摘要
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。
Chemical processes are characterized by large scale, high complexity and multi variables. Existing fault location methods are cumbersome and will introduce a large amount of redundant computation. Thus, their practicability in locating faults in chemical process is reduced. In order to diagnose and identify the root causes of failure in time, a causal model of chemical process failure was built, based on DPCA, contribution chart and statistics methods. A comparison of data was made between real time and normal working conditions to determine the variable which has the largest contribution rate and determine the fault propagation path to find the initial variable causing the disturbance （root cause of failure）. A fault case in the TE process was used to verify the model. The results show that the model can be used to determine the fault propagation path and the initial variable causing the disturbance to realize the fault location.
出处
《中国安全科学学报》
CAS
CSCD
北大核心
2016年第11期133-138,共6页
China Safety Science Journal
基金
国家自然科学基金资助(51574263)
中国石油大学(北京)科研基金资助(YJRC-2013-35)
中国石油化工股份有限公司科学研究与技术开发项目(P14004)
