可解释人工智能在工业智能诊断中的挑战和机遇：先验赋能

doi:10.3901/JME.2024.12.001

摘要/Abstract

摘要： 进入“大数据”时代，人工智能技术因其强大的数据挖掘与学习能力，成为工业智能诊断领域的重要方法，在机械装备的异常检测、故障诊断和寿命预测等方面发挥重要作用。随着机械装备日益向大型化、高速化、集成化和自动化发展，诊断方法的可信度变得至关重要。因此弱可解释性正成为人工智能技术在诊断领域实际应用的巨大障碍。为了推动人工智能技术在工业智能诊断领域的发展，对可解释人工智能方法进行综述。首先介绍可解释性技术的概念与作用原理，并对目前可解释性技术的主要观点与分类进行总结。接着，从工业诊断中常用的信号处理先验和物理知识先验角度，概述内在可解释的先验赋能可解释技术的研究现状。最后指出先验赋能可解释技术存在的挑战与机遇。

关键词: 智能诊断, 可解释性, 先验赋能, 信号处理, 物理知识

Abstract: In the era of “big data”, artificial intelligence(AI) has emerged as an important approach in the field of industrial intelligent diagnosis, owing to its powerful data mining and learning capability. It plays a significant role in tasks such as anomaly detection, fault diagnosis, and remaining useful life prediction of mechanical equipment. As mechanical equipment continues to evolve towards larger scale, higher speed, integration and automation, the reliability of diagnostic methods has become crucial. Consequently, the lack of interpretability has become a major obstacle to the practical application of AI technology in the field of diagnosis. To promote the development of AI technology in industrial intelligent diagnosis, a comprehensive review of explainable AI(XAI) methods is provided. Firstly, the concept and principles of XAI are introduced, along with a summary of the main perspective and classifications of current XAI techniques. Subsequently, the research status of inherently explainable AI techniques empowered by signal processing priors and physical knowledge prior from industrial diagnosis is summarized. Finally, the challenges and opportunities associated with priori-empowered XAI are highlighted.

Key words: intelligent diagnosis, explainability, priori-empowered, signal processing, physical knowledge

中图分类号:

TH17

严如强, 商佐港, 王志颖, 许文纲, 赵志斌, 王诗彬, 陈雪峰. 可解释人工智能在工业智能诊断中的挑战和机遇：先验赋能[J]. 机械工程学报, 2024, 60(12): 1-20.

YAN Ruqiang, SHANG Zuogang, WANG Zhiying, XU Wengang, ZHAO Zhibin, WANG Shibin, CHEN Xuefeng. Challenges and Opportunities of XAI in Industrial Intelligent Diagnosis: Priori-empowered[J]. Journal of Mechanical Engineering, 2024, 60(12): 1-20.

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