Anomaly detection of rolling elements using fuzzy entropy and similarity measures

M. L. Dennis Wong; S. H. Lee; A. K. Nandi

Anomaly detection of rolling elements using fuzzy entropy and similarity measures

M. L. Dennis Wong^*, S. H. Lee, A. K. Nandi

^*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

2 Citations (Scopus)

Abstract

The ability of detecting faults in rotating elements is highly desired in machine condition monitoring application (MCM). On many MCM platforms, discriminating attributes based on time and/or frequency domain of the acquired vibration data are used to classify the element under monitoring into normal and abnormal conditions. However, having such diagnostic ability is still insufficient in our global goal towards predictive maintenance. To achieve true predictive maintenance, the development tool must be able to provide a certain level of real time computation capability. In this paper, the authors propose a novel method based on fuzzy entropy and similarity measure for monitoring the health conditions of ball bearings on-line. The practicalities of the effectiveness and speed of the method are verified empirically, and results are presented towards the end of this paper.

Original language	English
Title of host publication	Institution of Mechanical Engineers
Subtitle of host publication	10th International Conference on Vibrations in Rotating Machinery
Place of Publication	Cambridge, United Kingdom
Publisher	Woodhead Publishing Ltd.
Pages	693-702
Number of pages	10
ISBN (Electronic)	9780857094537
ISBN (Print)	9780857094520
Publication status	Published - 11 Sept 2012
Event	10th International Conference on Vibrations in Rotating Machinery 2012 - London, United Kingdom Duration: 11 Sept 2012 → 13 Sept 2012 Conference number: 10 http://events.imeche.org/ViewEvent?code=c1326# (Conference website)

Conference

Conference	10th International Conference on Vibrations in Rotating Machinery 2012
Abbreviated title	VIRM10
Country/Territory	United Kingdom
City	London
Period	11/09/12 → 13/09/12
Internet address	http://events.imeche.org/ViewEvent?code=c1326# (Conference website)

ASJC Scopus subject areas

Mechanical Engineering

Cite this

@inproceedings{5c103eb33fb54ecabe8fa542ffe2c7c4,

title = "Anomaly detection of rolling elements using fuzzy entropy and similarity measures",

abstract = "The ability of detecting faults in rotating elements is highly desired in machine condition monitoring application (MCM). On many MCM platforms, discriminating attributes based on time and/or frequency domain of the acquired vibration data are used to classify the element under monitoring into normal and abnormal conditions. However, having such diagnostic ability is still insufficient in our global goal towards predictive maintenance. To achieve true predictive maintenance, the development tool must be able to provide a certain level of real time computation capability. In this paper, the authors propose a novel method based on fuzzy entropy and similarity measure for monitoring the health conditions of ball bearings on-line. The practicalities of the effectiveness and speed of the method are verified empirically, and results are presented towards the end of this paper.",

author = "Wong, {M. L. Dennis} and Lee, {S. H.} and Nandi, {A. K.}",

year = "2012",

month = sep,

day = "11",

language = "English",

isbn = "9780857094520",

pages = "693--702",

booktitle = "Institution of Mechanical Engineers",

publisher = "Woodhead Publishing Ltd.",

note = "10th International Conference on Vibrations in Rotating Machinery 2012, VIRM10 ; Conference date: 11-09-2012 Through 13-09-2012",

url = "http://events.imeche.org/ViewEvent?code=c1326#",

}

Wong, MLD, Lee, SH & Nandi, AK 2012, Anomaly detection of rolling elements using fuzzy entropy and similarity measures. in Institution of Mechanical Engineers: 10th International Conference on Vibrations in Rotating Machinery. Woodhead Publishing Ltd., Cambridge, United Kingdom, pp. 693-702, 10th International Conference on Vibrations in Rotating Machinery 2012, London, United Kingdom, 11/09/12.

Anomaly detection of rolling elements using fuzzy entropy and similarity measures. / Wong, M. L. Dennis; Lee, S. H.; Nandi, A. K.
Institution of Mechanical Engineers: 10th International Conference on Vibrations in Rotating Machinery. Cambridge, United Kingdom: Woodhead Publishing Ltd., 2012. p. 693-702.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

TY - GEN

T1 - Anomaly detection of rolling elements using fuzzy entropy and similarity measures

AU - Wong, M. L. Dennis

AU - Lee, S. H.

AU - Nandi, A. K.

N1 - Conference code: 10

PY - 2012/9/11

Y1 - 2012/9/11

N2 - The ability of detecting faults in rotating elements is highly desired in machine condition monitoring application (MCM). On many MCM platforms, discriminating attributes based on time and/or frequency domain of the acquired vibration data are used to classify the element under monitoring into normal and abnormal conditions. However, having such diagnostic ability is still insufficient in our global goal towards predictive maintenance. To achieve true predictive maintenance, the development tool must be able to provide a certain level of real time computation capability. In this paper, the authors propose a novel method based on fuzzy entropy and similarity measure for monitoring the health conditions of ball bearings on-line. The practicalities of the effectiveness and speed of the method are verified empirically, and results are presented towards the end of this paper.

AB - The ability of detecting faults in rotating elements is highly desired in machine condition monitoring application (MCM). On many MCM platforms, discriminating attributes based on time and/or frequency domain of the acquired vibration data are used to classify the element under monitoring into normal and abnormal conditions. However, having such diagnostic ability is still insufficient in our global goal towards predictive maintenance. To achieve true predictive maintenance, the development tool must be able to provide a certain level of real time computation capability. In this paper, the authors propose a novel method based on fuzzy entropy and similarity measure for monitoring the health conditions of ball bearings on-line. The practicalities of the effectiveness and speed of the method are verified empirically, and results are presented towards the end of this paper.

UR - http://www.scopus.com/inward/record.url?scp=84868631119&partnerID=8YFLogxK

M3 - Conference contribution

AN - SCOPUS:84868631119

SN - 9780857094520

SP - 693

EP - 702

BT - Institution of Mechanical Engineers

PB - Woodhead Publishing Ltd.

CY - Cambridge, United Kingdom

T2 - 10th International Conference on Vibrations in Rotating Machinery 2012

Y2 - 11 September 2012 through 13 September 2012

ER -

Anomaly detection of rolling elements using fuzzy entropy and similarity measures

Abstract

Conference

ASJC Scopus subject areas

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