A fuzzy rule-based algorithm to improve the performance of statistical process control in quality systems

Abstract

Statistical process control (SPC) is an important part of quality control systems in industrial applications. It is widely used to monitor parameters in production processes and detect abnormal parameter values that indicate a fault in the process. Measurements of controlled parameters commonly exhibit random variations that arise from either environmental changes or random variations in the measuring instrument itself. SPC uses control charts to determine whether variations in measurements are due only to random changes within the range expected or whether they indicate a real process fault. Inevitably, traditional control charts sometimes generate Type I errors (false alarms), indicating a process fault when none actually exists, and causing an unnecessary stoppage of the plant. In other cases, Type II errors are generated, where real faults are either not detected at all, or are detected only after some time delay during which product quality has been impaired. This paper describes an investigation into the use of fuzzy logic to modify SPC rules, with the aim of reducing the generation of false alarms and also improving the detection and detection-speed of real faults.

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