Generalized harmonic wavelet as an adaptive filter for machine health diagnosis

Ruqiang Yan; Robert X. Gao

doi:10.1117/12.599925

Generalized harmonic wavelet as an adaptive filter for machine health diagnosis

Ruqiang Yan
, Robert X. Gao

University of Massachusetts

Research output: Contribution to journal › Conference article › peer-review

2 Scopus citations

Abstract

This paper presents an adaptive filtering technique for the health diagnosis of mechanical systems, based on the generalized harmonic wavelet transformation. Through selection of two wavelet level parameters, a series of sub-frequency band wavelet coefficients corresponding to equi-bandwidth vibration signals measured from a machine were constructed. The energy and entropy associated with each sub-frequency band were then calculated, and the band with the maximum energy-to-entropy ratio was chosen to form a band-limited filter for the vibration signals. Experimental studies using rolling bearings that contain structural defects have confirmed that, the developed new technique enables high signal-to-noise ratio for effective machine failure detection and health diagnosis.

Original language	English
Article number	87
Pages (from-to)	786-793
Number of pages	8
Journal	Proceedings of SPIE - The International Society for Optical Engineering
Volume	5765
Issue number	PART 2
DOIs	https://doi.org/10.1117/12.599925
State	Published - 2005
Externally published	Yes
Event	Smart Structures and Materials 2005 - Sensors and Smart Structures Technologies for Civil, Mechanical, and Aerospace Systems - San Diego, CA, United States Duration: 7 Mar 2005 → 10 Mar 2005

Keywords

Energy-to-entropy ratio
Generalized harmonic wavelet transform
Health diagnosis

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10.1117/12.599925

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title = "Generalized harmonic wavelet as an adaptive filter for machine health diagnosis",

abstract = "This paper presents an adaptive filtering technique for the health diagnosis of mechanical systems, based on the generalized harmonic wavelet transformation. Through selection of two wavelet level parameters, a series of sub-frequency band wavelet coefficients corresponding to equi-bandwidth vibration signals measured from a machine were constructed. The energy and entropy associated with each sub-frequency band were then calculated, and the band with the maximum energy-to-entropy ratio was chosen to form a band-limited filter for the vibration signals. Experimental studies using rolling bearings that contain structural defects have confirmed that, the developed new technique enables high signal-to-noise ratio for effective machine failure detection and health diagnosis.",

keywords = "Energy-to-entropy ratio, Generalized harmonic wavelet transform, Health diagnosis",

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year = "2005",

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TY - JOUR

T1 - Generalized harmonic wavelet as an adaptive filter for machine health diagnosis

AU - Yan, Ruqiang

AU - Gao, Robert X.

PY - 2005

Y1 - 2005

N2 - This paper presents an adaptive filtering technique for the health diagnosis of mechanical systems, based on the generalized harmonic wavelet transformation. Through selection of two wavelet level parameters, a series of sub-frequency band wavelet coefficients corresponding to equi-bandwidth vibration signals measured from a machine were constructed. The energy and entropy associated with each sub-frequency band were then calculated, and the band with the maximum energy-to-entropy ratio was chosen to form a band-limited filter for the vibration signals. Experimental studies using rolling bearings that contain structural defects have confirmed that, the developed new technique enables high signal-to-noise ratio for effective machine failure detection and health diagnosis.

AB - This paper presents an adaptive filtering technique for the health diagnosis of mechanical systems, based on the generalized harmonic wavelet transformation. Through selection of two wavelet level parameters, a series of sub-frequency band wavelet coefficients corresponding to equi-bandwidth vibration signals measured from a machine were constructed. The energy and entropy associated with each sub-frequency band were then calculated, and the band with the maximum energy-to-entropy ratio was chosen to form a band-limited filter for the vibration signals. Experimental studies using rolling bearings that contain structural defects have confirmed that, the developed new technique enables high signal-to-noise ratio for effective machine failure detection and health diagnosis.

KW - Energy-to-entropy ratio

KW - Generalized harmonic wavelet transform

KW - Health diagnosis

UR - https://www.scopus.com/pages/publications/25144520858

U2 - 10.1117/12.599925

DO - 10.1117/12.599925

M3 - 会议文章

AN - SCOPUS:25144520858

SN - 0277-786X

VL - 5765

SP - 786

EP - 793

JO - Proceedings of SPIE - The International Society for Optical Engineering

JF - Proceedings of SPIE - The International Society for Optical Engineering

IS - PART 2

M1 - 87

T2 - Smart Structures and Materials 2005 - Sensors and Smart Structures Technologies for Civil, Mechanical, and Aerospace Systems

Y2 - 7 March 2005 through 10 March 2005

ER -

Generalized harmonic wavelet as an adaptive filter for machine health diagnosis

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Keywords

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