大数据

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工业数字化转型:故障诊断方法研究进展

杨  彪1,2,熊  贇1,2,傅  玲3,徐蔚峰3,李  婧3   

  1. 1. 复旦大学计算机科学技术学院,上海,200433;
    2. 上海市数据科学重点实验室,上海,200433;
    3. 西门子中国研究院,北京,100102

Industrial Digital Transformation: Research on Fault Diagnosis Digital Methods

YANG Biao1, 2, XIONG Yun1,2, FU Ling3, XU Weifeng3, LI Jing3   

  1. 1. School of Computer Science, Fudan University, Shanghai 200433, China
    2. Shanghai Key Laboratory of Data Science, Shanghai 315100, China
    3. Siemens Ltd., Beijing 100102, China 

摘要:

工业数字化是我国工业产业转型升级的重要手段,数字化转型成为我国工业发展的重要趋势。工业系统的可靠性和稳定性对工业生产的高质量和可持续发展具有重要作用。故障影响工业系统运行,甚至造成重大的安全事故和经济损失。为应对这一问题,故障诊断技术应运而生并逐步发展。高效、高质的故障诊断数字化技术已经成为工业数字化转型的关键技术。本文分析了工业领域故障诊断数字化方法的研究进展,按照其发展划分为以领域经验主导的建模方法、数据驱动与领域经验结合的数字化方法、数据驱动主导与可解释性结合的数字化方法等三个阶段,重点探究每个阶段方法的基本思想及其特点等,并探讨未来的研究方向,为推动工业数字化转型提供方法参考。

关键词:

工业数字化, 数字化转型, 故障诊断, 数字化方法

Abstract:

Industrial digitalization is an important way for industrial transformation and upgrading of China's  industry, and digital transformation has become an important trend in the development of China's industry. The reliability and stability of industrial systems play an important role in the high quality and sustainable development of industrial production. Failures affect the operation of industrial systems and even cause major safety accidents and economic losses. To deal with this problem, fault diagnosis technology was born and gradually developed. Efficient and high-quality fault diagnosis digital technology has become a key technology for industrial digital transformation. This paper analyzes the research progress of digital methods for fault diagnosis in industry, and divides them into three stages according to their development: domain experience-led modeling methods, data-driven digital methods combining with domain experience, and data-driven digital methods combining with interpretability. This paper focuses on exploring the basic ideas and characteristics of the methods in each stage, and discusses the future research directions to provide methodological references for promoting industrial digital transformation.

Key words:

 , industrial digitization, digital transformation, fault diagnosis, digital methods

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