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

Author

Listed:
  • 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)

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.

Suggested Citation

  • Ze Wei & Hui Liu & Xuewen Tao & Kai Pan & Rui Huang & Wenjing Ji & Jianhai Wang, 2023. "Insights into the Application of Machine Learning in Industrial Risk Assessment: A Bibliometric Mapping Analysis," Sustainability, MDPI, vol. 15(8), pages 1-29, April.
    • Handle: RePEc:gam:jsusta:v:15:y:2023:i:8:p:6965-:d:1128746
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    References listed on IDEAS

    as
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