Modified independent component analysis and Bayesian network-based two-stage fault diagnosis of process operations
Modified independent component analysis and Bayesian network-based two-stage fault diagnosis of process operations
复制标题
DOI：
10.1021/ie503530v
复制
发表时间：
2015-03
影响因子：
4.2
通讯作者：
Hongyang Yu;F. Khan;V. Garaniya
中科院分区：
文献类型：
--
作者：
Hongyang Yu;F. Khan;V. Garaniya
统计故障检测技术能够从被监测的过程变量中检测故障并诊断根本原因(S)。对于复杂的过程操作，由于监控成本和警报泛滥，筛选所有过程变量是不可行的。因此，如果故障源于未被监控的过程变量，则传统的统计技术不能定位真正的根本原因。为了放宽这一限制，提出了一种适用于过程操作的两阶段故障诊断技术。在第一阶段，利用改进的独立分量分析进行故障检测和故障监测变量的辨识。在第二阶段，考虑从工艺流程图中获得的工艺变量及其相关性，建立贝叶斯网络模型。然后，在网络节点处生成对应于在第一阶段中识别的故障变量的证据。随后，使用演绎和诱因对网络进行了更新和分析。
Statistical fault detection techniques are able to detect fault and diagnose root-cause(s) from the monitored process variables. For complex process operations, it is not feasible to screen all the process variables due to monitoring cost and flooding of alarms. Thus, if a fault is originated from a process variable that is not monitored, conventional statistical techniques are incapable of locating the true root-cause. To relax this limitation, a two-stage fault diagnosis technique is proposed for process operations. In the first-stage, the modified independent component analysis is used for fault detection and to identify the faulty monitored variable. In the second-stage, a Bayesian Network model is constructed considering the process variables and their dependence obtained from the process flow diagram. Evidence is then generated at the network node corresponding to the faulty variable identified in the first-stage. Subsequently, the network is updated and analyzed using deductive and abductive reason...