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Structural Geometric Morphology Monitoring for Bridges Using Holographic Visual Sensor Read chapter Read first chapter

2021 | OriginalPaper | Chapter

New Modes of Inference for Probabilistic SHM

Authors : Lawrence A. Bull, Paul Gardner, Timothy J. Rogers, Elizabeth J. Cross, Nikolaos Dervilis, Keith Worden

Published in: European Workshop on Structural Health Monitoring

Publisher: Springer International Publishing

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Abstract

In data-driven SHM, the signals recorded from systems in operation can be noisy and incomplete. Data corresponding to each of the operational, environmental, and damage states are rarely available a priori; furthermore, labelling to describe what each the measured signals represent is often unavailable. In consequence, the algorithms used to implement SHM should be robust and adaptive, while accommodating for missing information in the training-data – such that new information can be included if it becomes available. By collecting three novel techniques for statistical learning (originally proposed in previous work) – including semi-supervised, active, and transfer learning – it is argued that probabilistic algorithms offer a natural solution to model the signals recorded from systems in practice.

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previous chapter Damage Detection on an Operating Wind Turbine Blade via a Single Vibration Sensor: A Feasibility Study
next chapter Early Damage Detection for Partially Observed Structures with an Autoregressive Spectrum and Distance-Based Methodology
Footnotes
1
If indirectly, diagnostic labels can be inferred through post-processing of the pattern recognition outputs \(y_i\).
 
2
Discrete classification is presented in this work, although, SHM is regularly informed by regression models – i.e. \(y_i\) is continuous. This is application specific, and most of the motivational arguments remain the same.
 
3
Note, the cluster assumption does not necessarily imply that each class is represented by a single, compact cluster; instead, the implication is that observations from different classes are unlikely to appear in the same cluster [9].
 
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Metadata
Title
New Modes of Inference for Probabilistic SHM
Authors
Lawrence A. Bull
Paul Gardner
Timothy J. Rogers
Elizabeth J. Cross
Nikolaos Dervilis
Keith Worden
Copyright Year
2021
Book
European Workshop on Structural Health Monitoring

Print ISBN: 978-3-030-64907-4

Electronic ISBN: 978-3-030-64908-1

Copyright Year: 2021

DOI
https://doi.org/10.1007/978-3-030-64908-1_39