Effects of non-normality on statistical and neural network based control chart
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Date
2007
Authors
Khai Ping, Lim
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Abstract
In recent years, when quality becomes the priority in every field of manufacturing process,
demand on quality control in non-normality is increasing accordingly. To achieve good
quality control in non-normality, traditional control chart can no longer satisfy the user. In
this dissertation, an artificial intelligent based control chart is introduced, developed and
studied, which is Neural Network Control Chart.
Neural network control chart is developed and analyzed using C++ programming in this
dissertation. Then, the neural network control chart is compared with Shewhart control chart
and memory control chart - EWMA in its performance in non-normality environment. The
comparison criteria are average run length (ARL) performance during in-control stage,
detection speed when out-of-control and type I errors when detecting the shift.
The results in this dissertation show that neural network control chart performs better than
the others in analyzing the non-normal data. It is good in low false signal during in-control
stage, low rejection of in-control data at the same time quick detection when out-of-control,
and consistent performance for different shapes of non-normal observations.
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Keywords
Non-normality on statistical , Network based control chart