Fault Detection And Isolation Method Research Articles

A Comprehensive Fault Detection and Isolation Method for DC Microgrids Using Reduced-Order Unknown Input Observers

Fault diagnosis is of critical importance to the safety of power electronic devices in DC microgrids. To detect and isolate different component faults in DC microgrids, this paper introduces a comprehensive protection scheme using reduced-order unknown input observers (ROUIOs). As opposed to conventional protection strategies, the proposed method provides a centralized fault detection and isolation (FDI) solution for DC microgrids that covers multiple faults occurring in different components in a unified process. Moreover, it reduces the complexity of observer model and relaxes the requirements of measurement signals compared with existing observer-based FDI methods for DC microgrids. To this end, the state-space model of a multi-terminal DC microgrid with different faults is first established. On this basis, a bank of ROUIOs are designed with the aim of classifying different component faults in the system. At last, the performance of the proposed FDI method is verified through numerical simulations with MATLAB/Simulink and hardware tests. Test results show that the proposed method can accurately detect and isolate different component faults in DC microgrids in a short response time of 1 ms.

Output Feedback Active Fault Tolerant Control for a 3-DOF Laboratory Helicopter With Sensor Fault

In this paper, an output feedback-based active fault tolerant control (FTC) scheme is proposed for an unstable three degree-of-freedom (3-DOF) helicopter equipped only with angular position sensors, of which a single sensor can be faulty. Limited by the available outputs, existing fault estimation (FE) and FTC schemes cannot be applied to this helicopter because when the sensor measuring the elevation or travel angle is faulty, it does not satisfy the minimum-phase and matching conditions required by standard observers. To circumvent this problem, an adaptive interval observer is firstly designed as a fault detection and isolation (FDI) unit to indicate the occurrence and location of a fault, in contrast to the existing FDI methods that require a bank of observers and incur a high computational cost. Then a high-gain observer is combined with a sliding mode observer to form an FE unit that does not require the matching and minimum-phase conditions. Based on the estimate of the fault, an FTC scheme is constructed to ensure an <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\mathcal{H}_{\infty}$</tex-math> </inline-formula> performance of the faulty system. Finally, physical experiments on the 3-DOF helicopter verify the effectiveness of the proposed scheme. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —The 3-DOF lab helicopter serves as an ideal experimental platform for control strategies. In this paper, an active fault tolerant control (FTC) scheme is developed for a 3-DOF lab helicopter when any single sensor can be faulty. The helicopter is nonlinear and subject to external disturbances. There are only encoders measuring the attitude angles and no sensors for angular velocities. In this paper, we firstly design a fault detection and isolation (FDI) unit based on an adaptive interval observer to detect the fault and identify its location; it incurs a lower computational cost compared to existing methods that use a bank of observers. Then a high-gain observer is combined with a sliding mode observer to estimate the fault. Based on the estimate of the fault, an FTC scheme is constructed and designed using Linear Matrix Inequalities to ensure an <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\mathcal{H}_{\infty}$</tex-math> </inline-formula> performance of the faulty system.

Graph Complexity Reduction of Exergy-Based FDI—A Tennessee Eastman Process Case Study

When applying graph-based fault detection and isolation (FDI) methods to the attributed graph data of large and complex industrial processes, the computational abilities and speed of these methods are adversely affected by the increased complexity. This paper proposes and evaluates five reduction techniques for the exergy-graph-based FDI method. Unlike the graph reduction techniques available in literature, the reduction techniques proposed in this paper can easily be applied to the type of attributed graph used by graph-based FDI methods. The attributed graph data of the Tennessee Eastman process are used in this paper since it is a popular process to use for the evaluation of fault diagnostic methods and is both large and complex. To evaluate the proposed reduction techniques, three FDI methods are applied to the original attributed graph data of the process and the performance of these FDI methods used as control data. Each proposed reduction technique is applied to the attributed graph data of the process, after which all three FDI methods are applied to the reduced graph data to evaluate their performance. The FDI performance obtained with reduced graph data is compared to the FDI performance using the control data. This paper shows that, using the proposed graph reduction techniques, it is possible to significantly reduce the size and complexity of the attributed graph of a system while maintaining a level of FDI performance similar to that achieved prior to any graph reduction.

Bus network decomposition for fault detection and isolation through power line communication

This paper deals with fault detection and isolation (FDI) in complex embedded wired communication networks. The considered faults are soft faults which do not prevent the communication, but may evolve into hard faults, i.e. short or open circuit. A novel FDI method based on power line communication (PLC) transmission systems is proposed. In these PLC systems, the transmission coefficients between the source and each receiver are estimated for communication purposes using orthogonal frequency division multiplexing (OFDM). Health indicators and residuals are computed by comparing the online estimated transmission coefficients with the reference coefficients. A methodology for dealing with complex networks, such as bus networks, is proposed. It is based on the decomposition of the network into several Y-shaped sub-networks. Each of these sub-networks is monitored to detect the presence of a fault. The FDI method is first validated using real data extracted from a Y-shaped network test bench. Then, the proposed approach is validated on a more complex network using realistic simulated data.

A comparison of PCA and energy graph-based visualisation FDI on a heated two-tank process

In this study, the well-known Principal Component Analysis (PCA) Fault Detection and Isolation (FDI) method is compared against a recently developed hybrid FDI method, namely Energy Graph-Based Visualisation (EGBV) using twenty different process faults from a practical heated two-tank process. It was found that PCA is the more robust method as it has a lower overall False Alarm Rate (FAR), while EGBV is the superior fault isolation method. The best method for sensitive fault detection, determined using the True Alarm Rate (TAR), is dependent on the detection philosophy followed for EGBV.

A Dynamic Principal Component Analysis and Fréchet-Distance-Based Algorithm for Fault Detection and Isolation in Industrial Processes

Fault Detection and Isolation (FDI) methodology focuses on maintaining safe and reliable operating conditions within industrial practices which is of crucial importance for the profitability of technologies. In this work, the development of an FDI algorithm based on the use of dynamic principal component analysis (DPCA) and the Fréchet distance δdF metric is explored. The three-tank benchmark problem is studied and utilized to demonstrate the performance of the FDI method for six fault types. A DPCA transformation for the system was established, and fault detection was conducted based on the Q statistic. Fault isolation is also of critical importance for proper intervention to mitigate fault effects. To identify the type of detected faults, the fault responses within the PC subspace were analyzed using the δdF metric. The use of the Fréchet distance metric for the isolation of faults combined with DPCA for feature extraction is a novel technique to the best of the authors’ knowledge that provides a robust computational tool with low computational cost for FDI purposes that fits well into the Industry 4.0 framework.The robustness and sensitivity of the method was validated for a wide variety of signal-to-noise ratio (SNR) conditions, with findings indicating a possible average false and missed alarm rate of 0.1 and a macro-averaged F-score above 0.8 in all cases.

Stochastic Event-Triggered Fault Detection and Isolation Based on Kalman Filter.

In this article, the robust fault detection and isolation (FDI) Kalman filter is extended based on stochastic event-triggered schedulers for discrete linear systems consisting of deterministic/stochastic unknown inputs with nonzero mean and colored measurement noise. In the proposed FDI method, first, a subspace of the main system that significantly attenuates disturbance effects is proposed. After that, the fusion method is proposed for dealing with the colored measurement-noise problem in designing the Kalman filter and preventing from leading to measurement noise with zero mean and zero covariance. The stochastic event-triggered schedulers are considered as Gaussian functions. Therefore, the Gaussian property of innovation sequence is preserved, and consequently, the recursive equation of the Kalman filter need not be extended based on approximation techniques. Finally, design parameters are chosen based on the convex optimization problem such that the lowest communication rate between the sensor node and FDI filter, and also the best FDI performance are achieved. The proposed FDI method is evaluated to detect and isolate stator interturn short circuit and broken rotor bars faults with unbalanced voltage as disturbance in three-phase induction motors.

A New Fault Diagnosis Approach for Heavy-Duty Gas Turbines

Effective fault detection, estimation, and isolation are essential for the safety and reliability of gas turbines. In this article, a hybrid fault detection and isolation (FDI) approach is presented for condition monitoring of heavy-duty gas turbines. First, nonlinear dynamical models are constructed using an orthonormal basis function and an adaptive neuro-fuzzy inference system through experimental data. Following that, a fuzzy inference system is employed to estimate the fault severity. For this aim, fuzzy rules are extracted from the fault patterns by defining appropriate features. Later, various faults are isolated using an ensemble decision tree classifier. The proposed nonlinear modeling compensates for disturbances and uncertainty in the system and leads to adaptive thresholds for fault detection, which reduces the false alarm rate. Moreover, the proposed FDI method brings high accuracy in fault estimation by properly modeling a bounded uncertainty using the adaptive threshold. Experimental data are applied to validate the gas turbine model. Test results indicate that the proposed hybrid FDI method via adaptive threshold overwhelms the other FDI methods, where the misclassified data are 5.6%.

Fault-tolerant control with high-order sliding mode for manipulator robot

&lt;p&gt;The safety of manipulators systems is receiving a lot of attention these days. Faults have the ability to carry out harmful activities that harm equipment, the environment, or persons. As a result, it is critical to detect and diagnose problems as soon as possible, as well as to incorporate fault tolerance to avoid performance deterioration and harmful circumstances. Robust sensor fault detection and isolation (FDI) as well as fault-tolerant control (FTC) for a robotic system are discussed in this work. The goal of this research is to create an FDI method that uses a super-twisting third-order sliding mode (STW-TOSM) observer to estimate residual signals. A suggested system based on a higher-order sliding mode (HOSM) observer/controller approaches is used to accomplish this. In comparison to an active FTC technique, the test results demonstrate high level of performance. Finally, simulation results illustrate the efficacy of the presented techniques.&lt;/p&gt;

Fault Detection and Isolation for a Class of Nonlinear Systems Based on Gerschgorin Theorem and Optimization Approach

This article is concerned with the fault detection and isolation (FDI) problem for a class of nonlinear systems described by the T–S fuzzy models. Based on the concept of minimum unobservability subspace and geometric property of factor space, a set of FDI filters where each residual is only affected by one fault and completely decoupled from other faults is designed. Furthermore, in the decoupling space, the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$H_{\infty }/H_{-}$ </tex-math></inline-formula> performance indexes are provided to enhance the sensitivity of residual to faults and robustness to disturbances. In particular, to solve the nonconvex filter design problem caused by introducing the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$H_{-}$ </tex-math></inline-formula> index, the Gerschgorin theorem is first used to linearize the corresponding filter design conditions in the outer region of a ball. Then, the FDI filter design problem is converted into a convex optimization one, which is solved via the linear matrix inequality (LMI) control Toolbox, and the advantages and effectiveness of the proposed FDI method are verified through two simulation examples.

Fault Diagnosis for Networked Switched Systems: An Improved Dynamic Event-Based Scheme.

The issue of fault detection and isolation (FDI) under an event-triggered mechanism (ETM) is investigated for switched linear systems. An improved dynamic ETM (DETM), which includes some existing ETMs as special cases, is devised. Such a DETM contains two internal dynamic variables (IDVs), the mode information and seven adjustable parameters, and thus is flexible in adjusting the data packet transmissions to save network resources. The aim is to design a fault detection (FD) filter (FDF) and fault isolation filters (FIFs) such that the resultant filtering error systems are exponentially stable with prescribed exponential H∞ performance. A new Lyapunov function, which depends on the switching mode and two IDVs, is constructed. By utilizing the Lyapunov method and the average dwell time approach, sufficient conditions are derived to guarantee the existence of the desired FDF and FIFs, whose design methods are given accordingly. A numerical example is provided to demonstrate the effectiveness of the FDI method and the superiority of the devised DETM in reducing the waste of network resources while maintaining the FD filtering performance.

Enhanced chiller faults detection and isolation method based on independent component analysis and k-nearest neighbors classifier

The fault detection and isolation (FDI) of centrifugal chiller system is essential for improving energy efficiency and reducing energy consumption. Considering the non-Gaussianity of chiller measurement, this paper proposes an effective non-Gaussian chiller FDI method based on independent component analysis (ICA) and k-nearest neighbor (KNN) classifier. First of all, an enhanced fault detection method is developed by combining exponentially weighted moving average and ICA (EWICA). The exponentially weighted moving average is employed to develop a dynamic threshold scheme, which reduces the false alarm rate (FAR) and improves the fault detection rate (FDR) of chiller faults. Then, based on the KNN with cosine similarity metric, a novel fault isolation method using the direction of the residual vector is presented to isolate the chiller faults. To further improve the isolation performance, the number of neighbors (k) is optimized by 10-fold cross validation. Finally, the proposed FDI method is validated by using a data set from the ASHRAE Research Project 1043 (RP-1043). The FDI performance of the proposed method is comprehensively analyzed and compared with six state-of-the-art methods. Both of detection and isolation results suggest that the EWICA-based method delivers superior performance for chiller FDI.

Model-Free Geometric Fault Detection and Isolation for Nonlinear Systems Using Koopman Operator

This paper presents a model-free fault detection and isolation (FDI) method for nonlinear dynamical systems using Koopman operator theory and linear geometric technique. The key idea is to obtain a Koopman-based reduced-order model of a nonlinear dynamical system and apply the linear geometric FDI method to detect and isolate faults in the system. Koopman operator is an infinite-dimensional, linear operator which lifts the nonlinear dynamic data into an infinite-dimensional space where the correlations of dynamic data behave linearly. However, due to the infinite dimensionality of this operator, an approximation of the operator is needed for practical purposes. In this work, the Koopman framework is adopted toward nonlinear dynamical systems in combination with the linear geometric approach for fault detection and isolation. In order to demonstrate the efficacy of the proposed FDI solution, a mathematical nonlinear dynamical system, and an experimental three-tank setup are considered. Results show a remarkable performance of the proposed geometric Koopman-based fault detection and isolation (K-FDI) technique.

Robust Detection of Minute Faults in Uncertain Systems Using Energy Activity

Fault detection is one of the key steps in Fault Detection and Isolation (FDI) and, therefore, critical for subsequent prognosis or implementation of Fault Tolerant Control (FTC). It is, therefore, advisable to utilize detection algorithms which are quick and can detect the smallest faults. Model-based detection methods satisfy both these criteria and should be preferred. However, a big limitation for model-based methods is that they require the accurate value of the component parameters, which is difficult to obtain in real situations. This limits the accuracy of model-based methods. This paper proposes a new method for fault detection using Energy Activity (EA) which can detect minute levels of fault in systems with high component uncertainty. Different forms of EA are developed for use as an FDI metric. The proposed forms are simulated using a two-tank system under various types of faults. The results are compared with each other and with the traditional model-based FDI method using Analytical Redundancy Relations (ARRs). The simulations are performed considering model uncertainties to check the inherent performance of the methods. From initial simulations, it is established that the integral form of EA is most suited for fault detection. The integral for if EA is then tested using a real two-tank system considering both the model and measurement uncertainties.

Energy-based fault detection and isolation of a Brayton cycle-based HTGR power conversion unit – A comparative study

The amount of data associated with condition monitoring of large scale industrial plants has led to a “big data problem”. Energy-based fault detection and isolation (FDI) has been proposed as a hybrid FDI method to reduce data requirements and improve FDI performance. In this paper, a comparative study is performed on three energy-based FDI techniques, namely: an enthalpy–entropy error-based technique, a residual-based technique and an eigendecomposition-based technique. A representative Brayton cycle-based power conversion unit (PCU) is considered as the case study and simulated under normal and fault conditions. From the simulation data, the PCU is represented in terms of energy, either as an enthalpy–entropy diagram or an attributed graph. The graph representation allows structural information to be captured which contributes towards fault isolation capability. For each FDI technique, operational energy signatures are compared to normal and fault reference energy signatures to detect and isolate fault conditions. This paper contributes by comparing the FDI techniques based on the attributes of quick detection and isolation. The residual-based technique had the best performance in detecting and isolating faults. The results also indicate that a combination of techniques will result in an FDI technique with improved detection and isolation performance over the individual techniques.

Data-driven design of fault detection and isolation method for distributed homogeneous systems

Driven by the increasing needs in large-scale industrial processes for maintaining stable operation using process data, a data-driven fault detection and isolation (FDI) method for distributed homogeneous systems is proposed in this paper. To this end, the original distributed homogeneous system is first decomposed into several separate subsystems using invertible transformations of the state, input, and output variables. On this basis, the fault detection method according to the one-step identification of the stable kernel representation (SKR) is developed for the transformed subsystems. It is followed by the fault estimation for the transformed subsystems along with implementation on fault isolation for the original distributed system. Experiments comparing the proposed FDI method and the centralized FD method are performed using a numerical example. In this specific example, the proposed FDI method has a computational complexity of 2.32×107, while the centralized FD method has a computational complexity of 9.31×107. This shows the effectiveness and advantages of the proposed method.

Fault-Tolerant Control for Three-Tank System in Case of Sensor Faults

This research paper would be devoted to the application of a fault-tolerant control (FTC) for a benchmark system composed of three interconnected tanks in case of sensor faults. The control scheme includes two blocks: fault detection and isolation (FDI) block and a control law reconfiguration block. The strategy of the FDI method is based on a bank of high gain observers; each of them is constructed to estimate the system state vector. Thus, the diagnostic signal-residuals are generated by the comparison of measured and estimated outputs and the faulty sensor is isolated. The reconfiguration block performs an update of the controller parameters according to the operating mode. The application of this method to a pilot plant demonstrates that the hydrographic system maintains quite performances after sensor faults occurrence.

Fault detection and isolation using sliding mode observers with sensor fault in robot manipulator

Interest in fault detection and isolation (FDI) for nonlinear systems has grown significantly in recent years. The design of a residual generator for FDI in nonlinear systems which are affine in the control signals and in the failure modes is studied. The (FDI) approach is used to detect sensor faults on a robot manipulator that occurs on a system's specific component. The sliding mode observer is one of the FDI's methods, which are used to generate residuals when sensor faults is presented. This paper aims to improve the detection of sensor faults for an affine nonlinear system class. For a better detection of faults, a proportional derivative (PD) controller is used to improve the system's stability. Both the aforementioned method and the (PD) controller are based on the Lyapunov equation. The proposed approach is verified in simulation on a robot manipulator.

Fuzzy FDI System for Flyback Converters

In this article, the design of a fault detection and isolation (FDI) system for a flyback converter, based on a fuzzy unknown input observer, is presented. The proposed FDI system aims to detect the saturation in the inductor core as a fault, considering the current increment can cause an avalanche effect provoking a failure in the system. A Takagi–Sugeno model of the flyback converter is used. The observer's convergence and the stability of the fault diagnosis system are guaranteed by Lyapunov's theory and the estimation error used to determine the necessary LMIs. A comparison of different FDI methods applied in fuzzy systems is performed in order to select the most robust system, capable of estimating the current in the system even in the presence of external disturbances. The system validation is performed in both, simulation and online implementation, using experimental data.

Distributed Fault Detection and Isolation for Multiagent Systems: An Interval Observer Approach

This paper presents a distributed method for detecting and isolating the faults in multiagent systems (MASs). First, for the output-feedback-based closed-loop MAS in normal case and faulty cases, a bank of fault detection and isolation (FDI) interval observers are constructed by taking into account the outputs of local and received from neighbors, the bounds of one node fault signal and the whole disturbances. Then, the observer gains can be determined by solving the disturbance attenuation, fault sensitivity, and nonnegativity conditions, simultaneously. Furthermore, an alarm signal is released when the zero value is excluded from any one of the intervals, a node fault is indicated when the zero value is included in one residual interval and excluded from the surplus intervals. Different from the classical FDI methods, the proposed interval observers are able to both generate the residual signals and imply the thresholds. Under such framework, each agent has the capacity to determine which neighbor agent is faulty. Finally, the developed techniques are demonstrated in a simulation example.