A spin on active learning analysis for health monitoring

被引：0
|
作者：
Clarkson, D. [1 ]
Bull, L.A. [2 ]
Dardeno, T.A. [1 ]
Wickramarachchi, C.T. [1 ]
Cross, E.J. [1 ]
Rogers, T.J. [1 ]
Worden, K. [1 ]
Dervilis, N. [1 ]
Hughes, A.J. [1 ]
机构：
[1] University of Sheffield, Department of Engineering, Mappin St, S1 3JD, United Kingdom
[2] University of Cambridge, Department of Engineering, 7a JJ Thomson Ave, Cambridge, United Kingdom
来源：
e-Journal of Nondestructive Testing | 2024年 / 29卷 / 07期
基金：
英国工程与自然科学研究理事会;
关键词：
Artificial intelligence - Decision making - Risk assessment - Structural health monitoring;
D O I：
10.58286/29629
中图分类号：
学科分类号：
摘要：
This article combines the effects of the population-based approach to structural health monitoring(SHM) with the efficiencies of active learning with an aim to improve decision-making, reduce costs and more effectively allocate resources for SHM. This model applies a decision theoretic analysis to mandate inspections of a population of machining tools according to risk. © 2024, NDT. net GmbH and Co. KG. All rights reserved.
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