Simulation Software Reduces Turbine Maintenance Downtime

Aug. 10, 2016
The maintenance arm of a South African electricity provider is better able to plan for ultrasonic testing of the steam turbine blades in its power plants.

Part of why people seem skeptical of the Industrial Internet of Things (IIoT), in my opinion, is that they expect it to be some sort of magic pill, and then they don’t think it can possibly live up to the hype. Well, if you expect it to be a magic pill, then it won’t live up to the hype.

But if you understand the benefits of beginning with relatively small changes that can pay off in bits and pieces of improved maintenance regimes, for example, it shouldn’t be that difficult to get started.

That’s what struck me about how an electric utility in South Africa is using simulation to considerably reduce the downtime it needs for maintenance of the turbine blades in its power plants. Rotek, the maintenance branch of state-owned electricity provider Eskom, is using technical computing software from Maplesoft to design a comprehensive pre-inspection simulation model for use in the ultrasonic inspections of its turbine blades. The use of the Maple software is allowing inspections to be conducted quicker and more precisely, knocking several days off the typical inspection time.

The blades in a steam turbine—commonly subject to cracks—must be inspected and maintained regularly. Pressurized steam creates the thermal energy that is used by steam turbines to drive the electrical generator in a power plant. But that pressure can create a lot of stress on the components. The blade root, the portion of the turbine blade that connects to the rotor, is not only the most critical element of a blade, but also the most vulnerable. It is typically there that a crack can initiate and then propagate deeper into the blade.

Because it is too time-consuming to remove the blades for inspection, they must be inspected in situ. That adds complexity to the process, however, due to the location of the blades and limited access points. Blade shape and varying surfaces of the rotor and blades also add complexity.

To minimize the time required for inspection and to optimize the inspection results, Rotek needed to create a comprehensive pre-inspection simulation model, which would specify an accurate, detailed plan for the ultrasonic examination with the highest reliability. Using Maple, Rotek was able to simulate the analytical process, including scanning surfaces, curves of the defect location and optimal shot conditions. These results could then be entered into CIVA non-destructive testing software to determine the optimal equipment settings. With these results, Rotek could perform an ultrasonic examination with the greatest confidence and reduce the turbine downtime needed to conduct the examination.

“Using Maple, we are able to obtain a precise model of the inspection parameters before the actual testing takes place,” says Jean Michel Puybouffat, leader of Rotek’s in-service inspection team. “Using this model, we can better understand the exact equipment settings and most critical shot locations, thereby reducing the time normally required for setup and performance of the actual inspection.”

Different factors influence the conditions of an ultrasonic examination, such as the shape of the defect areas, the shape of the scanning surfaces and the shot conditions required for best detection. Maple can be used to represent unique inspection conditions. It also sets the parameters for optimal shot positions in a 2D phased array, which is essential for dealing with the inherent complexities of the blade root. Unlike conventional arrays, a 2D phased array allows the steering, transmitted and received waves to target the suspected failed area. In Rotek’s case, an array belt of seven arrays was used for the extrados (the exterior curve of the blade) and an array belt of six arrays was used for the intrados (the interior curve of the blade).

Using Maple, the entire inspection package was completely pre-designed and evaluated prior to implementation. The model ensured efficiency, reliability and scanning optimization when the actual ultrasonic inspection was performed. “The use of Maple to pre-design our inspection routines has generated fantastic time and cost savings to our company,” Puybouffat says.

The end result was a considerable reduction in costly machine downtime. The time required to perform an inspection of a single rotor with 88 blades has been reduced from seven days to two.

About the Author

Aaron Hand | Editor-in-Chief, ProFood World

Aaron Hand has three decades of experience in B-to-B publishing with a particular focus on technology. He has been with PMMI Media Group since 2013, much of that time as Executive Editor for Automation World, where he focused on continuous process industries. Prior to joining ProFood World full time in late 2020, Aaron worked as Editor at Large for PMMI Media Group, reporting for all publications on a wide variety of industry developments, including advancements in packaging for consumer products and pharmaceuticals, food and beverage processing, and industrial automation. He took over as Editor-in-Chief of ProFood World in 2021. Aaron holds a B.A. in Journalism from Indiana University and an M.S. in Journalism from the University of Illinois.

Sponsored Recommendations

Crisis averted: How our AI-powered services helped prevent a factory fire

Discover how Schneider Electric's services helped a food and beverage manufacturer avoid a factory fire with AI-powered analytics.

How IT Can Support More Sustainable Manufacturing Operations

This eBook outlines how IT departments can contribute to amanufacturing organization’s sustainability goals and how Schneider Electric's products and...

Three ways generative AI is helping our services experts become superheroes

Discover how we are leveraging generative AI to empower service experts, meet electrification demands, and drive data-driven decision-making

How AI can support better health – for people and power systems

Discover how AI is revolutionizing healthcare and power system management. Learn how AI-driven analytics empower businesses to optimize electrical asset performance and how similar...