A case model for predictive maintenance

This project is to respond to a need by Varian Semiconductor Equipment Associates, Inc. (VSEA) to help predict failure of ion implanters. Predictive maintenance would help to reduce the unscheduled downtime of ion implanters, whose throughput and uptime is highly important to customers. Statistical analysis is performed on historical data to extract metadata that can reflect the machine health, and statistical process control (SPC) is applied to detect deviations from normal or in-control behavior. Methods for failure prevention are also investigated. Challenging points in this project are the noise in raw signal data and the difference in data signals of different robots. To address these challenges, we apply signal filtering to extract cycle motions from raw data, and develop different generic as well as specific metadata extraction techniques for different robots. We test the extraction approaches and results using healthy data of ten machines, and find that the metadata on which we chose to perform SPC is suitable and can serve as a consistent indicator of a machine's health. We further develop an application using Visual Basic based on our study, and provide a user guide on how to generate the analysis reports on new data using our application.

Department dc:contributor.department

Massachusetts Institute of Technology. Dept. of Mechanical Engineering.

Grantor dc:publisher

Massachusetts Institute of Technology

Year dc:date.issued

2008

Author dc:creator

• Li, Jiawei, M. Eng. Massachusetts Institute of Technology

Advisor dc:contributor.advisor

• Duane S. Boning.

• Mechanical Engineering.

Statement dc:rights

• M.I.T. theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. See provided URL for inquiries about permission.

Licence dc:rights.uri

• http://dspace.mit.edu/handle/1721.1/7582

Language dc:language.iso

eng

Handle dc:identifier.uri

http://hdl.handle.net/1721.1/43139

OAI identifier oai:identifier

oai:dspace.mit.edu:1721.1/43139