Vibration based health monitoring for a lightweight truss structure: experimental assessment of several statistical time series methods
TL;DR: In this article, an experimental assessment of several vibration based statistical time series methods for Structural Health Monitoring (SHM) is presented via their application to a lightweight aluminum truss structure.
About: This article is published in Mechanical Systems and Signal Processing.The article was published on 2010-10-01. It has received 142 citations till now. The article focuses on the topics: Structural health monitoring.
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TL;DR: This paper aims to fulfill the gap by presenting the highlights of the traditional methods and provide a comprehensive review of the most recent applications of ML and DL algorithms utilized for vibration-based structural damage detection in civil structures.
440 citations
Cites background from "Vibration based health monitoring f..."
...Figure 17 – The laboratory truss structure and the damage locations (left); the FRF magnitude based SDD results (right) in Kopsaftopoulos and Fassois [137]....
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...On a comparative study on experimental assessment of several statistical time series methods, Kopsaftopoulos and Fassois [137] investigated the efficiency of vibration based statistical time series methods for SDD via a lightweight aluminum truss structure tested in the laboratory (Figure 17)....
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TL;DR: In this paper, a comparative study among different vibration-based damage detection methods: fundamental modal examination, local diagnostic method, non-probabilistic methodology and the time series method was made.
Abstract: Structural health monitoring (SHM) techniques have been studied for several years. An effective approach for SHM is to choose the parameters that are sensitive to the damage occurring in the structure but not sensitive to operational or environmental damages. This paper deals with a comparative study among the different vibration-based damage detection methods: fundamental modal examination, local diagnostic method, non-probabilistic methodology and the time series method. All these strategies contemplate different parameters of a structure to recognize damage. Out of the study made, time series analysis proves to more successfully in damage identification than the rest of the methods.
271 citations
TL;DR: The progress in the area of vibration-based damage identification methods over the past 10 years is reviewed to help researchers and practitioners in implementing existing damage detection algorithms effectively and developing more reliable and practical methods for civil engineering structures in the future.
200 citations
TL;DR: The reconstruction results of the structural health monitoring data show that the proposed Bayesian multi-task learning methodology affords an excellent performance, while the Bayesian single- task learning method is unreliable in certain cases; yet, the selection of covariance function has a significant impact on the reconstruction performance of the proposed methodology.
Abstract: Reconstruction of structural health monitoring data is a challenging task, since it involves time series data forecasting especially in the case with a large block of missing data In this study, w
132 citations
TL;DR: In this paper, the synergy between a wavelet transform (WT) and a Teager energy operator (TEO) is explored, with the aim of ameliorating the curvature mode shape.
125 citations
References
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Book•
01 Jan 1987
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.
Abstract: Das Buch behandelt die Systemidentifizierung in dem theoretischen Bereich, der direkte Auswirkungen auf Verstaendnis und praktische Anwendung der verschiedenen Verfahren zur Identifizierung hat. Da ...
20,436 citations
"Vibration based health monitoring f..." refers background or methods in this paper
...The obtained signals are subsequently analyzed by parametr ic or non–parametric time series methods and appropriate models are identified and properly validated [5 ,6,16]....
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...512], and use of “stabilization diagrams” (Figure 6 ) which depict the estimated modal parameters (usually frequencie s) as a function of increasing model order [16,29]....
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...On the other hand, parametric methods utilize a statistic Q based on parametric time series representations, such as AutoRegressive with eXogenous excitation (ARX) or other re presentations [16, 29]....
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...Parametric time series methods for SHM are those based on cor resp nding time series representations, such as the AutoRegressive Moving Average (ARMA) representatio n [5,6,16]....
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Book•
01 Jan 1971
TL;DR: A revised and expanded edition of this classic reference/text, covering the latest techniques for the analysis and measurement of stationary and nonstationary random data passing through physical systems, is presented in this article.
Abstract: From the Publisher:
A revised and expanded edition of this classic reference/text, covering the latest techniques for the analysis and measurement of stationary and nonstationary random data passing through physical systems. With more than 100,000 copies in print and six foreign translations, the first edition standardized the methodology in this field. This new edition covers all new procedures developed since 1971 and extends the application of random data analysis to aerospace and automotive research; digital data analysis; dynamic test programs; fluid turbulence analysis; industrial noise control; oceanographic data analysis; system identification problems; and many other fields. Includes new formulas for statistical error analysis of desired estimates, new examples and problem sets.
6,693 citations
5,341 citations
TL;DR: Krystek as discussed by the authors provides a comprehensive and self-contained overview of random data analysis, including derivations of the key relationships in probability and random-process theory not usually found to such extent in a book of this kind.
Abstract: This is a new edition of a book on random data analysis which has been on the market since 1966 and which was extensively revised in 1971. The book has been a bestseller since. It has been fully updated to cover new procedures developed in the last 15 years and extends the discussion to a broad range of applied fields, such as aerospace, automotive industries or biomedical research. The primary purpose of this book is to provide a practical reference and tool for working engineers and scientists investigating dynamic data or using statistical methods to solve engineering problems. It is comprehensive and self-contained and expands the coverage of the theory, including derivations of the key relationships in probability and random-process theory not usually found to such extent in a book of this kind. It could well be used as a teaching textbook for advanced courses on the analysis of random processes. The first four chapters present the background material on descriptions of data, properties of linear systems and statistical principles. They also include probability distribution formulas for one-, two- and higher-order changes of variables. Chapter five gives a comprehensive discussion of stationary random-process theory, including material on wave-number spectra, level crossings and peak values of normally distributed random data. Chapters six and seven develop mathematical relationships for the detailed analysis of single input/output and multiple input/output linear systems including algorithms. In chapters eight and nine important practical formulas to determine statistical errors in estimates of random data parameters and linear system properties from measured data are derived. Chapter ten deals with data aquisition and processing, including data qualification. Chapter eleven describes methods of data analysis such as data preparation, Fourier transforms, probability density functions, auto- and cross-correlation, spectral functions, joint record functions and multiple input/output functions. Chapter twelve shows how to handle nonstationary data analysis, classification of nonstationary data, probability structure of nonstationary data, calculation of nonstationary mean values or mean square values, correlation structures of nonstationary data and spectral structures of nonstationary data. The last chapter deals with the Hilbert transform including applications for both nondispersive and dispersive propagation problems. All chapters include many illustrations and references as well as examples and problem sets. This allows the reader to use the book for private study purposes. Altogether the book can be recommended for practical working engineers and scientists to support their daily work, as well as for university readers as a teaching textbook in advanced courses. M Krystek
3,390 citations