ORION-AE: Multisensor acoustic emission datasets reflecting supervised untightening of bolts in a jointed vibrating structure

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.<br>
<br>
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.<br>
<br>
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. <br>
<br>
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. <br>
<br>
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.<br>
<br>
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. <br>
<br>
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. <br>
<br>

<b> Details of the folders and files</b> <br>
<br>
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. <br>
<br>

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. <br>
<br>
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:
<li>A = micro80 sensor.</li>
<li>B = F50A sensor.</li>
<li>C = micro200HF sensor.</li>
<li>D = velocimeter.</li>
<br>
Note that the measurements are stored in <b>mV</b>.<br>
<br>
Sample Matlab codes are provided to read the files provided.<br>
<br>
The characteristics of the sensors are provided in the enclosed documentation. <br>

本实验旨在复现航空航天、汽车或土木工程领域螺栓连接（bolted joints）结构中出现的松动现象。螺栓在振动工况下确实可能发生自松动，因此研发用于早期松动预估的传感策略与算法至关重要。本试验台专为实现可重复的振动测试而设计。

数据集ORION-AE包含通过对螺栓施加7种不同松脱工况得到的多组时间序列（time-series）测量数据。该数据集由4类传感器以5兆赫兹（5 MHz）的采样率采集得到，其中包括3个永久贴合在结构上的声发射（Acoustic Emission, AE）传感器，以及1台激光非接触式测量装置。本数据集可用于监督学习（supervised learning）、半监督学习（semi-supervised learning）、无监督学习（unsupervised learning）算法（含面向时间序列数据的深度学习（deep learning）与迁移学习（transfer learning））的性能基准测试，共涵盖7个类别。此外，该数据集还可用于验证去噪方法与波拾取算法，其中激光测振仪的测量数据可作为验证基准。

ORION为螺栓连接结构，由两块采用2024铝合金（2024 aluminium alloy）加工的板材通过3颗螺栓紧固而成。板材间通过机加工的搭接面实现接触，接触斑块尺寸为12×12 mm²，厚度为1 mm。该结构承受约10秒的100赫兹（100 Hz）简谐激励力，激励由Tyra电磁激振器（electromagnetic shaker）提供，其最大输出力可达200牛（200 N）。激励力通过PCB压电式载荷传感器（piezoelectric load cell）采集，结构端部的振动幅值则通过Polytec激光测振仪（laser vibrometer）测得。

ORION-AE数据集包含5个批次的目录数据，后续分别记为B、C、D、E、F批次。试验对上方螺栓施加7种拧紧扭矩工况，初始扭矩通过扭矩螺丝刀设置为60厘牛米（cNm）。完成10秒振动测试后，关闭激振器，将扭矩调整为50 cNm后再次开展振动测试，随后依次将扭矩调整为40、30、20、10与5 cNm并重复测试。需注意，C批次缺少40 cNm的工况数据。

在特定拧紧扭矩工况的振动测试周期中，不同的声发射源会产生信号，而这些信号的激活与否取决于板材接触界面未受控的摩擦学（tribological）条件。拧紧扭矩工况可作为基准，用于比对聚类（clustering）或分类（classification）算法的测试结果。此时的核心假设为：每10秒测试周期内的扭矩始终保持与初始设置值接近，但在当前试验配置下无法验证该假设的合理性。

每个测试批次均采用4类传感器：1台激光测振仪与3款不同频段的声发射传感器（分别为Euro-Physical Acoustics公司的micro-200-HF、micro-80与F50A），传感器均安装在下板材上（位于板材端部上方5厘米处）。所有数据通过Picoscope 4824采集设备与增益设置为60分贝（dB）的前置放大器（Euro-Physical Acoustics出品）以5 MHz的采样率完成采集。该激光测速仪可用于多种用途，尤其用于控制上方板材顶端的位移幅值，确保其在不同拧紧扭矩工况下保持恒定。

传感器可用于检测界面间的粘滑过渡或冲击现象——这类现象在振动过程中会产生小型声发射事件。此类事件产生的声波与螺栓拧紧扭矩高度相关，从数据流中检测并识别这类声发射信号源正是本数据集的核心挑战所在。

### 文件夹与文件详情

每个测试批次对应1个主文件夹，每个主文件夹包含7个子文件夹，分别对应7种拧紧扭矩工况：5 cNm、10 cNm、20 cNm、30 cNm、40 cNm、50 cNm与60 cNm。因此除C批次缺少40 cNm工况外，其余批次均包含全部7种工况。

每种工况对应约10秒的连续数据录制（精确时长可通过子文件夹内的文件数量计算得出）。所有通道均通过Picoscope 4824采集设备与增益为60 dB的前置放大器（Europhysical Acoustics出品的2/4/6型前置放大器）以5 MHz采样率完成采集。采集设备与前置放大器的详细参数见随附文档。

每个子文件夹均由.mat格式的文件组成，每秒约生成1个文件（受缓存机制影响可能存在小幅波动）。子文件夹内的文件以录制时间戳命名。每个文件包含以下命名的数据向量：
• A = micro80 传感器
• B = F50A 传感器
• C = micro200HF 传感器
• D = 激光测速仪

需注意，测量数据以毫伏（mV）为单位存储。

随附用于读取数据文件的Matlab示例代码。

传感器的详细参数见随附文档。

搜集汇总

背景与挑战

背景概述

ORION-AE数据集是一个多传感器声发射数据集，记录了螺栓在振动结构中不同扭矩水平下的松动现象。数据集包含五个实验活动，每个活动下七个扭矩水平的数据，采样频率为5 MHz，适合用于机器学习和信号处理算法的性能测试和研究。