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Duane S. Boning and Jeffrey H. Lang. Martin, Damien W. Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science. Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science 2021-05-24T19:52:17Z 2021-05-24T19:52:17Z 2021 2021 https://hdl.handle.net/1721.1/130698 1251800255 Thesis: M. Eng., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, February, 2021 Cataloged from the official PDF of thesis. Includes bibliographical references (pages 87-90). We investigate the use of unsupervised deep learning to create a general purpose automated fault detection system for manufacturing equipment. Unexpected equipment faults can be costly to manufacturing lines, but data driven fault detection systems often require a high level of application specific expertise to implement and continued human oversight. Collecting large labeled datasets to train such a system can also be challenging due to the sparse nature of faults. To address this, we focus on unsupervised deep learning approaches, and their ability to generalize across applications without changes to the hyper-parameters or architecture. Previous work has demonstrated the efficacy of autoencoders in unsupervised anomaly detection systems. In this work we propose a novel variant of the deep auto-encoding Gaussian mixture model, optimized for time series applications, and test its efficacy in detecting faults across a range of manufacturing equipment. It was tested against fault datasets from three milling machines, two plasma etchers, and one spinning ball bearing. In our tests, the model is able to detect over 80% of faults in all cases without the use of labeled data and without hyperparameter changes between applications. We also find that the model is capable of classifying different failure modes in some of our tests, and explore other ways the system can be used to provide useful diagnostic information. We present preliminary results from a continual learning variant of our fault detection architecture aimed at tackling the problem of system drift. by Damien W. Martin. M.Eng. M.Eng. Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science 90 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 Electrical Engineering and Computer Science. Fault detection in manufacturing equipment using unsupervised deep learning Thesis application/pdf 2021-05-24T19:52:17Z Publication Master EECS unknown unknown <Publication xmlns="https://www.openaire.eu/cerif-profile/1.1/" id="c36496a5-19e6-4e85-843a-fb15a185d863"> <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>Fault detection in manufacturing equipment using unsupervised deep learning</Title> <PublishedIn> <Publication> </Publication> </PublishedIn> <PublicationDate>2021</PublicationDate> <Authors> <Author> <DisplayName>Martin, Damien W.</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>Electrical Engineering and Computer Science.</Keyword> <Abstract>We investigate the use of unsupervised deep learning to create a general purpose automated fault detection system for manufacturing equipment. Unexpected equipment faults can be costly to manufacturing lines, but data driven fault detection systems often require a high level of application specific expertise to implement and continued human oversight. Collecting large labeled datasets to train such a system can also be challenging due to the sparse nature of faults. To address this, we focus on unsupervised deep learning approaches, and their ability to generalize across applications without changes to the hyper-parameters or architecture. Previous work has demonstrated the efficacy of autoencoders in unsupervised anomaly detection systems. In this work we propose a novel variant of the deep auto-encoding Gaussian mixture model, optimized for time series applications, and test its efficacy in detecting faults across a range of manufacturing equipment. It was tested against fault datasets from three milling machines, two plasma etchers, and one spinning ball bearing. In our tests, the model is able to detect over 80% of faults in all cases without the use of labeled data and without hyperparameter changes between applications. We also find that the model is capable of classifying different failure modes in some of our tests, and explore other ways the system can be used to provide useful diagnostic information. We present preliminary results from a continual learning variant of our fault detection architecture aimed at tackling the problem of system drift.</Abstract> <Access xmlns="http://purl.org/coar/access_right" > </Access> </Publication>