Operational data analytics for failure prediction and availability improvement in gas turbine power plants

Abstract

Operational data analytics is crucial in enhancing failure prediction and improving the availability of gas turbine power plants. However, existing research lacks a comprehensive approach that integrates real-time operational data with predictive maintenance models to address high failure rates and prolonged downtime. This study bridges this gap by investigating five GE MS5001 single-shaft, open-cycle gas turbine units, utilizing real-time operational data, historical maintenance records, and performance metrics. Predictive models, including Bayesian simulation and MATLAB-based analysis, were employed to assess failure probabilities and optimize maintenance planning. Key findings reveal a strong correlation between maintenance efficiency and turbine availability, with units exhibiting lower failure rates and shorter mean time to repair (MTTR) demonstrating higher reliability. Conversely, units with frequent failures and extended downtime underscore the limitations of traditional maintenance approaches. The study emphasizes the importance of implementing advanced predictive maintenance strategies to mitigate operational inefficiencies, prevent unexpected failures, and enhance turbine performance. By integrating data analytics with reliability engineering, this research presents a data-driven framework for enhancing the reliability of gas turbine plants. The study contributes to bridging the research gap by demonstrating how predictive analytics can transform maintenance strategies. The study recommends that future research should focus on refining predictive maintenance models through machine learning and AI-based analytics to further improve turbine efficiency and operational resilience. By integrating data analytics with reliability engineering, this study contributes to the advancement of maintenance practices in gas turbine power plants.