The implementation of fault detection techniques in industrial systems for process monitoring has proven to be a useful tool to process operators supervising the plant’s operation conditions. As plants become more instrumented, more data is available for fault detection applications, if they are capable of demonstrate anticipation and low false alarm rates. A regional Natural Gas transportation system deals with these types of drawbacks. While the improvements are carried out, some effort should be done in order to improve the safety in operations. In this paper a data-driven technique was used to detect fault conditions along the pipeline, sectioning it in five partitions to increase the detection sensibility. To overcome the lack of quality in data, simulation software intended to gas controllers training and pipeline operation was used to simulate leaks scenarios. Some historic data with high quality is also used to create normal operation condition models by means of Principal Component Analysis. All simulated faults were detected in a reduced time gap and recent events related to third-party actions showed the tool proficiency to detecting faults in real time. In addition, it considers a fault normalized index per section indicating the fault persistence and aggressiveness in a single plot.

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