CASE STUDIES » EMPIRICAL CORRELATIONS

Case Studies

Explore how Turbine Logic helps companies like yours optimize the performance of your assets through advanced analytics, diagnostics, and engineering expertise.

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Other Studies

Firing Temperature Assessment for Heavy-Duty Gas Turbines

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.

Legacy Software Modernization for Continued Platform Compatibility

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.

Software Review and Technical Support for Power Industry Analytics

A client in the power generation sector needed specialized support for an internal software tool used to analyze and benchmark operational data. Over time, the tool had grown in complexity and adoption, creating a need for expert review of its underlying architecture and hands-on assistance with applied use cases.

Predictive Maintenance Analytics for Solar Generation Assets

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.

Digital Twin Model Development for Industrial Gas Turbines

A client needed to expand their gas turbine monitoring and diagnostic capabilities to cover additional equipment types within their fleet. Existing digital twin models were limited to certain turbine configurations, creating gaps in their ability to perform comprehensive performance analysis across all assets.

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