A practical signal processing approach for condition monitoring of low speed machinery using Peak-Hold-Down-Sample algorithm

Lin, Tian Ran, Kim, Eric, & Tan, Andy C.C. (2012) A practical signal processing approach for condition monitoring of low speed machinery using Peak-Hold-Down-Sample algorithm. Mechanical Systems and Signal Processing, 36(2), pp. 256-270.

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Abstract

A simple and effective down-sample algorithm, Peak-Hold-Down-Sample (PHDS) algorithm is developed in this paper to enable a rapid and efficient data transfer in remote condition monitoring applications. The algorithm is particularly useful for high frequency Condition Monitoring (CM) techniques, and for low speed machine applications since the combination of the high sampling frequency and low rotating speed will generally lead to large unwieldy data size. The effectiveness of the algorithm was evaluated and tested on four sets of data in the study. One set of the data was extracted from the condition monitoring signal of a practical industry application. Another set of data was acquired from a low speed machine test rig in the laboratory. The other two sets of data were computer simulated bearing defect signals having either a single or multiple bearing defects. The results disclose that the PHDS algorithm can substantially reduce the size of data while preserving the critical bearing defect information for all the data sets used in this work even when a large down-sample ratio was used (i.e., 500 times down-sampled). In contrast, the down-sample process using existing normal down-sample technique in signal processing eliminates the useful and critical information such as bearing defect frequencies in a signal when the same down-sample ratio was employed. Noise and artificial frequency components were also induced by the normal down-sample technique, thus limits its usefulness for machine condition monitoring applications.

Impact and interest:

12 citations in Scopus
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10 citations in Web of Science®

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ID Code: 54828
Item Type: Journal Article
Refereed: Yes
Keywords: Down-sample algorithm, Condition monitoring, Acoustic emission, Bearing defect detection, Low speed machine
DOI: 10.1016/j.ymssp.2012.11.003
ISSN: 1096-1216
Subjects: Australian and New Zealand Standard Research Classification > ENGINEERING (090000) > ELECTRICAL AND ELECTRONIC ENGINEERING (090600) > Signal Processing (090609)
Australian and New Zealand Standard Research Classification > ENGINEERING (090000) > MECHANICAL ENGINEERING (091300) > Dynamics Vibration and Vibration Control (091304)
Australian and New Zealand Standard Research Classification > ENGINEERING (090000) > MECHANICAL ENGINEERING (091300) > Microelectromechanical Systems (MEMS) (091306)
Divisions: Current > Schools > School of Chemistry, Physics & Mechanical Engineering
Current > Research Centres > CRC Integrated Engineering Asset Management (CIEAM)
Current > QUT Faculties and Divisions > Science & Engineering Faculty
Copyright Owner: Copyright 2012 Elsevier Ltd. All rights reserved.
Copyright Statement: This is the author’s version of a work that was accepted for publication in Mechanical Systems and Signal Processing. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Mechanical Systems and Signal Processing, VOL 36, ISSUE 2, (2012) DOI: 10.1016/j.ymssp.2012.11.003
Deposited On: 08 Jan 2013 04:51
Last Modified: 16 May 2013 23:43

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