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摘要: 针对一类带随机丢包的异步多传感器网络化系统，提出了基于网络化异步交互式多模型（Interacting multiple model，IMM）融合滤波的故障诊断方法.考虑不同传感器通道具有不同丢包概率的情况，将未知的故障幅值看作扩维的系统状态，利用提出的网络化异步IMM融合滤波算法对由系统正常模型和各种可能的故障模型构成的模型集进行滤波，根据模型概率进行故障检测和定位，同时得到故障幅值和系统状态的联合估计.提出的方法避免了传统IMM故障诊断方法模型集设计中故障大小难以确定的问题，适用于具有任意采样速率和任意初始采样时刻的异步多传感器网络化系统，并且通过融合多个传感器的信息提高了故障诊断的准确性.仿真实例验证了所提出方法的可行性和有效性.Abstract: A fault diagnosis method is proposed based on networked asynchronous interacting multiple model (IMM) fusion filtering for a kind of networked systems with multiple asynchronous sensors and stochastic packet dropouts. The proposed networked asynchronous IMM fusion filtering algorithm is used to perform fusion filtering for the model set consisting of the normal model and all kinds of possible fault models of the system, where different arriving probabilities are considered for different sensor communication channels and the unknown fault amplitude is taken as the augmented system state. Fault detection and location are achieved based on the model possibilities, and the estimates of system state and fault amplitude can be obtained simultaneously. The proposed method avoids the problem of determining fault amplitude in the model set design of traditional IMM fault diagnosis approaches, improves the accuracy of fault diagnosis by fusing information from multiple sensors, and can be used to asynchronous multi-sensor networked systems with arbitrary sampling rates and arbitrary initial sampling time instants. The feasibility and effectiveness of the proposed algorithm are illustrated by simulation examples.
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图 1 融合区间$(t_{k-1}, t_k]$内异步多传感器网络化测量
Fig. 1 Networked measurements from asynchronous multi-sensors during fusion interval $(t_{k-1}, t_k]$
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