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Application of Fuzzy Classification Methods for Diagnosis of Reject Root Causes in Manufacturing Environment
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Abstract
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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Citation
Chen, Y., Gravel, D., Filev, D., and Nagisetty, I., "Application of Fuzzy Classification Methods for Diagnosis of Reject Root Causes in Manufacturing Environment," SAE Technical Paper 981334, 1998, https://doi.org/10.4271/981334.Also In
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