Canlin Cui1, Junyu Yao1,*, Heng Xia2,*
CMC-Computers, Materials & Continua, Vol.85, No.3, pp. 4275-4306, 2025, DOI:10.32604/cmc.2025.069097
- 23 October 2025
Abstract High-quality data is essential for the success of data-driven learning tasks. The characteristics, precision, and completeness of the datasets critically determine the reliability, interpretability, and effectiveness of subsequent analyzes and applications, such as fault detection, predictive maintenance, and process optimization. However, for many industrial processes, obtaining sufficient high-quality data remains a significant challenge due to high costs, safety concerns, and practical constraints. To overcome these challenges, data augmentation has emerged as a rapidly growing research area, attracting considerable attention across both academia and industry. By expanding datasets, data augmentation techniques improve greater generalization and more… More >
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