Knowledge architecture and system design for intelligent operation support systems

Abstract

Intelligent operation support systems emerged from the complexity of modern industrial plants and the availability of inexpensive computer hardware. Modern industrial plants often collect vast amount of process data in distributed control systems and management information systems. Time stress due to data overload and decision uncertainty increases the risk of operator errors. Comprehensive operation support to the operator in abnormal situations to reduce operator errors is strongly suggested. The solution to this problem is to design intelligent operation support systems that reduce the cognitive load placed on operators by providing guidance for knowledge-based decision making. This paper presents an intelligent operation support system (IOSS) structure using rich knowledge representation and hybrid reasoning strategy. The functional requirements and desired features for IOSS are defined. The human operator's recognition behavior is analyzed. It is shown that a hybrid reasoning environment that combines case-based reasoning (CBR), model-based reasoning (MBR) and rule-based reasoning (RBR) is consistent with operator's problem solving. A multidimensional problem solving model is proposed to incorporate these requirements and human recognition behavior. The IOSS is designed by using the problem solving model as the guide.