Multiway PCA for Early Leak Detection in a Pipeline System of a Steam Boiler—Selected Case Studies

Abstract

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. 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. 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.

Real-World Data-Driven Machine-Learning-Based Optimal Sensor Selection Approach for Equipment Fault Detection in a Thermal Power Plant

“…Boiler tube leakage detection [11] 2020 Used multiway PCA model to detect boiler tube leakage -Performance highly dependent on the number of input sensor variables -Need to find optimal sensors necessary for fault detection Boiler tube leakage detection [9] 2017 Applied PCA to tube temperature data to detect boiler tube leakage Turbine fault detection [15] 2011…”

“…Recently, Natarianto et al [10] used process control data and introduced a data analytics-based approach by combining PCA, canonical variate, and linear discriminant analysis (LDA) for water wall tube leakage detection in a 650 MW supercritical coal-fired thermal power plant. Swiercz et al [11] proposed a multiway PCA approach for boiler riser and downcomer tube leakage detection using expert-provided sensor data. The proposed method could successfully detect the tube leak 3-5 days before boiler shutdown.…”

A Novel Leak Detection Approach in Water Distribution Networks

“…The idea behind leak detection water distribution systems is to maintain disinfection levels, pressure and reduce water loss are equally important. To address the leak detection purposes, several approaches have been developed including principal component analysis (PCA) [1], [2], nonlinear PCA (NPCA) [3], Multi-Regional PCA (MRPCA) [4], probabilistic PCA (PPCA) [5], [6] and attribute PCA (APCA) [7]. However, most industrial systems are nonlinear.…”

Intelligent Steam Power Plant Boiler Waterwall Tube Leakage Detection via Machine Learning-Based Optimal Sensor Selection

“…On the other hand, various methods of analyzing process control data are currently being investigated [16,17]. Swiercz et al [18] proposed a leak detection model based on multiway principal component analysis (MPCA) for boiler riser and downcomer tubes that uses process variables determined by experts. Kornel et al [19] used ANN to develop models for early tube leak detection that are based on process variables.…”