Characteristics recognition for the supervision of knowledge-based control systems

This paper presents a pattern-based information-processing approach, called characteristics recognition (CR), for on-line supervision of knowledge-based control systems. This approach provides a method for the analysis and automatic extraction of different types of process behaviour features during operation without a precise description of the process model. By combining the use of symbolic manipulation, graded fuzzy membership and numeric computation, the characteristic patterns that can capture system behaviour features are deduced from on-line data, and subsequently used for process monitoring and control. A multi-mode information-extraction and representation mechanism is provided to meet the needs of different tasks of system supervision. This leads to an effective and practical way of taking full advantage of on-line information. The basic methodology and a general application of CR to process monitoring and supervisory control are given. Copyright (C) 1996 Elsevier Science Ltd