Monitoring and diagnosis of a multi-stage manufacturing process using Bayesian networks Public Deposited

http://ir.library.oregonstate.edu/concern/graduate_thesis_or_dissertations/70795b799

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  • This thesis describes the application of Bayesian networks for monitoring and diagnosis of a multi-stage manufacturing process, specifically a high speed production part at Hewlett Packard. Bayesian network "part models" were designed to represent individual parts in-process. These were combined to form a "process model", which is a Bayesian network model of the entire manufacturing process. An efficient procedure is designed for managing the "process network". Simulated data is used to test the validity of diagnosis made from this method. In addition, a critical analysis of this method is given, including computation speed concerns, accuracy of results, and ease of implementation. Finally, a discussion on future research in the area is given.
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  • File scanned at 300 ppi (Monochrome) using ScandAll PRO 1.8.1 on a Fi-6670 in PDF format. CVista PdfCompressor 4.0 was used for pdf compression and textual OCR.
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  • description.provenance : Made available in DSpace on 2012-09-18T19:26:40Z (GMT). No. of bitstreams: 1 WolbrechtEricT1999.pdf: 1716376 bytes, checksum: 5bda0153ef8b5777021015ae20263317 (MD5) Previous issue date: 1998-06-25
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  • description.provenance : Approved for entry into archive by Patricia Black(patricia.black@oregonstate.edu) on 2012-09-18T19:26:40Z (GMT) No. of bitstreams: 1 WolbrechtEricT1999.pdf: 1716376 bytes, checksum: 5bda0153ef8b5777021015ae20263317 (MD5)
  • description.provenance : Approved for entry into archive by Patricia Black(patricia.black@oregonstate.edu) on 2012-09-18T19:25:21Z (GMT) No. of bitstreams: 1 WolbrechtEricT1999.pdf: 1716376 bytes, checksum: 5bda0153ef8b5777021015ae20263317 (MD5)

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