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Friction

, Volume 7, Issue 6, pp 572–586 | Cite as

Tribological behaviour diagnostic and fault detection of mechanical seals based on acoustic emission measurements

  • Hossein TowsyfyanEmail author
  • Fengshou Gu
  • Andrew D. Ball
  • Bo Liang
Open Access
Research Article
  • 264 Downloads

Abstract

Acoustic emission (AE) has been studied for monitoring the condition of mechanical seals by many researchers, however to the best knowledge of the authors, typical fault cases and their effects on tribological behaviour of mechanical seals have not yet been successfully investigated. In this paper, AE signatures from common faults of mechanical seals are studied in association with tribological behaviour of sealing gap to develop more reliable condition monitoring approaches. A purpose-built test rig was employed for recording AE signals from the mechanical seals under healthy and faulty conditions. The collected data was then processed using time domain and frequency domain analysis methods. The study has shown that AE signal parameters: root mean squared (RMS) along with AE spectrum, allows fault conditions including dry running, spring out and defective seal faces to be diagnosed under a wide range of operating conditions. However, when mechanical seals operate around their transition point, conventional signal processing methods may not allow a clear separation of the fault conditions from the healthy baseline. Therefore an auto-regressive (AR) model has been developed on recorded AE signals to classify different fault conditions of mechanical seals and satisfactory results have been perceived.

Keywords

tribology acoustic emission condition monitoring 

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Copyright information

© The author(s) 2018

Open Access: The articles published in this journal are distributed under the terms of the Creative Commons Attribution 4.0 International License (https://doi.org/creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.

Authors and Affiliations

  • Hossein Towsyfyan
    • 1
    Email author
  • Fengshou Gu
    • 2
  • Andrew D. Ball
    • 2
  • Bo Liang
    • 2
  1. 1.Institute of Sound and Vibration Research (ISVR), Digital Signal Processing and Control GroupUniversity of SouthamptonSouthamptonUK
  2. 2.School of Computing and EngineeringUniversity of HuddersfieldHuddersfieldUK

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