Acoustic emission monitoring of mechanical seals using music algorithm based on higher order statistics

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12 Citations (Scopus)

Abstract

Abstract: This paper presents the use of the MUSIC algorithm improved by higher order statistics (HOS) to extract key features from the noisy acoustic emission (AE) signals. The low signal-tonoise ratio of AE signals has been identified as a main barrier to the successful condition monitoring of pump mechanical seals. Since HOS methods can effectively eliminate Gaussian noise, it is possible in theory to identify a change in seal conditions from AE measurements even with low signal-to-noise ratios. Tests conducted on a test rig show that the developed algorithm can successfully detect the AE signal generated from the friction of seal faces under noisy conditions.

LanguageEnglish
Pages811-816
Number of pages6
JournalKey Engineering Materials
Volume413-414
DOIs
Publication statusPublished - 2009

Fingerprint

Higher order statistics
Acoustic emissions
Seals
Monitoring
Condition monitoring
Signal to noise ratio
Pumps
Friction

Cite this

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title = "Acoustic emission monitoring of mechanical seals using music algorithm based on higher order statistics",
abstract = "Abstract: This paper presents the use of the MUSIC algorithm improved by higher order statistics (HOS) to extract key features from the noisy acoustic emission (AE) signals. The low signal-tonoise ratio of AE signals has been identified as a main barrier to the successful condition monitoring of pump mechanical seals. Since HOS methods can effectively eliminate Gaussian noise, it is possible in theory to identify a change in seal conditions from AE measurements even with low signal-to-noise ratios. Tests conducted on a test rig show that the developed algorithm can successfully detect the AE signal generated from the friction of seal faces under noisy conditions.",
keywords = "Acoustic emission, Higher order statistics, Mechanical seals, MUSIC algorithm",
author = "Yibo Fan and Fengshou Gu and Andrew Ball",
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AU - Gu, Fengshou

AU - Ball, Andrew

PY - 2009

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N2 - Abstract: This paper presents the use of the MUSIC algorithm improved by higher order statistics (HOS) to extract key features from the noisy acoustic emission (AE) signals. The low signal-tonoise ratio of AE signals has been identified as a main barrier to the successful condition monitoring of pump mechanical seals. Since HOS methods can effectively eliminate Gaussian noise, it is possible in theory to identify a change in seal conditions from AE measurements even with low signal-to-noise ratios. Tests conducted on a test rig show that the developed algorithm can successfully detect the AE signal generated from the friction of seal faces under noisy conditions.

AB - Abstract: This paper presents the use of the MUSIC algorithm improved by higher order statistics (HOS) to extract key features from the noisy acoustic emission (AE) signals. The low signal-tonoise ratio of AE signals has been identified as a main barrier to the successful condition monitoring of pump mechanical seals. Since HOS methods can effectively eliminate Gaussian noise, it is possible in theory to identify a change in seal conditions from AE measurements even with low signal-to-noise ratios. Tests conducted on a test rig show that the developed algorithm can successfully detect the AE signal generated from the friction of seal faces under noisy conditions.

KW - Acoustic emission

KW - Higher order statistics

KW - Mechanical seals

KW - MUSIC algorithm

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EP - 816

JO - Key Engineering Materials

T2 - Key Engineering Materials

JF - Key Engineering Materials

SN - 1013-9826

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