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首页> 外文期刊>International Journal of Adaptive Control and Signal Processing >Rapid isolation of small oscillation faults via deterministic learning
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Rapid isolation of small oscillation faults via deterministic learning

机译：通过确定性学习快速隔离小振荡故障

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摘要

In this paper, we investigate the small fault isolation problem for a class of nonlinear uncertain systems. First, by utilizing the learned knowledge obtained through a recently proposed deterministic learning (DL) approach, a bank of estimators is constructed to represent the training normal mode and oscillation faults. Second, two isolation schemes based on the norms of the residuals are provided. The occurrence of a fault can be isolated if all the norms of the residuals associated with the matched fault estimator become smaller than the ones of the residuals associated with the other estimators in a finite time. Rigorous analysis of the performance of the both isolation schemes is also given, which includes the fault isolability condition and isolation time. The attraction of the paper lies in that an approach for fault isolation is proposed, in which the knowledge of modeling uncertainty and nonlinear faults obtained through DL is utilized to enhance the sensitivity of the isolation scheme. Simulation studies are included to demonstrate the effectiveness of the approach.
机译：在本文中，我们研究了一类非线性不确定系统的小故障隔离问题。首先，通过利用通过最近提出的确定性学习（DL）方法获得的学习知识，构造了一组估计器来表示训练正常模式和振荡故障。其次，提供了两种基于残差范数的隔离方案。如果与匹配的故障估计器关联的残差的所有范数在有限时间内变得小于与其他估计器关联的残差的范数，则可以隔离故障的发生。还对两种隔离方案的性能进行了严格的分析，其中包括故障隔离条件和隔离时间。本文的吸引力在于提出了一种故障隔离方法，该方法利用对通过DL获得的不确定性和非线性故障进行建模的知识来提高隔离方案的灵敏度。包括仿真研究以证明该方法的有效性。

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