络实现系统分布式多变量参数的实时采集,基于粒子滤波算法在线处理各变量数据,并基于简易模式识别算法获得系统真实状态的准确估计,实现系统故障的智能诊断与故障预示.智能故障诊断系统由ZigBee无线传感数据采集网络、粒子滤波算法、系统状态模型和故障模式识别四部分构成.粒子滤波算法基于粒子序贯重要性重采样和蒙特卡洛方法对传感器采集数据滤波,抑制或消除干扰及显著性误差对系统状态估计的影响,可避免粒子退化.故障模式识别就是求取与粒子滤波输出的系统状态估计曲线残差之和最小的系统状态模型.智能故障诊断系统的实现和实例实验结果表明该系统能实现对象的远程监测、对象状态的精确估计、对象故障的准确诊断,拓宽了分布式传感网络的应用范围,并具有成本低、可靠性高、实时性好和易实现的优点.

Research on Distributed Intelligent Fault Diagnosis System Based on Particle Filter

Due to the shortcomings of traditional fault diagnosis system, such as too complicated hardware system and fault recognition algorithm, a distributed intelligent fault diagnosis system based on particle filter was proposed and studied. Real-time collection of distributed multi-variable parameters was realized 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 system includes ZigBee network, particle filter algorithm, system states model and malfunction mode recognition. 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 realize 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.