Integration of principal component analysis and fuzzy logic systems for comprehensive process fault detection and diagnosis
Integration of principal component analysis and fuzzy logic systems for comprehensive process fault detection and diagnosis
复制标题
DOI：
10.1021/ie050695i
复制
发表时间：
2006-03
影响因子：
4.2
通讯作者：
E. Musulin;I. Yélamos;L. Puigjaner
中科院分区：
文献类型：
--
作者：
E. Musulin;I. Yélamos;L. Puigjaner
将主元分析（PCA）与模糊逻辑知识库（FLKB）系统相结合，开发了一个故障诊断系统。使用正常状态数据创建PCA模型。然后，它被用来投影正常和故障数据在训练阶段。它评估过程变量值及其相关性，从而实现快速可靠的故障检测。一旦执行检测，FLKB系统用于评估每个变量对过程变化的贡献，找到检测到的异常事件的根本原因。提出了一种自动提取紧凑工艺信息的简单方法。然后，优化算法的实施，以提高隔离性能。该方法是证明在学术案例研究和田纳西州伊士曼过程基准。
A fault diagnosis system is developed by integrating principal component analysis (PCA) with fuzzy logic knowledge-based (FLKB) systems. A PCA model is created using normal state data. Then it is used to project normal and faulty data during the training stage. It evaluates the process variables values and their correlation, allowing fast and reliable fault detection. Once detection is performed, a FLKB system is used to evaluate the contributions of each variable to changes in the process, finding the root causes of the abnormal event detected. A simple methodology to automatically extract compact process information is presented. Then, an optimization algorithm is implemented to improve the isolation performance. The methodology is demonstrated in an academic case study and in the Tennessee Eastman process benchmark.