Sensor Fault Estimation Research Articles

Real‐time nonlinear moving horizon observer with pre‐estimation for aircraft sensor fault detection and estimation

SummaryThis paper presents a real‐time nonlinear moving horizon observer (MHO) with pre‐estimation and its application to aircraft sensor fault detection and estimation. An MHO determines the state estimates by minimizing the output estimation errors online, considering a finite sequence of current and past measured data and the available system model. To achieve the real‐time implementability of such an online optimization–based observer, 2 particular strategies are adopted. First, a pre‐estimating observer is embedded to compensate for model uncertainties so that the calculation of disturbance estimates in a standard MHO can be avoided without losing much estimation performance. This strategy significantly reduces the online computational complexity. Second, a real‐time iteration scheme is proposed by performing only 1 iteration of sequential quadratic programming with local Gauss‐Newton approximation to the nonlinear optimization problem. Since existing stability analyses of real‐time moving horizon observers cannot address the incorporation of the pre‐estimating observer, a new stability analysis is performed in the presence of bounded disturbances and noises. Using a nonlinear passenger aircraft benchmark simulator, the simulation results show that the proposed approach achieves a good compromise between estimation performance and computational complexity compared with the extended Kalman filtering and 2 other moving horizon observers.

Sensor fault estimation for fractional-order descriptor one-sided Lipschitz systems

Up to now, the problem of sensor fault estimation for integer-order descriptor systems has been tackled by several researchers. However, no attempt has been done to estimate sensor faults for fractional-order descriptor systems. In this context, this paper presents a complete methodology to solve this problem. On the other hand, to the best of our knowledge, among all the existing works dealing with the observer design task for fractional-order descriptor systems, no paper has treated the special class of one-sided Lipschitz systems. In this paper, the designed observer is capable of estimating sensor faults for one-sided Lipschitz systems, thanks to a linear matrix inequality technique. In order to validate the theoretical results, a second-order numerical example as well as a third-order one are studied in the simulation section. The simulation results show that the sensor fault estimates are satisfactory.

SENSOR FAULTS DETECTION AND ESTIMATION FOR A DFIG EQUIPPED WIND TURBINE

Doubly Fed Induction Generator (DFIG) based on wind turbines demand a high degree of reliability and availability and they are characterized by expensive and safety critical maintenance work. This paper deals with a new strategy for detection and estimation of current sensor faults in the stator and rotor of a DFIG. First, a state space model of a DFIG is developed based on voltages and flux equations, which can be used in order to estimate states and to generate residuals by using a Luenberger observer. Then, the residuals results are exploited for faults detection and estimation. Finally, the effectiveness of the developed approach is validated through simulation tests performed by different faults scenarios.

Data-based Projection Method for Sensor Fault Tolerant Control of Switched Systems

This paper addresses the problem of sensor fault estimation and compensation of switched hybrid systems. Indeed, we propose to extend the Data-based Projection Method (EDPM) for the switched system in order to estimate the fault. The fault compensation is ensured by a fault tolerant control (FTC) based on the EDPM. This FTC is performed by combining the state feedback control with integral action and an additive control. In fact, the gain matrices of the state feedback control with integral action are computed by developing a new Linear Matrix Inequalities (LMI). This technique provides optimal values of the gain matrices which allow to ensure the stability of the system and to compensate the effect of the fault correctly. A simulation example is provided to show the effectiveness of the proposed method and to compare its performance with existing methods.

Active fault tolerant control design approach for the flexible spacecraft with sensor faults

In this paper, the problem of active fault tolerant control (FTC) is studied for a class of flexible spacecraft attitude systems with Lipschitz nonlinearity and sensor fault. Firstly, a functional observer is designed for the attitude systems of flexible spacecraft in order to detect the time of unknown sensor fault occurred. Next, the sensor fault estimation is performed by filtering the output estimation error, as usually done in the residual generation framework. Then, a dynamic output feedback-based FTC approach is proposed to the flexible spacecraft in sensor faulty case, it not only attenuates flexible appendage disturbance with a given level γ, but also tolerates the effect of unknown sensor fault. Finally, the effectiveness of the proposed FTC method is demonstrated in the attitude systems of flexible spacecraft subject to a time-varying sensor fault.

Robust sensor fault estimation for fractional-order systems with monotone nonlinearities

This paper investigates the problem of sensor fault estimation for systems with monotone nonlinearities and unknown inputs. To the best of our knowledge, such a particular problem is treated for the first time, as only actuator faults have been estimated for this class of nonlinear systems in the literature. The design method is based on an unknown input observer and is particularly effective for integer-order systems and fractional-order systems. This can be regarded as a second contribution in the paper. Indeed, to the best of our knowledge, no papers dealing with the observer design problem for fractional-order systems with monotone nonlinearities exist in the literature. In order to validate the theoretical results, two simulation examples are given, including fractional-order examples and the “integer-order” flexible joint electric drive system.

Fault tolerant shape control for particulate process systems under simultaneous actuator and sensor faults

The problem of fault tolerant shape control for particle size distribution (PSD) process subject to simultaneous time-varying actuator and sensor faults is studied. In this work, sensor and actuator faults are taken into consideration in a unified framework, and estimated by using adaptive observer technique. For the PSD process, the output distribution, rather than the system output signal itself, is adopted for shape control. Square root rational B-spline approximation is used to fit the distribution shape. An innovative on-line fault estimation scheme is proposed for simultaneous actuator and sensor faults estimation. Then an augmented controller based on the virtual actuator and virtual sensor techniques is designed to compensate for the faults and realise PSD shape tracking. A simulation example of emulsion polymerisation is used to validate the proposed scheme and satisfactory performance is obtained.

A data-driven approach to actuator and sensor fault detection, isolation and estimation in discrete-time linear systems

In this work, we propose and develop data-driven explicit state-space based fault detection, isolation and estimation filters that are directly identified and constructed from only the available system input–output (I/O) measurements and through only the estimated system Markov parameters. The proposed methodology does not involve a reduction step and does not require identification of the system extended observability matrix or its left null space. The performance of our proposed filters is directly related to and linearly dependent on the Markov parameters identification errors. The estimation filters operate with a subset of the system I/O data that is selected by the designer. It is shown that our proposed filters provide an asymptotically unbiased estimate by invoking a low order filter as long as the selected subsystem has a stable inverse. We have derived the estimation error dynamics in terms of the Markov parameters identification errors and have shown that they can be directly synthesized from the healthy system I/O data. Consequently, our proposed methodology ensures that the estimation errors can be effectively compensated for. Finally, we have provided several illustrative case study simulations that demonstrate and confirm the merits of our proposed schemes as compared to methodologies that are available in the literature.

Simultaneous actuator and sensor fault estimation for discrete‐time Lipschitz nonlinear systems in finite‐frequency domain

SummaryThis paper addresses the problem of actuator and sensor fault reconstruction simultaneously with finite‐frequency specifications for discrete‐time Lipschitz nonlinear systems. First, an augmented system description is given by extending the sensor fault as an auxiliary state vector. Then, a multiobjective unknown input proportional‐integral observer is designed, and the nonlinear error dynamics are transformed into a linear parameter‐varying system based on the use of a reformulated Lipschitz property. Sufficient conditions for the design of a fault estimator with 2 finite‐frequency performances are derived in terms of linear matrix inequalities where a slack variable is introduced to reduce the conservativeness of the design. The proposed method can provide less restrictive linear matrix inequality conditions and get a better fault estimation performance than the existing one in the full frequency domain. Simulation results are given to show the effectiveness of the proposed techniques.

State and Faults Estimation Based on Proportional Integral Sliding Mode Observer for Uncertain Takagi–Sugeno Fuzzy Systems and its Application to a Turbo-Reactor

This paper deals with the problem of state and faults estimation for nonlinear uncertain systems described by Takagi–Sugeno fuzzy structures (called also multiple models). In this work, actuator faults are considered as unknown inputs. The state and faults estimation is made using a structure of sliding mode observer where an integral term is added. This new structure of observer is called proportional integral sliding mode observer. The added integral term permits the unknown input estimation. For the sensor faults estimation, a mathematical transformation is used. The application of this mathematical transformation to the initial system output let to conceive an augmented system where the initial sensor fault appears as an unknown input. The observer convergence conditions are formulated in the form of Linear Matrix Inequalities allowing computing the observer gains. The proposed proportional integral sliding mode observer is applied to a numerical example showing the efficiency of the fault and the state estimation. In order to show the efficiency of the proposed method, it is applied to a turbo-reactor system.

Fault diagnosis and fault tolerant control for discrete-time linear systems with sensor fault

In this paper, the problems of sensor fault estimation and accommodation for discrete-time dynamic systems are investigated. Firstly, an augmented system is constructed where the sensor fault variable is added to the state vector. Then based on the descriptor observer technique, the efficient conditions of fault estimation under the disturbance and without the disturbance are both obtained. Finally, a static output feedback controller is employed to tolerate the sensor fault such that the closed-loop system is asymptotically stable. At last, a numerical example is provided to illustrate the proposed technique.

Sensor fault estimation for Lipschitz nonlinear systems in finite-frequency domain

ABSTRACTIn this study, the problem of sensor fault estimation observer design for Lipschitz nonlinear systems with finite-frequency specifications is investigated. First, the sensor fault is considered as an auxiliary state vector and an augmented system is established. Then, by transforming the nonlinear error dynamics into a linear parameter varying system, a sufficient condition for the observer-error system with a finite-frequency H∞ performance is derived in terms of linear matrix inequalities (LMIs). Based on the obtained condition, novel nonlinear observers are designed to simultaneously estimate the system states and the fault signals and attenuate the disturbances in the finite-frequency domain. The proposed design method can provide less restrictive LMI conditions and get a better disturbance-attenuation performance when the frequency ranges of disturbances are known beforehand. A numerical example is given to show the effectiveness and superiority of the new results.

Robust Fault-Tolerant Control of Wind Turbine Systems Against Actuator and Sensor Faults

This paper develops a novel observer-based fault-tolerant control (FTC) for wind turbine blade pitch system subjected to simultaneous actuator and sensor faults. The main contribution of this paper is the proposal of new architecture based on a combination of sliding mode control (SMC) and a proportional–proportional–integral-observer (PPIO) to provide tight reference blade pitch angle tracking regardless of the effects of actuator and sensor faults. Within this architecture, an integral sliding surface-based SMC (ISMC) has been developed to stabilize tracking error dynamics during sliding phase while the system is subjected to actuator faults. The robust PPIO-based sensor fault estimation is utilized to compensate sensor fault from the input of ISMC and thereby guarantee closed-loop system robustness against actuator and sensor faults. Stability analysis has clearly demonstrated using linear matrix inequality and Lyapunov approach. The proposed method is applied to 4.8 MW wind turbine FTC benchmark model.

Active Fault-Tolerant Control for a Quadrotor with Sensor Faults

An active fault-tolerant control scheme for a quadrotor with velocity sensor faults is presented in this paper. A two-level control scheme is designed to guarantee the quadrotor to track the given trajectory in case of no faults. The control scheme consists of an external-loop Proportion Differentiation (PD) control law and an internal-loop Proportion Integration Differentiation (PID) control law. A fault diagnosis unit is designed to detect and estimate sensor faults. The fault detection is achieved by using a Luenberger observer based residual generator and the fault estimation problem is solved by utilizing a new proposed augment variable observer. A sufficient condition on the existence of the augment variable observer is given based on Linear Matrix Inequalities (LMIs). The uniformly ultimately bounded property of state and fault estimation errors is proved. By combining the external-loop PD control law and the result of fault estimation, a fault-tolerant control law is proposed. Finally, the effectiveness of the scheme is demonstrated by the simulation and experimental results.

Sliding Mode Observers Based-Simultaneous Actuator and Sensor Faults Estimation for Linear Parameter Varying Systems

This paper proposes a scheme to estimate actuator and sensor faults simultaneously for a class of linear parameter varying system expressed in polytopic structure where its parameters evolve in the hypercube domain. Transformed coordinate system design is adopted to decouple faults in actuators and sensors during the course of the system’s operation coincidentally, and then two polytopic subsystems are constructed. The first subsystem includes the effect of actuator faults but is free from sensor faults and the second one is affected only by sensor faults. The main contribution is to conceive two polytopic sliding mode observers in order to estimate the system states and actuator and sensor faults at the same time. Meanwhile, in linear matrix inequality optimization formalism, sufficient conditions are derived withH∞performances to guarantee the stability of estimation error and to minimize the effect of disturbances. Therefore, all parameters of observers can be designed by solving these conditions. Finally, simulation results are given to illustrate the effectiveness of the proposed simultaneous actuator and sensor faults estimation.

Data-Driven Incipient Sensor Fault Estimation with Application in Inverter of High-Speed Railway

Incipient faults in high-speed railway have been rarely considered before developing into faults or failures. In this paper, a new data-driven incipient fault estimate (FE) methodology is proposed under multivariate statistics frame, which incorporates with Kullback-Leibler divergence (KLD) in information domain and neural network approximation in machine learning. By defining one sensitive fault indicator (SFI), the incipient fault amplitude can be precisely estimated. According to the experimental platform of China Railway High-speed 2 (CRH2), the proposed incipient FE algorithm is examined, and the more sensitivity and accuracy to tiny abnormality are demonstrated. Followed by the incipient FE results, several factors on FE performance are further analyzed.

Sensor Fault Estimation of Switched Fuzzy Systems with Unknown Input

This paper focuses on the problem of sensor fault estimation of switched fuzzy systems with unknown input. By treating the sensor fault as a virtual state, an augmented system can be obtained. A dynamic unknown input observer is designed for the au-gmented system to estimate the sensor fault and the system state simultaneously. Based on the average dwell time and the piecewise Lyapunov function, the observer parameter calculation method is presented in terms of solutions to a set of linear matrix inequalities. The unknown input is decoupled from the estimation error, and the effect of the disturbance is attenuated by the weighted $H_\infty$ performance level. At last, simulation results are provided to illustrate the effectiveness of the proposed techniques.

Descriptor reduced-order sliding mode observers design for switched systems with sensor and actuator faults

This paper investigates the state and fault estimation problem for linear continuous-time switched systems with simultaneous disturbances, sensor and actuator faults, and two types of observer approaches are presented to solve this design issue. The first one is a linear descriptor reduced-order observer, which generates the exact estimation of state and sensor faults together with disturbances by decoupling them from the state vector without any supplementary design. The second one refers to a descriptor sliding mode observer approach, where the state, disturbances, sensor and actuator faults can be reconstructed simultaneously. In the second method, the reachability of sliding surface is not necessary to be investigated, since the actuator faults can be estimated directly without using the equivalent output error injection scheme as in traditional sliding mode observers. Therefore, both developed observer approaches avoid the sliding surface switching problem of traditional sliding mode observers in application to switched systems. Finally, an example is given to demonstrate the effectiveness of the two designed observer methods.

Sensor Fault Detection, Isolation, and Estimation in Lithium-Ion Batteries

In battery management systems (BMSs), real-time diagnosis of sensor faults is critical for ensuring the safety and reliability of the battery. For example, a current sensor fault leads to erroneous estimates of state of charge and other parameters, which in turn affects the control actions in the BMS. A temperature sensor fault may lead to ineffective thermal management. In this brief, a model-based diagnostic scheme is presented that uses sliding mode observers designed based on the electrical and thermal dynamics of the battery. It is analytically shown how the extraction of the equivalent output error injection signals on the sliding manifolds enables the detection, the isolation, as well as the estimation of the temperature, voltage, and current sensor faults. This brief includes simulation and experimental studies to demonstrate and evaluate the effectiveness of the proposed scheme. Discussions are also included on the effects of uncertainty and on threshold design.

Fault-tolerant cooperative output regulation for multi-vehicle systems with sensor faults

ABSTRACTThis paper presents a unified framework of fault diagnosis and fault-tolerant cooperative output regulation (FTCOR) for a linear discrete-time multi-vehicle system with sensor faults. The FTCOR control law is designed through three steps. A cooperative output regulation (COR) controller is designed based on the internal mode principle when there are no sensor faults. A sufficient condition on the existence of the COR controller is given based on the discrete-time algebraic Riccati equation (DARE). Then, a decentralised fault diagnosis scheme is designed to cope with sensor faults occurring in followers. A residual generator is developed to detect sensor faults of each follower, and a bank of fault-matching estimators are proposed to isolate and estimate sensor faults of each follower. Unlike the current distributed fault diagnosis for multi-vehicle systems, the presented decentralised fault diagnosis scheme in each vehicle reduces the communication and computation load by only using the information of the vehicle. By combing the sensor fault estimation and the COR control law, an FTCOR controller is proposed. Finally, the simulation results demonstrate the effectiveness of the FTCOR controller.