Non-stationary random vibration modelling and analysis via functional series time-dependent ARMA (FS-TARMA) models – A critical survey

A critical overview of non-stationary random vibration modelling and analysis via the class of Functional Series Time-dependent AutoRegressive Moving Average (FS-TARMA) models is presented. FS-TARMA models feature deterministic parameter evolution and have been shown to effectively represent non-sta...

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Published inMechanical systems and signal processing Vol. 47; no. 1-2; pp. 175 - 224
Main Authors Spiridonakos, M.D., Fassois, S.D.
Format Journal Article
LanguageEnglish
Published Elsevier Ltd 03.08.2014
Subjects
Computer simulation
Evolution
Functional series models
Identification methods
Mechanical systems
Modelling
Monte Carlo methods
Non-stationary random vibration
Random vibration
System identification
Time-dependent ARMA models
Time-varying structural systems
Time–frequency analysis
Non-stationary random vibration
Functional series models
Time–frequency analysis
System identification
Time-varying structural systems
Time-dependent ARMA models
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Abstract A critical overview of non-stationary random vibration modelling and analysis via the class of Functional Series Time-dependent AutoRegressive Moving Average (FS-TARMA) models is presented. FS-TARMA models feature deterministic parameter evolution and have been shown to effectively represent non-stationary random vibration in various engineering applications. Their conventional and adaptable forms are reviewed, along with pertinent identification methods. The subproblems of parameter estimation, model structure (including basis function) selection, and model validation are discussed, and recent developments are outlined. The relative performance characteristics of the various identification methods are illustrated via Monte Carlo experiments using two different, simulated, random vibration signals. Through these, the potential of functional series models for elegant and effective modelling and analysis of non-stationary random vibration are further revealed. •Critical review of FS-TARMA models for non-stationary vibration modeling.•Model forms are classified and compared.•Adaptable models are introduced.•Identification techniques systematically presented.•The methods use and performance are assessed via Monte Carlo simulations.
AbstractList A critical overview of non-stationary random vibration modelling and analysis via the class of Functional Series Time-dependent AutoRegressive Moving Average (FS-TARMA) models is presented. FS-TARMA models feature deterministic parameter evolution and have been shown to effectively represent non-stationary random vibration in various engineering applications. Their conventional and adaptable forms are reviewed, along with pertinent identification methods. The subproblems of parameter estimation, model structure (including basis function) selection, and model validation are discussed, and recent developments are outlined. The relative performance characteristics of the various identification methods are illustrated via Monte Carlo experiments using two different, simulated, random vibration signals. Through these, the potential of functional series models for elegant and effective modelling and analysis of non-stationary random vibration are further revealed.
A critical overview of non-stationary random vibration modelling and analysis via the class of Functional Series Time-dependent AutoRegressive Moving Average (FS-TARMA) models is presented. FS-TARMA models feature deterministic parameter evolution and have been shown to effectively represent non-stationary random vibration in various engineering applications. Their conventional and adaptable forms are reviewed, along with pertinent identification methods. The subproblems of parameter estimation, model structure (including basis function) selection, and model validation are discussed, and recent developments are outlined. The relative performance characteristics of the various identification methods are illustrated via Monte Carlo experiments using two different, simulated, random vibration signals. Through these, the potential of functional series models for elegant and effective modelling and analysis of non-stationary random vibration are further revealed. •Critical review of FS-TARMA models for non-stationary vibration modeling.•Model forms are classified and compared.•Adaptable models are introduced.•Identification techniques systematically presented.•The methods use and performance are assessed via Monte Carlo simulations.
Author Spiridonakos, M.D.
Fassois, S.D.
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  surname: Fassois
  fullname: Fassois, S.D.
  email: fassois@mech.upatras.gr
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Issue 1-2
Keywords Non-stationary random vibration
Functional series models
Time–frequency analysis
System identification
Time-varying structural systems
Time-dependent ARMA models
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Snippet A critical overview of non-stationary random vibration modelling and analysis via the class of Functional Series Time-dependent AutoRegressive Moving Average...
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SubjectTerms Computer simulation
Evolution
Functional series models
Identification methods
Mechanical systems
Modelling
Monte Carlo methods
Non-stationary random vibration
Random vibration
System identification
Time-dependent ARMA models
Time-varying structural systems
Time–frequency analysis
Title Non-stationary random vibration modelling and analysis via functional series time-dependent ARMA (FS-TARMA) models – A critical survey
URI https://dx.doi.org/10.1016/j.ymssp.2013.06.024
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https://search.proquest.com/docview/1677939814
Volume 47
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