用于故障诊断的区间奇偶校验关系设计

IF 3.9 4区计算机科学 Q2 AUTOMATION & CONTROL SYSTEMS

International Journal of Adaptive Control and Signal Processing Pub Date : 2024-04-15 DOI:10.1002/acs.3816

Alexey Zhirabok, Alexander Zuev, Vladimir Filaretov

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引用次数: 0

摘要

摘要本文考虑了外部干扰下线性和非线性模型描述系统的区间奇偶关系设计问题。该问题基于原始系统的降阶模型求解。得到的关系允许设计对干扰不敏感或敏感度最小的区间奇偶校验关系。获得的区间奇偶关系可用于解决故障诊断问题。举例说明了理论结果。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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Interval parity relations design for fault diagnosis

The problem of interval parity relations design for systems described by linear and nonlinear models under the external disturbances is considered. The problem is solved based on the reduced-order model of the original system. The relations allowing designing interval parity relations insensitive or having minimal sensitivity to the disturbances are obtained. The obtained interval parity relations are used to solve the problem of fault diagnosis. Theoretical results are illustrated by example.

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来源期刊

International Journal of Adaptive Control and Signal Processing 工程技术-工程：电子与电气

CiteScore

5.30

自引率

16.10%

发文量

163

审稿时长

5 months

期刊介绍： The International Journal of Adaptive Control and Signal Processing is concerned with the design, synthesis and application of estimators or controllers where adaptive features are needed to cope with uncertainties.Papers on signal processing should also have some relevance to adaptive systems. The journal focus is on model based control design approaches rather than heuristic or rule based control design methods. All papers will be expected to include significant novel material. Both the theory and application of adaptive systems and system identification are areas of interest. Papers on applications can include problems in the implementation of algorithms for real time signal processing and control. The stability, convergence, robustness and numerical aspects of adaptive algorithms are also suitable topics. The related subjects of controller tuning, filtering, networks and switching theory are also of interest. Principal areas to be addressed include: Auto-Tuning, Self-Tuning and Model Reference Adaptive Controllers Nonlinear, Robust and Intelligent Adaptive Controllers Linear and Nonlinear Multivariable System Identification and Estimation Identification of Linear Parameter Varying, Distributed and Hybrid Systems Multiple Model Adaptive Control Adaptive Signal processing Theory and Algorithms Adaptation in Multi-Agent Systems Condition Monitoring Systems Fault Detection and Isolation Methods Fault Detection and Isolation Methods Fault-Tolerant Control (system supervision and diagnosis) Learning Systems and Adaptive Modelling Real Time Algorithms for Adaptive Signal Processing and Control Adaptive Signal Processing and Control Applications Adaptive Cloud Architectures and Networking Adaptive Mechanisms for Internet of Things Adaptive Sliding Mode Control.