CAREER: Model Free Fault Detection for Nonlinear Systems

该提案将涉及非线性动力系统故障检测方法的开发和应用。线性系统的故障检测现已很成熟，但将这些技术应用于非线性系统可能会导致性能损失。故障检测市场正在不断增长，因为越来越多的制造和工业中心有兴趣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力。此外，许多制造商安装了复杂的传感器和数据收集网络，以便密切监控其过程，这将使得非常复杂的故障检测方案的应用成为可能。故障检测可以像限制传感器测量的允许范围一样简单，但是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，在表征正常和故障操作条件的系统变量之间具有时间关系。挑战在于将这些时间关系编入法典。一种方法是根据第一原理开发分析模型。在大多数基于模型的故障检测方案中，设计输出观测器来生成测量模型与测量数据之间的差异的残余信号。然而，这些模型有时很难获得。上述系统是非线性热流体系统，难以准确建模，并且所得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。该提案旨在发展系统识别和故障检测过程之间相互作用的理论。重点是一种通用、实用的非线性故障检测方法，该方法很容易在现代非线性系统识别方法生成的输入/输出模型上实现。解决的基本问题是确定系统识别过程中如何做出选择，例如模型结构。目标函数、正则化的选择和验证程序影响故障检测过程或与故障检测过程相互作用。

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.