Sensor adaptive fault tolerant control for non-linear processes

Adaptive fault tolerant control of non-linear processes is an open problem. In this paper, on the basis of a strong tracking filter (STF), an approach to sensor adaptive fault tolerant generic model control for non-linear processes is proposed. When the process runs normally, Adaptive Generic Model Control (AGMC) based on parameter estimation is used to control non-linear time-varying processes. A sensor fault model is set up by introducing a bias vector into the output equation of the process. The bias vector is estimated on-line based on the STF during every control period. With the estimated sensor bias vector and the time-varying parameters, a fault detection mechanism is developed to supervise sensors. When a sensor fault is detected, AGMC will be switched to a state estimation and soft-sensor-based GMC. This strategy constitutes a sensor-adaptive fault tolerant generic model control for non-linear processes. Experimental results on a three-tank system demonstrate the effectiveness of the proposed approach.     

An analytical fault diagnosis method for yaw estimation of quadrotors

A new analytical fault diagnosis method is presented for the yaw and yaw rate estimation of a quadrotor. In the existing methods, the faults of the inertial sensors and the measurement sensors are treated separately. The presented method can deal with the faults of both the z-axis gyro and the magnetic sensor by introducing the torque model, which is used as a virtual sensor. The filter design, fault detection, isolation and recovery strategies are discussed. Real flight data is used to validate the proposed approach, showing that both the z-axis gyro fault and the magnetic sensor fault can be detected.     

Closed-loop diesel engine combustion phasing control based on crankshaft torque measurements

Methods for closed-loop combustion phasing control in a diesel engine, based on measurements of crankshaft torque, are developed and evaluated. A model-based method for estimation of cylinder individual torque contributions from the crankshaft torque measurements is explained and a novel approach for identification of crankshaft dynamics is proposed. The use of the combustion net torque concept for combustion phasing estimation in the torque domain is also described. Two different control schemes, one for individual cylinder control and one for average cylinder control, are studied. The proposed methods are experimentally evaluated using a light-duty diesel engine equipped with a crankshaft integrated torque sensor. The results indicate that it is possible to estimate and control on a cylinder individual basis using the measurements from the crankshaft torque sensor. Combustion phasing is estimated with bias levels of less than 0.5 crank angle degrees (CAD) and cycle-to-cycle standard deviations of less than 0.7 CAD for all cylinders and the implemented combustion phasing controllers manage to accurately counteract disturbances in both fuel injection timing and EGR fraction.     

Fault-tolerant traction control of electric vehicles

This paper investigates a new traction control approach that requires neither chassis velocity nor information about tire-road conditions. Plant fault subject to the uncertainties of the mathematical model and slightly sensor fault are concerned. For general traction control of vehicles, the variation of model behavior may break down the steering stability if the chassis velocity is not monitored. This paper presents a fault-tolerant approach based on the maximum transmissible torque estimation (MTTE) scheme which has the ability to prevent electric vehicles from skidding. A PI-type disturbance observer is employed to enhance the steering stability of the MTTE approach. This proposed approach does not require both the differentiator and the inversion of the controlled plant. Finally, illustrated examples are given for evaluating the fault-tolerant performance and feasibility of the presented anti-slip strategy.     

Bayesian Fault Probability Estimation: Application in Wind Turbine Drivetrain Sensor Fault Detection

In this paper, the extension of the Bayesian framework for sensor fault detection of nonlinear systems proposed in [25] is studied utilizing particle filtering and the expectation maximization (EM) algorithm, in which the fault probability is calculated. The proposed algorithm is implemented on a wind turbine benchmark model to detect drivetrain sensor faults, which are one of the most addressed and likely faults in offshore wind turbines. The fault probability estimation effectively eliminates the need for installing identical redundant sensors. Indeed, because of the use of the unknown wind speed estimator, the residual signal, constructed based on the drivetrain estimated states, is not able to clearly signify the fault periods, a situation in which the fault probability accurately does this task. Also, using the proposed algorithm, the fault size for each sensor is estimated via a one‐step calculation, which decreases the complexity of this algorithm. The fault identification is performed using the recursive least square method and two other modifications, including exponentially weighted and windowed estimates. Additionally, in the fault accommodation step, the concept of a virtual sensor is used to remove the need for reconfiguring the current controller, which reduces complexity and expense. In the simulation section, using a real measured wind speed for two different fault scenarios, the proposed algorithm is evaluated and finally, conclusions are stated.     

Current-sensor fault detection and isolation for induction-motor drives using a geometric approach

This work presents the design of a current-sensor fault detection and isolation system for induction-motor drives. A differential geometric approach is addressed to determine if faults can be detected and isolated in drives with two line current sensors by using a model based strategy. A set of subsystems is obtained based on the observability co-distribution, whose outputs are decoupled from the load torque (detectability) and only affected by one of the sensors (isolability). A bank of observers is designed for these subsystems in order to obtain residuals for the fault detection and isolation. It is demonstrated that the proposed strategy allows detecting single and multiple sensor faults, including disconnection, offset and gain faults. Experimental results validate the proposal.     

Design of a bilinear fault detection observer for singular bilinear systems

A bilinear fault detection observer is proposed for a class of continuous time singular bilinear systems subject to unknown input disturbance and fault. By singular value decomposition on the original system, a bilinear fault detection observer is proposed for the decomposed system via an algebraic Riccati equation, and the domain of attraction of the state estimation error is estimated. A design procedure is presented to determine the fault detection threshold. A model of flexible joint robot is used to demonstrate the effectiveness of the proposed method.     

Robust fault detection and isolation in stochastic systems

This article outlines the formulation of a robust fault detection and isolation (FDI) scheme that can precisely detect and isolate simultaneous actuator and sensor faults for uncertain linear stochastic systems. The given robust fault detection scheme based on the discontinuous robust observer approach would be able to distinguish between model uncertainties and actuator failures and therefore eliminate the problem of false alarms. Since the proposed approach involves estimating sensor faults, it can also be used for sensor fault identification and the reconstruction of true outputs from faulty sensor outputs. Simulation results presented here validate the effectiveness of the proposed robust FDI system.     

一种非线性系统容错控制的混合方法

针对连续搅拌箱式反映器,提出一种闭环非线性系统容错控制的混合方法。即利用“等价偏差”的新概念建立传感器的故障模型,然后基于一种伪偏差分离估计算法,在线得到系统状态和“等价偏差”的估计值。数值仿真结果验证了该方法不仅具有很强的关于传感器故障的容错能力,而且还可检测、分离和估计出相应的故障。     

An improved PCA scheme for sensor FDI: Application to an air quality monitoring network

In this paper a sensor fault detection and isolation procedure based on principal component analysis (PCA) is proposed to monitor an air quality monitoring network. The PCA model of the network is optimal with respect to a reconstruction error criterion. The sensor fault detection is carried out in various residual subspaces using a new detection index. For our application, this index improves the performance compared to classical detection index SPE. The reconstruction approach allows, on one hand, to isolate the faulty sensors and, on the other hand, to estimate the fault amplitudes.     

The influence of modeling hypothesis and experimental methodologies in the accuracy of muscle force estimation using EMG-driven models

This paper discusses some methodological questions regarding the application of EMG-driven models to estimate muscle forces, for the triceps surae performing isometric contractions. Ankle torque is estimated from a Hill-type muscle model driven by EMG data, collected from the three components of triceps surae and tibialis anterior. Ankle joint torque is synchronously collected from a dynamometer, which is compared to the sum of each muscle force multiplied by the respective ankle moment arm. A protocol consisting of two steps of low and medium/high loads is used. Raw EMG signal is processed and used as the input signal for the muscle model. The difference between simulated and dynamometer measured torque is calculated as the RMS error between the two curves. A set of nominal muscle model parameters is initially chosen from literature (e.g., OpenSim), which allows observing the characteristics of the error distribution. One possibility to improve model accuracy is using individual muscle parameters. We investigated the effect of applying simple scale factors to the nominal muscle model parameters and using ultrasound for estimating muscle maximum force. Other questions regarding muscle model improvements are also addressed, such as using a nonlinear formulation of activation dynamics and variable pennation angle. Surface EMG signals acquisition and processing can also affect force estimation accuracy. Electrodes positioning can influence signal amplitude, and the one-channel EMG may not represent actual excitation for the whole muscle. We have shown that high density EMG reduces, in some cases, the torque estimation error.     

基于缺失数据的误差生成策略及其在故障检测中的应用

误差生成是基于机理模型故障检测方法的核心本质,但鲜有应用于统计过程监测方法中.为此,提出一种基于缺失数据的误差生成策略,将能反映出采样数据对统计模型拟合程度的误差作为新的被监测对象实施故障检测.所提出的基于缺失数据的主元分析(MD-PCA)方法通过逐一假设各变量测量数据缺失后,利用缺失数据处理方法推测出相应缺失数据的估计值,并对缺失数据的实际值与估计值之间的误差实施基于PCA模型的故障检测.利用误差实施故障检测的优势在于,生成的误差能在一定程度上降低原测量变量的非高斯性程度,而且误差体现的是对应缺失变量中与其他测量变量不相关的成分信息,更能揭示各测量变量的本质.通过在TE过程上的实验充分验证了所提出方法的优势,以及MD-PCA方法用于故障检测的可行性与优越性.     

基于自适应观测器的四旋翼无人飞行器多传感器故障诊断方法

为了实现对四旋翼无人飞行器多传感器故障检测与诊断,提出一种基于自适应观测器的多传感器故障诊断方法。首先,在建立飞行器动力学模型和传感器模型的基础上,将传感器故障视为虚拟执行器故障,构建四旋翼无人飞行器多传感器故障检测与诊断系统;其次,设计非线性观测器实现多故障检测和与隔离,基于Laypunov方法设计非线性自适应观测器实现对多故障偏差值的估计;最后,在传感器测量噪声存在的情况下,证明自适应律的稳定性和参数收敛性。实验结果表明,该方法能有效进行多传感器的故障检测与隔离,实现对多传感器故障偏差的同时估计与跟踪。     

基于自适应观测器的四旋翼无人飞行器传感器故障诊断方法

针对四旋翼无人飞行器传感器故障诊断问题,提出一种用于四旋翼无人飞行器加速度计和陀螺仪故障同时发生的故障检测与隔离以及故障偏差值估计的非线性诊断方法.首先,在建立飞行器动力学模型和传感器模型的基础上,构建四旋翼无人飞行器传感器故障检测与诊断系统.其次,利用故障观测器完成传感器故障的检测与隔离,基于Laypunov方法设计非线性自适应观测器对未知故障偏差值进行估计.最后,在传感器测量噪声存在的情况下,证明自适应律的稳定性和参数收敛性.实验结果表明,该方法能有效进行传感器的故障检测与隔离,实现对传感器故障偏差的估计与跟踪.     

一类非线性时滞过程的传感器主动容错控制

通过结合非线性过程的一般模型控制(GMC)、强跟踪预测器(STP)和强跟踪滤波器(STF),本文提出了一类具有输入时滞非线性时变过程的传感器主动容错控制方法.基于强跟踪预测器对未来状态的预测,传统的一般模型控制被扩展到一类具有输入时滞的非线性过程.然后采用强跟踪滤波器估计过程状态及传感器偏差,传感器偏差估计用于驱动一个故障检测逻辑.当某一传感器故障被检测出来时,STF的状态估计值将用于重构过程输出(代替真实输出),此重构输出被STP用于继续进行状态预测,从而确保系统性能.最后,三容水箱系统仿真结果证明该方法的有效性.     

Robust parameter-dependent fault-tolerant control for actuator and sensor faults

In this article, we study a robust fault-tolerant control (FTC) problem for linear systems subject to time-varying actuator and sensor faults. The faults under consideration are loss of effectiveness in actuators and sensors. Based on the estimated faults from a fault detection and isolation scheme, robust parameter-dependent FTC will be designed to stabilise the faulty system under all possible fault scenarios. The synthesis condition of such an FTC control law will be formulated in terms of linear matrix inequalities (LMIs) and can be solved efficiently by semi-definite programming. The proposed FTC approach will be demonstrated on a simple faulty system with different fault levels and fault estimation error bounds.     

Control‐oriented fault detection of solid oxide fuel cell system unknown input on fuel supply

As the solid oxide fuel cell (SOFC) system work environment is a high‐temperature environment for a long time, it is difficult to obtain the SOFC stack internal state change directly. When the fault occurs, it is difficult to determine where the fault occurs. Moreover, the existing literature ignores the impact of faults, which creates many problems for SOFC system control. Therefore, a state observer‐based fault detection method, which is used to detect the input flow sensor fault and the fuel input fault, is proposed. Their advantage is that they do not need data processing. To realize the fault detection, the observer is used to track the changes of SOFC stack chamber temperature. To obtain the observer estimation parameter, an approach from the actual stack structure parameters is employed to approximate the observer parameters. The results show the proposed fault detect method can judge fuel input fault type quickly and shield the disturbances signals from the sensor effectively. The proposed method also can be used to other operating points or air input fault.     

基于碰撞检测的护理型操作臂的安全性设计与实现

针对与人近距离交互的护理型操作臂的安全性问题,提出并实现了一种基于碰撞检测的安全性设计方法,它根据由动力学模型计算获得的参考输出力矩与力矩传感器测得的实际输出力矩间的偏差实现碰撞检测.在关节力矩传感器设计中,采用有限元分析方法来优化应变片的位置布置,并开发了高抗干扰能力的信号处理电路.根据护理型操作臂的低速特点,提出了...     

基于瞬时幅值的光伏系统电流传感器微小故障检测及估计

电流传感器是光伏系统中用于系统控制和状态监测的重要元件,然而受运行环境影响,电流传感器易出现性能退化,影响系统运行安全.为了准确检测和估计出电流传感器微小故障,提出基于瞬时幅值的传感器微小故障检测和估计方法.首先,建立基于瞬时幅值的电流传感器微小故障模型,利用Hilbert变换(HT)估计相电流瞬时幅值将测量的三相正弦电流转换为相互独立的三维直流信号分量;其次,利用快速移动窗主成分分析(FWMPCA)对三维直流信号组成的数据矩阵进行特征提取,获得主元和残差子空间向量的概率密度分布函数;然后,利用Kullback-Leibler(KL)距离定量度量实际运行数据相对于无故障运行数据的微小变化,在此基础上,设置故障检测阈值,构建故障幅值估计模型,实现电流传感器微小故障检测和估计;最后,利用RT-LAB实验平台验证所提方法的有效性.