Problem Of Fault Isolation Research Articles

Event‐triggered fault detection and isolation for discrete‐time linear systems

In this study, the problem of event-triggered fault detection and isolation (FDI) for discrete-time linear time-invariant systems is considered. Using a Leunberger observer as the residual generator, a multi-objective formulation of the FDI problem is presented based on l 1 , H _ and H ∞ performance indices. For each performance index, sufficient conditions for the design of FDI observer are presented based on linear matrix inequalities (LMIs). To reduce the conservativeness of the multi-objective problem, extended LMIs are used to eliminate the couplings of Lyapunov matrices with the system state space matrices. It is shown that through an event-triggered data transmission mechanism, the amount of data that is sent to the FDI module is significantly decreased. Simulation results corresponding to a remotely operated vehicle demonstrate the effectiveness and capabilities of the proposed design methodology.

Design of Fault Detection and Isolation Filter for Switched Control Systems Under Asynchronous Switching

The problem of fault detection (FD) and isolation, in the presence of disturbances and noise, for continuous-time linear switched control systems is addressed in this paper. The residual generator is proposed, which is based on asynchronously switching filters, where asynchronous means that there is a lag between switching of filters and subsystems. To address the issue, the FD filter problem is formulated as a mixed $H_{-}/ H_{\infty }$ filtering problem. In the proposed $H_{-}/ H_{\infty }$ technique, residual is generated such that it is sensitive to faults and robust against process disturbances and measurement noise. In addition, the proposed filter has prominence of granting fault isolation capability along with FD. To deal with the major issue of asynchronous switching, during matched and unmatched time of switched systems, a piecewise Lyapunov function along with average dwell time scheme is employed, and sufficient conditions are derived in terms of linear matrix inequalities. To simplify the application procedure, an algorithm is also presented in the light of the proposed framework. Then, a solution is designed for two different case studies of highly maneuverable aircraft technology and boost–buck power converter. Finally, the designed filter parameters are simulated to illustrate the efficacy of the proposed framework.

Sequential FDIA for Autonomous Integrity Monitoring of Navigation Maps on Board Vehicles

This paper addresses the problem of fault detection, isolation, and adaptation (FDIA) in navigation systems on board passenger vehicles. The aim is to prevent malfunctions in systems such as advanced driving assistance systems and autonomous driving functions that use data provided by the navigation system. The integrity of the estimation of the vehicle position provided by the navigation system is continuously monitored and assessed. The proposed approach uses an additional estimate of vehicle position that is independent of the navigation system and based on data from standard vehicle sensors. First, fault detection consists in comparing the two estimates using a sequential statistical test to detect discrepancies despite the presence of noise. Second, fault isolation and adaptation is introduced to identify faulty estimates and to provide a correction where necessary. The FDIA framework presented here utilizes repeated trips along the same roads as a source of redundancy. Relevant properties of this formalism are given and verified experimentally using an equipped vehicle in rural and urban conditions and with various map faults. Results show that sequential FDIA performed well, even in difficult GNSS conditions.

Sampled-data [formula omitted] fault detection and isolation for nonlinear systems in T–S form: An approximate model approach

A direct discrete-time design methodology for the sampled-data fault detection and isolation (FDI) problem is proposed for a nonlinear system in Takagi–Sugeno form. The generalized observer scheme is adopted, accompanied by a bank of the sensors' number of observers. A sufficient condition to find observer and residual gains, based on an accessible approximate—rather than unavailable exact—discrete-time model is proposed in terms of matrix inequalities so that it exhibits H−/H∞ performance and asymptotic stability. An algorithm involving a convex optimization is presented using the cone complementary linearization technique. We show that the FDI observer ensures (modified) H−/H∞ performance and Lagrange stability, when it is connected to the actual nonlinear system.

Fault detection and isolation for uncertain closed‐loop systems based on adaptive and switching approaches

SummaryThis paper deals with the fault detection and isolation (FDI) problem for uncertain closed‐loop systems with external disturbances and nonlinear perturbations. To address the system uncertainties and the nonlinear perturbations in different faulty models, adaptive and switching techniques are introduced to construct a bank of FDI observers, such that one of them can match the current system, and the corresponding observer estimate errors can converge asymptotically to zero. An effective FDI scheme is then presented by introducing some model‐matching indexes. Moreover, the introduced switching laws liberate the equality constraints often used in the existing FDI approaches, which are hard to satisfy if the system matrices include uncertainties. Finally, a simulation example of F/A‐18A automatic carrier landing system is used to illustrate the effectiveness of the proposed method. Copyright © 2015 John Wiley & Sons, Ltd.

Fault detection and isolation of sensors in aeration control systems.

In this paper, we consider the problem of fault detection (FD) and isolation in the aeration system of an activated sludge process. For this study, the dissolved oxygen in each aerated zone is assumed to be controlled automatically. As the basis for an FD method we use the ratio of air flow rates into different zones. The method is evaluated in two scenarios: using the Benchmark Simulation Model no. 1 (BSM1) by Monte Carlo simulations and using data from a wastewater treatment plant. The FD method shows good results for a correct and early FD and isolation.

Multivariate fault isolation via variable selection in discriminant analysis

In multivariate statistical process monitoring (MSPM), isolation of faulty variables is a critical step that provides information for analyzing causes of process abnormalities. Although statistical fault detection has received considerable attention in academic research, studies on multivariate fault isolation are relatively fewer, because of the difficulty in analyzing the influences of multiple variables on monitoring indices. The commonly used tools for fault isolation, such as contribution plots, reconstruction-based methods, etc., have several shortcomings limiting their implementation. To solve the problems of the existing methods, this paper reveals the relationship between the problems of multivariate fault isolation and variable selection for discriminant analysis. Furthermore, by revealing the equivalence between discriminant analysis and regression analysis, the problem of multivariate fault isolation is further formulated in a form of penalized regression which can be solved efficiently using state-of-the-art algorithms. Instead of offering a single suggested set of faulty variables, the proposed method provides more information on the relevance of process variables to the detected fault, facilitating the subsequent root-cause diagnosis step after isolation. The benchmark Tennessee Eastman (TE) process is used as a case study to illustrate the effectiveness of the proposed method.

Failure identification for linear repetitive processes

This paper investigates the fault detection and isolation (FDI) problem for discrete-time linear repetitive processes using a geometric approach, starting from a 2-D model for these processes that incorporates a representation of the failure. Based on this model, the FDI problem is formulated in the geometric setting and sufficient conditions for solvability of this problem are given. Moreover, the processes's behaviour in the presence of noise is considered, leading to the development of a statistical approach for determining a decision threshold. Finally, a FDI procedure is developed based on an asymptotic observer reconstruction of the state vector.

Fault Detection and Isolation in Inertial Measurement Units Based on Bounding Sets

This work addresses the problem of Fault Detection and Isolation (FDI) for navigation systems equipped with sensors providing inertial measurements and vector observations. Assuming upper bounded sensor noise, two strategies are proposed: i) the first one takes advantage of existing hardware redundancy, requiring at least five sensor measurements to isolate faults; ii) the second approach exploits the analytical redundancy between the angular velocity measurements and the vector observations, by resorting to set-valued observers (SVOs). Necessary and sufficient conditions on the magnitude of the faults are provided, in order to guarantee successful detection and isolation, when hardware redundancy is available. Due to the set-based construction of the methods, none of the solutions generates false detections and no decision threshold is required. Using a simulation scenario, the proposed strategies are compared with two alternatives available in the literature.

Variable selection method for fault isolation using least absolute shrinkage and selection operator (LASSO)

In statistical fault isolation, contribution plots are most commonly used tools, whose results are often influenced by the smearing effect. To solve this problem, reconstruction-based methods were developed. Unfortunately, the conventional reconstruction-based methods rely on the knowledge of fault directions or abundant historical fault data which are seldom available in industrial applications. The branch and bound (B&B) algorithm can be adopted to relieve such limitation. However, the computational burden of B&B is usually heavy, especially for a large number of variables. In this paper, a fault isolation method based on variable selection is proposed to overcome these shortcomings of the existing methods. The fault isolation problem is transformed into a quadratic programming problem with a sparsity constraint, which has a unified form for different monitoring statistics and can be solved efficiently using the least absolute shrinkage and selection operator (LASSO). The effectiveness of this method is illustrated by case studies.

Fault detection and isolation of the attitude control subsystem of spacecraft formation flying using extended Kalman filters

In this paper, the problem of fault detection and isolation (FDI) of the attitude control subsystem (ACS) of spacecraft formation flying systems is considered. For developing the FDI schemes, an extended Kalman filter (EKF) is utilised which belongs to a class of nonlinear state estimation methods. Three architectures, namely centralised, decentralised, and semi-decentralised, are considered and the corresponding FDI strategies are designed and constructed. Appropriate residual generation techniques and threshold selection criteria are proposed for these architectures. The capabilities of the proposed architectures for accomplishing the FDI tasks are studied through extensive numerical simulations for a team of four satellites in formation flight. Using a confusion matrix evaluation criterion, it is shown that the centralised architecture can achieve the most reliable results relative to the semi-decentralised and decentralised architectures at the expense of availability of a centralised processing module that requires the entire team information set. On the other hand, the semi-decentralised performance is close to the centralised scheme without relying on the availability of the entire team information set. Furthermore, the results confirm that the FDI results in formations with angular velocity measurement sensors achieve higher level of accuracy, true faulty, and precision, along with lower level of false healthy misclassification as compared to the formations that utilise attitude measurement sensors.

A Hamiltonian Approach to Fault Isolation in a Planar Vertical Take–Off and Landing Aircraft Model

Abstract The problem of fault detection and isolation in a class of nonlinear systems having a Hamiltonian representation is considered. In particular, a model of a planar vertical take-off and landing aircraft with sensor and actuator faults is studied. A Hamiltonian representation is derived from an Euler-Lagrange representation of the system model considered. In this form, nonlinear decoupling is applied in order to obtain subsystems with (as much as possible) specific fault sensitivity properties. The resulting decoupled subsystem is represented as a Hamiltonian system and observer-based residual generators are designed. The results are presented through simulations to show the effectiveness of the proposed approach.

Vibration based fault diagnosis for railway vehicle suspensions via a functional model based method: A feasibility study

The design of a vibration based fault detection and isolation (FDI) unit that can tackle the combined problem of fault detection, isolation (or identification) and magnitude estimation (collectively known as fault diagnosis), in railway vehicle suspensions is presented. The unit is initially “trained” in a baseline phase based on data obtained from a simplified physics-based model of a railway vehicle suspension. Fault diagnosis is subsequently achieved in an inspection phase through a single, properly preselected, pair of vibration signals acquired from the vehicle, and a recently introduced data-based method, referred to as the functional model based method (FMBM), without resorting to the physics-based model of the baseline phase. The method’s cornerstone is the novel class of stochastic ARX-type models capable of accurately representing a system in a faulty state for its continuum of fault magnitudes. Fault diagnosis feasibility in a railway vehicle suspension is demonstrated via Monte Carlo simulations using different types and magnitudes of faults in the physics-based model and generating vibration signals corresponding to the healthy and faulty suspension. Two vibration signals are used by the diagnosis unit: the track velocity profile and the vehicle body acceleration above the trailing airspring. Fault diagnosis based on the FMBM is effective in a compact and unified statistical framework accounting for experimental and modelling uncertainty through appropriate interval estimates and hypothesis testing procedures. The unit is shown to exhibit high sensitivity and accurate estimation of even very small fault magnitudes, to detect and isolate unknown faults for which it has not been trained, and to be robust to high measurement noise, car body mass variations, and varying track irregularity.

Fault Detection and Isolation for a Class of Uncertain State-Feedback Fuzzy Control Systems

This paper investigates the fault detection and isolation (FDI) problem for a class of nonlinear state-feedback control systems with parameter uncertainties. First, the considered nonlinear systems are described by multiple T–S fuzzy models with uncertain grades of membership and local nonlinear parts in normal case and faulty cases. Then, a switching technique is introduced to address the uncertain grades of membership, external disturbances, local nonlinear parts, faults, and the coupling of them. In a multiple-model scheme, a bank of FDI observers are constructed, each of which is based on the T–S model that describes the system in the presence of a particular fault, such that one of them can track the current system state, and the corresponding observer estimate error can converge exponentially to zero. Finally, two examples are given to illustrate the effectiveness and advantages of the new results.

Robust Fault Detection and Isolation for Uncertain Switched Systems under Asynchronous Switching with Adaptive Threshold

The problem of fault detection and isolation for uncertain continuous-time linear switched systems in the presence of disturbances and noise is addressed in this paper. A robust residual generator is proposed which is based on asynchronously switching filters, where “asynchronous” means there is a lag between switching of filters and subsystems. To address the issue, fault detection problem is formulated as mixed H-/H∞ filtering problem. In proposed H-/H∞ technique, the effect of fault on residual signal is fixed to some maximum possible index and then influence of unknown inputs (disturbances and noise) on residual is minimized. In addition proposed filter has prominence of having fault isolation capability along with fault detection. To improve the fault detection capability adaptive threshold is set which takes into account local disturbance levels, the current operational mode and applied input signal. Then, solution is designed for boost converter switched system application and simulations are presented to illustrate the efficacy of the proposed framework.

Fault detection and isolation for a wind turbine benchmark using a mixed Bayesian/Set-membership approach

This paper addresses the problem of fault detection and isolation of wind turbines using a mixed Bayesian/Set-membership approach. Modeling errors are assumed to be unknown but bounded, following the set-membership approach. On the other hand, measurement noise is also assumed to be bounded, but following a statistical distribution inside the bounds. To avoid false alarms, the fault detection problem is formulated in a set-membership context. Regarding fault isolation, a new fault isolation scheme that is inspired on the Bayesian fault isolation framework is developed. Faults are isolated by matching the fault detection test results, enhanced by a complementary consistency index that measures the certainty of not being in a fault situation, with the structural information about the faults stored in the theoretical fault signature matrix. The main difference with respect to the classical Bayesian approach is that only models of fault-free behavior are used. Finally, the proposed FDI method is assessed against the wind turbine FDI benchmark proposed in the literature, where a set of realistic fault scenarios in wind turbines are proposed.

Distributed fault detection for a class of second‐order multi‐agent systems: an optimal robust observer approach

In this study, the distributed fault detection and isolation problem for a class of second-order discrete-time multi-agent systems (MASs) is studied by using an optimal robust observer approach. The work is based on an MAS with a mean square bounded consensus protocol. The authors prove that for this kind of system, a bank of optimal robust observers can be constructed for a certain agent to detect and isolate the possible faults of its neighbour agents. The main result of this study is to bring forward an existence condition for these observers, which is closely related to the topology structure of the MAS. For the detection of the faults injected in position states, they show that only the position information of the MAS is needed, but for the case of detecting faults in velocity states, both position and velocity measurements are needed. Simulation results are presented to illustrate the effectiveness of the results proposed in this study.

Multiple-Model Sensor and Components Fault Diagnosis in Gas Turbine Engines Using Autoassociative Neural Networks

In this paper the problem of fault diagnosis in an aircraft jet engine is investigated by using an intelligent-based methodology. The proposed fault detection and isolation (FDI) scheme is based on the multiple model approach and utilizes autoassociative neural networks (AANNs). This methodology consists of a bank of AANNs and provides a novel integrated solution to the problem of both sensor and component fault detection and isolation even though possibly both engine and sensor faults may occur concurrently. Moreover, the proposed algorithm can be used for sensor data validation and correction as the first step for health monitoring of jet engines. We have also presented a comparison between our proposed approach and another commonly used neural network scheme known as dynamic neural networks to demonstrate the advantages and capabilities of our approach. Various simulations are carried out to demonstrate the performance capabilities of our proposed fault detection and isolation scheme.

Robust fault detection and isolation for a class of uncertain single output non‐linear systems

The problem of robust fault detection and isolation (FDI) is studied for a class of uncertain single output non-linear systems with q faults. By designing a switching law, a robust state observer is developed such that its state responses can asymptotically converge to the true system states for both fault-free and faulty cases. Based on this robust state observer and other q + 1 FDI observers, an efficient scheme is presented such that the FDI can be achieved for the considered single output non-linear systems. The theoretical results are illustrated through an example of a single-link flexible joint robot.

On fault isolation for rail vehicle suspension systems

Reliability of the railway vehicle suspension system is of critical importance to the safety of the vehicle. It is very desirable to monitor the health condition and the performance degradation of the suspension system online, which offers the important information of the suspension system and is critically important for the condition-based maintenance rather than scheduled maintenance in the future. Advanced fault diagnosis method is one of the most effective means for the health monitoring of the suspension system. In this paper, taking the lateral suspension system as an examcple, the fault isolation issue for different component faults occurring in the suspension system is concerned. The sensor configuration for obtaining the vehicle state information and the mathematical model for the lateral suspension system are presented. Four fault features in the time domain and three fault features in the frequency domain are used for each sensor signal. Three different methods, Dempster–Shafer (D–S) evidence theory, Fisher discrimination analysis (FDA) and support vector machine (SVM) techniques are applied to the fault isolation problem. Simulation study is carried out by means of the professional multi-body simulation tool, SIMPACK. The simulation results show that these methods can isolate the considered component faults effectively with a high accuracy. The D–S evidence-based fault isolation approach outperforms the other two methods.