Probabilistic active learning: An online framework for structural health monitoring

Bull, Larry; Rogers, Timothy J.; Wickramarachchi, Chandula T.; Cross, Elizabeth J.; Worden, Keith; Dervilis, Nikolaos

doi:10.1016/j.ymssp.2019.106294

Cited by 55 publications

(79 citation statements)

References 17 publications

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“…As such, only the most informative observations are queried, to make the most out of a limited labelling budget. Active learning has been applied to SHM data in the past, in the offline [7] and online setting [8]. The focus of this work, however, considers semi-supervised variants of partially-supervised learning.…”

Section: Partially-supervised Learningmentioning

confidence: 99%

Towards semi-supervised and probabilistic classification in structural health monitoring

Bull

Worden

Dervilis

2020

Mechanical Systems and Signal Processing

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In practical applications of data-driven Structural Health Monitoring (SHM), recording labels for each of the measured signals can be infeasible and expensive. In consequence, conventional methods for (supervised) machine learning can become irrelevant in certain applications of damage classification. Semisupervised methods, however, allow algorithms to learn from information in the available unlabelled measurements as well a limited set of labelled data. As such, this paper suggests a semi-supervised Gaussian mixture model for probabilistic damage-classification, informed by both labelled and unlabelled signals. The generative statistical model is shown to improve the classification performance, compared to supervised learning, with simulated and experimental SHM data, while requiring no further inspections of the system. Specifically, semi-supervised learning leads to 3.87% and 3.83% reductions in the classification error for the simulated and experimental datasets respectively. These results indicate that, through semi-supervised learning in SHM, the cost associated with labelling data could be managed, as the information in a small set of labelled signals can be combined with larger sets of unlabelled data.

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Section: Partially-supervised Learningmentioning

confidence: 99%

Towards semi-supervised and probabilistic classification in structural health monitoring

Bull

Worden

Dervilis

2020

Mechanical Systems and Signal Processing

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“…This missing information is usually due to the cost associated with manually inspecting structures (or data), as well as the practicality of investigating each observation. The absence of labels makes defining and updating (multi-class) machine learning models difficult, particularly in the online setting, as it can become difficult to determine if/when novel valuable information has been recorded, and what it represents (Bull et al 2019b). For example, consider streaming data, recorded from a sub-sea pipeline.…”

Section: Incomplete Data and Missing Informationmentioning

confidence: 99%

Probabilistic Inference for Structural Health Monitoring: New Modes of Learning from Data

Bull

Gardner

Rogers

et al. 2021

ASCE-ASME J. Risk Uncertainty Eng. Syst., Part A: Civ. Eng.

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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 the measurements 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 reviewing novel techniques for statistical learning (introduced in previous work), it is argued that probabilistic algorithms offer a natural solution to the modelling of SHM data in practice. In three case-studies, probabilistic methods are adapted for applications to SHM signals -including semi-supervised learning, active learning, and multi-task learning.

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“…related to Rytter's hierarchy. There are three main problems in machine learning: classification, regression and density estimation, with examples of all three within the SHM and NDE literature [15][16][17], [17][18][19][20] and [21,22] respectively (where the reference list is not intended to be exhaustive). In classification, the challenge is inferring a map from the input data to a set of categorical labels, e.g.…”

Section: Ultrasound: Shm or Nde?mentioning

confidence: 99%

Machine learning at the interface of structural health monitoring and non-destructive evaluation

Gardner

Fuentes

Dervilis

et al. 2020

Phil. Trans. R. Soc. A.

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While both non-destructive evaluation (NDE) and structural health monitoring (SHM) share the objective of damage detection and identification in structures, they are distinct in many respects. This paper will discuss the differences and commonalities and consider ultrasonic/guided-wave inspection as a technology at the interface of the two methodologies. It will discuss how data-based/machine learning analysis provides a powerful approach to ultrasonic NDE/SHM in terms of the available algorithms, and more generally, how different techniques can accommodate the very substantial quantities of data that are provided by modern monitoring campaigns. Several machine learning methods will be illustrated using case studies of composite structure monitoring and will consider the challenges of high-dimensional feature data available from sensing technologies like autonomous robotic ultrasonic inspection. This article is part of the theme issue ‘Advanced electromagnetic non-destructive evaluation and smart monitoring’.

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Probabilistic active learning: An online framework for structural health monitoring

Cited by 55 publications

References 17 publications

Towards semi-supervised and probabilistic classification in structural health monitoring

Towards semi-supervised and probabilistic classification in structural health monitoring

Probabilistic Inference for Structural Health Monitoring: New Modes of Learning from Data

Machine learning at the interface of structural health monitoring and non-destructive evaluation

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