Enhancing the accuracy of machinery fault diagnosis through fault source isolation of complex mixture of industrial sound signals
Author:
Funder
Engineering Research and Development Center - Information Technology Laboratory
Publisher
Springer Science and Business Media LLC
Link
https://link.springer.com/content/pdf/10.1007/s00170-024-14080-y.pdf
Reference62 articles.
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2. Dai J, Wang J, Huang W, Shi J, Zhu Z (2020) Machinery health monitoring based on unsupervised feature learning via generative adversarial networks. IEEE/ASME Trans Mechatron 25:2252–2263. https://doi.org/10.1109/TMECH.2020.3012179
3. Senanayaka A, Al Mamun A, Bond G, Tian W, Wang H, Fuller S, Falls TC, Rahimi S, Bian L (2022) Similarity-based multi-source transfer learning approach for time series classification. Int J Progn Health Manag 13:1–9. https://doi.org/10.36001/IJPHM.2021.v13i2.3267
4. Al Mamun A, Bappy MM, Mudiyanselage AS, Li J, Jiang Z, Tian Z, Fuller S, Falls TC, Bian L, Tian W (2023) Multi-channel sensor fusion for real-time bearing fault diagnosis by frequency-domain multilinear principal component analysis. Int J Adv Manuf Technol 124:1321–1334. https://doi.org/10.1007/s00170-022-10525-4
5. Calabrese F, Regattieri A, Bortolini M, Gamberi M, Pilati F (2021) Predictive maintenance: a novel framework for a data-driven, semi-supervised, and partially online prognostic health management application in industries. Appl Sci (Switzerland) 11: https://doi.org/10.3390/app11083380
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