Gas turbines are manufactured in such a manner that they are capable of functioning under harsh conditions. They are able to function for thousands of hours and withstand very high temperature, pressure, and speed. However, even the most durable gas turbines may become less efficient due to the wear and tear over time. 

In the past, it was the practice of turbine operation through regular inspections and maintenance. Now, there is a new strategy in monitoring turbines. 

Using digital twins, artificial intelligence (AI), Internet of Things (IoT) and real-time analytics makes it possible to detect and fix an abnormal situation before it causes any damage to the machine. 

What Is a Digital Twin for a Gas Turbine? 

The digital twin is essentially a digital version of the real gas turbine.  

It takes data from the actual turbine, such as temperatures, pressures, vibrations, speeds, fuel usage and other parameters, and creates an up-to-date digital image. This model allows one to assess whether the turbine performs in accordance with its expected working condition.  

For users, it becomes possible to get extremely important information about the turbine condition without being limited to just physical checks.  

Using a digital twin, one can detect performance changes even before they become visible during the normal work of the gas turbine. Such performance changes can include the increase in exhaust temperature, unusual vibration patterns, decreased compressor efficiency and other symptoms. 

How AI Helps Predict Turbine Failures 

The true value comes once AI and machine learning technologies are introduced into the digital twin technology. 

Modern turbines create considerable amounts of data during operation. AI algorithms can process this information and find patterns that would be hard to spot otherwise.  

It is one thing to just detect when a turbine is not working within the predefined boundaries, it is quite another thing when the algorithm can detect abnormal behavior looking into the historical data. 

This is especially important for the components in hot-gas path such as blades, combustors and other parts of the turbine working under high temperatures.  

A slow increase in temperature or vibrations, which may not necessarily be considered a cause for alarm, might be detected by the AI system as part of a dangerous pattern. This gives maintenance teams more time to investigate and plan corrective action. 

From Preventive to Predictive Maintenance 

There is one major distinction that needs to be made between preventive maintenance and predictive maintenance.  

Preventive maintenance is normally scheduled ahead of time. The parts will be checked, serviced, or replaced after a set number of hours of use regardless of their actual state. 

Predictive maintenance, however, works in a totally different manner. In this case, the decision to conduct any maintenance will be based on the current state and performance of the machinery. For operators of gas turbines, this may make quite a bit of difference.  

Should the part function normally, unnecessary maintenance can be avoided. Should there be an abnormal trend identified, then maintenance can be planned in advance before the problem turns into an unexpected failure. 

The Role of IoT Sensors and Edge Computing 

Digital twins and AI rely on dependable data. Sensors in the Internet of Things placed around a turbine gather continuous data on parameters like temperature, pressure, vibration, airflow, and so on. This data serves as the basis for assessing the status of the turbine in real-time. 

This procedure can become even more efficient using edge computing technology that enables data processing locally without transferring all the data to distant systems. 

Faster processing of the data is especially valuable for power generation facilities. 

Improving Efficiency and Extending Asset Life 

However, apart from being preventative, predictive maintenance solutions may be used to identify areas that need improvements and where performance losses occur.  

Even minor efficiency gains can prove to be very useful since a gas turbine will continue running continuously. Digital tools may point to the loss in performance and allow engineers to explore the potential causes of the decrease such as compressor fouling or deterioration, among others. 

It was noted that some of the digital-twin projects yielded efficiency improvements of several percentage points. Improvements of even several percentage points would be meaningful enough in the context of power generation applications, given their scale.  

In addition, the use of better monitoring solutions allows extending the lifecycle of the asset and maintaining higher performance levels throughout the asset lifetime. 

Why This Matters in Today’s Turbine Market 

This is linked to the existing context in which the gas turbines exist in the market. 

Given the rising demands for power-generation equipment, certain turbines may take a while to deliver. It is not easy for operators to replace their old turbines immediately. It makes sense to value the existing equipment. 

If the time taken to replace one’s asset takes several years, it makes sense for one to ensure that the equipment they have works well for as long as possible. 

The Future of Gas Turbine Maintenance 

Maintenance on gas turbines is evolving from a scheduled to a data-based system. 

The digital twin provides the virtual model while the IoT sensors provide the data. The AI processes the data to discover patterns that can be used to optimize decisions regarding operations.  

The system does not do away with experienced engineers and physical inspection. Rather, it provides them with more accurate data. 

With the increased connectivity and intelligence of gas turbines, predictive maintenance will likely be an integral part of the management process in terms of reliability, efficiency, and asset lifecycle management.  

For businesses running expensive power generating equipment, the future may not just be about better maintenance but rather understanding the status of the machines in order to take action before a costly failure occurs.