Damage diagnosis using time series analysis of vibration signals

By Hoon Sohn, Charles R. Farrar. 2001

672 citations recorded by the source corpus.

Subfields: Civil and Structural Engineering, Control and Systems Engineering, Mechanics of Materials

View Paper: https://www.semanticscholar.org/paper/67abdab7016e83abdd7ae51c5cbb5b9001475b14

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In Civil and Structural Engineering

In Control and Systems Engineering

In Mechanics of Materials

Citations in the corpus

A novel time-domain auto-regressive model for structural damage diagnosis (2005, methodology): “To highlight the improvement of the present method in detecting this kind of damage, a comparison is made between the performance of the present model as shown above and the model proposed by Sohn and Farrar [2].”
An overview of wireless structural health monitoring for civil structures (2007, methodology): “(b ) Autoregressive model fitting Sohn & Farrar (2001) propose a novel approach for detecting damage in structures whose vibration characteristics exhibit sensitivity to environmental and operational variations.”
A review of vibration-based structural health monitoring with special emphasis on composite materials (2006, methodology): “In order to address this issue in the context of continuous online monitoring, Sohn et al. (2005) proposed a technique which does not rely on any past baseline signals for assess-”
Piezoelectric Transducer-Based Structural Health Monitoring for Aircraft Applications (2019, neutral): “to , the”
Overview of Piezoelectric Impedance-Based Health Monitoring and Path Forward (2003, background): “A primitive application of statistical pattern recognition to vibration signals that has led to documented success can be found in the literature (Sohn and Farrar, 2001).”
Vibration based health monitoring for a lightweight truss structure: experimental assessment of several statistical time series methods (2010, methodology): “In a related work, Sohn and Farrar [19] employ the standard deviation ratio of the residual errors from a two–stage AR–ARX model, obtained from healthy measured signals, as the damage sensitive feature.”
Substructure Vibration NARX Neural Network Approach for Statistical Damage Inference (2013, methodology): “Note that it may be desirable to repeat Step 2 from the list for different environmental and operational conditions (Sohn and Farrar 2001) to build a baseline database consisting of a range of reference states of interest.”
Damage classification and estimation in experimental structures using time series analysis and pattern recognition (2010, background): “Several researchers examined the errors in structural responses produced by time series models fitted to data from undamaged and damaged structures and tried to detect damage [7-11].”
Statistical Damage Classification Using Sequential Probability Ratio Tests (2002, methodology): “11 α β − ≅ 1 ln b and α β − ≅ 1 ln a (14)”
Long-term monitoring of a PSC box girder bridge : operational modal analysis, data normalization and structural modification assessment. (2012, background): “principal component analysis (PCA) can be used instead, as long as damage produces a change in the measured signals that is in some way orthogonal or uncorrelated to the change caused by the environmental variability [31,32].”
Review of Bridge Structural Health Monitoring Aided by Big Data and Artificial Intelligence: From Condition Assessment to Damage Detection (2020, positive): “An intuition to address the problems is to separate these unfavorable variations, which is also defined as data normalization by Sohn and Farrar (2001).”