Abstract
The line of evolution of manufacturing systems indicates rapidly increasing complexity at every system level, which necessitates enhanced requirements for the monitoring and diagnostic sybsystems applied in these manufacturing complexes. This means they must correspond—in performance, complexity and intelligence—to the entire material and data-processing system. This paper summarizes the fundamental requirements for a new family of monitoring and diagnostic equipment and describes two multipurpose, flexible machine tool monitoring systems, which can be regarded as first attempts in this direction. Special emphasis is placed on the generation of reference data for such complex monitoring equipment. Process modelling and teaching approaches are discussed. Pattern recognition methods during learning and decision-making are suggested. Among the most important research and development trends, the use of AI techniques in monitoring and diagnostics is also investigated.
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