Robust fault detection and diagnosis for uncertain nonlinear systems

This paper considers robust fault detection and diagnosis for input uncertain nonlinear systems. It proposes a multi-objective fault detection criterion so that the fault residual is sensitive to the fault but insensitive to the uncertainty as much as possible. Then the paper solves the proposed criterion by maximizing the smallest singular value of the transformation from faults to fault detection residuals while minimizing the largest singular value of the transformation from input uncertainty to the fault detection residuals. This method is applied to an aircraft which has a fault in the left elevator or rudder. The simulation results show the proposed method can detect the control surface failures rapidly and efficiently.

Event-triggered fault detection for T-S fuzzy systems with local nonlinear models in finite-frequency domain

This article investigates the issue of event-triggered fault detection(FD)filter design for T-S fuzzy systems with local nonlinear models.A novel H−/H∞FD filter subject to the event triggering transmission mechanism is designed in finite-frequency domain.Then,a novel lemma,in which the nonlinear part and the event triggering mechanism are dealt appropriately,is presented to capture the sensitivity and robustness performances.In addition,the slack matrices are utilised to derive optimal filter parameters by solving a convex optimisation problem.The less conservative FD method can get better detection performances than those entire-frequency methods.Finally,an example is introduced to verify the new results.

Design of a fault diagnosis scheme for a class of singular nonlinear systems

A new fault detection and diagnosis approach is developed in this paper for a class of singular nonlinear systems via the use of adaptive updating rules. Both detection and diagnostic observers are established, where Lyapunov stability theory is used to obtain the required adaptive tuning rules for the estimation of the process faults. This has led to stable observation error systems for both fault detection and diagnosis. A simulated numerical example is included to demonstrate the use of the proposed approach and encouraging results have been obtained.

Parity Relation Based Fault Estimation for Nonlinear Systems: An LMI Approach

This paper proposes a parity relation based fault estimation for a class of nonlinear systems which can be modelled by Takagi-Sugeno （TS） fuzzy models. The design of a parity relation based residual generator is formulated in terms of a family of linear matrix inequalities （LMIs）. A numerical example is provided to illustrate the effectiveness of the proposed design techniques.

An Ensemble Application of Conflict-Resolving ART-Based Neural Networks to Fault Detection and Diagnosis

Accurate fault detection and diagnosis is important for secure and profitable operation of modern power systems.In this paper,an ensemble of conflict-resolving Fuzzy ARTMAP classifiers,known as Probabilistic Multiple Fuzzy ARTMAP with Dynamic Decay Adjustment(PMFAMDDA),for accurate discrimination between normal and faulty operating conditions of a Circulating Water(CW)system in a power generation plant is proposed.The decisions of PMFAMDDA are reached through a probabilistic plurality voting strategy that is in agreement with the Bayesian theorem.The results of the proposed PMFAMDDA model are compared with those from an ensemble of Probabilistic Multiple Fuzzy ARTMAP(PMFAM)classifiers.The outcomes reveal that PMFAMDDA,in general,outperforms PMFAM in discriminating operating conditions of the CW system.

INVESTIGATION ON FAULT DETECTION & DIAGNOSIS FOR POSITION SERVO SYSTEM OF AIRCRAFT ACTUATOR

A new approach to fault dignosis dealing with nonlinear system Hopfieldneural networks (HNN) is presented. The model parameters of the nonlinear systemtreated as functions of measured operating points and faults are estimated by HNN. Boththe nominal model of the healthy system and HNN training models corresponding to everyoperating point are recognized. In addition, the anticipated fault models corresponding toevery kind of fault and every operating point are obtaind in advance. The real systemmodel parameters of the system estimated by generalization process of HNN are matchedwith the nominal models of the healthy system and anticipated fault models. Consequent-ly, the final result of fault detection and diagnosis is acquired. The approach to fault diag-nosis is used in an aircraft actuating poisition servo system and the simulation resu1t is re-ported.

Fault detection for nonlinear networked control systems based on fuzzy observer

Security and reliability must be focused on control sys- tems firstly, and fault detection and diagnosis （FDD） is the main theory and technology. Now, there are many positive results in FDD for linear networked control systems （LNCSs）, but nonlinear networked control systems （NNCSs） are less involved. Based on the T-S fuzzy-modeling theory, NNCSs are modeled and network random time-delays are changed into the unknown bounded uncertain part without changing its structure. Then a fuzzy state observer is designed and an observer-based fault detection approach for an NNCS is presented. The main results are given and the relative theories are proved in detail. Finally, some simulation results are given and demonstrate the proposed method is effective.

Fault-tolerant Control of Nonlinear System Using Credit Assign Fuzzy CMAC

The adaptive fault-tolerant control scheme of dynamic nonlinear system based on the credit assigned fuzzy CMAC neural network is presented. The proposed learning approach uses the learned times of addressed hypercubes as the credibility, the amounts of correcting errors are proportional to the inversion of the learned times of addressed hypercubes. With this idea, the learning speed can indeed be improved. Based on the improved CMAC learning approach and using the sliding control technique, the effective control law reconfiguration strategy is presented. The system stability and performance are analyzed under failure scenarios. The numerical simulation demonstrates the effectiveness of the improved CMAC algorithm and the proposed fault-tolerant controller.

Synthetic Intelligent Fault Diagnosis Technology for Complex Process

A fault diagnosis method of knowledge based fuzzy neural network is proposed for complex process, which is hard to develop practical mathematical model. Fault detection is performed through a knowledge based system, where fault detection heuristic rules have been generated from deep and shallow knowledge of the process. The fuzzy neural network performs the fault diagnosis task. This method does not need practical mathematical models of objects, so it is a strong implement for complex process.

基于智能系统的光伏发电场故障检测研究

模糊推理引擎进而评估这些模糊信息,依据模糊规则库中的规则来确定是否存在潜在故障。该方案提供了光伏场在正常运行时的瞬时发电量的估计。然后,将估计功率与实际功率进行比较,若功率之间的差值超过阈值,则生成警报信号,其中模糊规则系统TSK-FRBS已在正常运行期间将光伏电站模拟器收集的数据进行了培训,通过再现正常和故障条件,在模拟框架中进行测试。结果表明,即使引入噪声数据,该系统也能识别90%以上的故障情况。

风力发电系统传感器故障诊断

针对非线性风力发电系统,提出了一种基于滑模观测器的传感器故障诊断方法.基于考虑传感器加性故障的非线性动态模型,利用T--S模糊理论建立风力发电系统全局T--S模型,设计模糊T--S系统滑模故障观测器,产生对故障具有敏感性的残差,实现故障检测.通过等价输出控制方法来维持滑模运动,直接获取故障信息,重构传感器故障.最后以三叶片水平轴风力发电系统为例,仿真验证了该方法的有效性与可靠性.

基于模糊动态模型的传感器故障诊断方法

提出了解决控制系统全工况运行条件下传感器故障的诊断方法。系统的动态特性用模糊动态模型描述,模糊动态模型由一组反映系统局部动态的线性子模型通过模糊关系连接而成。基于观测器理论进行故障残差的设计,提出了一种衡量故障残差鲁棒性能的指标,然后采用遗传算法优化该鲁棒性指标,得到故障诊断观测器的优化增益阵和加权阵。最后讨论了诊断系统的稳定性。仿真结果表明:该诊断方法能很好地适应系统运行的不同工况,并有效地提高了诊断策略的鲁棒性。

基于SVR的非线性系统故障诊断研究

针对非线性系统辨识建模和故障诊断难的问题,利用回归型支持向量机(support vector regression,SVR)分别设计了非线性系统的辨识建模系统和故障诊断系统,最后以某一非线性系统为例进行了仿真试验研究,建立了该非线性系统的SVR辨识模型,在此基础上进行了三种典型故障的诊断试验,仿真试验结果验证了该方法的有效性和先进性。

基于非线性输出频率响应函数的裂纹故障诊断方法研究

将非线性输出频率响应函数(nonlinear output frequency response function,NOFRF)引用到裂纹转子的故障诊断中,提出基于NOFRF的转子裂纹的故障诊断方法。该方法通过辨识不同位置、不同深度的裂纹转子的NOFRF值,对裂纹故障进行诊断,得到一些有价值的结论。实验结果表明,系统各阶NOFRF值对转子裂纹不同位置和深度的变化相当敏感,可有效辨识裂纹故障的严重程度。

非线性系统的鲁棒故障检测与诊断

研究了一类具有未建模动态或扰动的非线性系统的鲁棒故障检测与诊断问题 ,利用神经网络、模糊系统或小波网络等对非线性故障模式进行在线逼近的方法进行故障诊断 .第一步 ,对用于鲁棒故障检测的观测器 ,建立了保证观测器稳定的增益阵的选择条件 ;第二步 ,若检测出发生故障 ,则用神经网络、模糊系统或小波网络进行故障的在线估计 ,建立了估计误差界 。

航天发射系统运行安全性评估研究进展与挑战

航天发射作为人类太空活动最为基础和最为重要的环节之一,是评判一个国家综合国力的重要指标,而航天发射系统运行安全性评估作为现代航天发射控制指挥与决策系统的核心,是保证航天发射安全运行的基础.首先,本文概述了现代航天发射系统,简要回顾了系统安全性研究发展历程,阐述了航天发射系统运行安全性评估的内涵.其次,通过综述航天发射系统运行故障检测与诊断、异常运行工况识别、运行过程安全分析与预测、安全性动态评估技术等方面的研究现状的基础上,总结出了航天发射系统运行安全性评估面临着系统极度复杂、决策风险性极大、先验信息少以及评估结果要求高准确性与实时性等方面的挑战.最后,本文对航天发射系统运行安全性评估有待研究的基础前沿问题进行了思考.

旋转机械振动故障诊断的一种模糊神经网络方法研究

介绍了一种基于多层感知器的模糊神经网络分类器，并针对其在旋转机械故障诊断中的应用，研究了网络构造过程中输入和输出模糊化的问题。文中利用振动频谱特征就旋转机械中几种典型的故障模式，采用模糊神经网络方法作了识别，且将其与传统的ＢＰ网络及模糊诊断方法进行了比较。研究结果表明：将模糊神经网络方法应用于旋转机械工况识别是有效的，它在处理分类边界模糊的数据时比传统的ＢＰ网络和模糊诊断方法具有更大的优越性。

基于模糊模型的无线网络控制系统故障检测

考虑控制器与被控对象之间采用无线传输,且被控对象为非线性模型的一类网络控制系统,对其进行故障检测。首先基于T-S模糊模型将对象线性化,利用模糊主导子系统规则,设计了模糊观测器,并得出了观测器误差方程。然后将误差方程等效为与无线传输跳数相关的离散切换系统,并证明了误差系统的稳定性。最后,通过仿真实例验证了所提方法的有效性。

执行器故障检测的神经网络观测器方法

针对一类非线性系统,提出了一种用于执行器故障检测的神经网络观测器方法·这种非线性系统具有未知非线性函数,不需要满足结构匹配条件,并且不要求系统状态可测·观测器利用神经网络器逼近系统中的未知非线性项,提高了状态估计的精度·估计的残差提供了故障检测的手段,另一方面利用自适应律进行故障的识别·基于李亚普诺夫方法,从理论上证明了状态估计误差稳定且渐近收敛到零·最后,仿真结果表明该方法的有效性·

基于非线性时序模型盲辨识的因子隐Markov模型识别方法

基于模型辨识的机械有效故障特征提取方法中输入信号难以确定,以及机械设备运行过程中具有信息量大、非平稳、特征重复再现性差的特点,结合非线性时序模型盲辨识和因子隐Markov模型,提出一种基于非线性时序模型盲辨识的特征提取的因子隐Markov模型识别方法,并应用到旋转机械升降速过程故障诊断中。同时还与基于Fourier变换、小波变换的特征提取的因子隐Markov模型识别方法进行比较,试验结果表明该方法是有效的。