Decision Making Based on Machine Learning Algorithm for Identifying Failure Rates in the Oil Transportation Pipeline
E. B. Priyanka, S. Thangavel
Abstract
In Oil Industry Safety Directorate (OISD), it has been testified that nearly 33% of pipeline defects are due to improper pigging and improper precast-forecasting of the existence of a crack in the long run pipelines. Therefore, pipeline engineers are requisite to exploit effective and proficient intelligent approach to identify and pinpoint these pipeline imperfections. To sort out these issues, an unsupervised machine learning technique with partition clustering algorithm is implemented to figure out the occurrence of crack or sedimentation inside the pipelines in the premature stage during the long run passage of oil through pipelines. As a result, partition clustering best fits for the observation of performance by organizing clusters as in spherical shapes which affords similarity within the cluster is higher and the similarity between the clusters is minimum. In the proposed work, for the prediction on the occurrence of anomaly in oil pipeline system the well-suit partitioning cluster approach is combined with K-means clustering.