Uncertainty quantification in operational modal analysis with stochastic subspace identification: Validation and applications

Reynders, Edwin; Maes, Kristof; Lombaert, Geert; Roeck, Guido De

doi:10.1016/j.ymssp.2015.04.018

Cited by 157 publications

(101 citation statements)

References 28 publications

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“…All data have been analysed via the Covariance-driven Stochastic Subspace Identification method (SSI/Cov), an Operational Modal Analysis (OMA) technique in the time-domain implemented in the MACEC code (Reynders et al, 2016). To this end, the data have been divided into one-hour long datasets.…”

Section: Data Analysis and Resultsmentioning

confidence: 99%

Ambient Vibrations of Age-old Masonry Towers: Results of Long-term Dynamic Monitoring in the Historic Centre of Lucca

Azzara

Girardi

Iafolla

et al. 2019

International Journal of Architectural Heritage

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The paper presents the results of an ambient vibration monitoring campaign conducted on so-called "Clock Tower" (Torre delle Ore), one the best known and most visited monuments in the historic centre of Lucca. The vibrations of the tower were continuously monitored from November 2017 to March 2018 using high-sensitivity instrumentation. In particular, four seismic stations provided by the Istituto Nazionale di Geofisica e Vulcanologia and two three-axial accelerometers developed by AGI S.r.l., spin-off of the Istituto Nazionale di Astrofisica, were installed on the tower. The measured vibration level was generally very low, since the structure lies in the middle of a limited traffic area. Nevertheless, the availability of two different types of highly sensitive and accurate instruments allowed the authors to follow the dynamic behaviour of the tower during the entire monitoring period and has moreover provided cross-validation of the results.

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Section: Data Analysis and Resultsmentioning

confidence: 99%

Ambient Vibrations of Age-old Masonry Towers: Results of Long-term Dynamic Monitoring in the Historic Centre of Lucca

Azzara

Girardi

Iafolla

et al. 2019

International Journal of Architectural Heritage

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“…The assumptions on probability distributions of modal parameters come from the analysis of a set of data of the authors' personal experience as well as from a review of documented experimental cases found in scientific literature [29], [30].…”

Section: Numerical Study: Analyzing the Effects Of Motion Blur On Frfmentioning

confidence: 99%

Uncertainty in vision based modal analysis: probabilistic studies and experimental validation

Lavatelli

Zappa

2016

ACTA IMEKO

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This paper analyses the issues related with the application of photogrammetric methods in the context of modal analysis. In this field of mechanical testing, the vibrations of the measurand are monitored. The possibility to measure displacement of several monitored points without directly instrumenting those, pushes the interest toward the application of an image based measurement system. However, the process of acquiring a moving target with a camera is complex due to the motion blur phenomenon. The effects of this particular phenomenon on uncertainty generation and propagation are discussed in parallel with Monte Carlo simulation and experimental approach. The case study here proposed focuses on the estimation of the main modal parameters (first two resonant frequencies and amplitudes) of a structure with the help of 2D Blob Analysis, with particular focus in comparing the performances of photogrammetry with the ones of classic motion transducers. The results of both the approaches confirm that the choice of using photogrammetry as an input for modal analysis should be made carefully, since the actual behavior of a structure can be distorted. Nonetheless, the results show that particular care should be made in the choice of structural excitation when using vision based techniques.

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“…As a result, the outcomes from such mathematical models may not be consistent with the clinical observations. Uncertainty is an unavoidable feature that affects prediction capabilities in real-world domains such as healthcare [9,10], manufacturing [11,12], signal processing [13,14], and etc. A certain amount of uncertainty is always involved in decision-making systems that do not encounter samples when the experimental data are insufficient to calibrate.…”

Section: Introductionmentioning

confidence: 99%

DL-CHI: a dictionary learning-based contemporaneous health index for degenerative disease monitoring

Samareh

Huang

2018

EURASIP J. Adv. Signal Process.

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Developing a knowledge-driven contemporaneous health index (CHI) that can precisely reflect the underlying patient condition across the course of the condition's progression holds a unique value, like facilitating a range of clinical decision-making opportunities. This is particularly important for monitoring degenerative conditions such as Alzheimer's disease (AD), where the condition of the patient will decay over time. Detecting early symptoms and progression signs, and continuous severity evaluation, are all essential for disease management. While a few methods have been developed in the literature, uncertainty quantification of those health index models has been largely neglected. To ensure the continuity of the care, we should be more explicit about the level of confidence in model outputs. Ideally, decision-makers should be provided with recommendations that are robust in the face of substantial uncertainty about future outcomes. In this paper, we aim at filling this gap by developing an uncertainty quantification based contemporaneous longitudinal index, named UQ-CHI, with a particular focus on continuous patient monitoring of degenerative conditions. Our method is to combine convex optimization and Bayesian learning using the maximum entropy learning (MEL) framework, integrating uncertainty on labels as well. Our methodology also provides closed-form solutions in some important decision making tasks, e.g., such as predicting the label of a new sample. Numerical studies demonstrate the effectiveness of the proposed UQ-CHI method in prediction accuracy, monitoring efficacy, and unique advantages if uncertainty quantification is enabled in practice. (Shuai Huang) communications between clinicians, healthcare providers, and patients. It will also be a crucial enabling factor for the development of many envisioned AI systems to implement adaptive interventions for better healthcare management, given a representation of the dynamic evolution of the patient's condition.Thus, to ensure continuity of care, we should be more explicit about our level of confidence in model outputs. Ideally, decision-makers should be provided with recommendations that are robust in the face of substantial uncertainty about future outcomes. However, computational models are an abstraction of clinical observations, as such, they are usually built on analytically tractable assumptions that may simplify the realworld problem. Also, most of these models are estimated from

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Uncertainty quantification in operational modal analysis with stochastic subspace identification: Validation and applications

Cited by 157 publications

References 28 publications

Ambient Vibrations of Age-old Masonry Towers: Results of Long-term Dynamic Monitoring in the Historic Centre of Lucca

Ambient Vibrations of Age-old Masonry Towers: Results of Long-term Dynamic Monitoring in the Historic Centre of Lucca

Uncertainty in vision based modal analysis: probabilistic studies and experimental validation

DL-CHI: a dictionary learning-based contemporaneous health index for degenerative disease monitoring

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