The global gas turbine market, valued at $18.5 billion in 2023, is projected to grow at 5.8% CAGR through 2030 (Grand View Research), driven by increasing energy demands and industrial applications. However, unplanned downtime costs power plants an average of $500,000 per day (GE Power Report), highlighting the critical need for advanced operational and maintenance strategies. This comprehensive course, Gas Turbine Operations and Digital Maintenance Strategies, integrates Gas Turbine Operations and Maintenance Techniques with Digital-Age Maintenance Planning and Scheduling, providing professionals with cutting-edge expertise to optimize turbine performance in today’s technology-driven industrial landscape.
Modern gas turbines face complex challenges, from compressor fouling (responsible for 70% of performance degradation – ASME Journal) to combustion instability issues that reduce efficiency by 15-20%. Meanwhile, the digital transformation of maintenance practices is revolutionizing asset management, with predictive maintenance reducing downtime by 35% (Deloitte Insights) and AI-driven analytics improving failure prediction accuracy by 90% (McKinsey).
Key Highlights of the Training Include:
Advanced thermodynamic principles governing gas turbine cycles and their impact on operational efficiency
Digital twin technology applications for real-time performance monitoring and anomaly detection
Combustion system optimization techniques to meet stringent emissions regulations
Machine learning algorithms for predictive maintenance scheduling and failure prevention
Integration of CMMS/EAM systems with IoT sensors for comprehensive asset management
This course bridges theoretical knowledge with digital implementation strategies, empowering professionals to enhance reliability, extend asset lifespan, and reduce operational costs in gas turbine applications across power generation, oil & gas, and aviation sectors.

Key Takeaways:

• Advanced thermodynamic principles governing gas turbine cycles and their impact on operational efficiency
• Digital twin technology applications for real-time performance monitoring and anomaly detection
• Combustion system optimization techniques to meet stringent emissions regulations
• Machine learning algorithms for predictive maintenance scheduling and failure prevention
• Integration of CMMS/EAM systems with IoT sensors for comprehensive asset management

• Advanced thermodynamic principles governing gas turbine cycles and their impact on operational efficiency
• Digital twin technology applications for real-time performance monitoring and anomaly detection
• Combustion system optimization techniques to meet stringent emissions regulations
• Machine learning algorithms for predictive maintenance scheduling and failure prevention
• Integration of CMMS/EAM systems with IoT sensors for comprehensive asset management