From Artificial Intelligence to Explainable Artificial Intelligence in Industry 4.0: A Survey on What, How, and Where

被引:274
|
作者
Ahmed, Imran [1 ]
Jeon, Gwanggil [2 ]
Piccialli, Francesco [3 ]
机构
[1] Inst Management Sci, Ctr Excellence Informat Technol, Peshawar 25000, Pakistan
[2] Incheon Natl Univ, Dept Embedded Syst Engn, Incheon 406772, South Korea
[3] Univ Naples Federico II, Dept Math & Applicat R Caccioppoli, I-80138 Naples, Italy
关键词
Fourth Industrial Revolution; Artificial intelligence; Industries; Hidden Markov models; Manufacturing; Service robots; Robots; Artificial intelligence (AI); cloud computing; cyber-physical system; explainable artificial intelligence (XAI); Industry; 4; 0; Internet of Things (IoT); FAULT-DIAGNOSIS; BIG DATA; SMART; TECHNOLOGIES; PROGNOSIS; FRAMEWORK; FUTURE; IOT;
D O I
10.1109/TII.2022.3146552
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
Nowadays, Industry 4.0 can be considered a reality, a paradigm integrating modern technologies and innovations. Artificial intelligence (AI) can be considered the leading component of the industrial transformation enabling intelligent machines to execute tasks autonomously such as self-monitoring, interpretation, diagnosis, and analysis. AI-based methodologies (especially machine learning and deep learning support manufacturers and industries in predicting their maintenance needs and reducing downtime. Explainable artificial intelligence (XAI) studies and designs approaches, algorithms and tools producing human-understandable explanations of AI-based systems information and decisions. This article presents a comprehensive survey of AI and XAI-based methods adopted in the Industry 4.0 scenario. First, we briefly discuss different technologies enabling Industry 4.0. Then, we present an in-depth investigation of the main methods used in the literature: we also provide the details of what, how, why, and where these methods have been applied for Industry 4.0. Furthermore, we illustrate the opportunities and challenges that elicit future research directions toward responsible or human-centric AI and XAI systems, essential for adopting high-stakes industry applications.
引用
收藏
页码:5031 / 5042
页数:12
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