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

对机床的监督将从 故障检测系统的透视图。 故障识别意味着 这个系统能够做出预测， 测量值。 对比不能严格，噪音 可能导致错误或遗漏的条件。 一个深度耦合， 数字/符号模型，结合了启发式和物理 知识，并在多处理器架构中实现 将用于 实时信号处理算法 将在数字信号处理芯片中实现， 并将用真实的数据进行验证。 另一方面， 研究涉及符号处理速度的改进， 决策制定策略，在最大限度内为警报提供服务 反应时间 对于推理机制，神经网络 将被开发和评估，带来分布式并行 处理到最耗时的推理方面。 在 为了最大限度地提高系统的中断驱动反应时间 专家将实施。 这将集中象征性的 推理，并通过整合警报中的 多传感器信息。 作为一个最终的结果，一个独立的 能够进行基于知识的实时监控的计算机系统 将用于演示。

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.