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Assessment Criteria for Optimal Sensor Placement for a Structural Health Monitoring System

TINGNA WANG, DAVID J. WAGG, KEITH WORDEN, ROBERT J. BARTHORPE

Abstract


Machine learning algorithms have been extensively used to implement structural health monitoring (SHM) systems to detect the occurrence of damage within a structure. To obtain the most effective data for SHM decision making, it is desirable to perform sensor placement optimisation (SPO), with a particular focus on damage identification. However, comparatively little attention has been paid to systematic assessment criteria appropriate to the design of a sensor system for SHM. This paper focusses on studying the evaluation criteria at different stages of a sensor-system design process, ranging from the measurement of linear associations to the detailed evaluation of the overall probability of correct classification. The effects of the investigated criteria are demonstrated using a physics-based model with uncertain parameters related to material proprieties. Predictions of the dynamic response of the structure in different states of interest are used to derive features.


DOI
10.12783/shm2021/36279

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