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

Abstract

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.

Partitional Clustering-Hybridized Neuro-Fuzzy Classification Evolved through Parallel Evolutionary Computing and Applied to Energy Decomposition for Demand-Side Management in a Smart Home

“…In this research, the development of the neuro-fuzzy classifier involves the following two steps. First, k-Means clustering [19,22,23] embedding heuristic knowledge into the neuro-fuzzy classifier is used to construct the fuzzy IF-THEN rules of the neuro-fuzzy classifier from a training dataset. Each of the constructed fuzzy rules in the rule base is in charge of a partition of the feature space, where the considered electrical features are the universe of discourse.…”

Gas Leakage Identification and Prevention by Pressure Profiling for Sustainable Supply of Natural Gas

“…On the other hand, pipeline fluids are frequently hazardous, corrosive, and flammable media [22]. Therefore, pipeline leaking gravely contaminates the environment, causing economic loss of resources and products.…”

A data-driven early micro-leakage detection and localization approach of hydraulic systems

“…Few approaches of early micro-leakage detection and localization are proposed. For example, ZHOU et al [19] proposed a hybrid intelligent method that integrates kernel principal component analysis and cascade support vector data description for microleakage detection of pipeline. GUO et al [20] proposed an internal micro-leakage detection method for hydraulic cylinder.…”