KNOWLEDGE-BASED FAULT-DETECTION AND DIAGNOSIS IN FLOW-INJECTION ANALYSIS

The reliable operation of flow-injection analysis systems (FI systems) demands a high degree of knowledge and experience. This a priori know ledge is of great importance, especially for a fast detection and diag nosis of faults. Transferring it into the form of a knowledge-based sy stem and combining it with information received on-line from the recor ded detector signal enables automatic operational supervision of FI sy stems. This contribution presents a real-time knowledge-based system f or the supervision of FI systems applied in on-line bioprocess monitor ing. The special conditions of real-time systems are explained and the basic structure of the knowledge base is illustrated. Examples of typ ical faults of the FI system are given to explain how symbolic knowled ge processing can be combined with numerical analysis of data to perfo rm a fast and reliable fault detection and fault diagnosis.