车用直驱轮毂电机传感器故障诊断

针对分布式驱动电动汽车直驱轮毂电机系统电流、转速传感器故障问题,研究传感器鲁棒故障检测与定位方法。考虑电机模型中含有未知输入和噪声,通过系统降阶的方式对未知输入进行解耦,采用卡尔曼滤波器(Kalman filter,简称KF)滤除解耦后子系统的白噪声,并设计最优未知输入观测器(unknown input observer,简称UIO)实现系统状态估计,得到了一种较强鲁棒性的残差产生器。采用极大似然比(generalized likelihood ratio,简称GLR)的方法评估残差信号并确定阈值,提出了一种传感器故障定位方法。台架实验结果表明,提出的基于最优UIO的传感器故障诊断方法能够实现电动汽车直驱电机系统传感器故障辨识与定位。     

基于双解析模型的故障隔离与估计方案及应用

系统分析了基于观测器的故障检测与隔离方法存在的问题,创新性提出了一种基于标称模型和未知输入观测器的故障诊断方案—双解析方案。它从故障观测而非状态估计的角度设计未知输入观测器,并根据未知输入观测器与标称系统之间的状态误差修正并替换未知输入观测器的状态估计误差,从而构成实际可行的故障隔离与估计的解析表达式。该方案应用于三轴稳定卫星执行器/敏感器故障诊断,仿真设置的五种飞轮/陀螺组合故障均被准确隔离及估计,表明了该方案的有效性。从设计及应用过程可以看出,双解析方案设计简单、无存在性条件约束,有效拓展了基于观测器故障诊断的应用范围及深度。     

基于观测器的故障检测与诊断

本文论述了基于观测器的故障检测与诊断技术的研究现状,重点放在最新的成就:自适应观测器,未知输入观测器,以及产生鲁棒性残差的各种方法。还论述了故障诊断的整个过程,最后指出了该方法有待进一步研究的主要问题及其发展趋势。     

板带轧机主传动系统的鲁棒故障检测与重构

考虑模型的非线性摩擦阻尼和主传动系统在轧制过程中受到外部干扰的情况,建立了板带轧机主传动系统的数学模型。针对该系统,设计了一种非线性未知输入观测器(unknown input observer,简称UIO)并用于轧机主传动系统的故障检测和故障重构。为了增强残差对故障信号的灵敏度,提高观测器故障检测精度,构建未知输入观测器,将外部干扰从残差中解耦。利用H_∞性能指标提高观测器对故障重构的鲁棒性,采用Lyapunov稳定性理论进行误差动态系统的收敛性分析。为了改进观测器的设计过程,把增益矩阵求解问题转化为受线性矩阵不等式(linear matrix inequality,简称LMI)约束的优化问题。将产生的残差与设定的阈值进行对比,实现故障的检测并完成故障重构。通过对2 030 mm冷连轧机F4号机架主传动系统的仿真研究,验证了该观测器可以准确地对系统状态进行跟踪,并能够检测和估计出主传动系统的故障。     

基于观测器的防滑刹车系统鲁棒故障诊断初探

将基于观测器的鲁棒故障检测的概念应用到飞机防滑刹车系统的故障诊断中 ,建立一种简化的飞机防滑刹车系统的二阶数学模型 ,考虑未知输入矩阵已知时 ,对防滑刹车系统可能的特定的故障进行检测研究     

基于滑模观测器的风力机故障快速重构方法设计

针对风电机组的传动系统、发电机和桨距系统故障的有限时间重构和实际应用中气动转矩无法准确获取等问题.提出一种新的自适应非奇异终端滑模观测器.在观测器中引入自适应律,确保滑模观测器不受未知扰动的影响.设计的非奇异滑模面能有效解决常规滑模观测器的抖振问题,避免了抖振现象造成的故障误判和漏判等问题,提高了故障诊断效率.针对变桨系统故障,通过引入故障指示参数,将桨距执行机构的液压压降模型转化为加性故障.然后,利用两个级联滑模观测器对桨距系统进行观测,给出了有限时间状态的估计和故障重构.最后,仿真结果验证了风电机组状态的有限时间估计和执行器故障的重构,达到了风电机组故障诊断快速诊断的目的 .证明所提方法的正确性、可行性.     

基于奇异值分解的故障检测技术及其应用

针对基于未知输入观测器的诊断方法在诊断多故障时具有局限性，提出了一种基于奇异值分解的诊断方法，这种方法通过奇异值分解将不同故障对系统残差的影响进行分离，给出了多故障检测与分离的方法，仿真证明这种方法对于多故障诊断有很好的效果。     

基于动态GRNN模型的挖掘机液压系统故障检测

提出了一种针对工程机械液压系统的动态广义回归神经网络(GRNN)模型的故障诊断方法.动态GRNN模型是一种全局递归的动态模型,具有很强非线性收敛能力.首先建立系统正常状态故障建立动态GRNN模型;计算动态GRNN模型的检测阈值;然后将测试故障样本带入动态GRNN模型当中,其残差平方和在对应阈值范围内即可判定故障.通过实验分析,基于动态GRNN模型的故障检测方法准确地诊断出了90%以上的系统故障,实验结果表明,这一方法能够有效地应用于挖掘机液压系统的故障诊断.     

系统干扰分布阵未知的GPS/SINS故障诊断算法

针对系统模型不确定性、未知输入扰动,为对干扰解耦以及不依赖系统未知输入扰动分布阵先验信息,提出了系统干扰分布阵未知的GPS/SINS(global positioning system/strapdown interial navigation system)故障诊断算法.设计了MEP-UIO(model error prediction-unknow input observer)故障诊断观测器,改进了传统未知输入故障诊断观测器(UIO)假设系统未知扰动分布阵已知的不足;利用凸二次规划最优化原理,构造了关于未知扰动分布阵的目标函数,提出了满足目标函数最小的未知输入扰动分布阵的最优估计算法以及状态估计误差方差最小的故障诊断系统增益阵设计方法.仿真结果表明,提出的MEP-UIO故障诊断观测器设计算法相比传统Kalman滤波精度更高,验证了该故障诊断算法的有效性.     

基于深度残差网络的滚动轴承故障诊断方法

在实现滚动轴承故障诊断的过程中,需要通过时频分析方法对原始信号进行特征集构建,期间包含大量计算且对于人工经验有着很强依赖性.针对滚动轴承故障诊断中依赖特征集选取这一问题,提出了基于深度残差网络的故障诊断方法,凭借深度学习的自主学习及强泛化能力以实现故障特征的自我获取和训练,消除故障诊断中人为特征集选取环节,从而简化故障诊断的流程.主要内容包括:首先,构建残差网络模型,通过建立多组卷积层、池化层及残差块,共同组成深层次网络模型;其次,通过滚动轴承故障实验台获取不同类型的故障样本,对信号进行分组并构建训练样本和测试样本;进而,对网络进行初始化设定后,将训练集输入深度残差网络模型,利用多层卷积和池化运算实现对原始信号抽象化表征;最后,在网络模型末端集成Softmax分类器,实现对两类轴承故障样本的分类诊断.所提出方法在两组诊断实验中均达到了 100％的准确率,对于不同类型、转速和损伤程度的滚动轴承故障都具有很好效果.研究说明所建立模型能够自主地挖掘故障信号的特征集,可在一定程度上简化故障诊断研究中的预处理和特征计算环节,避免人工提取特征的主观盲目性和经验依赖性,具有广泛的工程应用前景.     

基于扩张状态观测器的故障诊断方法研究

在分析现有基于观测器故障诊断方法的基础上,提出基于扩张状态观测器(ESO)故障诊断新方法.该方法利用ESO估计出系统的动态特性,完成对动态系统的故障检测与识别,同时,由于ESO只依赖于系统的输入参数b.和观测器的整定参数ω,因而具有很强的鲁棒性.最后,借助于三水箱非线性动态系统验证了算法的有效性.     

Input fault detection and estimation using PI observer based on the ARX-Laguerre model

This work is dedicated to the synthesis of a new fault detection and identification scheme for the actuator and/or sensor faults modeled as unknown inputs of the system. The novelty of this scheme consists in the synthesis of a new structure of proportional-integral observer (PIO) reformulated from the new linear ARX-Laguerre representation with filters on system input and output in order to estimate the unknown inputs presented as faults. The designed observer exploits the input/output measurements to reconstruct the Laguerre filter outputs where the stability and the convergence properties are ensured by using Linear Matrix Inequality. However, a significant reduction of this model is subject to an optimal choice of both Laguerre poles which is achieved by a new proposed identification approach based on a genetic algorithm. The performances of the proposed identification approach and the resulting PIO are tested on numerical simulation and validated on a 2 ^{n d} order electrical linear system.     

Robust fault detection for the dynamics of high-speed train with multi-source finite frequency interference

This paper proposes a composite fault detection scheme for the dynamics of high-speed train (HST), using an unknown input observer-like (UIO-like) fault detection filter, in the presence of wind gust and operating noises which are modeled as disturbance generated by exogenous system and unknown multi-source disturbance within finite frequency domain. Using system input and system output measurements, the fault detection filter is designed to generate the needed residual signals. In order to decouple disturbance from residual signals without truncating the influence of faults, this paper proposes a method to partition the disturbance into two parts. One subset of the disturbance does not appear in residual dynamics, and the influence of the other subset is constrained by $H_{\infty }$ performance index in a finite frequency domain. A set of detection subspaces are defined, and every different fault is assigned to its own detection subspace to guarantee the residual signals are diagonally affected promptly by the faults. Simulations are conducted to demonstrate the effectiveness and merits of the proposed method.     

Fault detection and classification in chemical processes based on neural networks with feature extraction

Feed forward neural networks are investigated here for fault diagnosis in chemical processes, especially batch processes. The use of the neural model prediction error as the residual for fault diagnosis of sensor and component is analyzed. To reduce the training time required for the neural process model, an input feature extraction process for the neural model is implemented. An additional radial basis function neural classifier is developed to isolate faults from the residual generated, and results are presented to demonstrate the satisfactory detection and isolation of faults using this approach.     

A modified NARMAX model-based self-tuner with fault tolerance for unknown nonlinear stochastic hybrid systems with an input–output direct feed-through term

A modified nonlinear autoregressive moving average with exogenous inputs (NARMAX) model-based state-space self-tuner with fault tolerance is proposed in this paper for the unknown nonlinear stochastic hybrid system with a direct transmission matrix from input to output. Through the off-line observer/Kalman filter identification method, one has a good initial guess of modified NARMAX model to reduce the on-line system identification process time. Then, based on the modified NARMAX-based system identification, a corresponding adaptive digital control scheme is presented for the unknown continuous-time nonlinear system, with an input–output direct transmission term, which also has measurement and system noises and inaccessible system states. Besides, an effective state space self-turner with fault tolerance scheme is presented for the unknown multivariable stochastic system. A quantitative criterion is suggested by comparing the innovation process error estimated by the Kalman filter estimation algorithm, so that a weighting matrix resetting technique by adjusting and resetting the covariance matrices of parameter estimate obtained by the Kalman filter estimation algorithm is utilized to achieve the parameter estimation for faulty system recovery. Consequently, the proposed method can effectively cope with partially abrupt and/or gradual system faults and input failures by the fault detection.     

Fault diagnosis for manifold absolute pressure sensor(MAP) of diesel engine based on Elman neural network observer

Intake system of diesel engine is a strong nonlinear system, and it is difficult to establish accurate model of intake system; and bias fault and precision degradation fault of MAP of diesel engine can’t be diagnosed easily using model-based methods. Thus, a fault diagnosis method based on Elman neural network observer is proposed. By comparing simulation results of intake pressure based on BP network and Elman neural network, lower sampling error magnitude is gained using Elman neural network, and the error is less volatile. Forecast accuracy is between 0.015-0.017 5 and sample error is controlled within 0-0.07. Considering the output stability and complexity of solving comprehensively, Elman neural network with a single hidden layer and with 44 nodes is presented as intake system observer. By comparing the relations of confidence intervals of the residual value between the measured and predicted values, error variance and failures in various fault types. Then four typical MAP faults of diesel engine can be diagnosed: complete failure fault, bias fault, precision degradation fault and drift fault. The simulation results show: intake pressure is observable and selection of diagnostic strategy parameter reasonably can increase the accuracy of diagnosis;the proposed fault diagnosis method only depends on data and structural parameters of observer, not depends on the nonlinear model of air intake system. A fault diagnosis method is proposed not depending system model to observe intake pressure, and bias fault and precision degradation fault of MAP of diesel engine can be diagnosed based on residuals.     

非线性系统的集成故障诊断和容错控制

基于解析模型而建立的状态观测法是一种得到了广泛应用的故障诊断和容错控制方法,而该方法在非线性不确定系统中的实际应用却由于未知输入扰动的影响受到一定的局限.针对机电系统中常见的严格反馈型不确定非线性系统,并考虑含有未知输入扰动,提出一种集成故障诊断与容错控制的设计方案,使系统在对不确定模型具有鲁棒性的同时,对执行器故障具有较强的跟踪性.该方案给出一种基于滑模变结构的容错控制器设计方法,并利用滑模变结构中的等值控制方法设计状态观测器,利用自适应方法实现对不同形式故障的重构.将所提方法以电液伺服系统为例进行仿真分析.仿真结果表明,系统对不确定模型具有鲁棒性,对突变、缓变和间歇变化等3种常见形式的故障以及带噪声的缓变故障均可进行较好的重构.