A fuzzy expert system for fault detection in statistical process control of industrial processes

Little work has previously been reported on the use of fuzzy logic within statistical process control when this is used for fault detection as part of quality control systems in industrial manufacturing processes. Therefore, this paper investigates the potential use of fuzzy logic to enhance the performance of statistical process control (SPC). The cumulative sum of the deviation in the monitored parameter is combined with the deviation in an attempt to discriminate between false alarms and real faults and, consequently, to improve the quality of the solution. Combinations of control rules are utilized and trained to cope with different inputs such that rejection of false alarms is achieved and quick detection of real faults is obtained. The design and implementation of this fuzzy expert system (FES) are presented, and a comparative rule-based study is performed.