Fuzzy Heuristics and Decision Tree for Classification of Statistical Feature-Based Control Chart Patterns

Monitoring manufacturing process variation remains challenging, especially within a rapid and automated manufacturing environment. Problematic and unstable processes may produce distinct time series patterns that could be associated with assignable causes for diagnosis purpose. Various machine learning classification techniques such as artificial neural network (ANN), classification and regression tree (CART), and fuzzy inference system have been proposed to enhance the capability of traditional Shewhart control chart for process monitoring and diagnosis. ANN classifiers are often opaque to the user with limited interpretability on the classification procedures. However, fuzzy inference system and CART are more transparent, and the internal steps are more comprehensible to users. There have been limited works comparing these two techniques in the control chart pattern recognition (CCPR) domain. As such, the aim of this paper is to demonstrate the development of fuzzy heuristics and CART technique for CCPR and compare their classification performance. The results show the heuristics Mamdani fuzzy classifier performed well in classification accuracy (95.76%) but slightly lower compared to CART classifier (98.58%). This study opens opportunities for deeper investigation and provides a useful revisit to promote more studies into explainable artificial intelligence (XAI).

SPHINX — A System for Computer-Aided Diagnosis

This paper presents a system for computer-aided diagnosis, the SPHINX system, based on methods of inference and pattern matching used in artificial intelligence and on various heuristic features: fuzzy heuristics in relation to the suggestion power of the signs and heuristics based on the costs of complementary investigations. The first application was made in the diagnosis of epigastric pain. Its results are presented and discussed.