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In the automotive industry, the plant production and diagnostic information contain a large amount of data with hidden but rich and insightful knowledge that can be used to facilitate effective monitoring of machine status, early prediction of machine faults, and quick identification of fault root causes. Most of the diagnostic entries, however, were merely analyzed by simple visualization; hidden information is seldom extracted or explored.
This research aims to develop methods for intelligent interpreting and analyzing the diagnostic information to seek improvement opportunities and bring insights and values to vehicle manufacturing. Specifically, methods in statistical quality control, change-point detection, pattern recognition, and machine learning will be investigated.
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