Actuator Fault Isolation Research Articles

Detection, isolation and identification of multiple actuator and sensor faults in nonlinear dynamic systems: Application to a waste water treatment process

The goal in many fault detection and isolation schemes is to increase the isolation and identification speed. This paper, presents a new approach of a nonlinear model based adaptive observer method, for detection, isolation and identification of actuator and sensor faults. Firstly, we will design a new method for the actuator fault problem where, after the fault detection and before the fault isolation, we will try to estimate the output of the instrument. The method is based on the formation of nonlinear observer banks where each bank isolates each actuator fault. Secondly, for the sensor problem we will reformulate the system by introducing a new state variable, so that an augmented system can be constructed to treat sensor faults as actuator faults. A method based on the design of an adaptive observers’ bank will be used for the fault treatment. These approaches use the system model and the outputs of the adaptive observers to generate residues. Residuals are defined in such way to isolate the faulty instrument after detecting the fault occurrence. The advantages of these methods are that we can treat not only single actuator and sensor faults but also multiple faults, more over the isolation time has been decreased. In this study, we consider that only abrupt faults in the system can occur. The validity of the methods will be tested firstly in simulation by using a nonlinear model of waste water treatment process with and without measurement noise and secondly with the same nonlinear model but by using this time real data.

Detection, isolation and handling of actuator faults in distributed model predictive control systems

In this work, we focus on monitoring and reconfiguration of distributed model predictive control systems applied to general nonlinear processes in the presence of control actuator faults. Specifically, we consider nonlinear process systems controlled with a distributed control scheme in which two Lyapunov-based model predictive controllers manipulate two different sets of control inputs and coordinate their actions to achieve the desired closed-loop stability and performance specifications. To deal with control actuator faults which may reduce the ability of the distributed control system to stabilize the process, a model-based fault detection and isolation and fault-tolerant control system which detects and isolates actuator faults and determines how to reconfigure the distributed control system to handle the actuator faults while maintaining closed-loop stability is designed. A detailed mathematical analysis is carried out to determine precise conditions for the stabilizability of the fault detection and isolation and fault-tolerant control system. A chemical process example, consisting of two continuous stirred tank reactors and a flash tank separator with a recycle stream and involving stabilization of an unstable steady-state, is used to demonstrate the approach.

Fuzzy Nonlinear Unknown Input Observer Design with Fault Diagnosis Applications

An approach for nonlinear unknown input observer (NUIO) design is proposed for a class of nonlinear systems representable by a Takagi—Sugeno (TS) fuzzy system. The proposed NUIO design for TS fuzzy systems is carried out for two cases: (1) the premise variables do not depend on the unmeasured state variables; and (2) the premise variables depend on the unmeasured state variables. Sufficient conditions for the existence of NUIOs are derived, and a linear matrix inequality (LMI)-based design strategy is presented for NUIO design purposes. The proposed NUIO design approach is then applied to solve actuator fault detection and isolation problems for nonlinear systems described by TS fuzzy systems. To this end, a system structure with two groups of inputs where one group of inputs is treated as unknown inputs is developed. Based on the system structure, a bank of NUIOs are then designed using the developed NUIO design approach in order to investigate the following fault diagnosis problems. (1) How can the NUIOs be used for detecting faults? (2) Under what conditions is it possible to isolate single and/or multiple faults? (3) What is the maximum number of faults that can be isolated simultaneously? (4) How can multiple fault isolation be achieved? In this article we present a NUIO-based fault-detection scheme for problem (1), give sufficient conditions for problem (2), determine the maximum number of faults that can be isolated for problem (3), and propose a fault-diagnosis scheme using a bank of NUIOs to solve problem (4). As an illustrative example, Lorenz’s chaotic system with multi-inputs is chosen to show the effect of the designed NUIOs and the proposed fault detection and isolation scheme. Simulation results show that accurate state estimation is achieved and actuator faults can be detected and isolated successfully.

In-flight Aircraft Monitoring using a Frequency Domain Parameter Identification

AbstractTo improve A/C safety and autonomy, the early detection of unexpected events is of primary importance for handling the Flight Control System and providing enhanced cueing of the envelope limits to the pilot. The approach proposed for achieving this on-line monitoring relies on a real time parameter identification technique. Owing to its robustness to measurement noises, an output error method in the frequency domain is developed to estimate the stability/control derivatives of a linearized state-space model. The process includes also pre and post-processing stages, to prevent from and to filter out inaccurate estimations. Evaluation results involve actuator Fault Detection, Isolation and Estimation as well as icing detection, and are compared to a time domain batch algorithm used as a reference method.

Detection and isolation of actuator faults for a class of non-linear systems with application to electric motors drives

In this study, the diagnosis of actuator faults is addressed for a class of non-linear systems and applied to electric motors drives. The residuals are synthesised using a model-based strategy by applying a differential geometry perspective. According to the dynamic structure of the systems considered in this work, it is deduced that the observability co-distribution built for this class of systems can be independent of the state (constant) under certain conditions, so that the resulting coordinates transformations in the state and output spaces are linear mappings. Consequently, the dynamic structure of the resulting subsystems is not altered after the coordinates transformation, and departing from these subsystems, dedicated observers can be designed for residual generation. One important property of the analysis presented in the paper is that the studied dynamical structure can be fitted to several electrical motors. Hence simulation results are illustrated for a three-phase induction motor drive application that verify the ideas presented in this work.

Subspace method aided data-driven design of fault detection and isolation systems

This paper deals with data-driven design of fault detection and isolation (FDI) systems. The basic idea is to identify a primary form of residual generators, instead of the process model, directly from test data and, based on it, to design advanced FDI systems. The proposed method can be applied for the parity space and observer based detection and isolation of sensor and actuator faults as well as the construction of soft-sensors. The application of the proposed method is illustrated by a simulation study on the Tennessee Eastman process.

Robust actuator fault isolation and management in constrained uncertain parabolic PDE systems

This paper presents a methodology for the design of an integrated robust fault detection and isolation (FDI) and fault-tolerant control (FTC) architecture for distributed parameter systems modeled by nonlinear parabolic Partial Differential Equations (PDEs) with time-varying uncertain variables, actuator constraints and faults. The design is based on an approximate finite-dimensional system that captures the dominant dynamics of the PDE system. Initially, an invertible coordinate transformation–obtained through judicious actuator placement–is used to transform the approximate system into a form where the evolution of each state is excited directly by only one actuator. For each state, a robustly stabilizing bounded feedback controller that achieves an arbitrary degree of asymptotic attenuation of the effect of uncertainty is then synthesized and its constrained stability region is explicitly characterized in terms of the constraints, the actuator locations and the size of the uncertainty. A key idea in the controller synthesis is to shape the fault-free closed-loop response of each state in a prescribed fashion that facilitates the derivation of (1) dedicated FDI residuals and thresholds for each actuator, and (2) an explicit characterization of the state-space regions where FDI can be performed under uncertainty and constraints. A switching law is then derived to orchestrate actuator reconfiguration in a way that preserves robust closed-loop stability following FDI. Precise FDI rules and control reconfiguration criteria that account for model reduction errors are derived for the implementation of the FDI-FTC structure on the distributed parameter system. Finally, the results are demonstrated using a tubular reactor example.

Actuator Fault Detection and Isolation for a Network of Unmanned Vehicles

This technical note investigates development, design and analysis of actuator fault detection and isolation (FDI) filters for a network of unmanned vehicles. It is shown that actuator fault signatures in a network of unmanned vehicles are dependent and the network can be considered as an over-actuated system. An isolability index mu is defined for a family of fault signatures and a new structured residual set is developed that is selectively capable of properly detecting and isolating mu multiple faults in linear systems with dependent fault signatures, such as over-actuated systems. Our proposed algorithm is then applied to the actuator FDI problem in a network of unmanned vehicles configured according to centralized, decentralized and semi-decentralized architectures. A comparative analysis in terms of the capabilities and limitations of these architectures is performed. Simulation results presented for the formation flight of multiple satellites demonstrate the effectiveness of our proposed FDI algorithm.

A unified framework for detection, isolation and compensation of actuator faults in uncertain particulate processes

This paper presents a methodology for the robust detection, isolation and compensation of control actuator faults in particulate processes described by population balance models with control constraints and time-varying uncertain variables. The main idea is to shape the fault-free closed-loop process response via robust feedback control in a way that enables the derivation of performance-based fault detection and isolation (FDI) rules that are less sensitive to the uncertainty. Initially, an approximate finite-dimensional system that captures the dominant process dynamics is derived and decomposed into interconnected subsystems with each subsystem directly influenced by a single manipulated input. The decomposition is facilitated by the specific structure of the process input operator. A robustly stabilizing bounded feedback controller is then designed for each subsystem to enforce an arbitrary degree of asymptotic attenuation of the effect of the uncertainty in the absence of faults. The synthesis leads to (1) an explicit characterization of the fault-free behavior of each subsystem in terms of a time-varying bound on an appropriate Lyapunov function and (2) an explicit characterization of the robust stability region in terms of the control constraints and the size of the uncertainty. Using the fault-free Lyapunov dissipation bounds as thresholds for FDI in each subsystem, the detection and isolation of faults in a given actuator is accomplished by monitoring the evolution of the system within the stability region and declaring a fault if the threshold is breached. The thresholds are linked to the achievable degree of asymptotic uncertainty attenuation and can therefore be properly tuned by proper tuning of the controllers, thus making the FDI criteria less sensitive to the uncertainty. The robust FDI scheme is integrated with a robust stability-based controller reconfiguration strategy that preserves closed-loop stability following FDI. Finally, the implementation of the fault-tolerant control architecture on the particulate process is discussed and the proposed methodology is applied to the problem of robust fault-tolerant control of a continuous crystallizer with a fines trap.

Nonlinear Geometric Approach–Based Filtering Methods for Aircraft Actuator FDI

The paper addresses the problem of the design and the application of nonlinear filtering techniques for the detection and isolation of aircraft model actuator faults. In particular, the main contribution of this work is to combine the particle filtering technique with the nonlinear geometric approach for disturbance decoupled residual functions. By using aircraft simulator data in a flight condition characterised by tight–coupled longitudinal and lateral dynamics, the proposed fault diagnosis schemes are validated. Finally, in order to analyse and compare the performance capabilities of the developed fault diagnosis strategies, extensive simulations are performed in the presence of turbulence, measurement noise and modelling errors.

State Estimation for Polytopic LPV Descriptor Systems: Application to Fault Diagnosis

In this paper, the design of a polytopic Unknown Input Observer (UIO) for polytopic Linear Varying Parameter (LPV) descriptor systems is investigated. The method presented is based on the representation of affine LPV descriptor systems where parameters evolve in a hypercube domain. The considered polytopic UIO is able to estimate the states of the system in spite of the presence of unknown inputs. This approach is also applied to actuator fault detection, isolation and estimation. Stability conditions of such observer are expressed in terms of Linear Matrix Inequalities (LMI). An example illustrates the effectiveness and performances of such polytopic LPV observer.

Monitoring actuator failures for a large transport aircraft – the nominal case

We consider the problem of designing residual generators with least dynamical orders to solve actuator fault detection and isolation problems for a generic large transport aircraft. The main result of our analysis is the proof of feasibility of the complete isolation of all primary actuator/surface faults in the nominal case by using a minimal number of additional surface angle sensors. The analysis of the nominal case provides residual filter specifications (reference models) to be employed for robust synthesis of residual generators.

A Recurrent Neural-Network-Based Sensor and Actuator Fault Detection and Isolation for Nonlinear Systems With Application to the Satellite's Attitude Control Subsystem

This paper presents a robust fault detection and isolation (FDI) scheme for a general class of nonlinear systems using a neural-network-based observer strategy. Both actuator and sensor faults are considered. The nonlinear system considered is subject to both state and sensor uncertainties and disturbances. Two recurrent neural networks are employed to identify general unknown actuator and sensor faults, respectively. The neural network weights are updated according to a modified backpropagation scheme. Unlike many previous methods developed in the literature, our proposed FDI scheme does not rely on availability of full state measurements. The stability of the overall FDI scheme in presence of unknown sensor and actuator faults as well as plant and sensor noise and uncertainties is shown by using the Lyapunov's direct method. The stability analysis developed requires no restrictive assumptions on the system and/or the FDI algorithm. Magnetorquer-type actuators and magnetometer-type sensors that are commonly employed in the attitude control subsystem (ACS) of low-Earth orbit (LEO) satellites for attitude determination and control are considered in our case studies. The effectiveness and capabilities of our proposed fault diagnosis strategy are demonstrated and validated through extensive simulation studies.

Static and Dynamic Data Reconciliation for an Irrigation Canal

This paper deals with the problem of fault detection and isolation in irrigation canals. We develop a method which combines static and dynamic data reconciliation for the validation of measurements, detection and isolation of sensors and actuator faults and reconstruction of missing data. Static data reconciliation uses static models at a regulation gate to validate measurements and detect sensor and actuator faults. It also enabled us to detect a drift in the stage discharge rating curve. The dynamic data reconciliation uses additional measurements and a dynamic model of the canal in order to validate measurements and detect faults and withdrawals. The combination of the two methods allowed us to distinguish between withdrawals and faults. Both methods are evaluated on measurements from a real irrigation canal located in the South of France.

A Robust Fault Detection and Isolation Method in Load Frequency Control Loops

In this paper, a robust sensor and actuator fault detection and isolation (FDI) method based on unknown input observers (UIO) is adopted and applied to the load frequency control loops of interconnected power systems. Changes in load demand are regarded as the unknown disturbances in the system and the designed UIOs and thus the proposed method are robust to these disturbances. Using selected different sets of measured variables in the UIO design, simulations are performed for the dynamical model of a power control system composed of two areas. As we distinguish the cases for successful and unsuccessful sensor and controller FDI, it is shown that the proposed scheme is able to detect and isolate sensor and controller faults for proper selections of measured variables. By using residuals generated by the UIOs, the designed ldquofault detection and isolation logicrdquo system shows the operator which sensor or controller is faulty. Hence the faulty sensor or controller can be replaced by a healthy one for a more reliable operation.

Actuator fault detection and isolation of nonlinear systems by robust fuzzy observers

Actuator fault detection and isolation of nonlinear systems by robust fuzzy observers

Robust sliding mode observer-based actuator fault detection and isolation for a class of nonlinear systems

In this article, an actuator fault detection and isolation scheme for a class of nonlinear systems with uncertainty is considered. The uncertainty is allowed to have a nonlinear bound which is a general function of the state variables. A sliding mode observer is first established based on a constrained Lyapunov equation. Then, the equivalent output error injection is employed to reconstruct the fault signal using the characteristics of the sliding mode observer and the structure of the uncertainty. The reconstructed signal can approximate the system fault signal to any accuracy even in the presence of a class of uncertainty. Finally, a simulation study on a nonlinear aircraft system is presented to show the effectiveness of the scheme.

Fuzzy observer for fault detection and reconstruction of unknown input fuzzy models

This paper addresses the design of a fuzzy observer for unknown input Takagi-Sugeno (T?S) fuzzy model. The main contribution of this paper is the development of a robust fuzzy observer in the presence of disturbances. Based on Lyapunov function, it is shown how to determine the observers gains in Linear Matrix Inequalities (LMI) terms. The proposed observer structure allows to estimate simultaneously and systematically the unknown inputs and state variables. The designed T?S observer is used for detection and reconstruction of faults which can affect a non-linear model and can be applied directly for fault detection and isolation of actuator faults. The validity of the proposed methodology is illustrated by estimating the state and faults of an automatic steering vehicle.

Actuator Fault Diagnosis Using High-Order Sliding Mode Differentiator (HOSMD) and Its Application to a Laboratory 3D Crane

Abstract Actuator fault diagnosis problem is studied for a class of nonlinear systems with relative degrees from the inputs to the outputs higher than one. This type of nonlinear systems include many mechanical systems, and their actuator fault diagnosis problem can be very challenging because of nonlinearities and high relative degrees. In this paper, in order to solve the actuator fault diagnosis problem for the considered nonlinear systems, 2nd-order and 3rd-order sliding mode differentiators are used and actuator fault diagnosis schemes are proposed to achieve fault detection and isolation. Computer simulations are carried out to compare the efficacy of the proposed fault diagnosis schemes on a laboratory 3D Crane model with noisy measurements. The fault diagnosis schemes are further tested through experiments on a laboratory 3D Crane in terms of actuator fault detection and isolation.

Observer-based strategies for actuator fault detection, isolation and estimation for certain class of uncertain nonlinear systems

Two observer-based actuator fault isolation schemes for a class of uncertain nonlinear systems have been presented. To deal with a broader class of uncertain nonlinearities than previously considered, novel diagnostic observers are proposed, which combine two nonlinear observer design strategies, namely Thau's observer with sliding-mode observer concepts. The proposed observers are primarily designed for actuator fault diagnostic purposes. The nonlinearities that can be attacked may include both Lipschitz nonlinearities and those uncertain nonlinearities that are not Lipschitz but satisfy certain matching conditions. Design of the proposed observer boils down to solving a set of linear matrix inequalities (LMIs), which can easily be accomplished using the Matlab's LMI toolbox. Using the proposed observers, two actuator fault isolation schemes are designed. Unlike the existing techniques, using m observers (where m is the number of actuators) in the first approach, and only one observer in the second approach, the proposed schemes can isolate any number of actuator faults occurring at the same time. In addition, both the proposed schemes are capable of estimating the shape of the faults which is useful for fault accommodation purposes. A numerical example is provided to show the effectiveness of the proposed model-based actuator fault isolation strategies. The simulation results confirm that the two proposed techniques are effective in dealing with robust actuator fault detection, isolation and estimation in the studied class of nonlinear systems.