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

Hasan, Md. Junayed; Islam, M.M. Manjurul; Kim, Jong-Myon

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%.

Citation

HASAN, M.J., ISLAM, M.M.M. and KIM, J.-M. 2021. Multi-sensor fusion-based time-frequency imaging and transfer learning for spherical tank crack diagnosis under variable pressure conditions. Measurement [online], 168, article 108478. Available from: https://doi.org/10.1016/j.measurement.2020.108478

• Journal Article Type: Article
• Acceptance Date: Sep 15, 2020
• Online Publication Date: Sep 22, 2020
• Publication Date: Jan 15, 2021
• Deposit Date: May 13, 2022
• Publicly Available Date: Jun 7, 2022
• Journal: Measurement: Journal of the International Measurement Confederation
• Print ISSN: 0263-2241
• Publisher: Elsevier
• Peer Reviewed: Peer Reviewed
• Volume: 168
• Article Number: 108478
• DOI: https://doi.org/10.1016/j.measurement.2020.108478
• Keywords: Acoustic emissions; Convolutional neural network; Fault diagnosis; Multi-sensors; Transfer learning; Spherical tank
• Public URL: https://rgu-repository.worktribe.com/output/1664532