Actuator Fault Isolation Research Articles

Design of unknown input functional observers for nonlinear systems with application to fault diagnosis

This paper considers the design of low-order unknown input functional observers for robust fault detection and isolation of a class of nonlinear Lipschitz systems subject to unknown inputs. The proposed functional observers can be used to generate residual signals to detect and isolate actuator faults. By using the generalized inverse approach, the effect of the unknown inputs can be decoupled completely from the residual signals. Conditions for the existence and stability of reduced-order unknown input functional observer are derived. A design procedure for the generation of residual signals to detect and isolate actuator faults is presented using the proposed unknown-input observer-based approach. A numerical example is given to illustrate the proposed fault diagnosis scheme in nonlinear systems subject to unknown inputs.

A Neural Network-Based Multiplicative Actuator Fault Detection and Isolation of Nonlinear Systems

The problem of fault detection and isolation/identification (FDI) of nonlinear systems using neural networks is considered in this paper. The proposed FDI approach employs recurrent neural network-based observers for simultaneously detecting, isolating and identifying the severity of actuator faults in presence of disturbances and uncertainties in the model and sensory measurements. The neural network weights are updated based on a modified dynamic backpropagation scheme. The proposed FDI scheme does not rely on the availability of full state measurements. In most works in the literature the fault function acts as an additive term on the actuator, whereas in this work the fault acts as a multiplicative term. This will make the formal stability and convergence analysis of the overall FDI scheme nontrivial and challenging. Our stability analysis considers the presence of plant and sensor uncertainties through the use of Lyapunov's direct method with no restrictive assumptions on the system and/or the FDI algorithm. The performance of our proposed FDI approach is evaluated through simulations that are performed for two case studies, namely FDI of 1) reaction wheel type actuators that are commonly utilized in the attitude control subsystem (ACS) of a satellite and 2) actuators in a two-link flexible joint manipulator.

Invariant-set-based fault diagnosis in Lure systems

SUMMARY In this paper, we present an invariant-set-based method for actuator and sensor fault detection and isolation in Lure systems. The Lure plant is controlled by an observer-based feedback tracking controller, designed for the nominal (fault-free) system. Suitable residual signals are constructed from measurable system outputs and estimates associated with the nominal observer. Faults are diagnosed by online contrasting the residual signal trajectories against sets of values that the residuals are shown to attain under healthy or faulty operation. These values are obtained via set-invariance analysis of the system closed-loop trajectories. Copyright © 2013 John Wiley & Sons, Ltd.

Online fault diagnosis using recursive subspace identification: Application to a dam-gallery open channel system

The paper presents an online strategy for sensor and/or actuator fault detection and isolation applied to a dam-gallery. A recursive subspace identification algorithm is used to estimate the dam-gallery model parameters. The main contribution consists in developing a specific identification scheme, insensitive to a certain type of faults. That is, the identified parameters are invariant to the faults. A fault estimation procedure is proposed to detect potential faults. The proposed approach appears to be suitable for open channel systems for which the characteristics are not easily measurable.

Design and validation of a reconfiguration strategy for a redundantly actuated intelligent autonomous vehicle

This paper deals with the reconfiguration of an intelligent autonomous vehicle that utilizes an automatic navigation method and online supervision system and thus can be used to improve the safety, traffic management and space optimization inside the confined space of a port. The bond graph model of the vehicle’s dynamic system is developed in a modular and hierarchical modelling environment. An over-actuated intelligent autonomous vehicle with redundant actuators has four independent driven wheels, four independent braking wheels and a four-wheel steering system. This vehicle can be safely operated with appropriate control law restructuring even when some of its actuators are unusable due to a fault. For actuator fault detection, analytical redundancy relations, which are constraint relations that describe nominal system behaviour and are written in terms of the measured system variables, are derived from the bond graph model. Analytical redundancy relations are continuously evaluated to generate residual signals and the symptoms in these signals are monitored for actuator fault detection and isolation. Once one or more actuator faults are isolated, the system is reconfigured via the selection of an appropriate operating mode to prevent critical or accidental situations. This procedure is validated by considering a fault scenario with two reconfiguration options.

Synthesis of Nonlinear Observers for Actuator Fault Detection and Isolation

works, we use the approach based on such as the Luenberger observer and the sliding mode observer in order to introduce the diagnosis of nonlinear systems. The robustness of the proposed is tested through a physical example. The obtained results show that for non linear systems the performances of sliding mode observer observer is better than using a classic kind of observer. The synthesis of nonlinear will be used for actuator fault detection and isolation using residual generation. Finally, a comparison of observers performances will be interesting for judging the effectiveness of this approach. Keywordsnonlinear systems, Luenberger observer, Sliding mode observer, fault detection and isolation, residual generation.

Actuator Fault Detection and Isolation for Robot Manipulators with the Adaptive Observer

The fault detection and isolation (FDI) for industrial robot manipulators, subject to faults of actuator, is devised in this paper. An adaptive observer is designed to tackle the robustness problem for unknown parameters due to faults，based on a bank of state observers. By using an adaptive regulating algorithm, the observer is ensured to be stable and the estimated errors are guaranteed to converge. Experimental results are reported for a planar robot under gravity, considering partial failures of the motor torques.

FDI Based on High-gain Robust Sliding Mode Observers

This paper considers the problems of actuator and sensor fault detection and isolation for a class of uncertain linear systems. By introducing an augmented vector, an augmented system which contains a fault vector consisting of both the actuator and sensor faults of original system is constructed. An auxiliary output vector is proposed so that the observer matching condition for the new augmented system is satisfied. A high-order sliding mode observer is considered to obtain the exact estimates of the auxiliary outputs. Then, a robust sliding mode observer is developed as fault detection observer to detect actuator and sensor faults. Furthermore, multi-observers are used for the actuator and sensor fault isolation purpose. Finally, a numerical simulation example is given to show the effectiveness of the proposed methods.

Robust residual generator design for Takagi-Sugeno fuzzy bilinear system subject to unknown inputs

In this paper, a method to design a residual generator for Takagi-Sugeno fuzzy bilinear system subject to unknown inputs and unmeasurable disturbances is proposed. The aim of this approach is to detect and isolate actuator faults despite the presence of disturbances. Such residual generator is determined on the basis of Linear Matrix Inequalities (LMIs) conditions. An example illustrates the effectiveness of the designed residual generator with unknown inputs and unmeasurable disturbances.

Fault Detection and Isolation for Nonlinear Systems via HOSM Multiple-Observer

The fault detection and isolation problem is studied for a class of nonlinear systems. Under structural conditions, a bank of High-Order Sliding-Mode observers is proposed. The value of the equivalent output injection is used for detecting and isolating particular faults in the system. The proposed method provides finite-time isolation of actuator and plant faults. Simulation results support the proposed approach.

State-Space GMDH Neural Networks for Actuator Robust Fault Diagnosis

1 Abstract—Most fault diagnosis methods focus on the fault detection of the system or sensors and do not take into account the problem of the fault detection and isolation of the actuators, which are an important part of the contemporary industrial systems. To solve such a problem, the system outputs and inputs estimator based on a dynamic Group Method of Data Handling neural network in the state-space representation is proposed. In particular, the methodology of the adaptive thresholds calculation for system inputs and outputs is presented. The approach is based on the application of the Unscented Kalman Filter and Unknown Input Filter is presented. This result enables performing robust fault detection and isolation of the actuators. The final part of the paper presents an application study, which confirms the effectiveness of the proposed approach. The m ost desirable feature of the neural models applied to the FDI and FTC schemes is a small modeling uncertainty, which is defined as a mismatch between the model and the system being considered (3). Therefore, it is important to use an approach reducing the contribution of the structure errors and the parameter estimation inaccuracy of the neural model uncertainty. To solve such a challenging problem, a Group Method of Data Handling (GMDH) neural network (10), (12), (20), (13), (18) have been proposed. The concept of the GM DH approach relies on replacing the complex neural model by the set of the hierarchically connected partial models, which can be chosen with the application of appropriate selection methods. Besides the reduction of the neural m odel inaccuracy it is important to calculate the model uncertainty in the form of mathematical description allowing performing the robust fault diagnosis. In this paper, a new approach to the actuator and sensors fault detection and isolation is proposed. To solve such a challenging problem, the system outputs and inputs estimator based on the dynamic GMDH neural network is employed. In order to achieve this goal a new structure of the dynamic neuron in the state-space representation is proposed. This description enables to obtain constraints of the parameter estimates which warranty the stability of dynamic GMDH neural model. In order to obtain the constrained parameter estimates and the neural model uncertainty description the Unscented Kalman Filter (UKF) (7), (27), (28) was used. This knowledge enables to calculate the output adaptive threshold. Moreover, the methodology of estimation of the GMDH neural model inputs and the calculation of the input adaptive threshold with the application of the Unknown Input Filter (UIF) is proposed. The obtained input and output adaptive thresholds allow to perform the robust fault diagnosis and actuators isolation of the dynamic non-linear systems. The paper consists of six sections. After the introduction, presenting the subject of this paper, in Section II a concept of robust fault detection and isolation of sensors and actuators is presented. Section III presents the process of synthesis of GMDH neural model. In section IV, the estimation of neural model inputs is realized with the UIF. The experimental results of the robust fault diagnosis are shown in section V. Finally, section VI concludes the paper.

A hierarchical multiple-model approach for detection and isolation of robotic actuator faults

Modern robotic systems perform elaborate tasks in complicated environments and have close interactions with humans. Therefore fault detection and isolation (FDI) schemes must be carefully designed and implemented on robotic systems in order to guarantee safe and reliable operations. In this paper, we propose a hierarchical multiple-model FDI (HMM-FDI) scheme to detect and isolate actuator faults of robot manipulators. The proposed algorithm performs FDI in stages and refines the associated model set at each stage. Consequently only a small number of models are required to detect and isolate various types of unexpected actuator faults, including abrupt faults, incipient faults, and simultaneous faults. In addition, the computational load is alleviated due to the reduced-sized model set. The relation between the fault detection stage of the HMM-FDI scheme and the likelihood ratio test is explicitly revealed and theoretical upper bounds of the false alarm and missed detection probabilities are evaluated. Then we conduct experiments to demonstrate the ability of the HMM-FDI scheme in successful and immediate detection and isolation of actuator faults.

Fault detection and isolation in linear parameter‐varying descriptor systems via proportional integral observer

SUMMARYThe main contribution of this paper is the design of a polytopic unknown inputs proportional integral observer (UIPIO) for linear parameter‐varying (LPV) descriptor systems. This observer is used for actuator fault detection and isolation. The proposed method is based on the representation of the LPV descriptor systems in a polytopic form. Its parameters evolve in an hypercube domain. The designed polytopic UIPIO is also able to estimate both the states and the unknown inputs of the LPV descriptor system. Stability conditions of such observer are expressed in terms of linear matrix inequalities. An example illustrates the performances of such polytopic UIPIO. Copyright © 2011 John Wiley & Sons, Ltd.

Data‐based monitoring and reconfiguration of a distributed model predictive control system

SUMMARYIn this work, we develop a data‐based monitoring and reconfiguration system for a distributed model predictive control system in the presence of control actuator faults. Specifically, we first design fault detection filters and filter residuals, which are computed via exponentially weighted moving average, to effectively detect faults. Then, we propose a fault isolation approach that uses adaptive fault isolation time windows whose length depends on the rate of change of the fault residuals to quickly and accurately isolate actuator faults. Simultaneously, we estimate the magnitudes of the faults using a least‐squares method and based on the estimated fault values, we design appropriate control system reconfiguration (fault‐tolerant control) strategies to handle the actuator faults and maintain the closed‐loop system state within a desired operating region. A nonlinear chemical process example is used to demonstrate the approach. Copyright © 2011 John Wiley & Sons, Ltd.

On Using Distorted Sensors for Set-based Multi-scale Actuator Fault Diagnosis

AbstractThe off-line fault detection, isolation and identification of multiplicative actuator faults using sensors providing measurements affected by unknown nonlinear distortions and bounded noises are addressed. The only assumption about the unknown functions modeling the sensor distortions is their monotonicity. Bounded disturbances are also considered as unknown inputs of the system time-varying dynamics. Polytopic faulty parameter sets resulting from the search of robust output pairs and from zonotope computations are the basis for a decision scheme which makes use of multi-scale temporal windows. A numerical example modeling part of a distillation column illustrates the off-line diagnosis of three multiplicative actuator faults from a single low-cost noisy sensor with unknown nonlinear distortion and constant bias.

Aircraft Sensor and Actuator Fault Detection, Isolation, and Accommodation

The purpose of the paper is to present an approach to detect, isolate, and accommodate the aircraft sensor and actuator faults using unknown input observers (UIOs). Full-order observers, reduced-order observers, and UIOs are widely used in state estimations. After the estimation of states, fault detection can be provided by conducting residual analysis. Despite of the existence of unknown inputs, fault detection and isolation are implemented for a very large, four-engined, cargo jet aircraft model. Sensor accommodation is realized via switching under redundant sensor existence assumption. Actuator accommodation is provided by gain scheduling. Hence, if a fault occurs in an actuator corresponding to the control surfaces, the remainder (n−1) actuators are used to avoid hazardous flight. Sensor and actuator faults are detected by using residuals. Sensor faults are effective on the outputs, while actuator faults are effective on the state equations. Fault isolation is implemented by taking into account that each residual is sensitive to all of the other faults but one fault. Fault detection, isolation, and a recommended accommodation are shown through the simulations to be functional.

Hybrid fault detection and isolation strategy for non-linear systems in the presence of large environmental disturbances

The problem of designing and developing a hybrid fault detection and isolation (FDI) scheme for a non-linear system that is subject to large environmental disturbances is investigated. In the proposed FDI scheme, a hybrid architecture is introduced, which is composed of a bank of residual generators and a discrete-event system (DES) fault diagnoser. A novel set of residuals is generated so that a DES fault diagnoser will robustly detect and isolate actuator faults in the system by incorporating an appropriate combination of residuals and their sequential features. Necessary and sufficient conditions for the existence of a set of residuals that are used by the DES fault diagnoser are derived based on the non-linear geometric FDI approach. The proposed hybrid FDI scheme is then applied to actuator fault detection and isolation of an almost-lighter-than-air-vehicle. Simulation results presented demonstrate and validate the performance capabilities of the proposed hybrid FDI scheme.

A Hybrid Fault Detection and Isolation Strategy for a Network of Unmanned Vehicles in Presence of Large Environmental Disturbances

In this brief, the problem of designing and developing a hybrid fault detection and isolation (FDI) scheme for a network of unmanned vehicles (NUVs) that is subject to large environmental disturbances is investigated. The proposed FDI algorithm is a hybrid architecture that is composed of a bank of continuous-time residual generators and a discrete-event system (DES) fault diagnoser. A novel set of residuals is generated so that the DES fault diagnoser empowered by incorporating appropriate combinations of the residuals and their sequential features will robustly detect and isolate faults in the NUVs. Our proposed hybrid FDI algorithm is then applied to actuator fault detection and isolation in a network of quad-rotors. Simulation results demonstrate and validate the performance capabilities of our proposed hybrid FDI algorithm.

Sensor and Actuator Fault Isolation Using Parameter Interval based Method for Nonlinear Dynamic Systems

Fault isolation problem for sensor and actuator in nonlinear dynamic systems is studied. Parame- ter interval based fault isolation method has fast isolation speed and fits many kinds of nonlinear dynamic systems. This method is extended to the isolation of sensor fault and actuator fault. The example shows good performance.

A gain-varying UIO approach with adaptive threshold for FDI of nonlinear F16 systems

A discrete gain-varying unknown input observer (UIO) method is presented for actuator fault detection and isolation (FDI) problems in this paper. A novel residual scheme together with a moving horizon threshold is proposed. This design methodology is applied to a nonlinear F16 system with polynomial aerodynamics coefficient expressions, where the coefficient expressions for the F16 system and UIOs may be slightly different. The simulation results illustrate that a satisfactory FDI performance can be achieved even when the F16 system is under the environment of model uncertainties, exogenous noise and measurement errors.