Research Initiation: A Symbolic - Numeric Approach to Machine Tool Supervision

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Supervision of machine tools will be investigated from the perspective of a fault detect system. Recognition of faults mean that the system is able to make predictions and compare predicted with measured values. The comparison cannot be strict, the noise can cause false or missed conditions. A deeply coupled, numeric/symbolic model that incorporates heuristic and physical knowledge and is implemented in a multiprocessor architecture will be used. Real-time signal processing algorithms being developed will be implemented in digital signal processing chips, and will be validated with real data. Another aspect of this research deals with improvements on symbolic processing speed and a decision making strategy to service alarms within a maximum response time. For the inference mechanism, a neural network will be developed and evaluated, bringing distributed parallel processing to the most time consuming aspect of inference. In order to maximize the system reaction time interrupt driven specialists will be implemented. This will focus the symbolic reasoning and create belief levels in the alarm by integrating multiple sensor information. As an end result, a self-contained computer system capable of real-time knowledge-based supervision will be available for demonstrations.