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A Model-Based Fault-Detection and Prediction Scheme for Nonlinear Multivariable Discrete-Time Systems With Asymptotic Stability Guarantees

机译:具有渐近稳定性的非线性多变量离散时间系统的基于模型的故障检测与预测方案

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摘要

In this paper, a novel, unified model-based fault-detection and prediction (FDP) scheme is developed for nonlinear multiple-input–multiple-output (MIMO) discrete-time systems. The proposed scheme addresses both state and output faults by considering separate time profiles. The faults, which could be incipient or abrupt, are modeled using input and output signals of the system. The fault-detection (FD) scheme comprises online approximator in discrete time (OLAD) with a robust adaptive term. An output residual is generated by comparing the FD estimator output with that of the measured system output. A fault is detected when this output residual exceeds a predefined threshold. Upon detecting the fault, the robust adaptive terms and the OLADs are initiated wherein the OLAD approximates the unknown fault dynamics online while the robust adaptive terms help in ensuring asymptotic stability of the FD design. Using the OLAD outputs, a fault diagnosis scheme is introduced. A stable parameter update law is developed not only to tune the OLAD parameters but also to estimate the time to failure (TTF), which is considered as a first step for prognostics. The asymptotic stability of the FDP scheme enhances the detection and TTF accuracy. The effectiveness of the proposed approach is demonstrated using a fourth-order MIMO satellite system.
机译:本文针对非线性多输入多输出(MIMO)离散时间系统,开发了一种新颖的,基于模型的统一故障诊断和预测(FDP)方案。所提出的方案通过考虑单独的时间曲线来解决状态和输出故障。可以使用系统的输入和输出信号对可能是初期或突然的故障进行建模。故障检测(FD)方案包括具有鲁棒自适应项的离散时间在线近似器(OLAD)。通过将FD估计器输出与测得的系统输出进行比较,可以生成输出残差。当此输出残差超过预定义的阈值时,将检测到故障。在检测到故障后,将启动鲁棒的自适应项和OLAD,其中OLAD在线估计未知故障动态,而鲁棒的自适应项则有助于确保FD设计的渐近稳定性。使用OLAD输出,引入了故障诊断方案。开发了稳定的参数更新定律,不仅可以调整OLAD参数,而且可以估计故障时间(TTF),这被认为是进行预测的第一步。 FDP方案的渐近稳定性提高了检测和TTF的准确性。使用四阶MIMO卫星系统证明了该方法的有效性。

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