CASE STUDIES » Combustion Dynamics Monitoring Implementation

Combustion Dynamics Monitoring Implementation

Combustion Dynamics Monitoring Implementation

A power generation company sought to enhance their gas turbine fleet monitoring capabilities, particularly around combustion dynamics. They needed a solution that could provide early detection of anomalous behavior related to instrumentation issues, tuning problems, and potential hardware damage before these issues led to costly unplanned outages or equipment damage.

Turbine Logic was engaged to deploy an advanced combustion dynamics health monitoring algorithm at the client's monitoring center. The work included processing historical operational data through the algorithm, configuring the system for ongoing monitoring of select units, and providing technical training to the client's personnel on combustion dynamics fundamentals and monitoring best practices.

The engagement resulted in the client gaining enhanced visibility into their gas turbine combustion health, with monitoring capabilities that could identify developing issues earlier than traditional approaches. The client's team was equipped with both the tools and knowledge to leverage the new monitoring capabilities going forward.

Turbine Logic has extensive experience implementing advanced gas turbine monitoring solutions for power generators. If your organization is looking to improve fleet reliability through better combustion dynamics monitoring or other predictive analytics capabilities, contact Turbine Logic to discuss how we can help protect your assets and reduce unplanned downtime.

Ready to achieve similar results?

Let's discuss how Turbine Logic can help with your specific challenges.

Talk to an Expert

Related Case Studies

Turbine Monitoring and Technical Support Services

A client in the power generation industry sought specialized expertise to enhance their turbine monitoring capabilities and ensure reliable operational support for their rotating equipment assets. The organization needed access to advanced diagnostic tools and experienced engineers who could provide ongoing technical guidance for complex turbine systems.

Sensor Health Monitoring for Gas Turbine Operations

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.

AI-Powered Dispatch Optimization for Power Generation Assets

Power generation operators managing multiple assets often struggle to balance competing priorities: responding quickly to grid demands, maintaining optimal efficiency, and minimizing maintenance impacts across their fleet. Traditional dispatch approaches rely on static models that fail to account for real-time equipment health, leading to suboptimal bidding strategies and reduced profitability.

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.

Aeroderivative Gas Turbine Digital Twin Development

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.

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.