Multi-stage semi-automated methodology for modal parameters estimation adopting parametric system identification algorithms

Tronci, Eleonora M.; Angelis, Maurizio De; Betti, Raimondo; Altomare, Vittorio

doi:10.1016/j.ymssp.2021.108317

Cited by 30 publications

(21 citation statements)

References 20 publications

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“…Then, to further validate the obtained results, the modal features were extracted from the system’s response using a semi-automated identification procedure based on Data-Driven Stochastic Subspace Identification (DD-SSI) technique developed by the authors. The reader is referred to [ 37 ] for a detailed description of the guidelines followed to pick the parameters’ ranges for the DD-SSI technique and to [ 7 ] for a step-by-step walk-through of the multi-stage semi-automated overall procedure. Figure 14 shows the structural modes identified adopting the DD-SSI technique with the normalized power spectrum in the background.…”

Section: Field Experiments On a Long-span Suspension Bridgementioning

confidence: 99%

“…In particular, vibration-based monitoring strategies relying on parametric or non-parametric system identification techniques are among the most popular ones, as it emerges from the rich amount of publications in the research field [ 2 , 3 ]. The development of numerous semi-automated and automated approaches to carry on operational modal analysis has made handling and processing massive quantities of monitoring data easier [ 4 , 5 , 6 , 7 , 8 ]. This has automatically reflected in a lot of resources spent on developing robust and long-lasting sensor networks to acquire this tremendous amount of monitoring data that has been speeding up safely.…”

Section: Introductionmentioning

confidence: 99%

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Long-Range Low-Power Multi-Hop Wireless Sensor Network for Monitoring the Vibration Response of Long-Span Bridges

Tronci

Nagabuko

Hieda

et al. 2022

Sensors

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Recently, vibration-based monitoring technologies have become extremely popular, providing effective tools to assess the health condition and evaluate the structural integrity of civil structures and infrastructures in real-time. In this context, battery-operated wireless sensors allow us to stop using wired sensor networks, providing easy installation processes and low maintenance costs. Nevertheless, wireless transmission of high-rate data such as structural vibration consumes considerable power. Consequently, these wireless networks demand frequent battery replacement, which is problematic for large structures with poor accessibility, such as long-span bridges. This work proposes a low-power multi-hop wireless sensor network suitable for monitoring large-sized civil infrastructures to handle this problem. The proposed network employs low-power wireless devices that act in the sub-GHz band, permitting long-distance data transmission and communication surpassing 1 km. Data collection over vast areas is accomplished via multi-hop communication, in which the sensor data are acquired and re-transmitted by neighboring sensors. The communication and transmission times are synchronized, and time-division communication is executed, which depends on the wireless devices to sleep when the connection is not necessary to consume less power. An experimental field test is performed to evaluate the reliability and accuracy of the designed wireless sensor network to collect and capture the acceleration response of the long-span Manhattan Bridge. Thanks to the high-quality monitoring data collected with the developed low-power wireless sensor network, the natural frequencies and mode shapes were robustly recognized. The monitoring tests also showed the benefits of the presented wireless sensor system concerning the installation and measuring operations.

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Section: Field Experiments On a Long-span Suspension Bridgementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Long-Range Low-Power Multi-Hop Wireless Sensor Network for Monitoring the Vibration Response of Long-Span Bridges

Tronci

Nagabuko

Hieda

et al. 2022

Sensors

Self Cite

View full text Add to dashboard Cite

show abstract

“…It does not categorize outlier modal properties, that is, eliminates them. Let P be the set of identified poles, the similarity of modes presented by p i 2 P and p j 2 P can be expressed by the Euclidean distance as follows (Tronci et al, 2022)…”

Section: Dbscan Clustering Algorithm and Elimination Of Outlier And N...mentioning

confidence: 99%

“…It does not categorize outlier modal properties, that is, eliminates them. Let P be the set of identified poles, the similarity of modes presented by pi∈P and pj∈P can be expressed by the Euclidean distance as follows (Tronci et al, 2022)where the superscripts −1 and T denote transpose and the inverse of a matrix, respectively. The vector u∈U contains the identified natural frequency and damping ratio of the respective poles.…”

Section: System Identification Processmentioning

confidence: 99%

System identification of Karun IV Dam using balanced stochastic subspace algorithm considering the uncertainty of results

Pourgholi

Tarinejad

Khabir

et al. 2022

Journal of Vibration and Control

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Uncertainty in modal characteristics due to output-only system identification methods has been a challenge in operational modal analysis. The present study aims to extract modal parameters of Karun IV Dam (the highest arch dam in Iran) using the balanced stochastic subspace identification (B-SSI) and investigate the influence of user-defined parameters (i.e., columns and block rows of Hankel Matrix) on the uncertainty of the results. The effects of noise caused by numerical instabilities were first filtered using the inverse process by the condition number. Subsequently, the modal properties were homogenized with spatial clustering of applications with noise (DBSCAN) to remove the outlier and spurious characteristics. Then, the physical modes were validated by inspecting the complexity of the mode shapes based on the mode complexity factor criterion. Finally, the coefficient of variation (CV) of the validated clusters was employed to conduct a sensitivity analysis performed concerning the dimensions of the Hankel matrix to find the optimal models (with the minimum error in estimating the modal characteristics). The results indicated that the proposed method prevented the emergence of computational and noisy modes by regulating the extracted models, such that the first model of the structure was extracted with an error of less than 10% compared to the numerical model.

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“…A great amount of post-processing is required to remove these spurious modes. Researchers [ 8 , 9 ] select stringent validation criteria that include many levels of separation approaches such as blind clustering, hierarchical clustering, DBScan, mathematical mode reduction, and so on. Many SSI versions have been created to alleviate this burdensome post-processing.…”

Section: Introductionmentioning

confidence: 99%

A Data-Driven System Identification Method for Random Eigenvalue Problem Using Synchrosqueezed Energy and Phase Portrait Analysis

Mahato

Chakraborty

Griškevičius

2023

Sensors

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The primary purpose of this research is to evaluate the uncertainty associated with modal parameter estimation for an inverse dynamic problem in which the structural parameters are random. The random nature of the structure’s parameters will be reflected in the modal features of the respected system. However, this may result in additive/subtractive errors in modal parameter identification, affecting the identification technique’s efficiency. With this in mind, the present study aims to develop an automated modal identification algorithm for a random eigenvalue problem. This is achieved by a recently developed advanced version of the wavelet transform (i.e., synchrosqueezing), which offers better resolution. Using this technique, the measured responses are transformed into a time-frequency plane, which is further processed by unsupervised learning using K-means clustering for quantification of the modal parameters. This automated identification is repeated for an ensemble of measurements to quantify the random eigenvalues in a statistical sense. The proposed methodology is first tested using simulated time histories of a two degree-of-freedom (dof) system. It is followed by an experimental validation using a beam whose mass matrix is random. The numerical results presented in this work clearly demonstrate the performance (i.e., in terms of efficiency and accuracy) of the proposed output-only automated data-driven identification scheme for random eigenvalue problems.

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Multi-stage semi-automated methodology for modal parameters estimation adopting parametric system identification algorithms

Cited by 30 publications

References 20 publications

Long-Range Low-Power Multi-Hop Wireless Sensor Network for Monitoring the Vibration Response of Long-Span Bridges

Long-Range Low-Power Multi-Hop Wireless Sensor Network for Monitoring the Vibration Response of Long-Span Bridges

System identification of Karun IV Dam using balanced stochastic subspace algorithm considering the uncertainty of results

A Data-Driven System Identification Method for Random Eigenvalue Problem Using Synchrosqueezed Energy and Phase Portrait Analysis

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