The University of Southampton
University of Southampton Institutional Repository

Vibration source model estimation and state specificity perception of a rotor structure

Vibration source model estimation and state specificity perception of a rotor structure
Vibration source model estimation and state specificity perception of a rotor structure
Rotor structures in abnormal states will create non-stationary vibration sources, these non-stationary features are often ignored by traditional signal processing algorithms, thereby the determination of rotor running states is affected. A new state specificity perception method based on kernel density function estimation combined with higher moments feature extraction and multi-class support vector machine is proposed. Firstly, for rotors with non-stationary vibration source, kernel density estimation algorithm is used to estimate the probability density model, which fully considers band broadening features of non-stationary signals. Then higher order moments of non-stationary vibration source model are calculated, the values are assembled to state mark vectors. Finally, the multi-class support vector machine is used to classify the state mark vectors. Three types of abnormal states are set on cascade rotor system for experiments, acceleration data is collected and the state specificity perception process is carried out. All abnormal states are identified successfully. High accuracy proves the effectiveness of Kernel Density Estimation-Support Vector Machine (KDE-SVM) method. The proposed specificity extraction and perception method provides new ideas for running structure condition monitoring.
2321-3558
575-587
Ma, Sai
2334efaa-c23a-4b1e-94b7-6c1154b2208d
Li, Shun Ming
f462fe35-203f-4b2a-a81c-edf6dd11f126
Liu, Hai Long
e1135d26-9076-43df-b443-b2fbb04b69c9
Miao, Xiao Dong
6897fd3a-93fc-4f48-bacd-7eb82456838c
Wany, Yong
4049acde-d036-47a6-8f6c-751e73a05a03
Liu, Tian Wen
c2079cd5-b57e-457c-94c7-b707a48cb8f9
Xiong, Yeping
51be8714-186e-4d2f-8e03-f44c428a4a49
Ma, Sai
2334efaa-c23a-4b1e-94b7-6c1154b2208d
Li, Shun Ming
f462fe35-203f-4b2a-a81c-edf6dd11f126
Liu, Hai Long
e1135d26-9076-43df-b443-b2fbb04b69c9
Miao, Xiao Dong
6897fd3a-93fc-4f48-bacd-7eb82456838c
Wany, Yong
4049acde-d036-47a6-8f6c-751e73a05a03
Liu, Tian Wen
c2079cd5-b57e-457c-94c7-b707a48cb8f9
Xiong, Yeping
51be8714-186e-4d2f-8e03-f44c428a4a49

Ma, Sai, Li, Shun Ming, Liu, Hai Long, Miao, Xiao Dong, Wany, Yong, Liu, Tian Wen and Xiong, Yeping (2015) Vibration source model estimation and state specificity perception of a rotor structure. Journal of Vibration Engineering & Technologies, 3 (5), 575-587.

Record type: Article

Abstract

Rotor structures in abnormal states will create non-stationary vibration sources, these non-stationary features are often ignored by traditional signal processing algorithms, thereby the determination of rotor running states is affected. A new state specificity perception method based on kernel density function estimation combined with higher moments feature extraction and multi-class support vector machine is proposed. Firstly, for rotors with non-stationary vibration source, kernel density estimation algorithm is used to estimate the probability density model, which fully considers band broadening features of non-stationary signals. Then higher order moments of non-stationary vibration source model are calculated, the values are assembled to state mark vectors. Finally, the multi-class support vector machine is used to classify the state mark vectors. Three types of abnormal states are set on cascade rotor system for experiments, acceleration data is collected and the state specificity perception process is carried out. All abnormal states are identified successfully. High accuracy proves the effectiveness of Kernel Density Estimation-Support Vector Machine (KDE-SVM) method. The proposed specificity extraction and perception method provides new ideas for running structure condition monitoring.

Full text not available from this repository.

More information

Published date: October 2015
Organisations: Fluid Structure Interactions Group

Identifiers

Local EPrints ID: 383408
URI: http://eprints.soton.ac.uk/id/eprint/383408
ISSN: 2321-3558
PURE UUID: 5b518c4b-b6f2-483b-b971-8214d30526e8
ORCID for Yeping Xiong: ORCID iD orcid.org/0000-0002-0135-8464

Catalogue record

Date deposited: 10 Nov 2015 15:11
Last modified: 20 Jul 2019 01:07

Export record

Contributors

Author: Sai Ma
Author: Shun Ming Li
Author: Hai Long Liu
Author: Xiao Dong Miao
Author: Yong Wany
Author: Tian Wen Liu
Author: Yeping Xiong ORCID iD

University divisions

Download statistics

Downloads from ePrints over the past year. Other digital versions may also be available to download e.g. from the publisher's website.

View more statistics

Atom RSS 1.0 RSS 2.0

Contact ePrints Soton: eprints@soton.ac.uk

ePrints Soton supports OAI 2.0 with a base URL of http://eprints.soton.ac.uk/cgi/oai2

This repository has been built using EPrints software, developed at the University of Southampton, but available to everyone to use.

We use cookies to ensure that we give you the best experience on our website. If you continue without changing your settings, we will assume that you are happy to receive cookies on the University of Southampton website.

×