Case Studies
Explore how Turbine Logic helps companies like yours optimize the performance of your assets through advanced analytics, diagnostics, and engineering expertise.
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.
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.
Other Studies
A power generation client needed to understand whether maintenance activities had impacted the firing temperature of their heavy-duty gas turbine fleet. Changes in firing temperature can significantly affect both performance and component life, making accurate assessment critical for operational decision-making and long-term asset management.
A power generation operator sought to understand the feasibility of transitioning their existing gas turbine fleet to operate on hydrogen-blended fuels as part of a broader decarbonization strategy. The fleet included multiple turbine models from different manufacturers, and the operator needed clarity on technical limitations, operational impacts, and facility modifications that would be required to support alternative fuel blending.
A utility-scale solar plant operator sought to transition from reactive and preventative maintenance practices toward more cost-effective condition-based approaches. The challenge was identifying performance anomalies and equipment issues early enough to reduce energy losses and maintenance costs, while minimizing false alarms that waste operational resources.
A client needed a way to evaluate the economic impact of different air filtration strategies for their gas turbine operations. The challenge involved balancing multiple competing factors: filter efficiency, pressure drop effects on turbine performance, maintenance costs, water wash scheduling, and long-term operational expenses.
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