I am developing a real-time, sensor agnostic, monitoring system for predicting and identifying evolving failures in various pipelines and mechanical parts. The customized diagnostic system runs parallel to the SCADA and is based on a statistical algorithm that processes data obtained from sensors installed on the diagnosed pipeline/mechanical part. Whenever the machine-learning algorithm receives SCADA data, it calculates the “system health grade” (HG), makes a decision concerning system normality, and outputs alerts as necessary when it identifies deviations from the norm (anomalies). These alerts are very accurate and are delivered to control room operators in any form specified or required by the client.
As Project manager, I managed the development team for the Slovak gas transmission network simulator (SPP-DSTG, Nitra) to monitor the integrity of the pipeline. Predictive Analytics of pipeline failure allowed the company to save more then 15 million dollars due to preventive repairs of the pipeline and equipment.