Towards Population-Based Structural Health Monitoring, Part VI: Structures as Geometry

Worden, Keith

doi:10.1007/978-3-030-47634-2_26

Cited by 11 publications

(9 citation statements)

References 18 publications

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“…In particular, complex structures encountered in complex (networked) systems and structures, such as computer networks, social networks, infrastructure networks, sensor networks, as well as cyber-physical networks play an important role throughout our everyday life. However, the network concept transcends such explicit structures, towards more implicit networks observed in physical structures of interdependent elements or components (Bloemheuvel et al 2020;Worden 2021). In particular, in the field of complex networks and feature rich networks both the need as well as the opportunities in studying such complex network topologies, has made the use of complex network models pervasive in many fields of research such as computer science, physics, engineering and the social sciences, also joining into interdisciplinary research contexts, cf.…”

Section: Complex Networkmentioning

confidence: 99%

A Computational Framework for Modeling Complex Sensor Network Data Using Graph Signal Processing and Graph Neural Networks in Structural Health Monitoring

Bloemheuvel¹,

Hoogen²,

Atzmueller³

2021

Preprint

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Complex networks lend themselves to the modeling of multidimensional data, such as relational and/or temporal data. In particular, when such complex data and their inherent relationships need to be formalized, complex network modeling and its resulting graph representations enable a wide range of powerful options. In this paper, we target this -connected to specific machine learning approaches on graphs for structural health monitoring on an analysis and predictive (maintenance) perspective. Specifically, we present a framework based on Complex Network Modeling, integrating Graph Signal Processing (GSP) and Graph Neural Network (GNN) approaches. We demonstrate this framework in our targeted application domain of Structural Health Monitoring (SHM). In particular, we focus on a prominent real-world structural health monitoring use case, i. e., modeling and analyzing sensor data (strain, vibration) of a large bridge in the Netherlands. In our experiments, we show that GSP enables the identification of the most important sensors, for which we investigate a set of search and optimization approaches. Furthermore, GSP enables the detection of specific graph signal patterns (mode shapes), capturing physical functional properties of the sensors in the applied complex network. In addition, we show the efficacy of applying GNNs for strain prediction on this kind of data.

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Section: Complex Networkmentioning

confidence: 99%

A Computational Framework for Modeling Complex Sensor Network Data Using Graph Signal Processing and Graph Neural Networks in Structural Health Monitoring

Bloemheuvel¹,

Hoogen²,

Atzmueller³

2021

Preprint

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“…In consequence, if a framework can transfer this missing information between groups of similar structures, it should bring significant advantages to practical applications of SHM. This issue of missing information motivates a population-based approach to SHM [1,2,3,4,5,6,7]. The aim of this new technology is to facilitate the transfer of valuable knowledge between groups of similar systems, i.e.…”

Section: Population-based Shmmentioning

confidence: 99%

“…Importantly, this paper is the first in a series [2,5,6,7] introducing various methods for population-based SHM. While this work focusses on the most uniform case -the application of a population form to model nominally-identical systems -papers [2,4,5] concern more involved technologies, of a different nature.…”

Section: Population-based Shmmentioning

confidence: 99%

Towards Population-Based Structural Health Monitoring, Part I: Homogeneous Populations and Forms

Bull

Gardner

Gosliga

et al. 2020

Conference Proceedings of the Society for Experimental Mechanics Series

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Data-driven models in Structural Health Monitoring (SHM) generally require comprehensive datasets, recorded from systems in operation, which are rarely available. One potential solution to this problem, considers that information might be transferred, in some sense, between similar systems. As a result, a population-based approach to SHM suggests methods to both model and transfer this valuable information, by considering different groups of structures as populations. Specifically, in this work, a method is proposed to model a population of nominally-identical systems, where (complete) datasets are only available from a subset of members. The framework attempts to build a general model, referred to as the population form, which can be used to make predictions across a group of homogeneous systems. First, the form is demonstrated through applications to a simulated population -with a single experimental (test-rig) member; secondly, the form is applied to data recorded from a group of operational wind turbines.

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“…The concept of PBSHM is to incorporate the feature and label data from each aeroplane to generate a machine learning-based approach that generalises across the complete fleet for all damage scenarios, especially when many members of the fleet have no labelled data associated with them. This work is Part IV of a series of papers on PBSHM [2,3,4,5,6], where for an overview of PBSHM and further motivation the reader is referred to earlier parts [2,3,4].…”

Section: Introductionmentioning

confidence: 99%

Towards Population-Based Structural Health Monitoring, Part IV: Heterogeneous Populations, Transfer and Mapping

Gardner

Bull

Gosliga

et al. 2020

Conference Proceedings of the Society for Experimental Mechanics Series

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Population-based structural health monitoring (PBSHM) involves utilising knowledge from one set of structures in a population and applying it to a different set, such that predictions about the health states of each member in the population can be performed and improved. Central ideas behind PBSHM are those of knowledge transfer and mapping. In the context of PBSHM, knowledge transfer involves using information from a structure, defined as a source domain, where labels are known for a given feature, and mapping these onto the unlabelled feature space of a different, target domain structure. If the mapping is successful, a machine learning classifier trained on the transformed source domain data will generalise to the unlabelled target domain data; i.e. a classifier built on one structure will generalise to another, making Structural Heath Monitoring (SHM) cost-effective and applicable to a wide range of challenging industrial scenarios. This process of mapping features and labels across source and target domains is defined as domain adaptation, a subcategory of transfer learning. However, a key assumption in conventional domain adaptation methods is that there is consistency between the feature and label spaces. This means that the features measured from one structure must be the same dimension as the other (i.e. the same number of spectral lines of a transmissibility), and that labels associated with damage locations, classification and assessment, exist on both structures. These consistency constraints can be restrictive, limiting to which types of population domain adaptation can be applied. This paper, therefore, provides a mathematical underpinning for when domain adaptation is possible in a structural dynamics context, with reference to topology of a graphical representation of structures. By defining when conventional domain adaptation is applicable in a structural dynamics setting, approaches are discussed that could overcome these consistency restrictions. This approach provides a general means for performing transfer learning within a PBSHM context for structural dynamics-based features.

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Towards Population-Based Structural Health Monitoring, Part VI: Structures as Geometry

Cited by 11 publications

References 18 publications

A Computational Framework for Modeling Complex Sensor Network Data Using Graph Signal Processing and Graph Neural Networks in Structural Health Monitoring

A Computational Framework for Modeling Complex Sensor Network Data Using Graph Signal Processing and Graph Neural Networks in Structural Health Monitoring

Towards Population-Based Structural Health Monitoring, Part I: Homogeneous Populations and Forms

Towards Population-Based Structural Health Monitoring, Part IV: Heterogeneous Populations, Transfer and Mapping

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