Residual Evaluation Function Research Articles

Dynamic event-triggered fault detection for multi time scale systems: Application to grid connected converters

This paper deals with the fault detection problem for multi time scale systems. To save network resources, a novel dynamic event-triggered protocol with more generality and higher flexibility of parameters is proposed. The main novelties are condensed as follows, firstly, a modified fault detection observer and a quadratic residual evaluation function are synthetically designed; secondly, the sufficient condition is proved to guarantee that the estimation error system is asymptotically stable and satisfies the specified H∞ performance index; finally, a Lyapunov function is designed to composite the multi time scale perturbation parameter and the influence of the dynamic event-triggered scheme. Finally, a practical experiment on a type of grid connected converters is given to verify the proposed approach.

Stability analysis and fault detection filter design for discrete‐time switched systems with all modes unstable

AbstractIn this article, we investigate the stabilization and fault detection problem for discrete‐time switched systems with all modes unstable. A set of mode‐dependent fault detection filters are designed with respect to each subsystem and combined with the switched system into an augmented switched system. Sufficient conditions for both guaranteeing globally uniformly asymptotically stable (GUAS) and performance of the augmented switched system are derived by utilizing the discretized Lyapunov function technique and dwell time (DT) switching strategy. A novel pair of the residual evaluation function and threshold function are also provided for improving the sensitivity and practicability of fault detection logic. Numerical experiments demonstrate the effectiveness of the proposed method.

Robust fault detection and isolation for dynamics of high-speed train with uncertainties based on descriptor systems

This paper proposes a new type of robust fault detection and isolation filter for dynamics of HST based on descriptor systems with uncertainties in finite frequency. This filter is designed based on the unknown input filter to decouple the non-linear variables due to the aerodynamic drag pressing on the trains. The exogenous disturbance is partitioned into two parts-the decoupling one is regarded as the augmented variables of the non-linear part of the systems, and the non-decoupling one is seen as the augmented disturbance along with the uncertainties. Concurrent faults of different positions are considered, residual evaluation functions and adaptive threshold are given to judge if the faults occur. Fault isolation is implemented by a set of detection subspaces associated with every different fault which is assigned to its own detection subspace. The residual is not only sensitive to the fault, but also has a robustness against the non-decoupling disturbance and uncertainties in finite frequency. Simulation examples are given to demonstrate the effectiveness of this method.

Fault tolerant control in an unmanned bicycle robot via sliding mode theory

AbstractIn this work, a new active fault tolerant control (FTC) is developed for an unmanned bicycle robot based on an integration between a sliding mode control (SMC), fault detection (FD), and fault estimation (FE) via a residual signal. A sliding surface in accordance with the fault tolerant sliding mode control (FTSMC) is designed for the bicycle robot to get multiple exciting features such as fast transient response with finite time convergence, small overshoot and quick stabilisation in the presence of an actuator fault. To obtain an effective performance for the FTSMC, a fault estimation system is employed and in order to attain estimation, an extended Kalman filter (EKF) as an estimator and a change detection algorithm called cumulative sum (CUSUM) as a residual evaluation function are developed. The innovative features of the proposed approach, that is FTSMC, are verified when compared with the other up‐to‐date control techniques like fault tolerant model‐based predictive control with feedback linearisation (FTMPC + FBL) and fault tolerant linear quadratic regulator with feedback linearisation (FTLQR + FBL) on an unmanned bicycle robot.

Robust fault diagnosis for DC–DC Boost converters via switched systems

In this paper, an effective system for fault detection and fault identification is proposed for DC–DC Boost converters, and the effectiveness of the method is proved from theoretical analysis, simulation, and experimental verification respectively. The switched systems are modeled and some of the fault models are analyzed based on the switched characteristics of the DC–DC Boost converter firstly. Then, the core of the method in this paper is to design a robust linear switch fault detection observer and a set of fault recognition observers. The fault detection observer detects a fault based on the residual among physical output and estimated output. Once the system detects faults, it automatically turns on the identification observers. Meanwhile, the system selects appropriate adaptive rules to estimate the value of the faulty component and achieves the adaptive parameter identification. The location of the fault is identified by comparing several residual evaluation functions. Finally, simulation and semi-physical simulation results testify to the effectiveness of the proposed approach.

Quantized Fault Detection Filter Design for Networked Control System with Markov Jump Parameters

In this paper, the design problem of fault detection filter for a class of networked control system subject to Markov jump parameters, randomly occurring uncertainties, sensor faults and packet dropouts by using state feedback control is investigated. The fault detection filter is used as an optimal residual generator that generates the residual signal and also guarantees the sensitivity of residual signal to the faults. To be more specific, a suitable threshold is selected for the residual evaluation function to reduce the error between the sensor faults and the residual signal. Further, by employing a fault detection technique along with output quantization approach, a new set of sufficient conditions is derived to ensure the stochastic stabilization of the addressed system with sensor faults under a predefined $$H_{\infty }$$ performance level. Particularly, by constructing Lyapunov–Krasovskii functional and utilizing extended Wirtinger-based single integral inequality, the required sufficient conditions are derived in terms of linear matrix inequalities for getting the required result. At last, two numerical examples including high alpha research vehicle model are presented to demonstrate the usefulness of our developed results.

Robust fault detection and adaptive parameter identification for DC‐DC converters via switched systems

SummaryIn this paper, the problem of fault detection and identification for DC‐DC converters is presented. First, switched systems model and fault model are analyzed based the switched characteristics of the DC‐DC converters, taking the DC‐DC buck converter as an example. According to the switched Lyapunov function technique, a fault detection observer and a bank of linear switched fault identification observers are designed for the switched systems. Next, the fault detection observer detects the fault based on the residual produced by the observer output and actual output. After the fault is detected, fault identification observers are activated. The location of fault is identified by comparing the residual evaluation functions. Meanwhile, the adaptive parameter identification is achieved by choosing an appropriate adaptive law. Finally, in order to show the feasibility of the fault detection and identification, the simulation results are given in this article.

An Optimal Data-Driven Approach to Distribution Independent Fault Detection

In this article, an optimal data-driven approach is proposed to deal with the problem of distribution independent fault detection (FD) for stochastic linear discrete-time systems. For this purpose, an observer-based residual generator is first constructed using process input and output data. Without exact probability distributions for noises and faults, the so-called confidence sets are constituted in terms of mean and covariance matrix to characterize residual in fault-free and faulty cases. On this basis, a stochastic optimization FD problem is formulated, which allows an integrated design of residual evaluation function and threshold toward maximizing fault detection rate (FDR) for an acceptable false alarm rate (FAR) in the worst-case setting. Furthermore, a data-driven formulation of the underlying FD problem is studied, wherein the estimation uncertainties caused by the deviation of empirical mean and covariance matrix from their real values are concerned. The robustness of the FD system is investigated in the probabilistic context. Confidence levels of the obtained FAR and FDR are achieved quantitatively. The main advantages of the proposed FD approach lie in its independence of probability distributions for noises and faults, the robustness to the estimation uncertainties and the quantitative probabilistic evaluation to the confidence levels of FAR and FDR. A case study on a three-tank system illustrates the effectiveness of the demonstrated approach.

Fault diagnosis of open and short-circuit faults in a parallel multi-cell converter based on sliding mode observer

This paper presents an observer-based diagnosis approach of open and short-circuit switch faults in a parallel multi-cell converter composed of three buck type converters. Motivated by its convergence qualities in a finite time and its robustness to external uncertainties and disturbances, a first-order sliding mode switched observer is used in residual generation. The residuals are then compared to a fixed threshold to obtain the different faults signatures. The selected residual evaluation function provided more information about residual signals which allowed the isolation and identification of the faults. Taking into account the hybrid behavior of the studied system, this method ensures both the detection and the identification of the fault, and eventually, the isolation of the faulty switch in the case of single and multiple faults. Moreover, it provides a fast diagnosis and thus allows a timely intervention to reconfigure the system and ensure safety and continuity of operation. The performance of the proposed approach is established by numerical simulations.

Distributionally Robust Fault Detection by using Kernel Density Estimation

In this paper, a method of distributionally robust fault detection (FD) is proposed for stochastic linear discrete-time systems by using the kernel density estimation (KDE) technique. For this purpose, an H2 optimization-based fault detection filter is constructed for residual generation. Towards maximizing the fault detection rate (FDR) for a prescribed false alarm rate (FAR), the residual evaluation issue regarding the design of residual evaluation function and threshold is formulated as a distributionally robust optimization problem, wherein the so-called confidence sets are constituted to model the ambiguity of distribution knowledge of residuals in fault-free and faulty cases. A KDE based solution, robust to the estimation errors in probability distribution of residual caused by the finite number of samples, is further developed to address the targeting problem such that the residual evaluation function, threshold as well as the lower bound of FDR can be achieved simultaneously. A case study on a vehicle lateral control system demonstrates the applicability of the proposed FD method.

Concurrent fault diagnosis of modular multilevel converter with Kalman filter and optimized support vector machine

ABSTRACTIn this paper, concurrent fault diagnosis problem of modular multilevel converter (MMC) with Kalman filter and optimized support vector machine (SVM) is investigated. The state space model by synthesizing the circulating current and the output current is first established. Recurring to the Kalman filtering theory, the estimation on circulating and output current is realized, the residual is achieved by using the innovation which involved the predicted and measured current. Based on the obtained residual, the residual evaluation function and its threshold are constructed. Then, the fault can be detected according to the proposed fault detection strategy. Once the fault is detected, the fault localization unit is triggered and the residual data is adopted as data set. By employing the optimized SVM with genetic algorithm, the concurrent and intermittent fault localization of MMC can be accomplished. Finally, an 11-level MMC simulation systems with concurrent fault and intermittent fault are set up in MATLAB/Simulink, and the effectiveness of the proposed fault detection and localization method is verified.

Fault detection and isolation in batteries power electronics and chargers

This paper focuses on the residual-based fault detection and isolation (FDI) in batteries power electronics and chargers. Currently, isolation of multiple faults is performed by generating a bank of residuals, one residual signal for each fault. It is shown that, with a combination of different residual evaluation functions, various battery and electronics faults, including battery short/open circuit, sensor biases, input voltage drop, and switches short/open circuit, can be detected and isolated by using the magnitude and slope of a residual signal or its norm that is generated from the battery voltage. In this paper, the constant-current constant-voltage (CCCV) battery chargers based on buck and boost converters are studied. The FDI systems with observer- and historical-based residual generation methods are designed and simulation results are discussed.

Voltage Difference Residual-Based Open-Circuit Fault Diagnosis Approach for Three-Level Converters in Electric Traction Systems

This paper proposes a voltage difference residual-based fault diagnosis approach for three-level converters in electric traction systems. Compared to the existing open-circuit fault diagnosis approaches, the proposed approach cannot only detect and identify the faulty devices among the transistors and clamp diodes in both of rectifier and inverter, but also hierarchically diagnose the leg-level and device-level faults. Furthermore, no additional sensors are required and all of specified parameters can be directly identified. Specifically, the current related term in the dc-link voltage difference equations are analyzed and modeled under the normal case and open-circuit fault cases in the transistors and clamp diodes, respectively. Subsequently, the estimation models of the dc-link voltage difference are developed, and then, the residuals and their evaluation functions between estimation and actual are generated. Furthermore, by analyzing the residual evaluation functions for various cases and building the current similarity functions, the hierarchical diagnosis algorithms are proposed to identify the leg-level and device-level faults. Finally, hardware-in-the-loop experiments are performed. The results show the effectiveness and robustness of the proposed approach.

Sliding mode switching observer-based actuator fault detection and isolation for a class of uncertain systems

This paper proposes a fault detection and isolation (FDI) method for a class of uncertain systems with actuator faults. First, based on the multiple model schemes, a bank of parallel sliding mode switching observers are designed to match the fault-free, stuck and loss of effectiveness fault models. A switching strategy is introduced to maintain a sliding motion even in the presence of actuator faults. Stability analysis of the sliding motions and the reachability conditions are given in various fault models. Without the assumption that all states are available, the residual signals are constructed by using the measurable output estimation errors. Then, the fault detection scheme is designed by comparing the residual evaluation function with the threshold. The fault isolation scheme is derived by looking for the minimum residual evaluation function, which is generated by the observer that matches the current system model well. Finally, an L-1011 aircraft model is presented to illustrate the effectiveness of the FDI method obtained in this paper.

Finite-time sensor fault diagnosis observer design for attitude control systems of satellite based on sampled-data descriptor systems

In this paper, a finite-time robust sensor fault diagnosis observer with non-singular structure is proposed for Attitude Control Systems of satellite based on Lipschitz non-linear sampled-data descriptor systems with state time-varying delay. Firstly, an Attitude Control Systems model based on sampled-data descriptor system with time-varying delay is proposed and transformed into a discrete-time non-singular one. Then, a sensor fault diagnosis observer is designed upon on the state estimation error and the measurement residual. The finite-time stability and robustness of the augmented error dynamic model is analyzed, the H infinity performance index in finite-time is satisfied. As the confining matrices of the observer parameters do not meet the Linear Matrix Inequality, a cone complementary linearization iteration algorithm is proposed to solve this problem. Residual evaluation function and the threshold are introduced for judging if the faults occur. At the end, simulation examples are given to illustrate the effectiveness of this method.

Torus-Event-Based Fault Diagnosis for Stochastic Multirate Time-Varying Systems With Constrained Fault.

In this paper, the torus-event-based fault detection and isolation (FDI) problem is investigated for a class of time-varying multirate systems. An ellipsoidal constraint is first adopted to describe the fault in a more practical pattern, and a novel torus-event-triggering scheme is proposed to improve the unilateral triggering mechanism. The aim is to design the torus-event-based fault detection filter and fault isolation estimators such that both the prescribed variance constraint on the estimation error and the desired H∞ performance on the disturbance are guaranteed over the finite horizon. Especially, the residual evaluation function is employed to detect the fault, and the residual matching function is developed to isolate the fault. Furthermore, three optimization problems are provided to seek separately the minimal parameters on the H∞ performance level, the upper bound of the estimation error variance, and the triggering torus. Finally, two simulation examples are utilized to show the effectiveness of the FDI scheme proposed in this paper.

Annulus-event-based fault detection, isolation and estimation for multirate time-varying systems: Applications to a three-tank system

In this paper, the annulus-event-based fault detection, isolation and estimation (FDIE) problem is examined for a class of multi-rate time-varying systems with sensor degradations as well as unknown but bounded disturbances and faults. A new event-triggered communication mechanism is proposed under which the measurement signal is transmitted to the observer/estimator only when the relative error satisfies certain triggering-annulus. By employing the set-membership estimation method, we design the time-varying annulus-event-based fault detection estimator (AEFDE), fault isolation estimator (AEFIE) as well as fault estimator (AEFE) with the help of recursive matrix inequalities. Based on the obtained AEFDE, AEFIE and AEFE, a residual evaluation function method is first employed to detect the fault, a novel estimation-error-function matching approach is then developed to isolate the fault, and the extended vector technique is finally adopted to estimate the fault. Especially, two subsequent optimization problems are investigated, one is to seek the minimal ellipsoidal region for the best estimation performance, and the other is to minimize the triggering-annulus so as to reduce the triggering frequency with guaranteed estimation performance. A numerical simulation example on a three-tank system is employed to show the effectiveness of FDIE scheme proposed in this paper.

Distributed Soft Fault Detection for Interval Type-2 Fuzzy-Model-Based Stochastic Systems With Wireless Sensor Networks

In this paper, a distributed filtering scheme is presented to deal with the fault detection problem of nonlinear stochastic systems with wireless sensor networks (WSNs). The nonlinear stochastic systems, which are of discrete-time form, are represented by interval type-2 (IT2) Takagi–Sugeno (T–S) fuzzy models. Each sensor of the WSN can receive measurements from itself and its neighboring sensors subject to a deterministic interconnection topology. Independent random variables obeying the Bernoulli distribution are formulated to characterize the randomly occurred packet losses between the WSN and the filter unit. To generate residual signals for evaluation functions of the fault detection mechanism, a novel type of IT2 T–S fuzzy distributed fault detection filter is proposed corresponding to each sensor node. Additionally, a fault reference model is adopted for improving the performance of the fault detection system. A new overall fault detection system is formulated in an IT2 T–S fuzzy model framework. Applying Lyapunov functional approach, we concentrate on the analysis of stability and performance of the resulting fault detection system. New techniques are utilized to handle the decoupling problem in design procedure. The desired parametric matrices of the fuzzy filters are designed subject to a developed criterion, which is a sufficient condition of the robust mean-square asymptotic stability for the overall fault detection system with a disturbance attenuation performance. Finally, a truck-trailer system with a four-node WSN is established for simulation validation. In simulations, the mincx function of the MatLab 2017a in Windows 10 OS is used to optimize the level of the disturbance attenuation performance, and to obtain the filter gains for the established system. By comparing the different time instants when the residual evaluation functions exceed their respective thresholds, simulation results successfully validate the effectiveness and applicability of the presented distributed fault detection scheme.

Interval observer-based fault detection in finite frequency domain for discrete-time fuzzy systems

Abstract This paper presents a new method for detecting faults in discrete-time fuzzy systems. First, the piecewise interval observers are constructed based on the output-space partition technique. Second, l1 performance is introduced to attenuate the persistent bounded disturbances and H − performance in finite frequency domain is employed to improve fault sensitivity of the residual intervals. Then, the observer gains can be determined by solving the disturbance attenuation, fault sensitivity and non-negativity conditions, simultaneously. Different from the classical fault detection methods with residual evaluation functions and threshold generators, the proposed interval observers are able to generate the natural thresholds. Finally, the developed technique is demonstrated in a simulation example.

A New Residual Evaluation Function Based Fault Diagnosis for a Kind of Nonlinear Systems

AbstractIn this paper, a new residual evaluation function (EF) is proposed for the Hi/H∞ fault detection filter (FDF). Residuals are projected into the direction of fault, then energy of this projection is adopted as the new EF. Compared with the traditional EF, improvement in FD ability of the new EF is demonstrated rigorously, and a recursive way to calculate it is also proposed under the assumption that fault is slowly time varying. Furthermore, application of this new EF for fault isolation (FI) is discussed, which finally leads to a systematic FDI scheme for a kind of nonlinear systems. Simulations with a nonlinear unmanned aerial vehicle (UAV) model validate the proposed method.