On suitability of measures of amplitude for quality assurance of vibration sensing systems in structural health monitoring
Publication date
2025-04-12
Document type
Forschungsartikel
Author
Nichani, Kapil
Martin, Victor San
Frost, Kirstin
Hettwer, Karina
Uhlig, Steffen
Organisational unit
Publisher
Wiley
Series or journal
Structural concrete : official journal of the FIB
First page
1
Last page
19
Peer-reviewed
✅
Part of the university bibliography
✅
Language
English
Abstract
Accelerometers play a crucial role in measurement systems utilized for structural health monitoring (SHM) in the field of civil engineering. Structural vibrations are analyzed to gather data that helps evaluate the integrity, performance, and safety of infrastructure. Their accurate measurements allow for early identification of abnormalities, support proactive maintenance, and guarantee the resilience of civil constructions. Yet, practical conditions encountered in the field produce variations in signal response that impact the precision and performance of sensor responses. This highlights the necessity for a quality assurance approach for SHM measurements. This report systematically investigates the sources of variation contributing to measurement uncertainty in sensor responses. To study the factors causing these variations, data were collected from two accelerometers using a shaker table apparatus and a factorial design of experiments. Amplitude, a fundamental signal feature, was evaluated in eight different ways to characterize measurement uncertainty, study static characteristics such as repeatability and linearity, and perform in situ calibration. The characteristics were compared over both short and long time‐windows as well as between two sensors. The findings reveal that amplitude, depending on how one calculates it, is a simple feature that effectively demonstrates both linearity and proportionality when correlated with shaker acceleration. This makes them exceptionally suitable for fast, efficient quality assurance (QA) tasks. Such analyses help evaluate uncertainties caused by environmental, operational, instrumentation, and human factors affecting sensing systems. The presented methodologies for evaluating measurement uncertainty contribute to the essential set of tools needed for QA of sensing systems.
Version
Published version
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