深度专家网络:通过完全可解释的神经符号人工智能实现知识型故障诊断的统一方法

IF 12.2 1区 工程技术 Q1 ENGINEERING, INDUSTRIAL
Qi Li, Yuekai Liu, Shilin Sun, Zhaoye Qin, Fulei Chu
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引用次数: 0

摘要

近年来,基于人工智能(AI)的智能故障诊断(IFD)备受关注,并取得了显著突破。然而,人工智能智能故障诊断的黑箱特性可能会使其变得不可解释,而这对于安全关键型工业资产来说是至关重要的。在本文中,我们提出了一种完全可解释的 IFD 方法,该方法利用神经符号人工智能纳入了专家知识。该方法被命名为深度专家网络,定义了神经符号节点,包括信号处理算子、统计算子和逻辑算子,为网络建立了一个清晰的语义空间。所有算子都与可训练的权重相连,由权重决定连接。利用端到端学习和基于梯度的学习来优化模型结构权重和参数,以适应故障信号并获得完全可解释的决策路径。通过对旋转机械的案例研究,展示了模型的透明度、对未知工作条件的泛化能力以及对噪声攻击的鲁棒性,为未来的工业应用铺平了道路。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Deep expert network: A unified method toward knowledge-informed fault diagnosis via fully interpretable neuro-symbolic AI
In recent years, intelligent fault diagnosis (IFD) based on Artificial Intelligence (AI) has gained significant attention and achieved remarkable breakthroughs. However, the black-box property of AI-enabled IFD may render it non-interpretable, which is essential for safety-critical industrial assets. In this paper, we propose a fully interpretable IFD approach that incorporates expert knowledge using neuro-symbolic AI. The proposed approach, named Deep Expert Network, defines neuro-symbolic node, including signal processing operators, statistical operators, and logical operators to establish a clear semantic space for the network. All operators are connected with trainable weights that decide the connections. End-to-end and gradient-based learning are utilized to optimize both the model structure weights and parameters to fit the fault signal and obtain a fully interpretable decision route. The transparency of model, generalization ability toward unseen working conditions, and robustness to noise attack are demonstrated through case study of rotating machinery, paving the way for future industrial applications.
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来源期刊
Journal of Manufacturing Systems
Journal of Manufacturing Systems 工程技术-工程:工业
CiteScore
23.30
自引率
13.20%
发文量
216
审稿时长
25 days
期刊介绍: The Journal of Manufacturing Systems is dedicated to showcasing cutting-edge fundamental and applied research in manufacturing at the systems level. Encompassing products, equipment, people, information, control, and support functions, manufacturing systems play a pivotal role in the economical and competitive development, production, delivery, and total lifecycle of products, meeting market and societal needs. With a commitment to publishing archival scholarly literature, the journal strives to advance the state of the art in manufacturing systems and foster innovation in crafting efficient, robust, and sustainable manufacturing systems. The focus extends from equipment-level considerations to the broader scope of the extended enterprise. The Journal welcomes research addressing challenges across various scales, including nano, micro, and macro-scale manufacturing, and spanning diverse sectors such as aerospace, automotive, energy, and medical device manufacturing.
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