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

从故障检测系统的角度来研究机床的监督。对故障的识别意味着系统能够做出预测，并将预测值与实测值进行比较。比较不能严格，噪声可能导致错误或遗漏条件。将使用一种深度耦合的数字/符号模型，该模型结合了启发式和物理知识，并在多处理器架构中实现。正在开发的实时信号处理算法将在数字信号处理芯片中实现，并将用实际数据进行验证。本研究的另一个方面涉及符号处理速度的改进和在最大响应时间内服务警报的决策策略。对于推理机制，将开发和评估神经网络，将分布式并行处理引入到推理中最耗时的方面。为了最大限度地提高系统的反应时间，中断驱动专家将被实现。这将集中符号推理，并通过集成多个传感器信息在警报中创建置信级别。最终，一个能够实时以知识为基础的监督的独立计算机系统将可用于演示。

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.