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Technology Reviews

Detailed State of the Art Review for the Different Online/Inline Oil Analysis Techniques in Context of Wind Turbine Gearboxes

[+] Author and Article Information
Andrew Hamilton

Wind Energy Systems Doctoral Training Centre, Institute for Energy and the Environment,  University of Strathclyde, 204 George Street, Glasgow G1 1XW, United Kingdomandrew.hamilton@eee.strath.ac.uk

Francis Quail

Wind Energy Systems Doctoral Training Centre, Institute for Energy and the Environment,  University of Strathclyde, 204 George Street, Glasgow G1 1XW, United Kingdomfrancis.quail@eee.strath.ac.uk

J. Tribol 133(4), 044001 (Oct 06, 2011) (18 pages) doi:10.1115/1.4004903 History: Received October 12, 2010; Revised August 17, 2011; Published October 06, 2011; Online October 06, 2011

The main driver behind developing advanced condition monitoring (CM) systems for the wind energy industry is the delivery of improved asset management regarding the operation and maintenance of the gearbox and other wind turbine components and systems. Current gearbox CM systems mainly detect faults by identifying ferrous materials, water, and air within oil by changes in certain properties such as electrical fields. In order to detect oil degradation and identify particles, more advanced devices are required to allow a better maintenance regime to be established. Current technologies available specifically for this purpose include Fourier transform infrared (FTIR) spectroscopy and ferrography. There are also several technologies that have not yet been or have been recently applied to CM problems. After reviewing the current state of the art, it is recommended that a combination of sensors would be used that analyze different characteristics of the oil. The information individually would not be highly accurate but combined it is fully expected that greater accuracy can be obtained. The technologies that are suitable in terms of cost, size, accuracy, and development are online ferrography, selective fluorescence spectroscopy, scattering measurements, FTIR, photoacoustic spectroscopy, and solid state viscometers.

Copyright © 2011 by American Society of Mechanical Engineers
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References

Figures

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Figure 1

Failure frequency and downtime for wind turbine components [4]

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Figure 2

Computational model of a planetary gearbox [15]

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Figure 3

Left: hydroxyl, middle: carbonyl, left: carboxylic acid

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Figure 4

General effect of temperature in the viscosity of oils [22]

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Figure 5

Hydrolysis reaction of an ester (R represents a general molecule) [20]

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Figure 6

Oxidation of a hydroxyl group

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Figure 7

Coulometric titration diagram [29]

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Figure 8

First three vibrational modes for a simple molecule [32]

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Figure 9

Amount of IR radiation absorbed for each particular component of a sample [33]

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Figure 10

Diagram of classic PAS sensor equipment [39]

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Figure 11

Diagram of capillary tube viscometer [42]

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Figure 12

Distortion of material due to movement of shear wave

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Figure 13

Layout of acoustic wave resonator sensor [44]

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Figure 14

Total fluorescence spectra of a crude oil sample [46]

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Figure 15

Synchronous fluorescence spectra of a crude oil sample [46]

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Figure 16

Time resolved fluorescence spectra of a crude oil sample [46]

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Figure 17

PCA of data from samples taken at 5000 and 10,000 km [47]

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Figure 18

Attenuation spectra for different IR fiber optics [52]

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Figure 19

Use of FTIR to determine excitation wavelength for PAS sensor [39]

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Figure 20

Fluorescence spectra from oil samples at different mileages [47]

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Figure 21

Rubbing wear particle [56]

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Figure 22

Cutting wear particle [56]

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Figure 23

Spherical wear particle [56]

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Figure 24

Severe sliding wear particle [56]

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Figure 25

Moving of contact point along the pressure line (shown by movement of arrow)

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Figure 26

Scuffing wear particle [56]

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Figure 27

Diagram of SEM with EDS detector [58]

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Figure 28

Operation of a ferrogram

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Figure 29

Operation of a particle counter [71]

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Figure 30

Change in flow rate of a channel with increasing blockages [72]

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