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Fault Detection for a Class of Uncertain Sampled-Data Systems Using Deterministic Learning | IEEE Journals & Magazine | IEEE Xplore

Fault Detection for a Class of Uncertain Sampled-Data Systems Using Deterministic Learning


Abstract:

In this article, we propose a learning-based fault diagnosis approach for a class of nonlinear sampled-data systems. First, the unmodeled sampled dynamics is acquired by ...Show More

Abstract:

In this article, we propose a learning-based fault diagnosis approach for a class of nonlinear sampled-data systems. First, the unmodeled sampled dynamics is acquired by the using deterministic learning method. The knowledge of the sampled dynamics of the normal and fault patterns is stored in the form of constant neural networks. Second, a fault detection scheme is designed in which memories of the learned knowledge can be recalled to give a rapid response to a fault. Third, analytical results concerning the fault detection condition and detection time are derived. It is shown that the mismatch function plays an important role in the performance properties of the diagnosis scheme. To analyze the effect of mismatch function on the residual, the concept of duty ratio is developed. Moreover, by comparing the constant neural networks of the normal and fault patterns, an extraction operator is designed to capture the feature of the mismatch function. By using this method, the performance of the diagnosis scheme can be improved. A simulation study is included to demonstrate the effectiveness of the approach.
Published in: IEEE Transactions on Cybernetics ( Volume: 51, Issue: 12, December 2021)
Page(s): 5930 - 5940
Date of Publication: 17 January 2020

ISSN Information:

PubMed ID: 31976924

Funding Agency:


References

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