Skip to main content
Kent Academic Repository

Intelligent condition monitoring of rotating machinery through electrostatic sensing and signal analysis

Wang, Lijuan and Yan, Yong and Hu, Yonghui and Qian, Xiangchen (2013) Intelligent condition monitoring of rotating machinery through electrostatic sensing and signal analysis. In: 2013 IEEE International Conference on Smart Instrumentation, Measurement and Applications (ICSIMA). IEEE, pp. 1-4. ISBN 978-1-4799-0842-4. E-ISBN 978-1-4799-0843-1. (doi:10.1109/ICSIMA.2013.6717951) (The full text of this publication is not currently available from this repository. You may be able to access a copy if URLs are provided) (KAR id:41396)

The full text of this publication is not currently available from this repository. You may be able to access a copy if URLs are provided.
Official URL:
http://dx.doi.org/10.1109/ICSIMA.2013.6717951

Abstract

Condition monitoring is a key step to identify the health status of working machinery and establish a necessary maintenance strategy. This paper proposes a novel intelligent system for the online monitoring of the operating conditions of rotating machinery using electrostatic sensors and signal processing techniques. This system is capable of providing simultaneous measurements of rotational speed, angular acceleration, vibration direction and frequency as well as an indication of mechanical wear. These parameters usually contain abundant fault-related information about the rotating machinery, which is to be extracted by detecting the electrostatic charge on the surface of the moving part. The general principle and system design considerations are presented. Preliminary experimental results obtained from laboratory tests demonstrate the effectiveness of the monitoring system.

Item Type: Book section
DOI/Identification number: 10.1109/ICSIMA.2013.6717951
Subjects: T Technology
Divisions: Divisions > Division of Computing, Engineering and Mathematical Sciences > School of Engineering and Digital Arts
Depositing User: Tina Thompson
Date Deposited: 12 Jun 2014 09:01 UTC
Last Modified: 16 Nov 2021 10:16 UTC
Resource URI: https://kar.kent.ac.uk/id/eprint/41396 (The current URI for this page, for reference purposes)

University of Kent Author Information

  • Depositors only (login required):

Total unique views for this document in KAR since July 2020. For more details click on the image.