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Multi-sensor fusion-based time-frequency imaging and transfer learning for spherical tank crack diagnosis under variable pressure conditions

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Abstract
In this paper, a crack diagnosis framework is proposed that combines a new signal-to-imaging technique and transfer learning-aided deep learning framework to automate the diagnostic process. The objective of the signal-to-imaging technique is to convert one-dimensional (1D) acoustic emission (AE) signals from multiple sensors into a two-dimensional (2D) image to capture information under variable operating conditions. In this process, a short-time Fourier transform (STFT) is first applied to the AE signal of each sensor, and the STFT results from the different sensors are then fused to obtain a condition-invariant 2D image of cracks; this scheme is denoted as Multi-Sensors Fusion-based Time-Frequency Imaging (MSFTFI). The MSFTFI images are subsequently fed to the fine-tuned transfer learning (FTL) model built on a convolutional neural network (CNN) framework for diagnosing crack types. The proposed diagnostic scheme (MSFTFI + FTL) is tested with a standard AE dataset collected from a self-designed spherical tank to validate the performance under variable pressure conditions. The results suggest that the proposed strategy significantly outperformed classical methods with average performance improvements of 2.36?20.26%.
Author(s)
JUNAYED MD이슬람 엠 엠 만주룰김종면
Issued Date
2021
Type
Article
Keyword
Acoustic emissionsConvolutional neural networkFault diagnosisMulti-sensorsTransfer learningSpherical tank
DOI
10.1016/j.measurement.2020.108478
URI
https://oak.ulsan.ac.kr/handle/2021.oak/9124
https://ulsan-primo.hosted.exlibrisgroup.com/primo-explore/fulldisplay?docid=TN_cdi_proquest_journals_2468689300&context=PC&vid=ULSAN&lang=ko_KR&search_scope=default_scope&adaptor=primo_central_multiple_fe&tab=default_tab&query=any,contains,Multi-sensor%20fusion-based%20time-frequency%20imaging%20and%20transfer%20learning%20for%20spherical%20tank%20crack%20diagnosis%20under%20variable%20pressure%20conditions&offset=0&pcAvailability=true
Publisher
MEASUREMENT
Location
영국
Language
영어
ISSN
0263-2241
Citation Volume
168
Citation Number
1
Citation Start Page
108478
Citation End Page
108478
Appears in Collections:
Engineering > IT Convergence
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