A Study on Multi-Data Source Fusion Method for Petroleum Pipeline Leak Detection

This paper describes a new approach of data source fusion based on process fusion entropy for leak detecting of product pipelines. Data sources are either single-channeled or multi-channeled: single-channeled data sources can be structured or semi-structured process steady entropy, whereas multi-channeled sources are singular spectrum entropy and power spectrum entropy. In order to develop data sources fusion systems for pipeline leak detection in real-time contexts, we need to study all issues raised by the matching paradigms. This challenging problem becomes crucial with the dominating role of the internet. Classical approaches of data integration, based on schemas mediation, are not suitable to the pipeline SCADA (Supervisory Control and Data Acquisition) environment where data is frequently modified or updated. Therefore, we develop a loosely integrated approach that takes into consideration both steady and transient states which must be separated correctly in order to integrate new data sources. Moreover, we introduce a process fusion entropy-based Multi-data source Fusion Method (MFM) that aims to define and retrieve conflicting data from multiple data sources.