CAREER: Model Free Fault Detection for Nonlinear Systems

职业：非线性系统的无模型故障检测

• 批准号: 0134132
• 负责人: Tyrone Vincent
• 金额: --
• 依托单位: Colorado School of Mines
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2002
• 资助国家: 美国
• 起止时间: 2002-02-01 至 2008-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0134132&HistoricalAwards=false
• 关键词: CAREER; Model; Free; Fault; Detection

This proposal will deal with concerns of the development and application of methods for fault detection of nonlinear dynamical systems. Fault detection of linear systems is by now well established, but application of these techniques to nonlinear systems can lead to a loss of performance. The market for fault detection is growing, as more and more manufacturing and industrial centers are interested in improving their productivity by reducing unplanned maintenance and limiting losses to product due to equipment failure. In addition, many manufactures have installed sophisticated sensor and data collection networks in order to closely monitor their processes, which would enable the application of very sophisticated fault detection schemes.Fault detection can be as simple as placing limits on the allowable range of sensor measurements, but when the systems that are monitored are dynamic systems, more sophisticated methods can and should be applied. This is because dynamic systems, by the their very nature, have temporal relationships between the system variables that characterize both normal and faulted operating conditions. The challenge is to codify these temporal relationships. One method is to develop analytical models from first principles. In most model based fault detection schemes, output observers are designed which generate residual signals that measure the disagreement between the model and the measured data. However, these models are sometimes difficult to come by. The systems described above are nonlinear thermo-fluid systems that are difficult to model accurately and the resulting models would have many unknown parameters. In this case, system identification methods can be employed.This proposal is aimed at developing a theory of the interactions between the system identification and fault detection processes. The focus is on a general, practical method of nonlinear fault detection which is easily implemented on input/output models that are generated by modern nonlinear system identification methods. The basic question that is addressed is to determine how the choices made during the system identification process, such as the model structure. The objective function, the choice of regularization, and the validation procedure influence or interact with the fault detection process.

这项建议将涉及非线性动态系统故障检测方法的发展和应用。到目前为止，线性系统的故障检测已经很成熟，但将这些技术应用于非线性系统可能会导致性能损失。随着越来越多的制造业和工业中心对通过减少计划外维护和限制因设备故障造成的产品损失来提高生产率感兴趣，故障检测市场正在增长。此外，许多制造商安装了复杂的传感器和数据采集网络，以便密切监控其过程，这将使非常复杂的故障检测方案得以应用。故障检测可以简单到限制传感器测量的允许范围，但当被监控的系统是动态系统时，可以而且应该应用更复杂的方法。这是因为动态系统，就其本质而言，在系统变量之间具有时间关系，这些关系表征了正常和故障操作条件。挑战在于将这些时间关系编入法典。一种方法是从基本原理出发建立分析模型。在大多数基于模型的故障检测方案中，输出观测器被设计为产生残差信号来衡量模型与测量数据之间的不一致。然而，这些模式有时很难买到。上述系统是难以精确建模的非线性热流体系统，所得到的模型将具有许多未知参数。在这种情况下，可以采用系统识别方法。该建议旨在发展一种系统识别和故障检测过程之间相互作用的理论。重点研究了一种通用的、实用的非线性故障检测方法，该方法易于在由现代非线性系统辨识方法产生的输入/输出模型上实现。要解决的基本问题是如何确定在系统识别过程中做出的选择，例如模型结构。目标函数、正则化的选择和验证过程影响故障检测过程或与其相互作用。