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Research Papers: Applications

Image Acquisition and Image Processing Algorithm for Movement Analysis of Bearings' Rolling Elements

[+] Author and Article Information
Eberhard Abele

Professor
Institute of Production Management,
Technology and Machine Tools,
Technical University of Darmstadt,
Otto-Berndt Straße 2,
Darmstadt 64287, Germany
e-mail: info@ptw.tu-darmstadt.de

Lars Holland

Institute of Production Management,
Technology and Machine Tools,
Technical University of Darmstadt,
Otto-Berndt Straße 2,
Darmstadt 64287, Germany
e-mail: holland@ptw.tu-darmstadt.de

Philipp Hönig

Institute of Production Management,
Technology and Machine Tools,
Technical University of Darmstadt,
Otto-Berndt Straße 2,
Darmstadt 64287, Germany
e-mail: info@ptw.tu-darmstadt.de

1Corresponding author.

Contributed by the Tribology Division of ASME for publication in the JOURNAL OF TRIBOLOGY. Manuscript received August 18, 2016; final manuscript received April 22, 2017; published online August 2, 2017. Assoc. Editor: Xiaolan Ai.

J. Tribol 140(1), 011103 (Aug 02, 2017) (9 pages) Paper No: TRIB-16-1266; doi: 10.1115/1.4037066 History: Received August 18, 2016; Revised April 22, 2017

Movement analyses of bearings with regard to stable and unstable motion behavior typically investigate the cage whirl. However, some experimental and simulation-based studies exist that analyze the movements of the rolling elements. The majority of these investigations focus on lower shaft speeds. This paper presents an image-based approach for investigating the rolling element motions under high-speed operation condition. A new evaluation algorithm is presented and its suitability is verified first by generic images and afterward by experiments on cylindrical roller bearings.

Copyright © 2018 by ASME
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References

Figures

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Fig. 1

Speed zones in a radial bearing (a) and rotational speed characteristics of a single rolling element (b) [5]

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Fig. 2

Schematic test rig setup for derotating prism analysis [13]

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Fig. 5

Image evaluation procedure

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Fig. 6

Conventional edge detection

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Fig. 7

Scanning procedure for grayscale leaps

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Fig. 8

Results grayscale gradient

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Fig. 9

Edge detection generic image: (a) detected edge, (b) magnification, and (c) weighting

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Fig. 10

Edge detection real image

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Fig. 11

Determination of cage edge

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Fig. 12

Deviation of values in real, nonoperating bearing images

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Fig. 13

Generic image with evenly and deliberately spread rolling elements

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Fig. 14

Deviation of values in generic, rotating images

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Fig. 15

Exemplary deviation of values for one roller in a rotating bearing at 3000 rpm

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Fig. 16

Distance between two rollers at 3000 rpm

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Fig. 17

Distance between two rollers at 6000 rpm

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Fig. 18

Calculation of relative movements of rollers

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Fig. 19

Roller speed for one exemplary roller at 3000 rpm

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Fig. 20

Roller speed for one exemplary roller at 6000 rpm

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