Development of a connected platform for industrial equipment monitoring to enable predictive maintenance using supervised machine learning methods
Name
1119537741-MIT.pdf
Size
7.51 MB
Format
Adobe PDF
Checksum (MD5)
405449b3c6ecc86a3dc5c44f4d6cfbc3
Author(s)
Wu, Jessica Madison.
Advisor(s)
Daniel Frey and John Carrier.
Date Issued
2019
2019
Publisher
Massachusetts Institute of Technology
Abstract
SHAPE Technologies is the world leader in ultra high pressure industrial waterjet systems for cutting and cleaning with applications from metal to food. Although SHAPE is the technological leader in this space, SHAPE must continuously look toward developing new capabilities to differentiate its products. SHAPE has historically outfitted its machines with a suite of sensors, however these systems in the field do not store the data, thereby losing the time series relationships and historical log of machine health. One opportunity is to create a connected platform that leverages this data to help SHAPE's customers move away from a break fix model to a predictive maintenance program. This project seeks to expand on a sensor connectivity proof of concept ("POC"), which the team successfully built on a prototype grade Raspberry Pi, and make the platform ready for customer beta trial. First, this project explores important infrastructure, legal, and supply chain challenges that impact the commercial business when connecting industrial equipment to the internet as well as the technological considerations to make the platform both backwards and forwards compatible. Second, this project helps define the minimum viable product requirements for industrial infrastructure and devices configuration. Third, this project merges the POC captured data and lab data to train and validate supervised machine learning models to predict failures several days in advance and demonstrates how such a system can help customers mitigate unplanned downtime.
Description
Thesis: M.B.A., Massachusetts Institute of Technology, Sloan School of Management, 2019, In conjunction with the Leaders for Global Operations Program at MIT
Thesis: S.M., Massachusetts Institute of Technology, Department of Mechanical Engineering, 2019, In conjunction with the Leaders for Global Operations Program at MIT
Cataloged from PDF version of thesis.
Includes bibliographical references (page 69).
Subjects
Sloan School of Management.
Mechanical Engineering.
Leaders for Global Operations Program.
MIT Department
Sloan School of Management
Massachusetts Institute of Technology. Department of Mechanical Engineering
Leaders for Global Operations Program
Terms of Use
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.
Persistent DSpace Link
