Multisensor acoustic emission data set reflecting supervised untightening of bolts in a jointed vibrating structure

TL;DR

The ORION-AE dataset provides multi-sensor acoustic emission data capturing bolt loosening in jointed structures under vibration, serving as a benchmark for developing and evaluating machine learning and signal processing methods in structural health monitoring.

Contribution

This work introduces a comprehensive, multi-sensor AE dataset for bolt loosening, facilitating comparison of data-driven methods in SHM and material characterization.

Findings

Dataset includes data from three AE sensors and a laser vibrometer.

Designed to challenge supervised and unsupervised learning methods.

Replicates loosening phenomena in jointed structures.

Abstract

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.