Lei Liang, Li Weibin, Zhang Shiwei, Wu Changyuan, Yu Hongxiang
School of Artificial Intelligence, Xidian University, Xi'an 710071, China.
School of Information Science and Technology, Northwestern University, Xi'an 710127, China.
Sensors (Basel). 2025 May 7;25(9):2952. doi: 10.3390/s25092952.
With the advent of Industry 5.0, fault diagnosis is playing an increasingly important role in routine equipment maintenance and condition monitoring. From the perspective of industrial big data, this paper systematically reviews the current mainstream industrial fault diagnosis methods. The content covers the main sources of industrial big data, commonly used datasets, and the construction of related platforms. In conjunction with the development of multi-source heterogeneous data, the paper explores the evolutionary path of fault diagnosis methods. Subsequently, it provides an in-depth analysis of data-driven fault diagnosis techniques in industrial applications, with particular emphasis on the pivotal role of deep learning algorithms in fault diagnosis. Next, it discusses the applications and development of large models in the field of fault diagnosis, focusing on their potential to enhance diagnostic intelligence and generalization under big data environments. Finally, the paper looks ahead to the future development of data-driven fault diagnosis methods, pointing out that data quality, interpretability of deep learning, and edge-based large models are important research directions that urgently require breakthroughs.
随着工业5.0的到来，故障诊断在日常设备维护和状态监测中发挥着越来越重要的作用。从工业大数据的角度出发，本文系统地综述了当前主流的工业故障诊断方法。内容涵盖工业大数据的主要来源、常用数据集以及相关平台的构建。结合多源异构数据的发展，探讨了故障诊断方法的演进路径。随后，深入分析了工业应用中数据驱动的故障诊断技术，特别强调了深度学习算法在故障诊断中的关键作用。接着，讨论了大模型在故障诊断领域的应用与发展，重点关注其在大数据环境下提升诊断智能和泛化能力的潜力。最后，展望了数据驱动故障诊断方法的未来发展，指出数据质量、深度学习的可解释性以及基于边缘的大模型是迫切需要突破的重要研究方向。
