Foundations of population-based SHM, Part I: Homogeneous populations and forms

Bull, Larry; Gardner, Paul A.; Gosliga, Julian; Rogers, Timothy J.; Dervilis, Nikolaos; Cross, Elizabeth J.; Papatheou, Evangelos; Maguire, Eoghan; Campos, Carles; Worden, Keith

doi:10.1016/j.ymssp.2020.107141

Cited by 88 publications

(103 citation statements)

References 18 publications

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“…In order to clarify these questions and their potential answers, it will be useful to introduce some terminology to guide the reader through the remainder of the paper without the mathematical complexities that can be found in the general framework described in Bull et al (2020a), Gosliga et al (2020a), and Gardner et al (2020a).…”

Section: The Potential Advantages Of Pbshm; Why It Is Neededmentioning

confidence: 99%

“…The basic concepts in the framework will be summarized here in a compact and comprehensive manner. Following the first steps discussed in the last section, many of the ideas were published in Bull et al (2020b), Gosliga et al (2020b), Gosliga et al (2020c), Gardner and Worden (2020), Worden (2020), and Lin et al (2020), and were then consolidated into (Bull et al, 2020a;Gardner et al, 2020a;Gosliga et al, 2020a).…”

Section: A General Framework For Pbshmmentioning

confidence: 99%

“…In this case study, a homogeneous population of 19 structurally-equivalent (8-DOF) systems was simulated, leading to a population of 19 members. Each individual is a model realization of an experimental rig, designed and constructed at the Los Alamos National laboratory (Bull et al, 2020a). The structure of interest is formed from eight very substantial masses connected by helical springs (Figure 3); it is as close to a lumpedmass system as one can construct, so the model division into eight masses and nine joints (springs) is entirely natural.…”

Section: Homogeneous Populationsmentioning

confidence: 99%

“…While SS did provide some useful ideas relating to spatio-temporal novelty detection (Hensman et al, 2011), it did not provide a rich enough framework for diagnostics in general populations of structures. Such a general framework was first outlined in the sequence of papers (Bull et al, 2020b;Gardner and Worden, 2020;Gosliga et al, 2020b,c;Lin et al, 2020;Worden, 2020), which were subsequently refined and consolidated into the series: (Bull et al, 2020a;Gardner et al, 2020a;Gosliga et al, 2020a). These papers pointed out that there are differing requirements for population-based SHM, depending on whether the populations are homogenous (i.e., composed of nominallyidentical structures) or heterogeneous.…”

Section: Introductionmentioning

confidence: 99%

“…To reiterate, the aim of this short paper is not to give an overview of SHM in general, or even PBSHM in particular, as there is not enough space; more extensive and detailed discussions can be found in Bull et al (2020a), Gosliga et al (2020a), and Gardner et al (2020a), including a range of challenging case studies and experimental investigations. Furthermore, the emphasis of the work presented here is on the general concepts, emphasizing and illustrating the practical benefits of PBSHM, explaining why it is needed in non-expert terms as far as possible.…”

Section: Introductionmentioning

confidence: 99%

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A Brief Introduction to Recent Developments in Population-Based Structural Health Monitoring

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Section: The Potential Advantages Of Pbshm; Why It Is Neededmentioning

confidence: 99%

Section: A General Framework For Pbshmmentioning

confidence: 99%

Section: Homogeneous Populationsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A Brief Introduction to Recent Developments in Population-Based Structural Health Monitoring

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On the use of domain adaptation techniques for bridge damage detection in a changing environment

Giglioni,

Poole,

Venanzi

et al. 2023

ce papers

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Structural Health Monitoring of civil infrastructures often suffers from the limited availability of damage labelled data. The work here seeks to overcome this issue by using Transfer Learning approaches, in the form of Domain Adaptation, for leveraging information from a source structure with determined health‐state labels to make inferences on an unlabeled monitored structure. The idea is to exploit source data to train a Machine Learning algorithm and achieve improved early‐stage damage detection capabilities across a population of bridges. To account for differences in the underlying distributions of each structure, Transfer Learning is seen as a strategy enabling population‐level bridge SHM. In this paper, the natural frequencies obtained from multiple vibration measurements are extracted to characterise different domains during pristine and abnormal conditions. Such damage‐sensitive features are aligned via Domain Adaptation and used to train a standard classifier within a shared feature space. The methodology is validated on the heterogeneous population composed of the Z24 and S101 bridges. The results prove the effectiveness to successfully exchange damage labels, thus increasing available information for health‐state inference for SHM applications with sparce datasets.

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Farm‐wide interface fatigue loads estimation: A data‐driven approach based on accelerometers

de N Santos,

Noppe,

Weijtjens

et al. 2024

Wind Energy

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Fatigue has become a major consideration factor in modern offshore wind farms as optimized design codes, and a lack of lifetime reserve has made continuous fatigue life monitoring become an operational concern. In this contribution, we discuss a data‐driven methodology for farm‐wide tower‐transition piece fatigue load estimation. We specifically debate the employment of this methodology in a real‐world farm‐wide setting and the implications of continuous monitoring. With reliable nacelle‐installed accelerometer data at all locations, along with the customary 10‐min supervisory control and data acquisition (SCADA) statistics and three strain gauge‐instrumented 'fleet‐leaders', we discuss the value of two distinct approaches: use of either fleet‐leader or population‐based data for training a physics‐guided neural network model with a built‐in conservative bias, with the latter taking precedence. In the context of continuous monitoring, we touch on the importance of data imputation, working under the assumption that if data are missing, then its fatigue loads should be modeled as under idling. With this knowledge at hand, we analyzed the errors of the trained model over a period of 9 months, with monthly accumulated errors always kept below . A particular focus was given to performance under high loads, where higher errors were found. The cause for this error was identified as being inherent to the use of 10‐min statistics, but mitigation strategies have been identified. Finally, the farm‐wide results are presented on fatigue load estimation, which allowed to identify outliers, whose behavior we correlated with the operational conditions. Finally, the continuous data‐driven, population‐based approach here presented can serve as a springboard for further lifetime‐based decision‐making.

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Foundations of population-based SHM, Part I: Homogeneous populations and forms

Cited by 88 publications

References 18 publications

A Brief Introduction to Recent Developments in Population-Based Structural Health Monitoring

A Brief Introduction to Recent Developments in Population-Based Structural Health Monitoring

On the use of domain adaptation techniques for bridge damage detection in a changing environment

Farm‐wide interface fatigue loads estimation: A data‐driven approach based on accelerometers

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