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The water-lubricated thrust bearings of the marine rim-driven thruster (RDT) are usually composed of polymer composites, which are prone to serious wear under harsh working conditions. Ultrasonic is an excellent non-destructive monitoring technology, but polymer materials are characterized by viscoelasticity, heterogeneity, and large acoustic attenuation, making it challenging to extract ultrasonic echo signals. Therefore, this paper proposes a wear monitoring method based on the amplitude spectrum of the ultrasonic reflection coefficient. The effects of bearing parameters, objective function, and algorithm parameters on the identification results are simulated and analyzed. Taking the correlation coefficient and root mean square error as the matching parameters, the thickness, sound velocity, density, and attenuation factor of the bearing are inversed simultaneously by utilizing the differential evolution algorithm (DEA), and the wear measurement system is constructed. In order to verify the identification accuracy of this method, an accelerated wear test under heavy load was executed on a multi-functional vertical water lubrication test rig with poly-ether-ether-ketone (PEEK) fixed pad and stainless-steel thrust collar as the object. The thickness of pad was measured using the high-precision spiral micrometer and ultrasonic testing system, respectively. Ultimately, the results demonstrate that the thickness identification error of this method is approximately 1%, and in-situ monitoring ability will be realized in the future, which is of great significance to the life prediction of bearings.


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Wear monitoring method of water-lubricated polymer thrust bearing based on ultrasonic reflection coefficient amplitude spectrum

Show Author's information Changxiong NING1Fei HU1Wu OUYANG2,3( )Xinpin YAN2,3Dongling XU4
School of Naval Architecture, Ocean and Energy Power Engineering, Wuhan University of Technology, Wuhan 430063, China
School of Transportation and Logistics Engineering, Wuhan University of Technology, Wuhan 430063, China
Reliability Engineering Institute, National Engineering Research Center for Water Transport Safety, Wuhan 430063, China
Alliance Manchester Business School, The University of Manchester, Manchester M156PB, UK

Abstract

The water-lubricated thrust bearings of the marine rim-driven thruster (RDT) are usually composed of polymer composites, which are prone to serious wear under harsh working conditions. Ultrasonic is an excellent non-destructive monitoring technology, but polymer materials are characterized by viscoelasticity, heterogeneity, and large acoustic attenuation, making it challenging to extract ultrasonic echo signals. Therefore, this paper proposes a wear monitoring method based on the amplitude spectrum of the ultrasonic reflection coefficient. The effects of bearing parameters, objective function, and algorithm parameters on the identification results are simulated and analyzed. Taking the correlation coefficient and root mean square error as the matching parameters, the thickness, sound velocity, density, and attenuation factor of the bearing are inversed simultaneously by utilizing the differential evolution algorithm (DEA), and the wear measurement system is constructed. In order to verify the identification accuracy of this method, an accelerated wear test under heavy load was executed on a multi-functional vertical water lubrication test rig with poly-ether-ether-ketone (PEEK) fixed pad and stainless-steel thrust collar as the object. The thickness of pad was measured using the high-precision spiral micrometer and ultrasonic testing system, respectively. Ultimately, the results demonstrate that the thickness identification error of this method is approximately 1%, and in-situ monitoring ability will be realized in the future, which is of great significance to the life prediction of bearings.

Keywords:

water-lubricated polymer thrust bearings, wear monitoring, ultrasonic reflection coefficient amplitude spectrum, parameter inversion, differential evolution
Received: 26 August 2021 Revised: 15 November 2021 Accepted: 29 April 2022 Published: 06 January 2023 Issue date: May 2023
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Publication history
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Publication history

Received: 26 August 2021
Revised: 15 November 2021
Accepted: 29 April 2022
Published: 06 January 2023
Issue date: May 2023

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© The author(s) 2022.

Acknowledgements

This study is supported by the National Key R&D Program of China (No. 2018YFE0197600), the European Union’s Horizon 2020 Research and Innovation Programme RISE under Grant Agreement No. 823759 (REMESH), and the National Natural Science Foundation of China (No. 52071244).

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