2026-09-18T22:53:33Zhttps://dspace.mit.edu/server/oai/requestoai:dspace.mit.edu:1721.1/1328982026-06-06T00:56:32Zcom_1721.1_7582com_1721.1_7581col_1721.1_131023
Brian W. Anthony.
Wilson, Sara M.
(Sara Mae)
Massachusetts Institute of Technology. Department of Mechanical Engineering.
Massachusetts Institute of Technology. Department of Mechanical Engineering
2021-10-08T17:10:53Z
2021-10-08T17:10:53Z
2020
2020
https://hdl.handle.net/1721.1/132898
1263358978
Thesis: M. Eng. in Advanced Manufacturing and Design, Massachusetts Institute of Technology, Department of Mechanical Engineering, September, 2020
Cataloged from the official PDF of thesis.
Includes bibliographical references (pages 103-105).
The fourth industrial revolution, known as Industry 4.0, has emerged in the past few decades. With its focus on digitization and interconnectivity between devices, data collection, and operator behavior, implementing Industry 4.0 in a factory gives manufacturers the ability to monitor manufacturing processes in real-time. By monitoring processes in real-time, operators can boost productivity and reduce waste by identifying issues in the manufacturing line faster and more frequently. This research was based on work completed at Industrial ML, a Cambridge-based, machine learning company that offers real-time production and quality monitoring to factories via their platform. The data used is from the manufacturing line of one of IML's clients, Industrial Steel, based in Japan. This thesis presents a comprehensive method for analyzing equipment data from a manufacturing line to determine which process control charts and equations are best-suited for real-time monitoring of the line. By evaluating the performance of X-Bar Charts, regressions, and S Charts in monitoring the various processes on the Industrial Steel manufacturing line, a different monitoring method was created. This method utilizes S Charts with 95th and 99th percentile limits calculated from historical data as upper limits and no lower limits to accommodate the low variance nature of many processes. This method's efficacy was tested by calculating the fraction of points from numerous long periods of continuous production (8 hours or more) that lay within these historical data percentile limits. For the variables analyzed, the percentile limits contained 95-99% of the data points. Some of the data ranges showed a higher variance of the data from the sensors; a set of higher variance limits were set for these ranges. A set of process control rules, adapted from the WECO rules, were established to guide how to determine out of control points on these S Charts with percentile limits.
by Sara M. Wilson.
M.Eng. in Advanced Manufacturing and Design
M.Eng.inAdvancedManufacturingandDesign Massachusetts Institute of Technology, Department of Mechanical Engineering
118 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
Mechanical Engineering.
Fault detection in a continuous production line using adaptive control chart limits
Thesis
application/pdf
2021-10-08T17:10:53Z
Publication
Master
MechE
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unknown
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<Abstract>The fourth industrial revolution, known as Industry 4.0, has emerged in the past few decades. With its focus on digitization and interconnectivity between devices, data collection, and operator behavior, implementing Industry 4.0 in a factory gives manufacturers the ability to monitor manufacturing processes in real-time. By monitoring processes in real-time, operators can boost productivity and reduce waste by identifying issues in the manufacturing line faster and more frequently. This research was based on work completed at Industrial ML, a Cambridge-based, machine learning company that offers real-time production and quality monitoring to factories via their platform. The data used is from the manufacturing line of one of IML's clients, Industrial Steel, based in Japan. This thesis presents a comprehensive method for analyzing equipment data from a manufacturing line to determine which process control charts and equations are best-suited for real-time monitoring of the line. By evaluating the performance of X-Bar Charts, regressions, and S Charts in monitoring the various processes on the Industrial Steel manufacturing line, a different monitoring method was created. This method utilizes S Charts with 95th and 99th percentile limits calculated from historical data as upper limits and no lower limits to accommodate the low variance nature of many processes. This method's efficacy was tested by calculating the fraction of points from numerous long periods of continuous production (8 hours or more) that lay within these historical data percentile limits. For the variables analyzed, the percentile limits contained 95-99% of the data points. Some of the data ranges showed a higher variance of the data from the sensors; a set of higher variance limits were set for these ranges. A set of process control rules, adapted from the WECO rules, were established to guide how to determine out of control points on these S Charts with percentile limits.</Abstract>
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