Picture of person typing on laptop with programming code visible on the laptop screen

World class computing and information science research at Strathclyde...

The Strathprints institutional repository is a digital archive of University of Strathclyde's Open Access research outputs. Strathprints provides access to thousands of Open Access research papers by University of Strathclyde researchers, including by researchers from the Department of Computer & Information Sciences involved in mathematically structured programming, similarity and metric search, computer security, software systems, combinatronics and digital health.

The Department also includes the iSchool Research Group, which performs leading research into socio-technical phenomena and topics such as information retrieval and information seeking behaviour.

Explore

Development of a novel wear detection system for wind turbine gearboxes

Hamilton, Andrew Wallace and Cleary, Alison and Quail, Francis (2014) Development of a novel wear detection system for wind turbine gearboxes. IEEE Sensors Journal, 14 (2). 465 - 473. ISSN 1530-437X

Full text not available in this repository. Request a copy from the Strathclyde author

Abstract

This paper presents a low-cost, inline, gearbox lubrication monitoring sensor. The purpose of the research was to develop a sensor that can analyze wear particles suspended in gearbox lubricant systems. Current inline sensor systems rely on methods that prevent significant morphological classification. The size and shape of the particles are often indicative of the type of wear that is occurring and is therefore significant in assessing the gearbox state. A demonstration sensor consisting of a webcam that uses an active pixel sensor combined with a rectangular cross section optically transparent acrylic pipe was developed. A rig that simulates a gearbox lubrication system was used to test the sensor. Images of wear particles suspended in the lubricant were captured in real time. Image analysis was then performed to distinguish particles from the lubricant medium. Object characteristics, such as area and major axis length, were used to determine shape parameters. It was found that the sensor could detect particles down to a major axis length of 125 μm. Classification was also demonstrated for four basic shapes: square, circular, rectangular and ellipsoidal. ellipsoidal, was also demonstrated.