This paper presents a robust fault diagnosis scheme for abrupt and incipient faults in nonlinear uncertain dynamic systems. A detection and approximation estimator is used for online health monitoring. Once a fault is detected, a bank of isolation estimators is activated for the purpose of fault isolation. A key design issue of the proposed fault isolation scheme is the adaptive residual threshold associated with each isolation estimator. A fault that has occurred can be isolated if the residual associated with the matched isolation estimator remains below its corresponding adaptive threshold, whereas at least one of the components of the residuals associated with all the other estimators exceeds its threshold at some finite time. Based on the class of nonlinear uncertain systems under consideration, an isolation decision scheme is devised and fault isolability conditions are given, characterizing the class of nonlinear faults that are isolable by the robust fault isolation scheme. The nonconservativeness of the fault isolability conditions is illustrated by deriving a subclass of nonlinear systems and of faults for which these conditions are also necessary for fault isolability. Moreover, the analysis of the proposed fault isolation scheme provides rigorous analytical results concerning the fault isolation time. Two simulation examples are given to show the effectiveness of the fault diagnosis methodology.

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A robust detection and isolation scheme for abrupt and incipient faults in nonlinear systems

This paper presents a unified methodology for detecting, isolating and accommodating faults in a class of nonlinear dynamic systems. A fault diagnosis component is used for fault detection and isolation. On the basis of the fault information obtained by the fault-diagnosis procedure, a fault-tolerant control component is designed to compensate for the effects of faults. In the presence of a fault, a nominal controller guarantees the boundedness of all the system signals until the fault is detected. Then the controller is reconfigured after fault detection and also after fault isolation, to improve the control performance by using the fault information generated by the diagnosis module. Under certain assumptions, the stability of the closed-loop system is rigorously investigated. It is shown that the system signals remain bounded and the output tracking error converges to a neighborhood of zero.

/pdf/adaptive-fault-tolerant-control-of-nonlinear-uncertain-f9h4q1dufi.pdf

Adaptive fault-tolerant control of nonlinear uncertain systems: an information-based diagnostic approach

Multivariable adaptive control

This paper deals with the problem of designing a distributed fault detection and isolation methodology for nonlinear uncertain large-scale discrete-time dynamical systems. As a divide et impera approach is used to overcome the scalability issues of a centralized implementation, the large scale system being monitored is modelled as the interconnection of several subsystems. The subsystems are allowed to overlap, thus sharing some state components. For each subsystem, a Local Fault Diagnoser is designed, based on the measured local state of the subsystem as well as the transmitted variables of neighboring states that define the subsystem interconnections. The local diagnostic decision is made on the basis of the knowledge of the local subsystem dynamic model and of an adaptive approximation of the interconnection with neighboring subsystems. The use of a specially-designed consensus-based estimator is proposed in order to improve the detectability and isolability of faults affecting variables shared among overlapping subsystems. Theoretical results are provided to characterize the detection and isolation capabilities of the proposed distributed scheme. Finally, simulation results are reported showing the effectiveness of the proposed methodology.

/pdf/distributed-fault-detection-and-isolation-of-large-scale-26ie7phcez.pdf

Distributed Fault Detection and Isolation of Large-Scale Discrete-Time Nonlinear Systems: An Adaptive Approximation Approach

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.

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

The learning approach to fault diagnosis provides a methodology for designing monitoring architectures which can be used for detection, identification and accommodation of failures in dynamical systems. This paper considers the issues of detectability conditions and detection time in a nonlinear fault diagnosis scheme based on the learning approach. First, conditions are derived to characterize the range of detectable faults. Then, nonconservative upper bounds are computed for the detection time of incipient and abrupt faults. It is shown that the detection time bound decreases monotonically as the values of certain design parameters increase. The theoretical results are illustrated by a simulation example of a second-order system.

Learning approach to nonlinear fault diagnosis: detectability analysis

The paper presents a robust fault diagnosis scheme for detecting and approximating state and output faults occurring in a class of nonlinear multiinput-multioutput dynamical systems. Changes in the system dynamics due to a fault are modeled as nonlinear functions of the control input and measured output variables. Both state and output faults can be modeled as slowly developing (incipient) or abrupt, with each component of the state/output fault vector being represented by a separate time profile. The robust fault diagnosis scheme utilizes on-line approximators and adaptive nonlinear filtering techniques to obtain estimates of the fault functions. Robustness with respect to modeling uncertainties, fault sensitivity and stability properties of the learning scheme are rigorously derived and the theoretical results are illustrated by a simulation example of a fourth-order satellite model.

Automated fault diagnosis in nonlinear multivariable systems using a learning methodology

The paper presents a robust fault diagnosis scheme for detecting and approximating state and sensor faults occurring in a class of nonlinear multi-input multi-output systems. The changes in the system dynamics due to a fault are modeled as nonlinear functions of the control input and measured output variables. Both state and sensor faults can be modeled as slowly developing (incipient) or abrupt, with each component of the state/sensor fault vector being represented by a separate time profile. The robust fault diagnosis scheme utilizes online approximators and adaptive nonlinear filtering techniques to obtain estimates of the fault functions. Robustness, fault sensitivity and stability conditions of the learning scheme are rigorously derived.

Robust fault diagnosis of state and sensor faults in nonlinear multivariable systems

Fault detection, diagnosis, and accommodation play an important role in the operation of autonomous robotic systems. Incipient faults in the actuators, sensors or in the dynamics of a robotic manipulator can lead to deterioration in performance and unsafe operating conditions. This paper presents a robust nonlinear fault diagnosis scheme for detecting and approximating faults occurring in a class of nonlinear MIMO systems. The proposed approach utilizes online approximators and adaptive nonlinear filtering techniques to obtain estimates of the fault functions. Performance of the nonlinear fault diagnosis scheme is analyzed by investigating its robustness and fault sensitivity properties in the presence of slowly developing or abrupt faults. A simulation example is presented to illustrate the ability of the fault diagnosis scheme to detect faults in a single link robotic manipulator with a revolute elastic joint.

Robust nonlinear fault diagnosis: application to robotic systems

The learning approach to fault diagnosis provides a methodology for designing monitoring architectures which can be used for detection, identification and accommodation of failures in dynamical systems. This paper considers the issues of detectability conditions and detection time in a nonlinear fault diagnosis scheme based on the learning approach. First, conditions are derived to characterize the range of detectable faults. Then, non-conservative upper bounds are computed for the detection time of incipient and abrupt faults. Finally, it is shown that the detection time decreases monotonically as the values of certain design parameters increase.

A.B. Trunov

Papers

Learning approach to nonlinear fault diagnosis: detectability analysis

Automated fault diagnosis in nonlinear multivariable systems using a learning methodology

Robust fault diagnosis of state and sensor faults in nonlinear multivariable systems

Robust nonlinear fault diagnosis: application to robotic systems

Detectability performance properties of learning-based nonlinear fault diagnosis