PRACTICAL GUIDELINES FOR THE NATURAL EXCITATION TECHNIQUE (NExT) AND THE EIGENSYSTEM REALIZATION ALGORITHM (ERA) FOR MODAL IDENTIFICATION USING AMBIENT VIBRATION

Caicedo, Juan M.

doi:10.1111/j.1747-1567.2010.00643.x

Cited by 127 publications

(56 citation statements)

References 22 publications

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“…By computing the cross‐spectral density function with respect to the reference acceleration and applying inverse Fourier transformation, the free vibration response can be obtained in the form of cross‐correlation:

{\overset{R}{true}}_{\ddot{bold-italicz}, {\overset{z}{true}}_{i}} = \frac{1}{N} \sum_{k = 0}^{N - 1} S_{\ddot{bold-italicz}, {\overset{z}{true}}_{i}} false(k false) e x p [j 0true \frac{2 π k n}{N}]

where

S_{\ddot{bold-italicz}, {\overset{z}{true}}_{i}} false(k false)

is the cross‐spectral density function of

\overset{z}{true}

and

{\ddot{z}}_{i}

, k is the frequency index, and n is the time index. More details on NExT are provided by Caicedo () and Caicedo et al. ().…”

Section: Methodsmentioning

confidence: 99%

Machine learning based novelty detection using modal analysis

Ozdagli

Koutsoukos

2019

Computer aided Civil Eng

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Among many structural assessment methods, the change of modal characteristics is considered a well‐accepted damage detection method. However, the presence of environmental or operational variations may pollute the baseline and prevent a dependable assessment of the change. In recent years, the use of machine learning algorithms gained interest within structural health community, especially due to their ability and success in the elimination of ambient uncertainty. This paper proposes an end‐to‐end architecture to detect damage reliably by employing machine learning algorithms. The proposed approach streamlines (a) collection of structural response data, (b) modal analysis using system identification, (c) learning model, and (d) novelty detection. The proposed system aims to extract latent features of accessible modal parameters such as natural frequencies and mode shapes measured at undamaged target structure under temperature uncertainty and to reconstruct a new representation of these features that is similar to the original using well‐established machine learning methods for damage detection. The deviation between measured and reconstructed parameters, also known as novelty index, is the essential information for detecting critical changes in the system. The approach is evaluated by analyzing the structural response data obtained from finite element models and experimental structures. For the machine learning component of the approach, both principal component analysis (PCA) and autoencoder (AE) are examined. While mode shapes are known to be a well‐researched damage indicator in the literature, to our best knowledge, this research is the first time that unsupervised machine learning is applied using PCA and AE to utilize mode shapes in addition to natural frequencies for effective damage detection. The detection performance of this pipeline is compared to a similar approach where its learning model does not utilize mode shapes. The results demonstrate that the effectiveness of the damage detection under temperature variability improves significantly when mode shapes are used in the training of learning algorithm. Especially for small damages, the proposed algorithm performs better in discriminating system changes.

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{\overset{R}{true}}_{\ddot{bold-italicz}, {\overset{z}{true}}_{i}} = \frac{1}{N} \sum_{k = 0}^{N - 1} S_{\ddot{bold-italicz}, {\overset{z}{true}}_{i}} false(k false) e x p [j 0true \frac{2 π k n}{N}]

where

S_{\ddot{bold-italicz}, {\overset{z}{true}}_{i}} false(k false)

is the cross‐spectral density function of

\overset{z}{true}

and

{\ddot{z}}_{i}

, k is the frequency index, and n is the time index. More details on NExT are provided by Caicedo () and Caicedo et al. ().…”

Section: Methodsmentioning

confidence: 99%

Machine learning based novelty detection using modal analysis

Ozdagli

Koutsoukos

2019

Computer aided Civil Eng

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“…Recalling from Section 2, the method uses reference signals. Caicedo [52] suggests that these signals should be far away of the in-test ones. In this regard, sensors 15 (x-direction) and 14 (y-direction) fulfill with the aforementioned suggestion and, hence, they are used as the references.…”

Section: Numerical Assessment Of the New Methodology Performancementioning

confidence: 99%

“…In an analogous way to the previous example, sensor 8 is used as the reference signal to estimate the free-decay response, since this sensor is far away from selected sensor 4 to estimate the modal parameters of structure. In this case, sensor 8 satisfies this specification recommended by Caicedo [52]. In this example, 1000 samples signal are selected to estimate the freedamped vibration form.…”

Section: Application To An Eight-story Buildingmentioning

confidence: 99%

A Two-Step Strategy for System Identification of Civil Structures for Structural Health Monitoring Using Wavelet Transform and Genetic Algorithms

et al. 2017

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Nowadays, the accurate identification of natural frequencies and damping ratios play an important role in smart civil engineering, since they can be used for seismic design, vibration control, and condition assessment, among others. To achieve it in practical way, it is required to instrument the structure and apply techniques which are able to deal with noise-corrupted and non-linear signals, as they are common features in real-life civil structures. In this article, a two-step strategy is proposed for performing accurate modal parameters identification in an automated manner. In the first step, it is obtained and decomposed the measured signals using the natural excitation technique and the synchrosqueezed wavelet transform, respectively. Then, the second step estimates the modal parameters by solving an optimization problem employing a genetic algorithm-based approach, where the micropopulation concept is used to improve the speed convergence as well as the accuracy of the estimated values. The accuracy and effectiveness of the proposal are tested using both the simulated response of a benchmark structure and the measurements of a real eight-story building. The obtained results show that the proposed strategy can estimate the modal parameters accurately, indicating than the proposal can be considered as an alternative to perform the abovementioned task.

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“…20,30,31 These assumptions are valid for common structures, and the Stochastic Subspace Identification method (SSI) 32 and Eigensystem Realisation Algorithm (ERA) 33 are two commonly used system identification algorithms in the state space. The ERA can be applied in cases in which the stochastic input is unknown, when used in combination with the Natural Excitation Technique (NExT).…”

Section: Introductionmentioning

confidence: 99%

Modal Identification of a Full-Scale Building Under Seismic Excitation Using the Fast Mode Identification Technique

2015

Self Cite

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The sensors used in structural monitoring systems have become more affordable in the last few decades. The number of sensors used for monitoring is expected to grow, creating the need for new algorithms for online modal identification of structural systems. The fast mode identification (FMI) technique uses experimental data from ambient vibration tests to identify operational modal shapes at a fraction of the time used by traditional modal identification techniques. This paper discusses the mathematical formulation and experimental validation of the FMI technique on a full-scale building under earthquake excitation. The method consists of two steps: (1) the identification of natural frequencies and modal damping ratios using traditional techniques, and (2) the identification of operational mode shapes using the FMI technique. Dividing the process in two reduces the computational time required for modal identification. Experimental results show good agreement between the results of FMI and those of other widely used modal identification methods on a full-scale building submitted to seismic excitation. Furthermore, FMI shows a higher consistency on the identified mode coordinates and lower processing time than traditional methods. FMI is envisioned to be used on dense wireless intelligent sensor with limited processing capabilities and battery life.

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PRACTICAL GUIDELINES FOR THE NATURAL EXCITATION TECHNIQUE (NExT) AND THE EIGENSYSTEM REALIZATION ALGORITHM (ERA) FOR MODAL IDENTIFICATION USING AMBIENT VIBRATION

Cited by 127 publications

References 22 publications

Machine learning based novelty detection using modal analysis

Machine learning based novelty detection using modal analysis

A Two-Step Strategy for System Identification of Civil Structures for Structural Health Monitoring Using Wavelet Transform and Genetic Algorithms

Modal Identification of a Full-Scale Building Under Seismic Excitation Using the Fast Mode Identification Technique

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