Affiliation:
1. College of Quality and Safety Engineering, China Jiliang University, Hangzhou 310018, China
2. Zhejiang Academy of Emergency Management Science, Hangzhou 310020, 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.
Funder
Zhejiang Provincial Natural Science Foundation of China
Fundamental Research Funds for the Provincial Universities of Zhejiang
Subject
Management, Monitoring, Policy and Law,Renewable Energy, Sustainability and the Environment,Geography, Planning and Development,Building and Construction
Reference88 articles.
1. Hazard and operability (HAZOP) analysis. A literature review;Fthenakis;J. Hazard. Mater.,2010
2. Vesely, W.E., Goldberg, F.F., Roberts, N.H., and Haasl, D.F. (1981). Fault Tree Handbook, Nuclear Regulatory Commission.
3. Smith, D.J., and Simpson, K.G. (2020). The Safety Critical Systems Handbook: A Straightforward Guide to Functional Safety: IEC 61508 (2010 Edition), IEC 61511 (2015 Edition) and Related Guidance, Butterworth-Heinemann.
4. Briand, L.C., Basili, V.R., and Thomas, W.M. (1991). A Pattern Recognition Approach for Software Engineering Data Analysis, IEEE Transactions on Software Engineering.
5. Spatial prediction models for shallow landslide hazards: A comparative assessment of the efficacy of support vector machines, artificial neural networks, kernel logistic regression, and logistic model tree;Tuan;Landslides,2016
Cited by
8 articles.
订阅此论文施引文献
订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献