ORION-AE: Multisensor acoustic emission datasets reflecting supervised untightening of bolts in a jointed vibrating structure 机器收录·待核验

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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>

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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.<br>

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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. <br>

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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 appli

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落地页

https://doi.org/10.7910/DVN/FBRDU0

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CC0-1.0 (置信:verified_official)

发布年份

2021

发布方

Harvard Dataverse

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