Case Studies

A client faced a growing compatibility challenge with a critical performance monitoring tool that had been in use for years. The software relied on older architecture that was becoming increasingly incompatible with modern computing environments as industry standards evolved.

A client in the power generation sector sought to explore how emerging digital twin technologies could be applied to improve asset reliability and operational intelligence. While the concept of digital twins had gained significant traction across industries, there was uncertainty about which specific applications would deliver meaningful value for their particular equipment and operational challenges.

As the energy industry increasingly explores low-carbon solutions, many organizations are evaluating the feasibility of combusting hydrogen or hydrogen-blended fuels in existing gas turbine assets. Understanding the performance implications of varying hydrogen content is essential for making informed decisions about fuel transitions, but these impacts are often non-obvious and require sophisticated analysis.

Gas turbine monitoring and diagnostics centers frequently encounter false alarms that consume valuable time and resources. Many of these false alarms stem from issues within the instrumentation chain: sensors that have drifted, failed, or produced corrupted data during transmission or storage.

A power generation operator sought to expand their monitoring and diagnostic capabilities for aeroderivative gas turbines. While digital twin technology had proven valuable for frame-type units, the client needed similar capabilities extended to a different class of equipment, along with support for implementing advanced combustion monitoring techniques.

A client sought to improve their ability to detect and diagnose equipment faults in their gas turbine fleet before they led to costly unplanned outages. Traditional monitoring approaches were reactive, often identifying problems only after performance had already degraded significantly.