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.
Original language | English |
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Pages (from-to) | 465 - 473 |
Number of pages | 9 |
Journal | IEEE Sensors Journal |
Volume | 14 |
Issue number | 2 |
Early online date | 9 Oct 2013 |
DOIs | |
Publication status | Published - Feb 2014 |
Keywords
- lubrication
- wear
- sensor
- wind turbine
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Dive into the research topics of 'Development of a novel wear detection system for wind turbine gearboxes'. Together they form a unique fingerprint.Datasets
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2D Rig Data Raw Images
Hamilton, A. (Creator), University of Strathclyde, 18 Apr 2016
DOI: 10.15129/f0990e57-d0c5-4638-8494-5550166b3263
Dataset
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2D Rig Data Extracted Data
Hamilton, A. (Creator), University of Strathclyde, 2 Feb 2015
DOI: 10.15129/708cc628-03d0-407b-8bc0-98ecb03549a6
Dataset
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2D Rig Data Raw Images
Hamilton, A. (Creator), University of Strathclyde, 18 Apr 2016
DOI: 10.15129/a5d104c0-0127-4b49-9fae-dc7c0b57f413
Dataset