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| Information divergence based process fault detection and diagnosis |
| CAO Yuping, TIAN Xuemin |
| College of Information and Control Engineering, China University of Petroleum, Dongying 257061, China |
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Abstract A nonlinear process fault detection and diagnosis method was presented based on the information divergence in order to utilize the nonGaussian fault characteristic in Kalman filter innovation. An unscented Kalman filter was used to produce the innovation sequence, and the kernel density estimation was applied to obtain the probability distribution of the innovation. Then an information divergence statistic was constructed to monitor the state of the process. Once a fault was detected, a symmetric information divergence was introduced to isolate the fault by measuring the distance between the monitored process and the fault process in fault database. Simulation results on the continuous stirred tank reactor demonstrate that the method can detect the fault in time and correctly distinguish the fault type.
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Published: 01 July 2010
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基于信息散度的过程故障检测与诊断
为了充分利用卡尔曼滤波新息中的非高斯故障特征,提出一种基于信息散度的非线性过程故障检测与诊断方法.通过unscented卡尔曼滤波产生新息序列,利用核密度估计方法计算概率分布.在此基础上建立信息散度统计量,监控过程的运行状态.一旦检测到故障后,引入对称信息散度计算待诊断过程与故障库中各类故障之间的距离,判断故障类型.在连续搅拌反应器上的仿真结果表明,提出的方法能够及时检测到故障的发生,正确判断故障类型.
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