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Parametric time-domain methods for non-stationary random vibration modelling and analysis — A critical survey and comparison

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TLDR
A critical survey and comparison ofparametric time-domain methods for non-stationary random vibration modelling and analysis based upon a single vibration signal realization confirms the advantages and high performance characteristics of parametric methods.
About
This article is published in Mechanical Systems and Signal Processing.The article was published on 2006-05-01. It has received 246 citations till now. The article focuses on the topics: Parametric statistics & Random vibration.

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A stochastic analysis method of transient responses using harmonic wavelets, Part 1: Time-invariant structural systems

TL;DR: In this article, a stochastic analysis method of the transient responses for multi-degree-of-freedom (MDOF) structural systems subjected to nonstationary (non-stationary) STF excitations based on periodic generalized harmonic wavelets (GHW) is proposed.
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Prediction of modified Mercalli intensity from PGA, PGV, moment magnitude, and epicentral distance using several nonlinear statistical algorithms

TL;DR: This paper will employ three methods of nonlinear regression, namely support vector regression, multilayer perceptrons, and genetic programming in order to find a functional dependence between the instrumental records and the modified Mercalli intensity scale.
Journal ArticleDOI

Asymptotic Analysis of Non-stationary Functional Series TARMA Estimators

TL;DR: In this paper, a general framework for the asymptotic analysis of general FS-TARMA estimators is developed and applied to the case of Weighted Least Squares, Maximum Likelihood and Multi Stage estimators.
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Vision-Based Moving Mass Detection by Time-Varying Structure Vibration Monitoring

TL;DR: The proposed vision-based moving mass detection (VMMD) technique has valuable potential for online detection, localization and classification of inferior products with abnormal mass in industrial automation.
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A novel recursive modal parameter estimator for operational time-varying structural dynamic systems based on least squares support vector machine and time series model

TL;DR: A novel recursive modal parameter estimator for operational linear time-varying structures based on least squares support vector machine (LSSVM) and vector time-dependent autoregressive moving average model is presented.
References
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Journal ArticleDOI

Estimating the Dimension of a Model

TL;DR: In this paper, the problem of selecting one of a number of models of different dimensions is treated by finding its Bayes solution, and evaluating the leading terms of its asymptotic expansion.

Estimating the dimension of a model

TL;DR: In this paper, the problem of selecting one of a number of models of different dimensions is treated by finding its Bayes solution, and evaluating the leading terms of its asymptotic expansion.
Book

System Identification: Theory for the User

Lennart Ljung
TL;DR: Das Buch behandelt die Systemidentifizierung in dem theoretischen Bereich, der direkte Auswirkungen auf Verstaendnis and praktische Anwendung der verschiedenen Verfahren zur IdentifIZierung hat.