Next Article in Journal
Experimental and Numerical Simulation Study on Co-Incineration of Solid and Liquid Wastes for Green Production of Pesticides
Previous Article in Journal
Effect of Cellulosic Waste Derived Filler on the Biodegradation and Thermal Properties of HDPE and PLA Composites
Previous Article in Special Issue
BISSO: Biomass Interface for Superstructure Simulation and Optimization
Open AccessArticle

An Integration Method Using Kernel Principal Component Analysis and Cascade Support Vector Data Description for Pipeline Leak Detection with Multiple Operating Modes

Department of Chemical Engineering, Zhejiang University of Technology, Hangzhou 310027, China
*
Author to whom correspondence should be addressed.
Processes 2019, 7(10), 648; https://doi.org/10.3390/pr7100648
Received: 15 August 2019 / Revised: 8 September 2019 / Accepted: 18 September 2019 / Published: 22 September 2019
(This article belongs to the Special Issue Process Systems Engineering à la Canada)
Pipelines are one of the most efficient and economical methods of transporting fluids, such as oil, natural gas, and water. However, pipelines are often subject to leakage due to pipe corrosion, pipe aging, pipe weld defects, or damage by a third-party, resulting in huge economic losses and environmental degradation. Therefore, effective pipeline leak detection methods are important research issues to ensure pipeline integrity management and accident prevention. The conventional methods for pipeline leak detection generally need to extract the features of leak signal to establish a leak detection model. However, it is difficult to obtain actual leakage signal data samples in most applications. In addition, the operating modes of pipeline fluid transportation process often have frequent changes, such as regulating valves and pump operation. Aiming at these issues, this paper proposes a hybrid intelligent method that integrates kernel principal component analysis (KPCA) and cascade support vector data description (Cas-SVDD) for pipeline leak detection with multiple operating modes, using data samples that are leak-free during pipeline operation. Firstly, the local mean decomposition method is used to denoise and reconstruct the measured signal to obtain the feature variables. Then, the feature dimension is reduced and the nonlinear principal component is extracted by the KPCA algorithm. Secondly, the K-means clustering algorithm is used to identify multiple operating modes and then obtain multiple support vector data description models to obtain the decision boundaries of the corresponding hyperspheres. Finally, pipeline leak is detected based on the Cas-SVDD method. The experimental results show that the proposed method can effectively detect small leaks and improve leak detection accuracy. View Full-Text
Keywords: multiple operating modes; cascade support vector data description; leak detection; K-means; kernel principal component analysis multiple operating modes; cascade support vector data description; leak detection; K-means; kernel principal component analysis
Show Figures

Figure 1

MDPI and ACS Style

Zhou, M.; Zhang, Q.; Liu, Y.; Sun, X.; Cai, Y.; Pan, H. An Integration Method Using Kernel Principal Component Analysis and Cascade Support Vector Data Description for Pipeline Leak Detection with Multiple Operating Modes. Processes 2019, 7, 648.

Show more citation formats Show less citations formats
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Back to TopTop