Application of Fuzzy Classification Methods for Diagnosis of Reject Root Causes in Manufacturing Environment
This paper presents an approach of using neural network and fuzzy logic methods for the diagnosis of fault root causes in a manufacturing environment. As the first step in this approach, data from all the valid test points were collected and studied based on their statistical characteristics. An information-gain-based procedure was then followed to quantitatively evaluate the relevance of each test point to the diagnosis process. Accordingly, an objective rank of all relevant test points was generated for a particular reject. The root cause of rejects was then identified by a procedure based on an information-gain-weighted radial basis function neural network and a fuzzy multiple voting classification algorithm. This method has been tested with the top five rejects of the transmission main control component at Ford and promising results have been obtained.
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Availability:
- Find a library where document is available. Order URL: http://worldcat.org/issn/01487191
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Supplemental Notes:
- Abstract reprinted with permission of SAE International.
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Authors:
- Chen, Yubao
- Gravel, Dave
- Filev, Demitar
- Nagisetty, Irena
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Conference:
- International Automotive Manufacturing Conference & Exposition
- Location: Detroit Michigan, United States
- Date: 1998-5-12 to 1998-5-14
- Publication Date: 1998-5-12
Language
- English
Media Info
- Media Type: Web
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Serial:
- SAE Technical Paper
- Publisher: Society of Automotive Engineers (SAE)
- ISSN: 0148-7191
- EISSN: 2688-3627
- Serial URL: http://papers.sae.org/
Subject/Index Terms
- TRT Terms: Fuzzy logic; Mathematical models; Neural networks; Statistical analysis; Transmissions
- Subject Areas: Highways; Vehicles and Equipment;
Filing Info
- Accession Number: 01792856
- Record Type: Publication
- Source Agency: SAE International
- Report/Paper Numbers: 981334
- Files: TRIS, SAE
- Created Date: Dec 9 2021 10:13AM