Explainable AI in Manufacturing and Industrial Cyber-Physical Systems: A Survey

被引:3
|
作者
Moosavi, Sajad [1 ]
Farajzadeh-Zanjani, Maryam [1 ]
Razavi-Far, Roozbeh [1 ,2 ]
Palade, Vasile [3 ]
Saif, Mehrdad [1 ]
机构
[1] Univ Windsor, Dept Elect & Comp Engn, Windsor, ON N9B 3P4, Canada
[2] Univ New Brunswick, Fac Comp Sci, Fredericton, NB E3B 5A3, Canada
[3] Coventry Univ, Ctr Computat Sci & Math Modelling, Coventry CV1 5FB, England
关键词
explainable AI; XAI; interpretable AI; industrial cyber-physical systems; manufacturing; industrial systems; CONVOLUTIONAL NEURAL-NETWORK; ARTIFICIAL-INTELLIGENCE; FAULT-DIAGNOSIS; CHALLENGES; FRAMEWORK; SELECTION; SECURITY; SIGNALS; SHAP;
D O I
10.3390/electronics13173497
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
This survey explores applications of explainable artificial intelligence in manufacturing and industrial cyber-physical systems. As technological advancements continue to integrate artificial intelligence into critical infrastructure and industrial processes, the necessity for clear and understandable intelligent models becomes crucial. Explainable artificial intelligence techniques play a pivotal role in enhancing the trustworthiness and reliability of intelligent systems applied to industrial systems, ensuring human operators can comprehend and validate the decisions made by these intelligent systems. This review paper begins by highlighting the imperative need for explainable artificial intelligence, and, subsequently, classifies explainable artificial intelligence techniques systematically. The paper then investigates diverse explainable artificial-intelligence-related works within a wide range of industrial applications, such as predictive maintenance, cyber-security, fault detection and diagnosis, process control, product development, inventory management, and product quality. The study contributes to a comprehensive understanding of the diverse strategies and methodologies employed in integrating explainable artificial intelligence within industrial contexts.
引用
收藏
页数:28
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