Sustainability of natural gas transmission infrastructure is highly related to the system’s ability to decrease emissions due to ruptures or leaks. Although traditionally such detection relies in alarm management system and operator’s expertise, given the system’s nature as large-scale, complex, and with vast amount of information available, such alarm generation is better suited for a fault detection system based on data-driven techniques. This would allow operators and engineers to have a better framework to address the online data being gathered.
This paper presents an assessment on multiple fault-case scenarios in critical infrastructure using two different data-driven based fault detection algorithms: Principal component analysis (PCA) and its dynamic variation (DPCA).
Both strategies are assessed under fault scenarios related to natural gas transmission systems including pipeline leakage due to structural failure and flow interruption due to emergency valve shut down. Performance evaluation of fault detection algorithms is carried out based on false alarm rate, detection time and misdetection rate. The development of modern alarm management frameworks would have a significant contribution in natural gas transmission systems’ safety, reliability and sustainability.