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Donna H Rhodes. Ye, Chen, S.M. Massachusetts Institute of Technology Massachusetts Institute of Technology. Integrated Design and Management Program. Massachusetts Institute of Technology. Engineering and Management Program Massachusetts Institute of Technology. Integrated Design and Management Program. 2018-10-15T20:22:46Z 2018-10-15T20:22:46Z 2018 2018 http://hdl.handle.net/1721.1/118502 1054648396 Thesis: S.M. in Engineering and Management, Massachusetts Institute of Technology, System Design and Management Program, 2018. Cataloged from PDF version of thesis. Includes bibliographical references (pages 87-91). Digital technology is changing the industrial sector, yet how to make rational use of some technologies and create considerable value in a variety of industrial scenarios is an issue. Many digital industrial companies have stated that they have helped clients with their digital transformation, create much value, but the real effects have not been shown in public. Venture capitals firms have made huge investment in potential digital industrial startups. Numerous industrial IoT platforms are emerging in the market, but a number of them fade soon after. Many people have heard about industrial maintenance technology, but they have difficulty in differentiate concepts such as reactive maintenance, planned maintenance, proactive maintenance, and predictive maintenance. Many people know that big data and Al are essential in industrial sector, but they do not know how to process, analyze, and extract value from industrial data and how to use Al algorithms and tools to implement a research project. This thesis analyzes the entire digital industrial ecosystem in various dimensions such as initiatives, technologies in related domains, stakeholders, markets, and strategies. This work also analyzes of the predictive maintenance solution in various dimensions such as background, importance, suitable scenarios, market, business model, and technology. The author plans an experiment for the predictive maintenance solution, including goal, data source and description, methods and steps, and flow and tools. Then author uses a baseline approach and an optimal approach to implement the experiment, including data preparation, selection and evaluation of both regression and classification models, and deep learning practice through neural network building and optimization. Finally, contributions and expectations, and limitations and future research are discussed. This work uses a system approach, including system architecting, system engineering, and project management, to complete the process of analysis, design, and implementation. by Chen Ye. S.M. in Engineering and Management 91 pages eng Massachusetts Institute of Technology MIT theses are protected by copyright. They may be viewed, downloaded, or printed from this source but further reproduction or distribution in any format is prohibited without written permission. http://dspace.mit.edu/handle/1721.1/7582 Engineering and Management Program. Integrated Design and Management Program. A system approach to implementation of predictive maintenance with machine learning Thesis application/pdf false Publication unknown unknown <Publication xmlns="https://www.openaire.eu/cerif-profile/1.1/" id="341e4f13-42eb-449d-b9df-3edd37dea1a7"> <Type xmlns="https://www.openaire.eu/cerif-profile/vocab/COAR_Publication_Types">http://purl.org/coar/resource_type/c_1843</Type> <Language>eng</Language> <Title>A system approach to implementation of predictive maintenance with machine learning</Title> <PublishedIn> <Publication> </Publication> </PublishedIn> <PublicationDate>2018</PublicationDate> <Authors> <Author> <DisplayName>Ye, Chen, S.M. Massachusetts Institute of Technology</DisplayName> <Affiliation> <OrgUnit> </OrgUnit> </Affiliation> </Author> </Authors> <Editors> </Editors> <Publishers> <Publisher> <DisplayName>Massachusetts Institute of Technology</DisplayName> <OrgUnit /> </Publisher> </Publishers> <License>http://dspace.mit.edu/handle/1721.1/7582</License> <Keyword>Engineering and Management Program.</Keyword> <Keyword>Integrated Design and Management Program.</Keyword> <Abstract>Digital technology is changing the industrial sector, yet how to make rational use of some technologies and create considerable value in a variety of industrial scenarios is an issue. Many digital industrial companies have stated that they have helped clients with their digital transformation, create much value, but the real effects have not been shown in public. Venture capitals firms have made huge investment in potential digital industrial startups. Numerous industrial IoT platforms are emerging in the market, but a number of them fade soon after. Many people have heard about industrial maintenance technology, but they have difficulty in differentiate concepts such as reactive maintenance, planned maintenance, proactive maintenance, and predictive maintenance. Many people know that big data and Al are essential in industrial sector, but they do not know how to process, analyze, and extract value from industrial data and how to use Al algorithms and tools to implement a research project. This thesis analyzes the entire digital industrial ecosystem in various dimensions such as initiatives, technologies in related domains, stakeholders, markets, and strategies. This work also analyzes of the predictive maintenance solution in various dimensions such as background, importance, suitable scenarios, market, business model, and technology. The author plans an experiment for the predictive maintenance solution, including goal, data source and description, methods and steps, and flow and tools. Then author uses a baseline approach and an optimal approach to implement the experiment, including data preparation, selection and evaluation of both regression and classification models, and deep learning practice through neural network building and optimization. Finally, contributions and expectations, and limitations and future research are discussed. This work uses a system approach, including system architecting, system engineering, and project management, to complete the process of analysis, design, and implementation.</Abstract> <Access xmlns="http://purl.org/coar/access_right" > </Access> </Publication>