| Summary: | 碩士 === 國立虎尾科技大學 === 工業工程與管理研究所 === 95 === Control chart patterns (CCPs) can be used to determine the status of system. Unnatural CCPs can be associated with a particular set of assignable causes for process variation. In recent years, artificial neural networks (ANNs) have been successfully used in the CCP recognition task. In intelligent SPC, most of researches used raw data (RB) as input vector and the other researches have used statistical feature data extracted from raw data (FB) as input vector for reducing network size. In this thesis, we present an ANN-based approach, in which an improved hybrid training data (HB) integrates both the time series data (Raw data) and the statistical feature data (Feature data). The training data set and testing data set used in this thesis were generated by Monte-Carlo Simulation Method for production line process data. Both HB and RB will be examined in normal environment and auto-correlated environments at a static state while performing the tests of abnormal CCP recognition and then simulating HB at a dynamic state. The static test result shows that the average recognition rates of RB and HB are 92.99% and 95.89%, respectively, in normal environment. The static test results of RB and HB are 92.11% and 95.12%, respectively, in auto-correlated environment. The experiment results show that HB has better recognition performances in normal environment than in auto-correlated environment. Besides, the dynamic test result shows that the average recognition rate of HB is 87% in normal environment and 82% in auto-correlated environment. Both statistics are worse than they are in static environment and the auto-correlated results are inferior to the normal results in dynamic state. However, the auto-correlated results can still be maintained over 80% in real time on-line test. Our experiments yield better performance than previous works by using the proposed new method. Hence, it can be conclude that HB has better recognition ability in normal environment and still has well-performed ability in auto-correlated environment.
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