Open Access

REVIEW

Data Augmentation: A Multi-Perspective Survey on Data, Methods, and Applications

Canlin Cui¹, Junyu Yao^1,*, Heng Xia^2,*

1 School of Information Science and Technology, Beijing University of Technology, Beijing, 100124, China
2 Department of Automation, Tsinghua University, Beijing, 100084, China

* Corresponding Authors: Junyu Yao. Email: ; Heng Xia. Email:

Computers, Materials & Continua 2025, 85(3), 4275-4306. https://doi.org/10.32604/cmc.2025.069097

Received 14 June 2025; Accepted 28 August 2025; Issue published 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 robust performance in actual applications. This paper provides a comprehensive, multi-perspective review of data augmentation methods for industrial processes. For clarity and organization, existing studies are systematically grouped into four categories: small sample with low dimension, small sample with high dimension, large sample with low dimension, and large sample with high dimension. Within this framework, the review examines current research from both methodological and application-oriented perspectives, highlighting main methods, advantages, and limitations. By synthesizing these findings, this review offers a structured overview for scholars and practitioners, serving as a valuable reference for newcomers and experienced researchers seeking to explore and advance data augmentation techniques in industrial processes.

---

Keywords

---