{"title":"Design of supervision solutions for industrial equipment: Schemes, tools and guidelines for the user","authors":"Mirko Mazzoleni","doi":"10.1016/j.jii.2024.100667","DOIUrl":null,"url":null,"abstract":"<div><p>The advent of Industry 5.0 envisages production systems that are more resilient, embrace human–machine collaboration and promote sustainability driven by technological research. The development of supervision solutions for industrial equipment fills in this picture as a basis for more proactive Condition-Based Maintenance strategies. The goal of this paper is to provide a self-contained set of guidelines to design such supervision solutions. With respect to existing literature on the topic, we provide a design process with a strong focus on experimental data collection and failure reproduction activities. Moreover, the connections between the steps of the proposed process are clearly highlighted to guide the user. First, the paper provides a set of tools to select the critical items and the methodological approaches for supervision. Then, these tools are used and referenced in the proposed design process. Finally, the proposed process is exemplified on two industrial case studies to show its effectiveness. Considerations, hints, and a user guidelines are given at the end of most sections.</p></div>","PeriodicalId":55975,"journal":{"name":"Journal of Industrial Information Integration","volume":"41 ","pages":"Article 100667"},"PeriodicalIF":10.4000,"publicationDate":"2024-07-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2452414X24001110/pdfft?md5=82cf1c4fdd6bc608641fc45fd377d1d4&pid=1-s2.0-S2452414X24001110-main.pdf","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Industrial Information Integration","FirstCategoryId":"94","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S2452414X24001110","RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS","Score":null,"Total":0}
引用次数: 0
Abstract
The advent of Industry 5.0 envisages production systems that are more resilient, embrace human–machine collaboration and promote sustainability driven by technological research. The development of supervision solutions for industrial equipment fills in this picture as a basis for more proactive Condition-Based Maintenance strategies. The goal of this paper is to provide a self-contained set of guidelines to design such supervision solutions. With respect to existing literature on the topic, we provide a design process with a strong focus on experimental data collection and failure reproduction activities. Moreover, the connections between the steps of the proposed process are clearly highlighted to guide the user. First, the paper provides a set of tools to select the critical items and the methodological approaches for supervision. Then, these tools are used and referenced in the proposed design process. Finally, the proposed process is exemplified on two industrial case studies to show its effectiveness. Considerations, hints, and a user guidelines are given at the end of most sections.
期刊介绍:
The Journal of Industrial Information Integration focuses on the industry's transition towards industrial integration and informatization, covering not only hardware and software but also information integration. It serves as a platform for promoting advances in industrial information integration, addressing challenges, issues, and solutions in an interdisciplinary forum for researchers, practitioners, and policy makers.
The Journal of Industrial Information Integration welcomes papers on foundational, technical, and practical aspects of industrial information integration, emphasizing the complex and cross-disciplinary topics that arise in industrial integration. Techniques from mathematical science, computer science, computer engineering, electrical and electronic engineering, manufacturing engineering, and engineering management are crucial in this context.