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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.

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A probabilistic analysis is an approach that allows to identify whether if preventive, predictive and corrective maintenance practices are efficient for keeping the overall system safety. The implementation of methodologies that evaluate the maintenance plans are a way to improve existing programs. Promigas S.A. E.S.P. is a company that must guarantee the reliability and availability of its natural gas transmission systems to its consumers. Therefore, since 2009, the company has implemented reliability centered maintenance practices to achieve it. However, considering the current scenario of the natural gas in Colombia and according to the growth plans of the company, it is necessary to guarantee that the reliability and availability indexes remain close to 100%. To achieve the aforementioned objective, it is necessary to diagnose the current maintenance plan. We propose a hybrid methodology combining functional and probabilistic analysis to assess the maintenance plan of a natural gas transmission system, specifically a turbo-compressor power pack. The proposed methodology includes a new priorization method to identify and select critical components and its critical failure modes, through a qualitative functional and quantitative characterization of the subsystems that conform the turbo-compressor power pack. The probabilistic analyses were simulated for five time periods: one, three, six, nine and thirteen years. The results allow to conclude in terms of availability that while the maintenance plan is optimal for the first-time period, from the second time period the preventive and predictive maintenance practices must be optimized increasing resources or modifying the equipment intervention frequencies.

Natural gas transmission infrastructure is a large-scale complex system often exhibiting a considerable operating states not only due to natural, slow and normal process changes related to aging but also to a dynamic interaction with multiple agents overall having different functional parameters, an irregular demand trend adjusted by the hour, and sometimes affected by external conditions as severe climate periods. As traditional fault detection relies in alarm management system and operator’s expertise, it is paramount to deploy a strategy being able to update its underlying structure and effectively adapting to such process shifts. This feature would allow operators and engineers to have a better framework to address the online data being gathered in dynamic on transient conditions. This paper presents an extended analysis on WARP technique to address the abnormal condition management activities of multiple-state processes deployed in critical natural gas transmission infrastructure. Special emphasis is made on the updating activity to incorporate effectively the operating shifts exhibited by a new operating condition implemented on a fault detection strategy. This analysis broadens the authors’ original algorithm scope to include multi-state systems in addition to process drifting behavior. The strategy is assessed under two different scenarios rendering a major shift in process’ operating conditions related to natural gas transmission systems: A transition between low and high natural gas demand to support hydroelectric generation matrix on severe tropical conditions. Performance evaluation of fault detection algorithm is carried out based on false alarm rate, detection time and misdetection rate estimated around the model update.