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化工过程具有大规模、高复杂性、多变量等特点,系统的关联性强,现有的故障定位方法较为繁琐,且会引入大量冗余计算,使其在化工过程故障定位中的实用性降低。为及时诊断故障并识别故障根源,基于动态主元分析(DPCA),结合贡献图和统计量,建立化工过程故障因果关系模型,确定对故障贡献率最大的变量,绘制故障传播路径图,找到引起扰动的初始变量,即故障的根源。采用田纳西-伊斯曼过程(TE)中的某一故障仿真作为案例,以验证该模型的有效性。结果表明,该模型利用故障的传播特点,在发生故障报警后,通过绘制故障传播路径图对扰动进行溯源,可实现化工过程故障定位。
The chemical process is characterized by large scale, high complexity and multivariate. The system has strong correlation. The existing fault location method is more complicated, and will introduce a large number of redundant calculations to reduce the practicality in chemical process fault location . In order to timely diagnose the fault and identify the root cause of the fault, a model of causal relationship of chemical process fault is established based on dynamic principal component analysis (DPCA), contribution map and statistics, and the variable with the largest contribution rate to fault is determined. The initial variable of disturbance is the root of the fault. A fault simulation in the Tennessee-Eastman process (TE) was used as a case to verify the validity of the model. The results show that the model exploits the characteristics of fault propagation and tracing the disturbance by drawing fault propagation path map after fault alarm, so as to realize the chemical process fault location.