Neural network-based event-triggered fault detection of discrete-time nonlinear uncertain systems

This article addresses fault detection, estimation, and compensation problem in a class of disturbance driven time delay nonlinear systems. The proposed approach relies on an iterative learning observer (ILO) for fault detection, estimation, and compensation. When there are no faults in the system, the ILO supplies accurate disturbance estimation to the control system where the effect of disturbances on estimation error dynamics is attenuated. At the same time, the proposed ILO can detect sudden changes in the nonlinear system due to faults. As a result upon the detection of a fault, the same ILO is used to excite an adaptive control law in order to offset the effect of faults on the system. Further, the proposed ILO-based adaptive fault compensation strategy can handle multiple faults. The overall fault detection and compensation strategy proposed in the paper is finally demonstrated in simulation on an automotive engine example to illustrate the effectiveness of this approach. Copyright © 2005 John Wiley & Sons, Ltd.

Fast and selective DC fault detection and location schemes are needed for fault protection of DC Grids. In this paper, the frequency domain characteristics of DC voltage besides the current limiting reactors are analyzed and used to put forward a new fault detection method for DC grids. Hilbert-Huang transform is applied on the DC voltage waves to extract the transient frequency and transient amplitude of the DC voltage. The influence of fault type, fault distance and fault resistance of DC faults and the noise on the frequency domain transient characteristics on voltage waves are then studied. Support vector machine learns the mapping between the transient frequency and transient amplitude of the DC voltage and desired outputs, fault type and fault location through training process. Then, a fast and accurate fault detection scheme based on frequency-domain characteristics of DC voltage is proposed by SVM for DC fault detection and location in MTDC systems. This fault detection scheme requires no telecommunication, has anti-interference ability and good sensitivity and selectivity for detection of long-distance fault and high resistance grounding fault. Based on the four-terminal HVDC system built by PSCAD/EMTDC, simulations under different fault conditions are carried out to verify the effectiveness of the proposed DC fault detection scheme.

In this paper, a novel model-based fault detection (FD) and prediction scheme is developed for a class of Takagi-Sugeno (T-S) fuzzy systems. Unlike other FD schemes, in the proposed design, an FD observer with online fault learning capability is utilized to generate a residual which is obtained by comparing the system output with respect to the observer output. A fault is declared active if the generated residual exceeds an a priori chosen threshold. Subsequently, the fault magnitude is estimated online by using a suitable parameter update law. Upon detection, the online estimate of the fault magnitude is used in a mathematical equation to determine time-to-failure (TTF) or remaining useful life. TTF is determined by projecting the estimated fault magnitude at the current time instant against a failure threshold. Note that the previously reported FD schemes could neither estimate the magnitude of a growing fault in real time nor were they able to predict the remaining useful life of the fuzzy system. In this paper, the stability of the proposed FD and prognostics scheme is verified using the Lyapunov theory. Finally, two different simulation case studies are considered to verify the theoretical conjectures presented in this paper.

This paper focuses on the fault detection (FD) observer design issue for a category of nonlinear discrete-time Takagi-Sugeno (T-S) fuzzy systems, in which measurement outliers are considered. A new FD observer is constructed to decrease the affect on anomalous measurement signals which exist in the considered system based on the scheme of saturation function. Then, sufficient conditions are provided to ensure the error system is stable and satisfies performance requirements. Measurement outliers may exist in the system and produce the false alarm, contrasted to the present works, the observer proposed in the article can avert the false alarm. At last, an example is provided to identify the superiority of the technique proposed in this paper.

ABSTRACTThis article studies the design of fault detection (FD) observers for nonlinear steering control (NSC) systems of unmanned surface vehicles (USVs) with steering machine faults. The considered NSC systems of USVs are characterized by discrete‐time T‐S fuzzy models with immeasurable states. First, a new fuzzy FD observer is constructed to handle the challenge caused by the introduction of membership functions (MFs) that are different from the ones in the system, in which the MFs in the FD are designed by scaling and biasing those of the T‐S model. Different from the existing uncertainty restrictions between the MFs in the systems and those in the observers, the deterministic restrictions are constructed, and the MFs of the observer are directly constructed according to the ones of the system without calculation. Then, by employing Lyapunov function (LF), it is proved that the constructed FD observers can ensure the stability and desired performance of the systems. Finally, an NSC system model of USVs is given to verify the effectiveness of the presented technical solution.

For the fault detection of mechatronic control system, based on observer a feedforward compensation approach was presented. The model of a mechatronic control system was investigated and divided into two loops for disturbance compensation and fault detection. The external disturbance of the vehicle was analyzed, and the relationship between the support shaft friction moment as main disturbance element and the attitude effect of the vehicle was built by the way of data curve fitting. The adjustment coefficients were proposed respectively in nine kinds of running section of the vehicle, because disturbance was very complicated and time-variant in different section. Fault detection observer was designed to generate residuals in the inner loop, and the whole residual was calculated. The compensation was deduced in accordance with responses produced by the disturbance, so the disturbance could be compensated. Band filter was proposed for other noises. Finally, an example was presented to illustrate the proposed approach. The simulation results showed that the disturbance elements can be compensated rapidly.

formula omitted] fault detection observer design for nonlinear conformable fractional-order systems

A novel distributed fault detection strategy for a class of nonlinear stochastic systems is presented. Different from the existing design procedures for fault detection, a novel fault detection observer, which consists of a nonlinear fault detection filter and a consensus filter, is proposed to detect the nonlinear stochastic systems faults. Firstly, the outputs of the nonlinear stochastic systems act as inputs of a consensus filter. Secondly, a nonlinear fault detection filter is constructed to provide estimation of unmeasurable system states and residual signals using outputs of the consensus filter. Stability analysis of the consensus filter is rigorously investigated. Meanwhile, the design procedures of the nonlinear fault detection filter are given in terms of linear matrix inequalities (LMIs). Taking the influence of the system stochastic noises into consideration, an outstanding feature of the proposed scheme is that false alarms can be reduced dramatically. Finally, simulation results are provided to show the feasibility and effectiveness of the proposed fault detection approach.

Robust Fault Detection Using Index of Kullback Discrimination Information

This paper examines a robust fault detection scheme that can be used to detect faulty sensors of jet engines. The fault detection scheme has to be insensitive to disturbances while being highly sensitive to sensor faults (robust). The paper presents a complete description of a robust fault detection approach based on eigenstructure assignment, both in continuousand discrete-time domains. By assigning the left (or right) eigenvectors of the observer orthogonal (or parallel) to the disturbance directions, the robust (disturbance decoupling) fault detection is achieved. The approach has been applied to a realistic jet engine simulation system. The system is a 17th-order system, and a reduced-order model is used to approximate the system. Modeling errors are considered as disturbances acting on the fault detection scheme. A particularly novel feature of the work is the development and use of a new method (new in this context) for estimating disturbance direction. The robust fault detection scheme design uses this estimated direction as that of the direction of unknown inputs (disturbances). Simulation results show that the scheme can detect soft or incipient faults efficiently.

Discriminative common vector in sufficient data Case: A fault detection and classification application on photovoltaic arrays

This paper addresses a zonotopic fault detection (FD) observer for discrete-time descriptor systems subject to additive actuator faults. The considered descriptor systems are also perturbed by unknown-but-bounded uncertainties including state disturbances and measurement noise. Under a set-based framework, the effects of uncertainties and faults are separated into two state zonotopes. Based on this decomposition, the FD observer gain is designed to be robust against uncertainties and meanwhile sensitive to faults based on a index in a finite-frequency domain. Then, two linear matrix inequality (LMI) conditions are obtained to design an observer that achieves robustness and fault sensitivity at the same time. The generalised KYP lemma is applied to address the fault sensitivity condition. The optimal FD observer gain can be obtained via an offline design procedure. Finally, the proposed fault detection method is applied to a chemical mixing system and the effectiveness is shown.

For discrete-time linear parameter varying systems with distributed time delays, the problem of fault detection under event triggering mechanism is studied. Based on the parameter dependent Lyapunov function method, the sufficient conditions for the system to be asymptotically stable and meet the H∞ performance criterion are given. The parameter matrix of fault detection filter is obtained by solving LMI technology. In addition, the event triggering strategy is introduced to save network resources.

The authors describe a robust scheme for use in the role of fault detection in sensors and actuators of process systems. The fault detection scheme is designed to be insensitive to disturbances, while being sensitive to faults. The authors present the robust fault detection approach based on eigenstructure assignment in the discrete-time domain. By assigning the left eigenvectors of the observer orthogonal to the disturbance, robust (disturbance decoupling) fault detection is achievable. The approach has been applied to an 11th-order nuclear reactor system. A reduced-order model is used to approximate this system and modeling errors are considered as disturbances acting on the reduced-order model contained in the observer. The work involves the development and use of a method for estimating the disturbance direction. The robust fault detection scheme design uses this direction as that of the direction of unknown input acting on a fourth-order model. Simulation results show that the scheme can detect soft or incipient faults efficiently. >

As explained in the literature, it is very hard to measure premise variables of a Takagi-Sugeno (TS) fuzzy system. Therefore, in this paper, a fault detection and prediction (FDP) scheme is designed for a class of TS fuzzy systems with immeasurable (unknown) premise variables and external disturbances. A fault detection (FD) observer is designed to approximate the system output and the premise variables. Subsequently, a FD residual is generated by comparing the observer output with respect to the system output. The FD residual is evaluated to detect any faults in the system. Further, time-to-failure (TTF) of the TS fuzzy system is obtained by using a mathematical equation. Note the parameter update law and TTF scheme utilize the approximated premise variables since they are not measurable. Stability of the fault detection and TTF prediction results are verified using Lyapunov theory. Finally, a simulation study using a truck-trailer system is presented to verify the theoretical claims.