  The data set 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 data set 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 data set
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. ORION-AE data set is available on a shared directory for Research
Data, Harvard Dataverse (https://doi.org/10.7910/DVN/FBRDU0). A Matlab code is
provided to extract the data stream from each sensor. This article is a
preprint, the published version is available at
https://doi.org/10.3390/data7030031.
