Well-Conditioned Observers for High-Performance, Low-Cost, Sensing Systems

该项目致力于分析和开发一种创新的传感系统，该系统结合了传统的遥感器和信号处理技术，可以提取信号中被破坏的部分。通常，在诊断系统中，传感器输出会被机器本身损坏，因为传感器通常不能放置在产生待测量信号的位置。例如，在机床中，一些切削力传感器依赖于机床外圈的应变测量，另一些依赖于在刀架底部测量的载荷，还有一些依赖于进给或主轴电机消耗的电流。研究中提出了一种可监控性指数(MI)，它不仅可以提高传感器的可靠性和性能，还可以为系统地优化特定传感系统的设计提供一种手段。设计程序提供了一种简单合理的方法来确定最合适的测量信号和使用的换能器类型。使用模型校正被破坏的信号的技术属于被称为基于模型的估计器的技术类别。该传感系统基于MI，称为基于模型的传感(Model Based Sensing，MBS)，用于现有机床的刀具力和主轴轴承监测，并将其性能与传统的基于模型的估计策略进行了比较。

This project deals with the analysis and development of an innovative sensing system that combines conventional remote sensors and signal processing techniques that extract the corrupted portion of signals. Typically, in diagnostic systems the sensor output is corrupted by the machine itself since the sensor cannot be generally placed at the site where the signal to be measured is generated. For example, in machine tools, some cutting force sensors depend upon the measurement of strain in the outer race of the machine tools, others depend upon the load measured at the base of the tool turret, and still others depend on the current consumed by the feed or spindle motor. A monitorability index (MI) is developed in this research which not only improves the sensor reliability and performance, but also provides a means of systematically optimizing the design of a particular sensing system. The design procedure yields a simple rational method to determine the most appropriate signals to measure and the type of transducers to use. The technique of using a model to correct the corrupted signal belongs to the class of techniques known as model-based estimators. This sensing system, which is based on MI and is called Model Based Sensing (MBS) is implemented for tool force and spindle bearing monitoring on existing machine tools and its performance is compared with traditional model-based estimation strategies.*** //