A novel scheme for current sensor faults diagnosis in the stator of a DFIG described by a T-S fuzzy model

Diagnosis of current sensor faults (CSF) for doubly fed induction generators (DFIGs) is of paramount importance for the reliable power generation of DFIG-based wind turbines (WT). In this paper, a new scheme is developed for current sensors faults diagnosis in the stator of a DFIG-based WT. The nonlinear model of the DFIG is first transformed into an equivalent Takagi-Sugeno (T-S) fuzzy model. Secondly, using this model, a novel fault detection and isolation (FDI) algorithm is proposed. This algorithm is based on a bank of Luenberger observers for residuals generation combined with a new proposed residual vector. Furthermore, a new binary decision logic is used for CSF isolation. Stability analysis of the observer bank is analyzed using a Lyapunov theorem, which allows deriving sufficient stability conditions by solving a system of Linear Matrix Inequalities (LMIs). A simulation study is carried out to assess the performance and the effectiveness of the new FDI scheme.     

Sliding mode observer based incipient sensor fault detection with application to high-speed railway traction device

This paper considers incipient sensor fault detection issue for a class of nonlinear systems with “observer unmatched” uncertainties. A particular fault detection sliding mode observer is designed for the augmented system formed by the original system and incipient sensor faults. The designed parameters are obtained using LMI and line filter techniques to guarantee that the generated residuals are robust to uncertainties and that sliding motion is not destroyed by faults. Then, three levels of novel adaptive thresholds are proposed based on the reduced order sliding mode dynamics, which effectively improve incipient sensor faults detectability. Case study of on the traction system in China Railway High-speed is presented to demonstrate the effectiveness of the proposed incipient senor faults detection schemes.     

Actuator fault tolerant control based on probabilistic ultimate bounds

In this work we introduce a novel set-based fault tolerant control scheme for linear systems under Gaussian disturbances. In the proposed strategy, actuator faults are detected and diagnosed when residual trajectories enter and remain in certain sets that are computed as probabilistic ultimate bounds. After a fault is diagnosed, the control scheme is reconfigured to take into account the corresponding actuator failure and preserve certain closed loop features. We show that our strategy can detect and diagnose the different faults considered with an arbitrarily small probability of misdetection.     

An anti-windup INDI fault-tolerant control scheme for flying wing aircraft with actuator faults

In this paper, an anti-windup incremental nonlinear dynamic inversion (INDI) fault-tolerant scheme is proposed for flying wing aircraft with actuator faults, actuator saturation and uncertainties of aerodynamic parameters. An optimal anti-windup compensator based on nonlinear partial differential inequalities is used to compensate the actuator saturation. INDI is used to control the fault system and compensate the uncertainties of the flight dynamics. Control allocation strategy is designed in consideration of the control scheme and configuration of the control surfaces. The proposed control method can guarantee the bounded tracking of the reference signals. Simulation results are given to show the effectiveness of the proposed method.     

Indirect adaptive fuzzy fault-tolerant tracking control for MIMO nonlinear systems with actuator and sensor failures

In this paper, an active fuzzy fault tolerant tracking control (AFFTTC) scheme is developed for a class of multi-input multi-output (MIMO) unknown nonlinear systems in the presence of unknown actuator faults, sensor failures and external disturbance. The developed control scheme deals with four kinds of faults for both sensors and actuators. The bias, drift, and loss of accuracy additive faults are considered along with the loss of effectiveness multiplicative fault. A fuzzy adaptive controller based on back-stepping design is developed to deal with actuator failures and unknown system dynamics. However, an additional robust control term is added to deal with sensor faults, approximation errors, and external disturbances. Lyapunov theory is used to prove the stability of the closed loop system. Numerical simulations on a quadrotor are presented to show the effectiveness of the proposed approach.     

Isolability of faults in sensor fault diagnosis

A major concern with fault detection and isolation (FDI) methods is their robustness with respect to noise and modeling uncertainties. With this in mind, several approaches have been proposed to minimize the vulnerability of FDI methods to these uncertainties. But, apart from the algorithm used, there is a theoretical limit on the minimum effect of noise on detectability and isolability. This limit has been quantified in this paper for the problem of sensor fault diagnosis based on direct redundancies. In this study, first a geometric approach to sensor fault detection is proposed. The sensor fault is isolated based on the direction of residuals found from a residual generator. This residual generator can be constructed from an input-output or a Principal Component Analysis (PCA) based model. The simplicity of this technique, compared to the existing methods of sensor fault diagnosis, allows for more rational formulation of the isolability concepts in linear systems. Using this residual generator and the assumption of Gaussian noise, the effect of noise on isolability is studied, and the minimum magnitude of isolable fault in each sensor is found based on the distribution of noise in the measurement system. Finally, some numerical examples are presented to clarify this approach.     

Fault diagnosis of complex system based on nonlinear frequency spectrum fusion

A novel fault detection and diagnosis approach is proposed for nonlinear complex systems by combining nonlinear frequency spectrum characteristics and evidence theory. In order to overcome the problem of calculated amount expansion of generalized frequency response functions, single-dimensional nonlinear output frequency response functions are used to obtain nonlinear frequency spectrum, from which, features of nonlinear frequency spectrum are extracted. The fault diagnosis model of multiple faults is given based on evidence theory. The mass functions of evidences are obtained according to the similarity among different modes. In order to solve the problem of evidence fusion in the situation of evidence confliction, a dynamic parameter conflicting evidence combination method is proposed based on the average credibility. Fault diagnosis of the transmission system of numerical control machine tool is studied through nonlinear frequency spectrum. Simulations indicate that the proposed approach has simple calculation and high recognition rate for faults.     

Adaptive-gain fast super-twisting sliding mode fault tolerant control for a reusable launch vehicle in reentry phase

In this paper, a novel adaptive-gain fast super-twisting (AGFST) sliding mode attitude control synthesis is carried out for a reusable launch vehicle subject to actuator faults and unknown disturbances. According to the fast nonsingular terminal sliding mode surface (FNTSMS) and adaptive-gain fast super-twisting algorithm, an adaptive fault tolerant control law for the attitude stabilization is derived to protect against the actuator faults and unknown uncertainties. Firstly, a second-order nonlinear control-oriented model for the RLV is established by feedback linearization method. And on the basis a fast nonsingular terminal sliding mode (FNTSM) manifold is designed, which provides fast finite-time global convergence and avoids singularity problem as well as chattering phenomenon. Based on the merits of the standard super-twisting (ST) algorithm and fast reaching law with adaption, a novel adaptive-gain fast super-twisting (AGFST) algorithm is proposed for the finite-time fault tolerant attitude control problem of the RLV without any knowledge of the bounds of uncertainties and actuator faults. The important feature of the AGFST algorithm includes non-overestimating the values of the control gains and faster convergence speed than the standard ST algorithm. A formal proof of the finite-time stability of the closed-loop system is derived using the Lyapunov function technique. An estimation of the convergence time and accurate expression of convergence region are also provided. Finally, simulations are presented to illustrate the effectiveness and superiority of the proposed control scheme.     

Fault prediction for nonlinear stochastic system with incipient faults based on particle filter and nonlinear regression

This paper is concerned with the fault prediction for the nonlinear stochastic system with incipient faults. Based on the particle filter and the reasonable assumption about the incipient faults, the modified fault estimation algorithm is proposed, and the system state is estimated simultaneously. According to the modified fault estimation, an intuitive fault detection strategy is introduced. Once each of the incipient fault is detected, the parameters of which are identified by a nonlinear regression method. Then, based on the estimated parameters, the future fault signal can be predicted. Finally, the effectiveness of the proposed method is verified by the simulations of the Three-tank system.     

Deep model integrated with data correlation analysis for multiple intermittent faults diagnosis

Currently, single fault diagnosis has received mass concern, and the related research achievements are remarkable. However, because of the mutual interaction of subsystems and the coupling of faults characteristics, the diagnosis of multiple intermittent faults commonly existing in industrial systems is still an intractable problem. In order to solve the problem, an improved Constrained Sparse Autoencoder integrated with Correlation Analysis (CA-CSAE) is proposed, further, a diagnosis scheme for multiple intermittent faults is formulated in this paper. The main strategies are as follows. (1) An adaptive loss function and a constraint for initial weight are designed to improve the diversity and accuracy of SAE feature learning. (2) A relational constraint term is constructed to mitigate the effect of data correlation. (3) The evaluation criterion of data correlation degree is put forward to quantify the scope of the method. (4) In order to improve the diagnostic efficiency, ReLU is introduced as the activation function of hidden layer, and L-BFGS algorithm is employed to obtain the optimal solution. (5) Softmax classifier is employed as the output layer to identify fault mode and ensure the reliability of diagnosis results. Finally, comparison experiments and results analysis are conducted to verify the effectiveness and practicability of the proposed method.     

Disturbance observer based fault estimation and dynamic output feedback fault tolerant control for fuzzy systems with local nonlinear models

This paper addresses the problems of fault estimation (FE) and fault tolerant control (FTC) for fuzzy systems with local nonlinear models, external disturbances, sensor and actuator faults, simultaneously. Disturbance observer (DO) and FE observer are designed, simultaneously. Compared with the existing results, the proposed observer is with a wider application range. Using the estimation information, a novel fuzzy dynamic output feedback fault tolerant controller (DOFFTC) is designed. The controller can be used for the fuzzy systems with unmeasurable local nonlinear models, mismatched input disturbances, and measurement output affecting by sensor faults and disturbances. At last, the simulation shows the effectiveness of the proposed methods.     

Sensor fault detection and isolation system for a condensation process

This article presents the design of a sensor Fault Detection and Isolation (FDI) system for a condensation process based on a nonlinear model. The condenser is modeled by dynamic and thermodynamic equations. For this work, the dynamic equations are described by three pairs of differential equations which represent the energy balance between the fluids. The thermodynamic equations consist in algebraic heat transfer equations and empirical equations, that allow for the estimation of heat transfer coefficients. The FDI system consists of a bank of two nonlinear high-gain observers, in order to detect, estimate and to isolate the fault in any of both outlet temperature sensors. The main contributions of this work were the experimental validation of the condenser nonlinear model and the FDI system.     

Sensors and actuator fault tolerant control applied in a double pipe heat exchanger

In this work, we present the design of a Fault-Tolerant Control (FTC) system, applied in the outlet temperature sensors and in the control valve (actuator) of a concentric double pipe counter-current flow heat exchanger. The FTC consists in a sensors Fault Detection and Isolation (FDI) system and an actuator FDI system. The sensors FDI system is based on analytical redundancy, in such a way that a bank of modified Kalman filters is developed in order to estimate the two outlet temperatures of the heat exchanger. To develop the modified Kalman filters a multi-linear models approach is used. So that, if a sensor fault is detected by the FDI system the measured temperature signal is replaced by the temperature estimation provided by the modified Kalman filter. Moreover, to detect an actuator fault a comparison between the control valve behaviors (the control valve voltage is used to estimate the water flow rate) and a predefined flow rate for each linear model is carried out. In order to keep the continuous operation of the heat exchanger even in fault presence a model-following control law is introduced, such that, when an actuator fault occurs, the FDI system detect the fault and immediately the model-following control makes the fault accommodation in order to compensate the actuator fault. The proposed scheme is presented with experimental data on-line. The successful tests are presented and discussed.     

Adaptive sensor fault-tolerant control for a class of multi-input multi-output nonlinear systems: Adaptive first-order filter-based dynamic surface control approach

This paper is concerned with the adaptive fault-tolerant control (FTC) problem for a class of multivariable nonlinear systems with external disturbances, modeling errors and time-varying sensor faults. The bias, drift, loss of accuracy and loss of effectiveness faults can be effectively accommodated by this scheme. The dynamic surface control (DSC) technique and adaptive first-order filters are brought together to design an adaptive FTC scheme which can reduce significantly the computational burden and improve further the control performance. The adaptation laws are constructed using novel low-pass filter based modification terms which enable under high learning or modification gains to achieve robust, fast and high-accuracy estimation without incurring undesired high-frequency oscillations. It is proved that all signals in the closed-loop system are uniformly ultimately bounded and the tracking-errors can be made arbitrary close to zero. Simulation results are provided to verify the effectiveness and superiority of the proposed FTC method.     

Sensor location optimization for fault diagnosis with a comparison to linear programming approaches

The critical importance of sustaining fault diagnosis, as a major system tool, is unquestionable if the high performance and reliability of increasingly complex engineering systems is to be sustained over time and across a wide operating range. However, it is quite difficult to retain the joint ability of fault detection and isolation as it requires a strong system architecture. That is why, before designing an industrial supervision system, the determination of a system’s monitoring ability based on technical specifications is important as finding the source of the failure is not trivial in systems with a large number of components and complex component relationships. This paper presents an efficient and cost-effective fault detection and isolation (FDI) scheme that evolved from an earlier work [1]. FDI specifications are translated into constraints of the optimization problem considering that the whole set of analytical redundancy relations has been generated, under the assumption that all candidate sensors are installed and later on tested by an optimization algorithm using binary and relaxed versions of linear and nonlinear programming. By doing so, critical information about the presence or absence of a fault is gained in the shortest possible time, with not only confirmation of the findings but also an accurate unfolding in time of the finer details of the fault, thus completing the overall diagnostic picture of the system under test. The proposed scheme is evaluated extensively on a two-tank process used in industry, exemplified by a benchmarked laboratory-scale coupled-tank system.     

Multiple incipient sensor faults diagnosis with application to high-speed railway traction devices

This paper deals with the problem of incipient fault diagnosis for a class of Lipschitz nonlinear systems with sensor biases and explores further results of total measurable fault information residual (ToMFIR). Firstly, state and output transformations are introduced to transform the original system into two subsystems. The first subsystem is subject to system disturbances and free from sensor faults, while the second subsystem contains sensor faults but without any system disturbances. Sensor faults in the second subsystem are then formed as actuator faults by using a pseudo-actuator based approach. Since the effects of system disturbances on the residual are completely decoupled, multiple incipient sensor faults can be detected by constructing ToMFIR, and the fault detectability condition is then derived for discriminating the detectable incipient sensor faults. Further, a sliding-mode observers (SMOs) based fault isolation scheme is designed to guarantee accurate isolation of multiple sensor faults. Finally, simulation results conducted on a CRH2 high-speed railway traction device are given to demonstrate the effectiveness of the proposed approach.     

Fault diagnosis and accommodation of a three-tank system based on analytical redundancy

This paper investigates the application of a fault diagnosis and accommodation method to a real system composed of three tanks. The performance of a closed-loop system can be altered by the occurrence of faults which can, in some circumstances, cause serious damage on the system. The research goal is to prevent the system deterioration by developing a controller that has some capabilities to compensate for faults, that is, the fault accommodation or fault-tolerant control. In this paper, a two-step scheme composed of a fault detection, isolation and estimation module, and a control compensation module is presented. The main contribution is to develop a unique structured residual generator able to isolate and estimate both sensor and actuator faults. This estimation is of paramount importance to compensate for these faults and to preserve the system performances. The application of this method to the three-tank system gives encouraging results which are presented and commented on in case of various kinds of faults.     

Neural networks applied for the identification and fault diagnosis of process valves and actuators

This paper is concerned with the instrumentation and technology of fault detection and isolation (FDI) in process valves and actuators. A classification of faults in process valves and actuators is followed by a brief review of EDI techniques. Artificial neural networks (ANNs) are classified and introduced as an effective way of modelling valves and actuators, which are severely nonlinear components. Experimental results obtained from tests conducted on a double acting, twin piston rack-and-pinion actuator, are presented.     

Multiple model-based detection and estimation scheme for gas turbine sensor and gas path fault simultaneous diagnosis

In this paper, a multiple model (MM)-based detection and estimation scheme for gas turbine sensor and gas path fault diagnosis is proposed, which overcomes the coupling effects between sensor faults and gas path faults, and simultaneously realizes an accurate diagnosis of sensor and gas path faults. First, an adaptive fault detection and isolation (FDI) framework based on the MM method was established to detect and isolate sensor faults and gas path faults. Then, a fault amplitude estimation method was proposed according to the FDI results, and a fault validation method based on the Chi-square test was proposed to confirm the actual fault. Finally, hardware in the loop (HIL) simulation platform was established to validate the effectiveness of the proposed method. Several simulation case studies were conducted based on a two-shaft marine gas turbine with common gas path faults and sensor faults. The simulation results show that the proposed method can accurately diagnose the fault and estimate the corresponding fault amplitude when both the sensor fault and the gas path fault coincide.

     

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.