Fault Detection For Nonlinear Systems Research Articles

Fault detection based on a residual intervals multigenerator for nonlinear systems

This paper is devoted to sensor fault detection for nonlinear systems using an interval multiobserver based on uncoupled multimodel. This proposed interval multiobserver is used to detect the faults through residual intervals multigenerator. The [Formula: see text] performance technique is introduced to improve the accuracy of the sensor fault detection. The stability analysis of the fault detection system is carried out using Lyapunov approach under LMI(s) formulation. A numerical simulation example is used to highlight the efficiency of the proposed residual intervals multigenerator and the robustness of the fault detection strategy.

A Hybrid Design of Fault Detection for Nonlinear Systems Based on Dynamic Optimization.

To ensure the safety of an automation system, fault detection (FD) has become an active research topic. With the development of artificial intelligence, model-free FD strategies have been widely investigated over the past 20 years. In this work, a hybrid FD design approach that combines data-driven and model-based is developed for nonlinear dynamic systems whose information is not known beforehand. With the aid of a Takagi-Sugeno (T-S) fuzzy model, the nonlinear system can be identified through a group of least-squares-based optimization. The associated modeling errors are taken into account when designing residual generators. In addition, statistical learning is adopted to obtain an upper bound of modeling errors, based on which an optimization problem is formulated to determine a reliable FD threshold. In the online FD decision, an event-triggered strategy is also involved in saving computational costs and network resources. The effectiveness and feasibility of the proposed hybrid FD method are illustrated through two simulation studies on nonlinear systems.

Fault detection combining adaptive degrees of freedom χ2-statistics and interval approach for nonlinear systems

An enlargement of the Adaptive degrees of freedom χ2-statistics (ADFC) method to fault detection for nonlinear systems with mixed uncertainties (stochastic and bounded uncertainties) is presented in this paper. The ADFC approach, primarily developped for fault detection in case of linear systems, is then combined with the Reinforced likelihood box particle filter (RLBPF). A residual generator is used, followed by the adaptive amplifier coefficient (a.a.c.) concept in the decision making stage. Then, the proposed approach is applied to a nonlinear Magneto-Rheological damper model to illustrate the efficiency of the method.

Sliding mode observer-based fault detection for helicopter system

ABSTRACT Fault detection of non-linear systems is of great importance in control systems reliability. Undetected faults could lead to irreparable damage. This paper deals with fault diagnosis of helicopter system in the presence of uncertainties and disturbances. To deal with sensor, actuator and component faults, the observer-based diagnosis scheme which employs sliding mode observer is designed. Faults are modelled as an additive and multiplicative fault which is introduced as an abrupt and intermittent fault into the system. Observer inequality constraints and gain matrices are solved using a Lyapunov-based approach. The results display the effectiveness of the designed observer and the ability to handle faults.

A Framework for Observer-Based Robust Fault Detection in Nonlinear Systems With Application to Synchronous Generators in Power Systems

A nonlinear robust fault detection scheme for synchronous generators in power systems is presented. The higher-order model of a synchronous generator having nonlinearity in dynamics is considered. A nonlinear observer-based fault detection (FD) scheme is proposed so that the designed FD is robust against disturbances, uncertainties, and sensitive to faults. To this end, a mixed <inline-formula><tex-math notation="LaTeX">$\mathcal {L}_{-}/\mathcal {L}_\infty$</tex-math></inline-formula> induced norm-based framework is proposed, and various faults that are encountered in synchronous generators are investigated. The success of the proposed scheme is demonstrated through an application to IEEE 9-bus and 3-machine systems.

Dynamic event-triggered security control and fault detection for nonlinear systems with quantization and deception attack

The event-triggered security control and fault detection for nonlinear systems, in which the output signals are wirelessly transmitted to the control and detection module, is investigated in this paper. In particular, quantization before data transmission and deception attacks during data transmission are considered. To ease the data transmission pressure of wireless networks, a dynamic event-triggered protocol is proposed. Specifically, the triggering threshold changes in accordance with the system state. A stochastic sequence is introduced for modelling the time of occurrence of cyber-attacks within an insecure communication environment. By taking the dynamic event-triggered protocol, signal quantization and randomly occurring deception attacks into account, an integrated dynamic output feedback controller and fault detection filter module is developed. With the proposed design, stochastic stability is obtained, while the expected levels of security control performance and detection performance are also guaranteed. Novel algorithms are proposed for determining the parameters of the controller, filter and dynamic event-triggered protocol. A practical example is given to demonstrate the effectiveness of the proposed design idea.

Non-fragile H∞ Fault Detection for Nonlinear Systems with Stochastic Communication Protocol and Channel Fadings

Non-fragile H∞ Fault Detection for Nonlinear Systems with Stochastic Communication Protocol and Channel Fadings

Nonlinear Parity Approach to Fault Detection in Nonlinear Systems Using Unknown Input Observer

The following study presents a novel fault detection approach, based on nonlinear parity design using nonlinear unknown input observers, for a class of nonlinear systems in the presence of sensor noise. Initially, fault and noise are decoupled via splitting the system into two subsystems by a series of transformations on the states and output equations. The nonlinear unknown input observer estimates the states in the subsystem subject to sensor noise, while the states in the other subsystem are estimated with the proposed equation. The objective in this study is to ensure the asymptotic stability of the error dynamics using the Lyapunov method. Hence, the results are formulated in the form of bilinear matrix inequalities. At the same time, the nonlinear parity approach is employed to design residue signals in the subsystem subject to actuator/component faults. Sufficient conditions are established to guarantee the convergence of the state estimation error. The goal in the parity approach is to generate a specific residue indicating the difference between nominal and faulty states. Finally, three simulations are provided to illustrate the efficiency of the proposed algorithm.

An Improved Just-in-Time Learning Scheme for Online Fault Detection of Nonlinear Systems

Just-in-time learning (JITL) scheme has been employed as an efficient tool of online soft sensor. It requires current measured data with high accuracy. However, in real industrial environments, it is difficult to ensure that no disturbance is added to the measurement. To solve this problem, this article proposes an improved JITL scheme, which employs leverage calculation to trim the weight of variables and abate the affect of disturbances. On this basis, an online modeling and output prediction algorithm is further presented. The experiment on a typical nonlinear system shows the better robustness and accuracy of the presented algorithm in comparison with conventional JITL-based approaches. Moreover, an online fault detection strategy for nonlinear systems is proposed based on JITL and partial least squares (PLS), for the purpose of simplifying the parameter setting and reducing the computational load of conventional fault detection approaches for nonlinear systems. Four statistic indexes are designed, including conventional T <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> and SPE for fault detection, T <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">h</sub> and T <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">t</sub> of two orthogonal decomposition input subspaces for fault birth subspace observation. A numerical nonlinear system and an industrial benchmark of Tennessee Eastman process are employed for fault detection experiments, testifying the effectiveness of the proposed strategy.

Parity-based robust data-driven fault detection for nonlinear systems using just-in-time learning approach

This paper discusses a framework for fault detection in nonlinear processes. A novel subspace aided parity-based data-driven technique is proposed using the so-called just-in-time learning (JITL) approach. The idea of the proposed technique is to choose a set of the most similar data samples from the database for each test data sample using the JITL approach to address the nonlinearity problem. The parity vector is constructed for each test sample using the selected data samples to generate the residual signal. The salient features of the proposed technique include easy and simple implementation together with effectiveness in fault detection in the presence of disturbances and measurement noise. The fault detection scheme is so designed that it exhibits robustness against sensor noise and disturbances and sensitivity to faults. A case study for the fault detection of a nuclear research reactor (NRR) is presented to demonstrate the efficacy of the technique. The NRR is a highly nonlinear and complex process. Two faults of NRR, namely external reactivity insertion and control rod withdrawal, are successfully detected using the proposed approach.

Bayesian Fault Probability Estimation: Application in Wind Turbine Drivetrain Sensor Fault Detection

AbstractIn this paper, the extension of the Bayesian framework for sensor fault detection of nonlinear systems proposed in [25] is studied utilizing particle filtering and the expectation maximization (EM) algorithm, in which the fault probability is calculated. The proposed algorithm is implemented on a wind turbine benchmark model to detect drivetrain sensor faults, which are one of the most addressed and likely faults in offshore wind turbines. The fault probability estimation effectively eliminates the need for installing identical redundant sensors. Indeed, because of the use of the unknown wind speed estimator, the residual signal, constructed based on the drivetrain estimated states, is not able to clearly signify the fault periods, a situation in which the fault probability accurately does this task. Also, using the proposed algorithm, the fault size for each sensor is estimated via a one‐step calculation, which decreases the complexity of this algorithm. The fault identification is performed using the recursive least square method and two other modifications, including exponentially weighted and windowed estimates. Additionally, in the fault accommodation step, the concept of a virtual sensor is used to remove the need for reconfiguring the current controller, which reduces complexity and expense. In the simulation section, using a real measured wind speed for two different fault scenarios, the proposed algorithm is evaluated and finally, conclusions are stated.

A Fault Detection Approach for Nonlinear Systems Based on Data-Driven Realizations of Fuzzy Kernel Representations

This paper is devoted to the data-driven fault detection of nonlinear systems. For our purpose, the definition of Takagi–Sugeno fuzzy data-driven forms of kernel representations for nonlinear systems is introduced first, which builds the basis of our work. The major contributions consist of two parts. In the first part, a data-driven method for fuzzy process modeling is proposed, and associated with it, some modeling issues are addressed with the aid of the so-called randomized algorithm technique in the probabilistic framework. It is followed by a data-driven realization of fuzzy kernel representation and its implementation in the fault detection system design. To link the data-driven methods to the well-established observer-based fault detection approaches, the recursive form of the fuzzy kernel representation is proposed. In the second part, the fuzzy-observer-based fault detection design scheme is investigated based on the recursive fuzzy kernel representation. The main results of our study are illustrated by an experimental study on the laboratory setup of a three-tank system.

An Improved Polytopic Adaptive LPV Observer Design Under Actuator Fault

In this paper, we treat the problem of both state estimation and fault detection of nonlinear systems. Those systems could be described by a poly-quadratic Linear Parameter-Varying (LPV) form. A new adaptive observer is developed in terms of Linear Matrix Inequalities (LMI) technique. A feasible algorithm is developed to compute the designed parameters. This algorithm provides more flexibility and mitigates the conservatism of quadratic approach. Finally, simulation results are presented to illustrate the efficiency of the proposed method..

Fault detection for nonlinear systems with unreliable measurements based on hierarchy cubature Kalman filter

AbstractThis paper is concerned with fault detection of a kind of nonlinear dynamic system. Based on the framework of hierarchy information processing, the scope of the fault is first located by use of the presented windowing cubature Kalman filter (WCKF), followed by point‐by‐point fault detection to locate the fault by use of the residuals of each moment within the suspected windows. Theoretical analysis and experiments show that the presented algorithm is more effective than the traditional point‐by‐point fault detection method that only uses the moment residuals. The presented algorithm has potential value in many application fields, such as fault detection, reliability evaluation, fault tolerant control, etc.

Neural adaptive observer-based sensor and actuator fault detection in nonlinear systems: Application in UAV

A new online detection strategy is developed to detect faults in sensors and actuators of unmanned aerial vehicle (UAV) systems. In this design, the weighting parameters of the Neural Network (NN) are updated by using the Extended Kalman Filter (EKF). Online adaptation of these weighting parameters helps to detect abrupt, intermittent, and incipient faults accurately. We apply the proposed fault detection system to a nonlinear dynamic model of the WVU YF-22 unmanned aircraft for its evaluation. The simulation results show that the new method has better performance in comparison with conventional recurrent neural network-based fault detection strategies.

Observer-Based Fault Detection for Nonlinear Systems With Sensor Fault and Limited Communication Capacity

In this technical note, a new fault detection design scheme is proposed for interval type-2 (IT2) Takagi-Sugeno (T-S) fuzzy systems with sensor fault based on a novel fuzzy observer. The parameter uncertainties can be captured by the membership functions of the IT2 fuzzy model. The premise variables of the plant are perfectly shared by the fuzzy observer. A stochastic process between the plant and the observer is considered in the system. A fault sensitive performance is established, and then sufficient conditions are obtained for determining the fuzzy observer gains. Finally, simulation results are provided to verify the effectiveness of the presented scheme.

Non‐parametric fault detection methods in non‐linear systems

In this study, two fault detection methods are proposed for non-linear systems. In these methods, Gaussian process (GP) is integrated into extended Kalman filter (EKF) and square root cubature Kalman filter (SCKF), which are called GP-EKF and GP-SCKF, respectively. The most important advantage of the proposed methods is that there is no need to know the accurate model of the system. Therefore, these methods are considered as non-parametric approaches of fault detection in non-linear systems. Moreover, by applying these methods, it is possible to detect the fault with high accuracy at early stage. First, GP-EKF and GP-SCKF are proposed for non-linear state estimation, and then GP-SCKF is compared with the GP-EKF and the results of this comparison prove the superiority of GP-SCKF regarding the complexity of computations and accuracy. In addition, simulation results show a good performance of GP-EKF and GP-SCKF in non-linear system's fault detection. To illustrate performance of these algorithms in state estimation and fault detection, they are used in aircraft tracking system. Both of the proposed methods are able to detect the sensors faults at early stage.

Applying GP-EKF and GP-SCKF for non-linear state estimation and fault detection in a continuous stirred-tank reactor system

This paper proposes two novel methods for fault detection in non-linear processes. These methods apply a Gaussian process (GP) to model the underlying process, and then the extended Kalman filter (EKF) and square root cubature Kalman filter (SCKF) are used to detect faults. Accordingly, two approaches called the Gaussian process–extended Kalman filter (GP-EKF) and Gaussian process–square root cubature Kalman filter (GP-SCKF) are proposed. The most important characteristic of these proposed methods is that there is no need for an accurate model of the system. Therefore, these methods are considered non-parametric approaches of fault detection in non-linear systems. To illustrate the performance of these algorithms in fault detection, they have been used in a continuous stirred-tank reactor system (CSTR). Both proposed methods are able to detect sensor faults at an early stage.

Detecting sensor fault for nonlinear systems in T–S form under sampled-data measurement: Exact direct discrete-time design approach

A direct discrete-time design methodology for sampled-data sensor fault detection for nonlinear systems in Takagi–Sugenos form is proposed. Contrary to the conventional schemes in this way that rely on an approximate discrete-time model of the nonlinear system, our result is established based on an exact one. Condition to design the observer and the residual gain under an H-/H∞ performance criterion is presented in matrix inequality format. An example is given to illustrate the effectiveness of the proposed methodology.

Robust fuzzy observer-based fault detection for nonlinear systems with disturbances

This paper studies the integrated design of fuzzy observer-based fault detection (FD) for nonlinear systems in the presence of external disturbances. To this end, we first approximate the nonlinear systems by a set of Takagi–Sugeno (T–S) fuzzy models. Then, the observer-based FD systems are studied with the aid of L2 stability theory. In the end, a numerical example is given to show the efficiency of the proposed method.