Kwok Tai Chui1,*, Brij B. Gupta2,3,4,5,6,*, Varsha Arya7,8,9, Miguel Torres-Ruiz10
CMC-Computers, Materials & Continua, Vol.78, No.1, pp. 1363-1379, 2024, DOI:10.32604/cmc.2023.046762
Abstract The visions of Industry 4.0 and 5.0 have reinforced the industrial environment. They have also made artificial intelligence incorporated as a major facilitator. Diagnosing machine faults has become a solid foundation for automatically recognizing machine failure, and thus timely maintenance can ensure safe operations. Transfer learning is a promising solution that can enhance the machine fault diagnosis model by borrowing pre-trained knowledge from the source model and applying it to the target model, which typically involves two datasets. In response to the availability of multiple datasets, this paper proposes using selective and adaptive incremental transfer learning (SA-ITL), which fuses three… More >
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