采样数据系统的故障诊断方法研究
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摘要
随着数字计算机技术、多传感器等技术在工业过程中的广泛应用,现代工业系统与设备本质上多数以采样数据系统的形式呈现。相比于一般系统,这类系统自动化程度更高、结构更复杂,相应发生故障的可能性更大。采样数据系统故障诊断的研究受到了广泛关注并取得了一些成果。然而由于采样数据系统所具有的混杂性、内采样信息的未知性、采样模式的多样性、采样时刻的不确定性等因素,增加了故障诊断的困难,而现有研究还存在诸多不足和深层次难题。为此,本文基于提升、混杂系统和时滞系统等技术和理论,开展了各类采样数据(单速率、多速率、非均匀)系统的故障诊断设计新方法的研究。论文的主要工作如下:
1.针对一类单速率采样数据系统,在连续提升框架内建立了诊断观测器这一结构灵活的残差产生器的直接方法。
2.针对具有连续过程噪声和离散测量噪声的采样数据系统,基于混杂系统方法和线性矩阵不等式技术提出了一种故障检测直接设计方法。不仅解决了连续提升要求系统必须服从严格正实的苛刻条件,也克服了现有方法求解跳变Riccati微分方程困难的不足。
3.针对具有一般噪声干扰的多速率采样数据系统,基于离散提升、扩展QR分解和初等变换等技术,提出了一种直观简单的快速率故障检测设计新方法。
4.针对随机多速率采样数据系统,应用序贯滤波的思想,建立了一种快速率稳态残差产生器和相应的残差评估方法,有效地避免了复杂的因果约束问题。同时,还对基于序贯滤波、左同步提升和右同步提升等异步多传感器融合估计算法的性能进行了分析和比较,相关结论为针对不同情况选用对应算法提供了一定依据。
5.针对一般的采样非均匀数据系统,基于输出时滞方法提出了一种鲁棒传感器故障检测设计方法。
6.从时滞系统角度率先研究了采样数据系统的故障估计问题。针对自适应诊断观测器不再有效适用于采样数据系统这一难题,提出一种新的增广故障估计滤波器设计方法能够保证估计误差收敛,该方法可以被推广于不确定采样数据系统的时变故障估计。
7.文中所提方法的有效性和优越性通过一系列数值仿真和飞行器系统的例子进行了验证。
With the wide application of digital comuputer and multisensor techniques in industrial process, most morden systems and equiments are essentially appeared as sampled-data systems. Comparing with general systems, these type of systems have higher level of automation, more complex structure and thus bigger possibility of occurring faults. The research of fault diagnosis for sampled-data systems has attracted considerable attention and some results have achieved. However, due to the hybridism of sampled-data systems, the unknownness of intersampling behaviour, the diversity of sampling pattern and the uncertainity of sampling instant, the difficulties of fault diagnosis are increasing and the existing research has many deficiencies and profound problems. Therefore, with the aid of lifting technique, the theories of hybrid system and time-delay system, the thesis develops several new fault diagnosis design methods for various kinds of sampled-data systems. The main contributions are as follows:
1. Under the framework of continuous lifting, the direct design approach to the diagnositic observer which has flexible structure has been establised for a class of sigle-rate sampled-data systems.
2. Based on the hybrid system approach and linear matrix inequality (LMI) technique, a direct design methodology of fault detection for a class of sampled-data system with both continuous process noise and discrete measurement noise is presented. It not only resolves the harsh condition of strictly properness caused by continuous lifting, but also avoids solving Riccati equation with jumps which are difficult.
3. With the aid of discrete lifting, the extended QR decomposition and elementary transformation, a new fast rate fault detection method, which is intuitive and simple, is proposed for the multirate sampled data systems corrupted by general disturbance.
4. Based on the idea of sequential filtering, a fast rate fault detection scheme consisting of a fast rate steady-state residual generation and the corresponding residual evaluation, which can avoid the complex problem of causality constraint, is further developed for multirate sampled-data systems with stochastic disturbance. Meanwhile, the performance of three asynchronous multisensor fusion estimation algorithms based on sequential filtering, the left synchronization lifting and the right synchronization lifting are analyzed and compared. Some conclusions are valuable in practical engineering applications.
5. Based on the output-delay approach, a robust sensor fault detection design approach is proposed for the general nonuniform sampled-data systems.
6. The problem of fault estimation for sampled-data systems is firstly investigated from the time delay viewpoint. Based on the analysis of the inapplicability of the adaptive fault diagnosis observer in sampled-data system, a novel augmented fault estimation observer design method is proposed to guarantee the exponential convergence of the estimation errors. Furthermore, an extension to the case of time varying fault estimation for the uncertain sampled-data system is studied.
7. The effectiveness and superiority of the proposed methods are demonstrated by numerical simulations and aircraft examples.
1.针对一类单速率采样数据系统,在连续提升框架内建立了诊断观测器这一结构灵活的残差产生器的直接方法。
2.针对具有连续过程噪声和离散测量噪声的采样数据系统,基于混杂系统方法和线性矩阵不等式技术提出了一种故障检测直接设计方法。不仅解决了连续提升要求系统必须服从严格正实的苛刻条件,也克服了现有方法求解跳变Riccati微分方程困难的不足。
3.针对具有一般噪声干扰的多速率采样数据系统,基于离散提升、扩展QR分解和初等变换等技术,提出了一种直观简单的快速率故障检测设计新方法。
4.针对随机多速率采样数据系统,应用序贯滤波的思想,建立了一种快速率稳态残差产生器和相应的残差评估方法,有效地避免了复杂的因果约束问题。同时,还对基于序贯滤波、左同步提升和右同步提升等异步多传感器融合估计算法的性能进行了分析和比较,相关结论为针对不同情况选用对应算法提供了一定依据。
5.针对一般的采样非均匀数据系统,基于输出时滞方法提出了一种鲁棒传感器故障检测设计方法。
6.从时滞系统角度率先研究了采样数据系统的故障估计问题。针对自适应诊断观测器不再有效适用于采样数据系统这一难题,提出一种新的增广故障估计滤波器设计方法能够保证估计误差收敛,该方法可以被推广于不确定采样数据系统的时变故障估计。
7.文中所提方法的有效性和优越性通过一系列数值仿真和飞行器系统的例子进行了验证。
With the wide application of digital comuputer and multisensor techniques in industrial process, most morden systems and equiments are essentially appeared as sampled-data systems. Comparing with general systems, these type of systems have higher level of automation, more complex structure and thus bigger possibility of occurring faults. The research of fault diagnosis for sampled-data systems has attracted considerable attention and some results have achieved. However, due to the hybridism of sampled-data systems, the unknownness of intersampling behaviour, the diversity of sampling pattern and the uncertainity of sampling instant, the difficulties of fault diagnosis are increasing and the existing research has many deficiencies and profound problems. Therefore, with the aid of lifting technique, the theories of hybrid system and time-delay system, the thesis develops several new fault diagnosis design methods for various kinds of sampled-data systems. The main contributions are as follows:
1. Under the framework of continuous lifting, the direct design approach to the diagnositic observer which has flexible structure has been establised for a class of sigle-rate sampled-data systems.
2. Based on the hybrid system approach and linear matrix inequality (LMI) technique, a direct design methodology of fault detection for a class of sampled-data system with both continuous process noise and discrete measurement noise is presented. It not only resolves the harsh condition of strictly properness caused by continuous lifting, but also avoids solving Riccati equation with jumps which are difficult.
3. With the aid of discrete lifting, the extended QR decomposition and elementary transformation, a new fast rate fault detection method, which is intuitive and simple, is proposed for the multirate sampled data systems corrupted by general disturbance.
4. Based on the idea of sequential filtering, a fast rate fault detection scheme consisting of a fast rate steady-state residual generation and the corresponding residual evaluation, which can avoid the complex problem of causality constraint, is further developed for multirate sampled-data systems with stochastic disturbance. Meanwhile, the performance of three asynchronous multisensor fusion estimation algorithms based on sequential filtering, the left synchronization lifting and the right synchronization lifting are analyzed and compared. Some conclusions are valuable in practical engineering applications.
5. Based on the output-delay approach, a robust sensor fault detection design approach is proposed for the general nonuniform sampled-data systems.
6. The problem of fault estimation for sampled-data systems is firstly investigated from the time delay viewpoint. Based on the analysis of the inapplicability of the adaptive fault diagnosis observer in sampled-data system, a novel augmented fault estimation observer design method is proposed to guarantee the exponential convergence of the estimation errors. Furthermore, an extension to the case of time varying fault estimation for the uncertain sampled-data system is studied.
7. The effectiveness and superiority of the proposed methods are demonstrated by numerical simulations and aircraft examples.
引文
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