The development of the Internet of Things, cloud computing, and artificial intelligence has given birth to industrial artificial intelligence (IAI) technology, which enables us to obtain fine perception and in-depth understanding capabilities for the operating conditions of industrial processes, and promotes the intelligent transformation of modern industrial production processes. At the same time, modern industry is facing diversified market demand instead of ultra-large-scale demand, resulting in typical variable conditions, which enhances the nonstationary characteristics of modern industry, and brings great challenges to the monitoring of industrial processes. In this regard, this paper analyzes the complex characteristics of nonstationary industrial operation, reveals the effects on operating condition monitoring, and summarizes the difficulties faced by varying condition monitoring. Furthermore, by reviewing the recent 30 years of development of data-driven methods for industrial process monitoring, we sorted out the evolution of nonstationary monitoring methods, and analyzed the features, advantages and disadvantages of the methods at different stages. In addition, by summarizing the existing related research methods by category, we hope to provide reference for monitoring methods of nonstationary process. Finally, combined with the development trend of industrial artificial intelligence technologies, some promising research directions are given in the field of nonstationary process monitoring.

物联网、云计算、人工智能的发展催生了工业人工智能（IAI）技术，使我们能够获得对工业过程运行状况的精细感知和深度理解能力，促进现代工业生产过程的智能化改造。同时，现代工业面临着多样化的市场需求，而非超大规模的需求，导致典型的多变条件，增强了现代工业的非平稳特性，给工业过程的监控带来了巨大挑战。对此，本文分析了非平稳工业运行的复杂特性，揭示了对运行状态监测的影响，并总结了变化状态监测所面临的困难。此外，通过回顾工业过程监测数据驱动方法近30年的发展历程，梳理了非平稳监测方法的演变过程，分析了不同阶段方法的特点、优缺点。此外，通过对现有相关研究方法的分类总结，希望为非平稳过程的监测方法提供参考。最后，结合工业人工智能技术的发展趋势，给出了非平稳过程监测领域一些有前景的研究方向。梳理了非平稳监测方法的演进过程，分析了不同阶段方法的特点、优缺点。此外，通过对现有相关研究方法的分类总结，希望为非平稳过程的监测方法提供参考。最后，结合工业人工智能技术的发展趋势，给出了非平稳过程监测领域一些有前景的研究方向。梳理了非平稳监测方法的演进过程，分析了不同阶段方法的特点、优缺点。此外，通过对现有相关研究方法的分类总结，希望为非平稳过程的监测方法提供参考。最后，结合工业人工智能技术的发展趋势，给出了非平稳过程监测领域一些有前景的研究方向。