Orthogonal On-Rotor Sensing Vibrations for Condition Monitoring of Rotating Machines

Abstract

Thanks to the fast development of micro-electro-mechanical systems (MEMS) technologies, MEMS accelerometers show great potentialities for machine condition monitoring. To overcome the problems of a poor signal to noise ratio (SNR), complicated modulation, and high costs of vibration measurement and computation using conventional integrated electronics piezoelectric accelerometers, a triaxial MEMS accelerometer-based on-rotor sensing (ORS) technology was developed in this study. With wireless data transmission capability, the ORS unit can be mounted on a rotating rotor to obtain both rotational and transverse dynamics of the rotor with a high SNR. The orthogonal outputs lead to a construction method of analytic signals in the time domain, which is versatile in fault detection and diagnosis of rotating machines. Two case studies based on an induction motor were carried out, which demonstrated that incipient bearing defect and half-broken rotor bar can be effectively diagnosed by the proposed measurement and analysis methods. Comparatively, vibration signals from translational on-casing accelerometers are less capable of detecting such faults. This demonstrates the superiority of the ORS vibrations in fault detection of rotating machines.

Publication DOI: https://doi.org/10.37965/jdmd.v2i2.47
Divisions: College of Engineering & Physical Sciences > School of Engineering and Technology > Mechanical, Biomedical & Design
Additional Information: © The Author(s) 2022. This is an open access article published under the CC BY license (https://creativecommons.org/licenses/by/4.0/).
Uncontrolled Keywords: On-rotor sensing,vibration,condition monitoring,rotating machines
Last Modified: 27 Dec 2023 09:40
Date Deposited: 05 Sep 2022 16:21
Full Text Link:
Related URLs: https://ojs.ist ... article/view/47 (Publisher URL)
PURE Output Type: Article
Published Date: 2022-03
Published Online Date: 2021-12-21
Accepted Date: 2021-12-10
Authors: Xu, Yuandong
Tang, Xiaoli (ORCID Profile 0000-0003-4428-0895)
Feng, Guojin
Wang, Dong
Ashworth, Craig
Gu, Fengshou
Ball, Andrew

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