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David E. Hardt and Roy E. Welsch. Gaudio, Brian Gabriel. Sloan School of Management. Massachusetts Institute of Technology. Department of Mechanical Engineering. Leaders for Global Operations Program. Sloan School of Management Massachusetts Institute of Technology. Department of Mechanical Engineering Leaders for Global Operations Program 2020-09-03T15:51:29Z 2020-09-03T15:51:29Z 2020 https://hdl.handle.net/1721.1/126899 1191622989 Thesis: M.B.A., Massachusetts Institute of Technology, Sloan School of Management, in conjunction with the Leaders for Global Operations Program at MIT, May, 2020 Thesis: S.M., Massachusetts Institute of Technology, Department of Mechanical Engineering, in conjunction with the Leaders for Global Operations Program at MIT, May, 2020 "May 2020." Cataloged from the official PDF of thesis. Includes bibliographical references (pages 110-115). Over the past one hundred years, maintenance concepts have evolved from a simple "fix when broken" approach to advanced prognostic methods used today that leverage large amounts of historical, operational, and primary sensor data to predict when and how failures will occur. For firms that produce complex assets, the ability to predict with accuracy when maintenance overhauls should occur can provide both an operational and economic competitive advantage. This research evaluates the hypothesis that the accuracy of predictive maintenance models for complex assets can be improved with the addition of historical operational data and failure modes can be more clearly identified by examining primary sensor data. This hypothesis is tested through data analysis on predictive maintenance models used by commercial turbofan jet engines. Because some engines have operated for decades, their entire operational records are not in the appropriate digital format and not utilized by current models. This research identifies alternate, available sources of this data. The additional data sources were processed and incorporated into the existing predictive maintenance models. The addition of the operational data sources did not reduce the error in the model used to forecast the useful life of assets for preventative maintenance, which suggests that the current coverage provided by existing data is sufficient. The examination of primary sensor data isolated one component that displayed age-related degradation and maintenance costs. by Brian Gabriel Gaudio. M.B.A. S.M. M.B.A. Massachusetts Institute of Technology, Sloan School of Management S.M. Massachusetts Institute of Technology, Department of Mechanical Engineering 128 pages eng Massachusetts Institute of Technology MIT theses may be protected by copyright. Please reuse MIT thesis content according to the MIT Libraries Permissions Policy, which is available through the URL provided. http://dspace.mit.edu/handle/1721.1/7582 Sloan School of Management. Mechanical Engineering. Leaders for Global Operations Program. Assessing the impact of historical operational data from complex assets on predictive maintenance models Thesis application/pdf 2020-09-03T15:51:29Z Publication Master Sloan MechE unknown unknown <Publication xmlns="https://www.openaire.eu/cerif-profile/1.1/" id="e0f3227d-b7ee-4244-9b08-11d8041e1ea3"> <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>Assessing the impact of historical operational data from complex assets on predictive maintenance models</Title> <PublishedIn> <Publication> </Publication> </PublishedIn> <PublicationDate>2020</PublicationDate> <Authors> <Author> <DisplayName>Gaudio, Brian Gabriel.</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>Sloan School of Management.</Keyword> <Keyword>Mechanical Engineering.</Keyword> <Keyword>Leaders for Global Operations Program.</Keyword> <Abstract>Over the past one hundred years, maintenance concepts have evolved from a simple &quot;fix when broken&quot; approach to advanced prognostic methods used today that leverage large amounts of historical, operational, and primary sensor data to predict when and how failures will occur. For firms that produce complex assets, the ability to predict with accuracy when maintenance overhauls should occur can provide both an operational and economic competitive advantage. This research evaluates the hypothesis that the accuracy of predictive maintenance models for complex assets can be improved with the addition of historical operational data and failure modes can be more clearly identified by examining primary sensor data. This hypothesis is tested through data analysis on predictive maintenance models used by commercial turbofan jet engines. Because some engines have operated for decades, their entire operational records are not in the appropriate digital format and not utilized by current models. This research identifies alternate, available sources of this data. The additional data sources were processed and incorporated into the existing predictive maintenance models. The addition of the operational data sources did not reduce the error in the model used to forecast the useful life of assets for preventative maintenance, which suggests that the current coverage provided by existing data is sufficient. The examination of primary sensor data isolated one component that displayed age-related degradation and maintenance costs.</Abstract> <Access xmlns="http://purl.org/coar/access_right" > </Access> </Publication>