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Uncertainty extraction based multi-fault diagnosis of rotating machinery

Uncertainty extraction based multi-fault diagnosis of rotating machinery
Uncertainty extraction based multi-fault diagnosis of rotating machinery
Feature extraction has always been a significant research topic for in-situ fault diagnosis applications. In this research, measurement uncertainty of vibration signal is defined and extracted as a pre-processing step for statistical feature calculation. An Empirical Mode Decomposition (EMD) detrending method combined with hurst exponent criterion is applied to extract uncertainty. Decision tree and Least Square Support Vector Machine (LS-SVM) algorithms are introduced as statistical feature selector and classifier respectively. Misalignment, rub-impact, pedestal looseness as well as eccentricity faults are set on experimental rig in sequence for data collecting and to test the proposed method. As the diagnosis accuracy shows, the extracted uncertain components are more sensitive to rotor faults compared to original vibration signal. HE-EMD (Hurst Exponent-Empirical Mode Decomposition) is proved a rational tool to pre-process vibration signal for an enhanced diagnosis ability. This paper shows effectiveness of a multi-fault diagnosis method with uncertainty components as state indicator and thus provides new approaches for condition monitoring of rotating machinery.
measurement uncertainty, feature extraction, multi-fault diagnosis.
139-150
Ma, Sai
dcf30dab-ab68-40f7-af71-7a3fa836f9d3
Li, Shunming
9c5b6b26-f1e7-47ee-badf-ce3829712f64
Xiong, Yeping
51be8714-186e-4d2f-8e03-f44c428a4a49
Ma, Sai
dcf30dab-ab68-40f7-af71-7a3fa836f9d3
Li, Shunming
9c5b6b26-f1e7-47ee-badf-ce3829712f64
Xiong, Yeping
51be8714-186e-4d2f-8e03-f44c428a4a49

Ma, Sai, Li, Shunming and Xiong, Yeping (2016) Uncertainty extraction based multi-fault diagnosis of rotating machinery. Journal of Vibroengineering, 18 (1), 139-150.

Record type: Article

Abstract

Feature extraction has always been a significant research topic for in-situ fault diagnosis applications. In this research, measurement uncertainty of vibration signal is defined and extracted as a pre-processing step for statistical feature calculation. An Empirical Mode Decomposition (EMD) detrending method combined with hurst exponent criterion is applied to extract uncertainty. Decision tree and Least Square Support Vector Machine (LS-SVM) algorithms are introduced as statistical feature selector and classifier respectively. Misalignment, rub-impact, pedestal looseness as well as eccentricity faults are set on experimental rig in sequence for data collecting and to test the proposed method. As the diagnosis accuracy shows, the extracted uncertain components are more sensitive to rotor faults compared to original vibration signal. HE-EMD (Hurst Exponent-Empirical Mode Decomposition) is proved a rational tool to pre-process vibration signal for an enhanced diagnosis ability. This paper shows effectiveness of a multi-fault diagnosis method with uncertainty components as state indicator and thus provides new approaches for condition monitoring of rotating machinery.

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More information

Accepted/In Press date: 28 August 2015
Published date: 15 February 2016
Keywords: measurement uncertainty, feature extraction, multi-fault diagnosis.
Organisations: Fluid Structure Interactions Group

Identifiers

Local EPrints ID: 386328
URI: http://eprints.soton.ac.uk/id/eprint/386328
PURE UUID: e6b0d63c-a31e-4292-8019-018d1086c887
ORCID for Yeping Xiong: ORCID iD orcid.org/0000-0002-0135-8464

Catalogue record

Date deposited: 14 Mar 2016 13:47
Last modified: 01 Oct 2019 00:54

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