{"title":"A conceptual framework proposal for the implementation of Prognostic and Health Management in production systems","authors":"Raffaele Abbate, Chiara Franciosi, Alexandre Voisin, Marcello Fera","doi":"10.1049/cim2.12122","DOIUrl":null,"url":null,"abstract":"<p>Prognostic and Health Management (PHM) is an emerging maintenance concept that is highly regarded by the scientific community and practitioners, as its adoption can bring economic, technical and environmental benefits to a company. PHM fully reflects the smart maintenance paradigm encompassing data collection, data manipulation, state detection, health assessment, prognostic assessment and advisory generation. Despite the undeniable benefits, there is still a large gap between the scientific and the real world. Several authors have investigated on the barriers to PHM implementation for companies, highlighting among them the lack of systematic approaches to its design and implementation. As a first contribution to this topic, the authors conducted a systematic literature review (SLR) to investigate the use of Decision Support Systems (DSSs) to support the PHM implementation. The SLR highlighted that few DSS had been developed and were limited to critical unit identification, maintenance strategy selection and data acquisition phase of PHM. Therefore, a conceptual framework for PHM implementation was provided as a second contribution. This framework summarises the decisions that should be addressed by a practitioner wishing to implement PHM services; moreover, it could lay the foundations for the development/improvement of the missing/existing DSSs for PHM implementation.</p>","PeriodicalId":33286,"journal":{"name":"IET Collaborative Intelligent Manufacturing","volume":null,"pages":null},"PeriodicalIF":2.5000,"publicationDate":"2024-10-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/cim2.12122","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"IET Collaborative Intelligent Manufacturing","FirstCategoryId":"1085","ListUrlMain":"https://onlinelibrary.wiley.com/doi/10.1049/cim2.12122","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"ENGINEERING, INDUSTRIAL","Score":null,"Total":0}
引用次数: 0
Abstract
Prognostic and Health Management (PHM) is an emerging maintenance concept that is highly regarded by the scientific community and practitioners, as its adoption can bring economic, technical and environmental benefits to a company. PHM fully reflects the smart maintenance paradigm encompassing data collection, data manipulation, state detection, health assessment, prognostic assessment and advisory generation. Despite the undeniable benefits, there is still a large gap between the scientific and the real world. Several authors have investigated on the barriers to PHM implementation for companies, highlighting among them the lack of systematic approaches to its design and implementation. As a first contribution to this topic, the authors conducted a systematic literature review (SLR) to investigate the use of Decision Support Systems (DSSs) to support the PHM implementation. The SLR highlighted that few DSS had been developed and were limited to critical unit identification, maintenance strategy selection and data acquisition phase of PHM. Therefore, a conceptual framework for PHM implementation was provided as a second contribution. This framework summarises the decisions that should be addressed by a practitioner wishing to implement PHM services; moreover, it could lay the foundations for the development/improvement of the missing/existing DSSs for PHM implementation.
期刊介绍:
IET Collaborative Intelligent Manufacturing is a Gold Open Access journal that focuses on the development of efficient and adaptive production and distribution systems. It aims to meet the ever-changing market demands by publishing original research on methodologies and techniques for the application of intelligence, data science, and emerging information and communication technologies in various aspects of manufacturing, such as design, modeling, simulation, planning, and optimization of products, processes, production, and assembly.
The journal is indexed in COMPENDEX (Elsevier), Directory of Open Access Journals (DOAJ), Emerging Sources Citation Index (Clarivate Analytics), INSPEC (IET), SCOPUS (Elsevier) and Web of Science (Clarivate Analytics).