Over the last decades, safety requirements have become of primary concern. In the context of safety, several strategies could be pursued in many engineering fields. Moreover, many techniques have been proposed to deal with safety, risk, and reliability matters, such as Machine Learning (ML) and Deep Learning (DL). ML and DL are characterised by a high variety of algorithms, adaptable for different purposes. This generated wide and fragmented literature on ML and DL for safety purposes, moreover, literature review and bibliometric studies of the past years mainly focus on a single research area or application field. Thus, this paper aims to provide a holistic understanding of the research on this topic through a Systematic Bibliometric Analysis (SBA), along with proposing a viable option to conduct SBAs. The focus is on investigating the main research areas, application fields, relevant authors and studies, and temporal evolution. It emerged that rotating equipment, structural health monitoring, batteries, aeroengines, and turbines are popular fields. Moreover, the results depicted an increase in popularity of DL, along with new approaches such as deep reinforcement learning through the past four years. The proposed workflow for SBA has the potential to benefit researchers from multiple disciplines, beyond safety science.

Machine learning and deep learning for safety applications: Investigating the intellectual structure and the temporal evolution / Leoni, Leonardo; BahooToroody, Ahmad; Abaei, Mohammad Mahdi; Cantini, Alessandra; BahooToroody, Farshad; De Carlo, Filippo. - In: SAFETY SCIENCE. - ISSN 0925-7535. - STAMPA. - 170:(2024), pp. 106363.1-106363.25. [10.1016/j.ssci.2023.106363]

Machine learning and deep learning for safety applications: Investigating the intellectual structure and the temporal evolution

Leoni, Leonardo;Cantini, Alessandra;De Carlo, Filippo
2024

Abstract

Over the last decades, safety requirements have become of primary concern. In the context of safety, several strategies could be pursued in many engineering fields. Moreover, many techniques have been proposed to deal with safety, risk, and reliability matters, such as Machine Learning (ML) and Deep Learning (DL). ML and DL are characterised by a high variety of algorithms, adaptable for different purposes. This generated wide and fragmented literature on ML and DL for safety purposes, moreover, literature review and bibliometric studies of the past years mainly focus on a single research area or application field. Thus, this paper aims to provide a holistic understanding of the research on this topic through a Systematic Bibliometric Analysis (SBA), along with proposing a viable option to conduct SBAs. The focus is on investigating the main research areas, application fields, relevant authors and studies, and temporal evolution. It emerged that rotating equipment, structural health monitoring, batteries, aeroengines, and turbines are popular fields. Moreover, the results depicted an increase in popularity of DL, along with new approaches such as deep reinforcement learning through the past four years. The proposed workflow for SBA has the potential to benefit researchers from multiple disciplines, beyond safety science.
2024
170
1
25
Goal 9: Industry, Innovation, and Infrastructure
Leoni, Leonardo; BahooToroody, Ahmad; Abaei, Mohammad Mahdi; Cantini, Alessandra; BahooToroody, Farshad; De Carlo, Filippo
File in questo prodotto:
File Dimensione Formato  
1-s2.0-S0925753523003053-main.pdf

accesso aperto

Tipologia: Pdf editoriale (Version of record)
Licenza: Open Access
Dimensione 1.47 MB
Formato Adobe PDF
1.47 MB Adobe PDF

I documenti in FLORE sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificatore per citare o creare un link a questa risorsa: https://hdl.handle.net/2158/1349391
Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus 10
  • ???jsp.display-item.citation.isi??? 6
social impact