燕山大学学报2016，Vol.40Issue(5)：456-461,6.
基于PCA和RBF神经网络的石化装置故障监测与诊断
Fault monitoring and diagnosis based on PCA and RBF neural network for petrochemical plant
摘要
Abstract
To extract effective fault feature from a large number of the process monitoring data in petrochemical plant, a fault monitoring and diagnosis approach based on PCA ( Principal Component Analysis) and RBF ( Radial Basis Function) neural network was developed to find the fault timely and identify the failure cause accurately in this paper.First, the obtained data samples was used to establish PCA model, in which the data feature was extracted through dimension reduction. SPE ( Squared Prediction Error) statistic threshold in normal condition was set, and the SPE statistic in real⁃time condition was established, thereby fault monitoring was conducted.Then, by using fault samples with low dimension by PCA, the multiple RBF neural network models were constructed for diagnosing on⁃line fault and identifying fault causes.Finally, a deisobutanizer unit in the gas fractionation plant in a petrochemical company was taken as a study case. Fault monitoring and diagnosis model was constructed with process monitoring samples obtained from the dynamic simulation by the UniSim Design software.Results show that the proposed method not only can effectively monitor conditions, but also can quickly and accurately diagnose the fault.关键词
PCA/RBF神经网络/故障监测与诊断/石化装置Key words
PCA/RBF/neural network/fault monitoring and diagnosis/petrochemical plant分类
资源环境引用本文复制引用
郭丽杰,赵明娟,康建新..基于PCA和RBF神经网络的石化装置故障监测与诊断[J].燕山大学学报,2016,40(5):456-461,6.基金项目
国家自然科学基金资助项目（51205340）；燕山大学博士基金资助项目 （）
