An Unsupervised Anomaly Detection Based on Self-Organizing Map for the Oil and Gas Sector

Anomaly detection plays a crucial role in preserving industrial plant and machinery health. Detecting and identifying anomalies helps prevent any production system from damage and failure. In this paper, an approach for fault detection and identification was developed using a Self-Organizing Map algorithm, as the results of the obtained map are intuitive and easy to understand. In order to assign each node in the output map a single class that is unique, the purity of each node is examined. The samples are identified and mapped in a two-dimensional space, clustering all readings into six macro-areas. Moreover, through the confusion matrix, it is found that the algorithm achieves an overall accuracy of 90 per cent and can classify and recognize the state of the system