A new generation of ultrasonic sensors will listen to the rubbing surfaces inside engines, gearboxes, and turbines to detect when they are about to fail—before they actually break. Current monitoring methods only catch failures after they happen, by measuring excessive vibration or counting metal fragments in oil. This leaves operators unable to plan maintenance or optimise performance. The Leonardo Centre at Sheffield has already developed ultrasonic techniques that measure oil film thickness and metal contact in bearings, pistons, and seals. This fellowship extends that work to measure contact load, stress history, oil viscosity, and friction in real time, using different ultrasonic wave types and signal processing. If successful, these sensors could be embedded in commodity machine components, turning standard parts into smart parts. For high-cost machinery in remote locations—such as offshore wind turbines—this means planned maintenance instead of emergency shutdowns, longer component life, and energy savings. The research also aims to become part of the UK’s research infrastructure, providing new measurement tools for both industry and academic tribology labs.
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Engineering machines, from car and planes, to power stations and production lines, have lots of moving parts. The reliability of these parts is key to the function and energy efficiency the machine. It is often these moving parts that fail and frequently that failure is associated with the rubbing surfaces. Machine elements like bearings, gears, seals, and pistons often wear out, exhibit high friction, or seize. Knowing if a machine element is performing at its optimum can save energy and lead to long life. Being able to monitor the components in-situ in a machine can speed up the development cycle time. Further, monitoring performance rather than failure, allows allows the machine operator to plan maintenance. This is particularly important for high capital cost machines, in remote locations, like offshore wind turbines. Current monitoring methods are based around measuring excessive vibration or the noise emitted by a failed component (acoustic emission AE) or by counting wear debris particles in a lubricant. Sensors that measure performance rather than failure, and so can be used to optimise operating parameters would be much more useful. This also opens the possibility of using advanced control based on sensor readings, Many machine components are commodities, and integrating sensors provides a way to add value to what would otherwise be a commodity product. The Leonardo Centre at Sheffield has developed unique methods for measuring machine contacts in-situ. The approaches are based on ultrasonic technologies adapted from the NDT and dynamics communities. By sending ultrasonic pulses through machine components and measuring transmission and reflection we have been able to non-invasively study various tribological machine components. In early work we developed methods to measure the oil film thickness, and the amount of metal contact. This has been well established, validated in laboratory experiments, and applied to journal bearings, trust pads, rolling bearings, pistons, and seals. Several industrial companies have adopted these approaches in their product development cycles. This fellowship seeks to explore new methods to learn more about contacts. Buy using different kinds of ultrasonic waves, transducer topologies, and signal processing we will develop methods to measure contact load, stress history, oil viscosity, and friction. These will be prototyped in the laboratory and we have industrial partners ready to provide field applications. In addition the fellowship seeks to collaborate with academic institutions; firstly to learn new acoustic sensor techniques and secondly to support research into machine element research with the provision of new measurement methods. This will support the Leonardo Centre aim to be, not only the leading centre for ultrasonic measurement in tribology, but to be a key part of the UK's research infrastructure in machine component research and development both in industry and academia.
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