Pattern recognition for statistical process control charts

Control charts are important statistical process control tools for determining whether a process is run in its intended mode or in the presence of unnatural patterns. Patterns displayed on control charts can provide information about the process. This paper describes the development of a pattern recognition system designed to detect and analyse various patterns that can occur on statistical quality control charts. The system looks not only for simple patterns, such as trend, shift and stratification, but also for superimposed patterns, such as trend + shift. The effect of noise associated with individual patterns is also analysed. The benefits of the approach compared with the alternatives are discussed.Notation N_i ith value of the noise series - N_T noise tolerance - x_i ith data item from a number sequence - r_i seed for random number simulation - adjacent difference - standard deviation - mean of the data - A slope of a straight line - B constant - C constant - i indexing integer - j indexing integer - k total number of samples - l starting point of a pattern on control chart - m ending point of a pattern on control chart - n size of samples - ptn pointer to the pattern identified - slope slope for trend patterns - X normally distributed variate arising from simulation - CL centre-line - LCL lower control limit - LOSL lower one-sigma limit - LWL lower warning limit - UCL upper control limit - UOSL upper one-sigma limit - UWL upper warning limit