Session: 20-02: Gas Turbine Operation and Maintenance

Paper Number: 152222

Optimizing Maintenance for Pipeline Gas Turbine Machinery 

Extending gas turbine maintenance intervals can reduce total operating costs but raises the risk of unit failures. Failures, specifically forced outages, require more extensive and costly repairs than preventative maintenance activities, highlighting a crucial risk balance. Maintenance strategies can be condition-based or scheduled on a strict interval, but often the development of these strategies and the resulting cost balance relies on limited failure data as gas turbines fail relatively infrequently when maintained regularly. In this study, scheduled maintenance (specifically the optimization of the maintenance interval) is explored. This paper explores both Bayesian and Fuzzy approaches to optimize maintenance scheduling and prioritization of maintenance resources. A Bayesian reliability analysis paired with a Cost Curve analysis to assess failure risks and financial implications of maintenance intervals. Bayesian statistics leverage prior knowledge, expert opinions, and existing data to strengthen the reliability analysis, while the Cost Curve analysis evaluates the financial impact of both failure and planned maintenance. The study considers a fleet of gas turbines and pipeline stations and prescribes a maintenance interval for the fleet using the proposed methodology. A fuzzy approach is used to combine both qualitative and quantitative information to help evaluate the impact of maintenance resources. Results indicated that for increasing failure rates, where parts are life-limited, higher failure costs justified shorter maintenance intervals.

Presenting Author: Christopher Perullo Turbine Logic

Presenting Author Biography: Chris Perullo is the Director of Engineering at Turbine Logic. Mr. Perullo has 15 years of experience in gas turbine and combined cycle design, modeling and simulation, and analysis. He leads digital twin development which encompasses health monitoring, instrumentation fault detection, anomaly detection, and performance prediction. Before joining Turbine Logic, he was a Senior Research Engineer at Georgia Tech where he focused on a wide variety of advanced gas turbine simulation and monitoring methods for entities including NASA, the FAA, and major large frame and aircraft engine OEMs.

Hemanth Satish is a Principal Machinery Engineer and Corporate Rotating Equipment Subject Matter Expert within TC Energy’s Facility Integrity Reliability group. He has over 22 years of Engineering experience working mainly in the area of Rotating Machinery, Vibration and Pulsation Analysis, Machinery Modeling and Predictive Analytics, Large Compression equipment, Pumps, Motors, Turbine and Engine failure investigations, Compression Asset Optimization, Heat Transfer and Stress Analysis. He has a diverse work experience supporting operations, greenfield and brown field projects within oil/gas pipelines as well as downstream oil and gas industries. He is a registered Professional Engineer with APEGA (Alberta, Canada). He holds a Bachelor’s Degree from VTU, India and a Master’s degree from McGill University Canada, in Mechanical Engineering. Hemanth has published journal papers and presented in several conferences. He currently sits on the PRCI (Pipeline Research Council International) compressor and pump technical committee, Turbo Pump symposium Advisory Committee, and part of a number of API technical committees.

Authors:

Hemanth Satish TC Energy
Christopher Perullo Turbine Logic
Corson Teasley Turbine Logic