Monitoring a Bolted Vibrating Structure Using Multiple Acoustic Emission Sensors: A Benchmark

Emmanuel Ramasso, Benoît Verdin and Gaël Chevallier
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Emmanuel Ramasso: FEMTO-ST, Department of Applied Mechanics, 24 Rue de l’Epitaphe, 25000 Besançon, France
Benoît Verdin: FEMTO-ST, Department of Applied Mechanics, 24 Rue de l’Epitaphe, 25000 Besançon, France
Gaël Chevallier: FEMTO-ST, Department of Applied Mechanics, 24 Rue de l’Epitaphe, 25000 Besançon, France

Data, 2022, vol. 7, issue 3, 1-14

Abstract: The dataset presented in this work, called ORION-AE, is made of raw AE data streams collected by three different AE sensors and a laser vibrometer during five campaigns of measurements by varying the tightening conditions of two bolted plates submitted to harmonic vibration tests. With seven different operating conditions, this dataset was designed to challenge supervised and unsupervised machine/deep learning as well as signal processing methods which are developed for material characterization or structural health monitoring (SHM). One motivation of this work was to create a common benchmark for comparing data-driven methods dedicated to AE data interpretation. The dataset is made of time series collected during an experiment designed to reproduce the loosening phenomenon observed in aeronautics, automotive, or civil engineering structures where parts are assembled together by means of bolted joints. Monitoring loosening in jointed structures during operation remains challenging because contact and friction in bolted joints induce a nonlinear stochastic behavior.

Keywords: acoustic emission; bolted joints; health monitoring; laser vibrometry; benchmarking (search for similar items in EconPapers)
JEL-codes: C8 C80 C81 C82 C83 (search for similar items in EconPapers)
Date: 2022
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