This paper present the development of an artificial neural network (ANN) Gas Path Diagnostics (GPD) technique applied to pipeline compression system for fault detection and quantification. The work detailed the various degradation mechanisms and the effect of such degradations on the performance of the natural gas compressors. A comprehensive review of different maintenance strategies was largely, discussed with particular focus on performance method of condition-based maintenance. The data used in demonstrating the ANN diagnostics is so derive using an advanced thermodynamic performance simulation model of integrated pipeline and compressor systems, which has embedded empirical compressor map data and pipeline resistance model. Within the model, fault degradation can be implanted to account for effects caused by fouling, erosion and corrosion, of various degrees of severity, and obtain wide range of corresponding simulated “true” measurements. In order to account for uncertainties normally encountered in field measurements, Gaussian noise distribution was, combined with simulated true measurements, which depends on the instruments tolerances. Furthermore, since judicious measurement selection are crucial in ensuring flawless GPD predictions, a sensitivity and correlation analysis of the available measurements revealed that discharge temperature, rotational speed and torque are the most effective measurement for the diagnostics with acceptable degrees of accuracies. An analytical case study of the developed method shows that, a selected ANN architecture has the capabilities to detect fault related to degradation in efficiency and flow capacities in the present of instrument error, varied operational and environmental conditions.


Application of Artificial Neural Network Based Gas Path Diagnostics on Gas Pipeline Compressors

Division / Sponsor:
International Gas Turbine Institute (IGTI)

Paper Proceedings: https://asmedigitalcollection.asme.org/GT/GT2020/volume/84058

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