Back-propagation pattern recognizers for X control charts: Methodology and performance

Abstract

The pattern recognition algorithm presented here is based on the perception that, as automated data collection becomes more widespread in manufacturing processes, the monitoring of control charts will be performed by computer-based algorithms. These algorithms will have to detect unnatural patterns to assist in the correction of assignable causes. The work currently being performed in addressing the application of pattern recognition to control charts is directed toward answering this need. In this paper, a control chart pattern recognition methodology based on the back-propagation algorithm, a neural computing theory, is presented. This classification algorithm, suitable for real-time statistical process control, evaluates observations routinely collected for control charting to determine whether a pattern, such as a trend or cycle, exists in the data. The foundation of the algorithm is based on the neural network concepts of constructing and training a network in the types of patterns to be detected. These concepts mimic the trained operator's ability to detect patterns. Here, the pattern recognizer is trained and tested with the consideration of Type I error (finding a pattern where none existed) as well as Type II error (failing to detect a known pattern). Performance measures sensitive to these types of errors are used to evaluate the algorithm's performance on an extensive series of simulated patterns of control chart data. This approach is promising because of its flexible training and high-speed computation. © 1993.