Statistical analytics, as a data extraction and fault detection strategy, may incorporate segmentation techniques to overcome its underlying limitations and drawbacks. Merging both techniques shall provide a more robust monitoring structure to address the proper identification of normal and abnormal conditions, to improve the extraction of fundamental correlation among variables, and to improve the separation of both main variation and natural variation (noise) subspaces. This additional feature is key to limit the false alarm rate and to optimize the fault detection time when it is implemented on industrial applications.
This paper presents an analysis to determine whether a segmentation approach, as a previous step of detection, enhances the fault detection strategies, specifically the principal component analysis performance. The data segmentation criteria assessed in this study includes two approaches: a) Sources (well) of the transmitted natural gas and b) Promigas’ natural gas pipeline division defined by the Energy and Gas Regulation Commission (CREG in Spanish).
The performance assessment of segmentation criteria was carried out evaluating the false alarm rate and detection time when the natural gas transmission network presents faults of different magnitude. The results show that the implementation of a segmentation criteria provides an advantage in terms of the detection time, but it depends of the fault magnitude and the number of clusters. The detection time is improved by 25% in the case scenario I, when transition zones are considered. On the other hand, the detection time is slightly better with less than 10% in the case scenario II, where the segmentation is geographical.