Insights into the Application of Machine Learning in Industrial Risk Assessment: A Bibliometric Mapping Analysis

Ze Wei, Hui Liu (), Xuewen Tao, Kai Pan, Rui Huang, Wenjing Ji and Jianhai Wang
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Ze Wei: College of Quality and Safety Engineering, China Jiliang University, Hangzhou 310018, China
Hui Liu: College of Quality and Safety Engineering, China Jiliang University, Hangzhou 310018, China
Xuewen Tao: Zhejiang Academy of Emergency Management Science, Hangzhou 310020, China
Kai Pan: College of Quality and Safety Engineering, China Jiliang University, Hangzhou 310018, China
Rui Huang: College of Quality and Safety Engineering, China Jiliang University, Hangzhou 310018, China
Wenjing Ji: College of Quality and Safety Engineering, China Jiliang University, Hangzhou 310018, China
Jianhai Wang: College of Quality and Safety Engineering, China Jiliang University, Hangzhou 310018, China

Sustainability, 2023, vol. 15, issue 8, 1-29

Abstract: Risk assessment is of great significance in industrial production and sustainable development. Great potential is attributed to machine learning in industrial risk assessment as a promising technology in the fields of computer science and the internet. To better understand the role of machine learning in this field and to investigate the current research status, we selected 3116 papers from the SCIE and SSCI databases of the WOS retrieval platform between 1991 and 2022 as our data sample. The VOSviewer, Bibliometrix R, and CiteSpace software were used to perform co-occurrence analysis, clustering analysis, and dual-map overlay analysis of keywords. The results indicate that the development trend of machine learning in industrial risk assessment can be divided into three stages: initial exploration, stable development, and high-speed development. Machine learning algorithm design, applications in biomedicine, risk monitoring in construction and machinery, and environmental protection are the knowledge base of this study. There are three research hotspots in the application of machine learning to industrial risk assessment: the study of machine learning algorithms, the risk assessment of machine learning in the Industry 4.0 system, and the application of machine learning in autonomous driving. At present, the basic theories and structural systems related to this research have been established, and there are numerous research directions and extensive frontier branches. “Random Forest”, “Industry 4.0”, “supply chain risk assessment”, and “Internet of Things” are at the forefront of the research.

Keywords: machine learning; industry; bibliometrics; knowledge mapping; risk assessment; safety (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2023
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