DOI: 10.7910/dvn/fbrdu0
title: ORION-AE: Multisensor acoustic emission datasets reflecting supervised untightening of bolts in a jointed vibrating structure
title: Challenge machine learning and signal processing methods

description: Experiments were designed to reproduce the loosening phenomenon observed in aeronautics, automotive or civil engineering structures where parts are assembled together by means of bolted joints. The bolts can indeed be subject to self-loosening under vibrations. Therefore, it is of paramount importance to develop sensing strategies and algorithms for early loosening estimation. The test rig was specifically designed to make the vibration tests as repeatable as possible.&lt;br&gt;
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The dataset ORION-AE is made of a set of time-series measurements obtained by untightening a bolt with seven different levels. The data have been sampled at 5 MHz on four different sensors, including three permanently attached acoustic emission sensors in contact with the structure, and one laser (contactless) measurement apparatus. This dataset can thus be used for performance benchmarking of supervised, semi-supervised or unsupervised learning algorithms, including deep and transfer learning for time-series data, with possibly seven classes. This dataset may also be useful to challenge denoising methods or wave-picking algorithms, for which the vibrometer measurements can be used for validation.&lt;br&gt;
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ORION is a jointed structure made of two plates manufactured in a 2024 aluminium alloy, linked together by three bolts. The contact between the plates is done through machined overlays. The contact patches has an area of 12x12 mm^2 and is 1 mm thick. The structure was submitted to a 100 Hz harmonic excitation force during about 10 seconds. The load was applied using a Tyra electromagnetic shaker, which can deliver a 200 N force. The force was measured using a PCB piezoelectric load cell and the vibration level was determined next to the end of the specimen using a Polytec laser vibrometer. &lt;br&gt;
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The ORION-AE dataset is composed of five directories collected in five campaigns denoted as B, C, D, E and F in the sequel. Seven tightening levels were applied on the upper bolt. The tightening was first set to 60 cNm with a torque screwdriver. After a 10 seconds vibration test, the shaker was stopped and this vibration test was repeated after a torque modification at 50 cNm. Then torque modifications at 40, 30, 20, 10 and 5 cNm were applied. Note that, for campaign C, the level 40 cNm is missing. &lt;br&gt;
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During each cycle of the vibration test for a given tightening level, different AE sources can generate signals and those sources may be activated or not, depending on the tribological conditions within the contact between the beams which are not controlled. The tightening levels can be used to represent a reference against which clustering or classification results can be compared with. In that case, the main assumption is that the torque remained close to the level which was set at the beginning of every period of 10 s. This assumption can not be checked in the current configuration of the tests.&lt;br&gt;
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For each campaign, four sensors were used: a laser vibrometer and three different AE sensors (micro-200-HF, micro-80 and the F50A from Euro-Physical Acoustics) with various frequency bands were attached onto the lower plate (5 cm above the end of the plate). All data were sampled at 5 MHz using a Picoscope 4824 and a preamplifier (from Euro-Physical Acoustics) set to 60 dB. The velocimeter is used for different purposes, in particular to control the amplitude of the displacement of the top of the upper beam so that it remains constant whatever the tightening level. &lt;br&gt;
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The sensors are expected to detect the stick-slip transitions or shocks in the interface that are known to generate small AE events during vibrations. The acoustic waves generated by these events are highly dependent on bolt tightening. These sources of AE signals have to be detected and identified from the data stream which constitute the challenge. &lt;br&gt;
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&lt;b&gt; Details of the folders and files&lt;/b&gt; &lt;br&gt;
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There is 1 folder per campaign, each composed of 7 subfolders corresponding to 7 tightening levels: 5 cNm, 10 cNm, 20 cNm, 30 cNm, 40 cNm, 50 cNm, 60 cNm. So, 7 levels are available per campaign, except for campaign C for which 40 cNm is missing. &lt;br&gt;
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There is about 10 seconds of continuous recording of data per level (the exact value can be found according to the number of files in each subfolder). The sampling frequency was set to 5 MHZ on all channels of a picoscope 4824 and a preamplifer of 60 dB (model 2/4/6 preamplifier made by Europhysical acoustics). The characteristics of both the picoscope and preamplifier are provided in the enclosed documentation. &lt;br&gt;
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Each subfolder is made of .mat files. There is about 1 file per second (depending on the buffering, it can vary a little). The files in a subfolder are named according to the timestamps (time of recording). Each file is composed of vectors of data named:
&lt;li&gt;A = micro80 sensor.&lt;/li&gt;
&lt;li&gt;B = F50A sensor.&lt;/li&gt;
&lt;li&gt;C = micro200HF sensor.&lt;/li&gt;
&lt;li&gt;D = velocimeter.&lt;/li&gt;
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Note that the measurements are stored in &lt;b&gt;mV&lt;/b&gt;.&lt;br&gt;
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Sample Matlab codes are provided to read the files provided.&lt;br&gt;
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The characteristics of the sensors are provided in the enclosed documentation. &lt;br&gt;