On Rotor Sensing (ORS) for Improved Rolling Stock Reliability, Efficiency and Performance.

A project with Digital Transit Limited (DTL) and the University of Huddersfield (UoH) to demonstrate the On Rotor Sensor (ORS) which provides timely condition, efficiency and performance data from wind turbines offshores. It has the potential to make a step-change improvement in the ability to identify and interpret incipient electrical and mechanical faults, along with changes in efficiency and operational performance. In doing so, the capability offers a significant opportunity to improve the reliability, availability and efficiency of offshore wind turbines.

Conventional vibration sensors are generally accelerometers that work on either piezoelectric or microelectromechanical systems (MEMS) principles. They are typically located on a rigid component as close as possible to the rotating assembly of interest; this is to minimise attenuation and distortion of the signal as it is transmitted from the moving component to the stationary sensor.

For over 10 years, we have been developing tri-axial MEMS-based vibration sensors with the specific aim of installing them on the rotating component of interest. These miniature devices harvest energy to power themselves and they communicate wirelessly. The ORS will bring a step change improvement in the quality and information content of the dynamic data that can be collected from a rotating element.

UoH has proven that when using ORS, the condition-indicating information is much more pronounced and much richer, significantly enhancing the abilities to detect, locate, diagnose, severity assess, and prognose a change in condition or performance. This improvement in ability stems from the fact that an ORS measures dynamic behaviour at source, rather than via an attenuating and distorting transmission path.

This is a first-of-a-kind innovation that has been proven to prototype stage, including testing on wind turbines and similar machines. These real machine applications have clearly shown a significant improvement in capability over conventional monitoring approaches.

The ORS is self-powered using energy harvesting capabilities and communicates to a local hub using either Wi-Fi or Bluetooth-LE. From the local hub, data and information can be accessed via the fibreoptic link that is generally incorporated with the power cable. According to need and system configuration, data analysis and feature extraction can take place within the sensor, in the local hub or in the cloud, or any combination thereof. The information gathered ranges from simple measures like operational parameters, to detailed machine condition and performance metrics providing high integrity insight into matters such as wear and fatigue in gear drives, and electromechanical modes of deterioration in generators.

The system is currently at TRL6 (Technology Readiness Level 6) and with the funding, this can be raised to TRL7/8 by deploying the ORS on the numerous representative wind farms in the USA.

Yes, it is true to say that there are several businesses which already provide rotating machinery monitoring technologies which can monitor a wind farm, but these use less capable and more narrow-application techniques such as measuring motor torque and housing IMUs.

DTL require the funding to instruct the work with UoH and to collaborate with wind farm operators in the US.

This ORS project is an innovative approach to the monitoring of offshore wind farms, will enhance their competitiveness and will be a key enabler in decarbonization.

To sum up, the ORS supplies critical data that enables offshore wind farm operators to detect gradual wear and tear in wind farm machine components, as well as emerging inefficiencies and deterioration in performance over time. Once these issues are identified, proactive measures can be implemented to optimize efficiency and minimize downtime—a key objective.

By pinpointing areas where efficiency falters, the ORS empowers operators to make data-driven adjustments that lead to reduced waste and energy consumption, aligning with sustainability and net-zero objectives. Enhancing machinery uptime in this way contributes to greater resilience within the industry.