Monitoring charts play an essential role in statistical process control. Shewhart charts are commonly used due to their computational simplicity, and have seen many extensions that attempt to improve their performance. Most univariate charts operate under the assumption that data follow a normal distribution, are independent and contain only a moderate level of noise. Unfortunately, most practical data violate one or more of these assumptions. Wavelet-based multiscale representation of data possess characteristics that can help address these assumptions violations, and may be exploited to improve the performance of the conventional Shewhart chart. In this paper, a multiscale Shewhart chart is developed to deal with violation of these assumptions. The advantages brought forward by the developed multiscale Shewhart chart fault detection algorithm are illustrated through simulated examples. The results clearly demonstrate that the developed method is able to provide lower missed detection and comparable false alarm rates under violation of the above mentioned assumptions.
['M. Ziyan Sheriff', 'Mohamed N. Nounou']
Enhanced performance of shewhart charts using multiscale representation