Faculty of Electrical Engineering, Bialystok University of Technology, Wiejska 45D, 15-351 Bialystok, Poland.
Enea Cieplo sp. z o.o., ul. Warszawska 27, 15-062 Bialystok, Poland.
Sensors (Basel). 2020 Mar 11;20(6):1561. doi: 10.3390/s20061561.
In the paper the usability of the Multiway PCA (MPCA) method for early detection of leakages in the pipeline system of a steam boiler in a thermal-electrical power plant is presented. A long segment of measurements of selected process variables was divided into a series of "batches" (representing daily recordings of normal behavior of the plant) and used to create the MPCA model of a "healthy" system in a reduced space of three principal components (PC). The periodically updated MPCA model was used to establish the confidence ellipsoid for the "healthy" system in the PC coordinates. [d=replaced]The staff's decision of the probable leak detection is supported by comparison of the current location of the operating point (on the "fault trajectory") with the boundaries of the confidence ellipsoid.The location of the process operating point created the "fault trajectory," which (if located outside the confidence ellipsoid) supported the decision of probable leak detection. It must be emphasized that due to daily and seasonal changes of heat/electricity demands, the process variables have substantially greater variability than in the examples of batch processes studied in literature. Despite those real challenges for the MPCA method, numerical examples confirmed that the presented approach was able to foresee the leaks earlier than the operator, typically 3-5 days before the boiler shutdown. The presented methodology may be useful in implementation of an on-line system, developed to improve safety and maintenance of boilers in a thermal-electrical power plant.
本文介绍了多向主成分分析（MPCA）方法在早期检测火电厂蒸汽锅炉管道系统泄漏中的可用性。选择的过程变量的长段测量被分为一系列“批次”（代表工厂正常行为的日常记录），并用于在三个主成分（PC）的简化空间中创建“健康”系统的 MPCA 模型。定期更新的 MPCA 模型用于在 PC 坐标中为“健康”系统建立置信椭球。[d=替换]工作人员通过将当前操作点的位置（在“故障轨迹”上）与置信椭球的边界进行比较，来支持可能发生泄漏的检测决策。过程操作点的位置创建了“故障轨迹”，如果该轨迹位于置信椭球之外，则支持可能发生泄漏的检测决策。必须强调的是，由于热/电需求的日常和季节性变化，过程变量的可变性比文献中研究的批量过程示例大得多。尽管 MPCA 方法面临这些实际挑战，但数值示例证实，所提出的方法能够比操作人员更早地预测泄漏，通常在锅炉停机前 3-5 天。所提出的方法可用于开发在线系统，以提高火电厂锅炉的安全性和维护。
