Fault Detection And Isolation Problem Research Articles

Design of a computationally efficient observer-based distributed fault detection and isolation scheme in second-order networked control systems

This paper presents a novel directional observer-based fault detection and isolation scheme for second-order networked control systems (NCS). The directional unknown input observer (UIO) tool is exploited to study the problem of distributed fault detection and isolation (FDI). Two design schemes with global and partial/local network models are proposed to solve the distributed FDI problem. Thresholds are computed for the application of the proposed schemes in a noisy environment. In addition, the salient features of the proposed schemes are that both fault detection and fault isolation are achieved in a single step using a single observer. The schemes are applied to power system models to validate their results. A detailed comparison with existing FDI schemes is also provided, which clearly shows the effectiveness of the proposed scheme in terms of computational requirements.

A Density-based Clustering Approach for Monitoring of Injection Moulding Machine

Fault Detection and Isolation (FDI) is crucial and challenging problem in many industrial applications and continues to be an on-going research issue in the control community. In the literature, model-based techniques are mostly employed to generate residuals for diagnosis and decision-making. In this paper, we focus on FDI problem using a novel based-clustering approach. The key idea is to restrict each fault to be a data cluster with high-density gathering the most similar objects. In this way, the algorithm does not require prior analytic models to start. It uses rather a density measurement to detect and isolate cluster’s regions. The overall algorithm is expanded around two fundamental steps: cluster domain description and density-based clustering. To address properly the requirements of system control and monitoring, the algorithm is designed to work in real-time as observations are acquired and it is endowed with specific tools for data mining and feature extraction. A study case is proposed consisting of plastic Injection Molding Machine (IMM) to prove the effectiveness of the method.

Fault Detection and Isolation for Linear Structured Systems

This paper deals with the fault detection and isolation (FDI) problem for linear structured systems in which the system matrices are given by zero/nonzero/arbitrary pattern matrices. In this paper, we follow a geometric approach to verify solvability of the FDI problem for such systems. To do so, we first develop a necessary and sufficient condition under which the FDI problem for a given particular linear time-invariant system is solvable. Next, we establish a necessary condition for solvability of the FDI problem for linear structured systems. In addition, we develop a sufficient algebraic condition for solvability of the FDI problem in terms of a rank test on an associated pattern matrix. To illustrate that this condition is not necessary, we provide a counterexample in which the FDI problem is solvable while the condition is not satisfied. Finally, we develop a graph-theoretic condition for the full rank property of a given pattern matrix, which leads to a graph-theoretic condition for solvability of the FDI problem.

Enhanced Control Algorithm in PMSMs with Machine Current Sensor FDI and Current Management for Hardware Protection

This paper proposes an enhanced control algorithm in permanent magnet synchronous machines (PMSMs) with machine current sensor fault detection and isolation (FDI) and current management for hardware protection. Different from existing work on machine current sensor FDI, the proposed method addresses the sensor FDI problem under co-existence of PMSM current sensor and non-sensor imbalance faults, and thereby help prevent the controller performance degradation caused by a faulty machine current sensor signal. Meanwhile, hardware protection is achieved through the proposed algorithm which has a unique feature of providing online overcurrent regulation and source power management at the same time to enhance protection of power inverter and power source. Benchmark comparison of the proposed sensor FDI method to state-of-the-art techniques is carried out to validate the good performance of the proposed FDI method in terms of fault detection time, false detection rate and missed detection rate. Sensitivity analysis of the proposed current management algorithm with overcurrent regulation and source power management is performed under changing rotor flux linkage and machine stator d-axis self-inductance, as an example to demonstrate the robustness of the proposed algorithm for hardware protection against system parameter variations. The effectiveness of the proposed algorithm is confirmed by simulation results in MATLAB for a PMSM.

Fault detection and isolation of two-dimensional (2D) systems

In this work, we develop a novel fault detection and isolation (FDI) scheme for discrete-time two-dimensional (2D) systems that are represented by the Fornasini–Marchesini model II (FMII). This is accomplished by generalizing the basic invariant subspaces including unobservable, conditioned invariant and unobservability subspaces of 1D systems to 2D models. These extensions have been achieved and facilitated by representing a 2D model as an infinite dimensional (Inf-D) system on a Banach vector space, and by particularly constructing algorithms that compute these subspaces in a finite and known number of steps. By utilizing the introduced subspaces, the FDI problem is formulated and necessary and sufficient conditions for its solvability are provided. Sufficient conditions for solvability of the FDI problem for 2D systems using both deadbeat and LMI-based filters have also been developed. Moreover, the capabilities and advantages of our proposed approach are demonstrated by performing an analytical comparison with the currently available 2D geometric methods in the literature.

Comparison of Model-based Vs. Data-driven Methods for Fault Detection and Isolation in Engine Idle Speed Control System

An internal combustion engine operating at idle is regulated by a feedback controller so that it runs at a preset idle speed without stalling when no acceleration is requested from the driver. Idle speed control is affected by numerous disturbances, ranging from accessory loads to environmental conditions. Because of the regulating behavior of the controller, faults, especially actuator faults, may affect sensor measurements in a way very similar to disturbances, system uncertainty or noise. This poses a challenge to the fault detection and isolation (FDI) problem for this system. In this paper, two fundamentally different fault diagnoses approaches are used to detect and isolate faults. A model-based residual generation scheme as well as a data-driven linear discriminant analysis scheme is developed to solve the FDI problem even when faults are concurring in addition to system uncertainty, disturbance and noise. Their performances are compared side by side using data gathered from an experimentally validated simulator for an engine idle system that considers an actuator fault, a sensor fault, several system uncertainties and disturbance (operating conditions), and sensor noise. The results show that comparable performance can be achieved with both schemes and some comments are made about each approach.

A geometric approach to fault detection and isolation of multi-dimensional (n-D) systems

In this work, we develop a novel fault detection and isolation (FDI) scheme for discrete-time multi-dimensional (n-D) systems for the first time in the literature. These systems represent as generalization of the Fornasini---Marchesini model II two- and three-dimensional (2-D and 3-D) systems. This is accomplished by extending the geometric FDI approach of one-dimensional (1-D) systems to n-D systems. The basic invariant subspaces including unobservable, conditioned invariant and unobservability subspaces of 1-D systems are generalized to n-D models. These extensions have been achieved and facilitated by representing an n-D model as an infinite dimensional system, and by particularly constructing algorithms that compute these subspaces in a finite and known number of steps. By utilizing the introduced subspaces the FDI problem is formulated and necessary and sufficient conditions for its solvability are provided. Sufficient conditions for solvability of the FDI problem for n-D systems using LMI filters are also developed. Moreover, the capabilities and advantages of our proposed approach are demonstrated by performing an analytical comparison with the only currently available 3-D geometric methods in the literature.

A Tractable Fault Detection and Isolation Approach for Nonlinear Systems With Probabilistic Performance

This article presents a novel perspective along with a scalable methodology to design a fault detection and isolation (FDI) filter for high dimensional nonlinear systems. Previous approaches on FDI problems are either confined to linear systems or they are only applicable to low dimensional dynamics with specific structures. In contrast, shifting attention from the system dynamics to the disturbance inputs, we propose a relaxed design perspective to train a linear residual generator given some statistical information about the disturbance patterns. That is, we propose an optimization-based approach to robustify the filter with respect to finitely many signatures of the nonlinearity. We then invoke recent results in randomized optimization to provide theoretical guarantees for the performance of the proposed filer. Finally, motivated by a cyber-physical attack emanating from the vulnerabilities introduced by the interaction between IT infrastructure and power system, we deploy the developed theoretical results to detect such an intrusion before the functionality of the power system is disrupted.

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.

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.

Combined Signal and Model-Based Sensor Fault Diagnosis for a Doubly Fed Induction Generator

The problem of multiplicative and/or additive fault detection and isolation (FDI) in the current sensors of a doubly fed induction generator (DFIG) is considered in the presence of model uncertainty. A residual generator based on the DFIG model is proposed using the structure of the classical generalized observer scheme. However, each observer in this scheme is replaced by a robust H_/H∞ fault detection filter followed by a Kalman-like observer. The latter further attenuates the effect of the modeling uncertainties on the residuals. It exploits the specific pattern induced by the balanced three-phase nature of all the electric signals. It turns out that the FDI problem then amounts to detecting an abrupt change in the mean of the residual vector in the additive fault case, or the appearance of sine waves superimposed on a white noise vector in the multiplicative fault case. A decision algorithm made of a combination of generalized likelihood ratio algorithms allows us to detect and isolate the additive and multiplicative sensor faults. The complete FDI system is tested through simulations on a controlled DFIG.

Geometric tools for solving the FDI problem for linear periodic discrete-time systems

This paper studies the problem of detecting and isolating faults in linear periodic discrete-time systems. The aim is to design an observer-based residual generator where each residual is sensitive to one fault, whilst remaining insensitive to the other faults that can affect the system. Making use of the geometric tools, and in particular of the outer observable subspace notion, the Fault Detection and Isolation (FDI) problem is formulated and necessary and solvability conditions are given. An algorithmic procedure is described to determine the solution of the FDI problem.

Fault Detection and Isolation on a Noisy Nonlinear Circuit

In this paper fault detection and isolation (FDI) schemes are applied in the context of the surveillance of emerging faults in an electrical circuit. The FDI problem is studied on a noisy nonlinear circuit, where both abrupt and incipient faults in the voltage source are considered. A rigorous analysis of fault detectability precedes the application of the fault detection (FD) scheme; then, the fault isolation (FI) phase is accomplished with two alternative FI approaches, proposed as new extensions of that FD approach. Numerical simulations illustrate the applicability of the mentioned schemes.

이산시간 상태지연 시스템을 위한 ℋ_/ℋ∞고장검출 및 분리

In this paper, an <TEX>$\mathfrak{H}$</TEX>_/<TEX>$\mathfrak{H}_{\infty}$</TEX> fault detection and isolation (FDI) observer design problem is investigated for discrete-time delayed systems. To that end, a bank consisting of the sensor's number of observers is introduced. Each residual should be sensitive to a certain partial group of faults, but robust against the disturbance as far as possible. We formulate this multiobjective FDI problem as <TEX>$\mathfrak{H}$</TEX>_/<TEX>$\mathfrak{H}_{\infty}$</TEX> observers design problem. Sufficient design condition is expressed as iterative linear matrix inequalities. The fault is then detected and isolated by evaluating the residuals through an FDI decision logic. A computer simulation is provided for verification of the proposed technique.

Integrated Computational Algorithm for Solving H∞-Optimal FDI Problems

AbstractThe synthesis problem of fault detection and isolation (FDI) filters can be formulated as a model matching problem and solved using an H∞-norm optimization approach. A systematic procedure is proposed to choose appropriate filter specifications which guarantee the existence of proper and stable solutions of the model matching problem. This selection is integral part of a numerically reliable computational method to design H∞-optimal FDI filters. The proposed design approach is completely general, being applicable to both continuous- and discrete-time systems, and can easily handle even unstable and/or improper systems.

Actuator Fault Detection and Isolation for a Network of Unmanned Vehicles

This technical note investigates development, design and analysis of actuator fault detection and isolation (FDI) filters for a network of unmanned vehicles. It is shown that actuator fault signatures in a network of unmanned vehicles are dependent and the network can be considered as an over-actuated system. An isolability index mu is defined for a family of fault signatures and a new structured residual set is developed that is selectively capable of properly detecting and isolating mu multiple faults in linear systems with dependent fault signatures, such as over-actuated systems. Our proposed algorithm is then applied to the actuator FDI problem in a network of unmanned vehicles configured according to centralized, decentralized and semi-decentralized architectures. A comparative analysis in terms of the capabilities and limitations of these architectures is performed. Simulation results presented for the formation flight of multiple satellites demonstrate the effectiveness of our proposed FDI algorithm.

Parity Relations based on elimination theory for Fault Detection in a bioprocess

Fault Detection and Isolation (FDI) is an important issue for safe operation in industrial processes. The FDI problem in nonlinear systems is studied within the scope of the analytical redundancy concept. The algebraic approach of parity relations based on the elimination theory is used to generate the analytical redundancy relations. A new method based on resultants is proposed for structuring these relations. The approach is applied to an alcoholic fermentation process.

An application of decentralized estimation in a fault detection problem

This paper presents a design of a decentralized fault detection and isolation (FDI) filter by means of an overlapping decentralized estimation algorithm based on a consensus strategy. An efficient solution to the FDI problem can be obtained by an adequate system decomposition into overlapping subsystems and the construction of local FDI estimators aimed at achieving the desired performance. The general aspects and properties of a consensus based estimator are described in the first part of the paper. An applicability of such an estimator to an FDI problem in a large scale system is discussed next. Namely, a case study related to the detection of fire dissymmetry in a thermal power plant boiler is presented, including the process description and identification procedure, comparison between the results obtained by local and decentralized estimators and conclusions concerning their validity.

Fault Detection and Isolation in Sewer Networks

In this paper, the problem of fault detection and isolation (FDI) in sewer networks will be presented as well as a methodology to address it. In particular, the paper will focus on the case of faults in rain gauges and limnimeters (water level sensors in the sewers). The proposed model based FDI methodology is based on the use of interval methods (in order to enhance robustness) in fault detection and on the use of an isolation algorithm based on several fault signature matrices that considers additional information to the typical binary one. More precisely, fault signature matrices containing information about residual fault sensitivity and time/order of residual activation are used. To exemplify the FDI problem in sewer networks and the proposed FDI methodology, the Barcelona network is used as the case study. Such network has a telemetry system containing 22 rain gauges and more than 100 limnimeters used for the real-time control system. As additional topics discussed in the paper, the problem of reconstructing measurements from faulty sensors will also be addressed after the FDI system has located them. Also, some ideas on how to address the problem of faults in actuators embedding fault tolerance in the controller used for the global control of the network will be provided.

Sensor Location for Diagnosis in Linear Systems: A Structural Analysis

We consider here the fault detection and isolation (FDI) problem for linear systems. We are interested in designing a set of observer-based residuals, in such a way that the transfer from the faults to the residuals is diagonal and the transfer from the disturbances to the residuals is zero. We deal with this problem when the system under consideration is structured, that is, the entries of the system matrices are either fixed zeros or free parameters. This problem can be solved in terms of the graph that can be associated in a natural way with a structured system. When the FDI solvability conditions are not satisfied, we assume that internal variables can be measured at a cost and look into the question of wether the problem is solvable with these new measurements. We give solvability conditions for a solution with a minimal number of additional sensors and among such solutions provide a minimal cost solution for the sensor location problem under consideration. We pay particular attention to the internal analysis of the system, and we propose a structural decomposition of the system associated graph based on some particular separators. This analysis leads to the definition of a reduced system. We prove that some potential additional sensors are inefficient for solving our FDI problem and that the FDI problem can be solved using only measurements on the reduced system