Fault Isolation Algorithm Research Articles

Three- and Seven-Point Optimally Weighted Recursive Median Filters for Gas Turbine Diagnostics

Measured health signals incorporate significant details about any malfunction in a gas turbine. The attenuation of noise and removal of outliers from these health signals while preserving important features is an important problem in gas turbine diagnostics. The measured health signals are a time series of sensor measurements such as the low rotor speed, high rotor speed, fuel flow, and exhaust gas temperature in a gas turbine. In this article, a comparative study is done by varying the window length of acausal and unsymmetrical weighted recursive median filters and numerical results for error minimization are obtained. It is found that optimal filters exist, which can be used for engines where data are available slowly (three-point filter) and rapidly (seven-point filter). These smoothing filters are proposed as preprocessors of measurement delta signals before subjecting them to fault detection and isolation algorithms.

A robust fault detection and diagnosis strategy for pressure-independent VAV terminals of real office buildings

A robust fault detection and diagnosis (FDD) strategy using a hybrid approach is presented for pressure-independent variable air volume (VAV) terminals in this paper. The residual-based cumulative sum (CUSUM) control charts are utilized to detect faults in VAV terminals. The residuals between the temperature error and its predication are generated using autoregressive time-series models. The standard CUSUM control charts are used to monitor the residuals which are statistically independent. If the CUSUM value exceeds the chart limits, it means the occurrence of fault or abnormity in the corresponding VAV terminal. The residual-based CUSUM control chart can improve the accuracy of fault detection through eliminating the effects of serial correlation on the performance of control charts. Also, the residual-based CUSUM control chart can enhance the robustness and reliability of fault detection through reducing the impacts of normal transient changes. A rule-based fault classifier consisting of expert rules and fault isolation algorithms is developed to isolate 15 fault sources. The FDD strategy was online tested and validated using in real time data collected from real VAV air-conditioning systems.

Fault Diagnosis of Wind Turbines using a Set-membership Approach

AbstractIn this paper, the problem of fault diagnosis in wind turbines will be addressed applying a set-membership approach. Fault detection is based on the use of parity equations and unknown but bound description of the noise and modelling errors. Fault detection test is based on checking consistency between measurements and the model by finding if there is parameter in the feasible parameter set (approximated by a zonotope) that is compatible with modelling and noise bounds. The fault isolation algorithm is based on analyzing the observed fault signatures on-line and matching them with the theoretical ones obtained using structural analysis. Finally, the proposed approach will be tested using the wind turbine benchmark proposed in the context of the WT FDI competition.

Reconfigurable Control Systems: A Nonhomogeneous Markovian Jump System Approach

AbstractThis paper deals with the problem of stochastic stability of a class of discrete-time reconfigurable control systems with Markovian parameters. In this class of system, two Markovian processes are considered. The first one is used to model random failures affecting the plant and the second one is used to describe the decisions of a fault detection and isolation (FDI) algorithm. These Makovian processes are supposed to be nonhomogeneous, that is there transition probabilities (TPs) are time-varying. The time-varying character is considered to be in a polytopic sense. The approach followed in this note is based on the use of a parameter dependent stochastic Lypaunov function. We give a stability condition in terms of a linear matrix inequalities (LMI) feasibility problem. The obtained results are illustrated on a numerical example.

The Application of Immune Guided Dtsvm in Fault Isolation

Support Vector Machine (SVM) is originally designed for linear binary pattern recognition. According to defect of common SVM, the Least square SVM is discussed to classify the small samples. In fault diagnosis, multi-category faults are common. Many multi-category SVMs are introduced in this paper briefly, and then an immune guided decision tree SVM (DTSVM) is presented which takes into account the fault occurring frequency. It proposes a fault isolation algorithm which adopts immune memory to optimize the configuration of DTSVM. Simulation and examples show that this method is useful in fault isolation. The kernel functions play an important role in the classification. The choice of kernel function and its parameters is neither easy nor trivial. In this paper, we have researched the kernel function of RBF and its effect on classification.

Attitude Estimation for Satellite Fault Tolerant System Using Federated Unscented Kalman Filter

We propose a spacecraft attitude estimation algorithm using a federated unscented Kalman filter. For nonlinear spacecraft systems, the unscented Kalman filter provides better performance than the extended Kalman filter. Also, the decentralized scheme in the federated configuration makes a robust system because a sensor fault can be easily detected and isolated by the fault detection and isolation algorithm through a sensitivity factor. Using the proposed algorithm, the spacecraft can continuously perform a given mission despite navigation sensor faults. Numerical simulation is performed to verify the performance of the proposed attitude estimation algorithm.

Analytic Confusion Matrix Bounds for Fault Detection and Isolation Using a Sum-of-Squared-Residuals Approach

Given a system which can fail in 1 of n different ways, a fault detection and isolation (FDI) algorithm uses sensor data to determine which fault is the most likely to have occurred. The effectiveness of an FDI algorithm can be quantified by a confusion matrix, also called a diagnosis probability matrix, which indicates the probability that each fault is isolated given that each fault has occurred. Confusion matrices are often generated with simulation data, particularly for complex systems. In this paper, we perform FDI using sum-of-squared residuals (SSRs). We assume that the sensor residuals are s-independent and Gaussian, which gives the SSRs chi-squared distributions. We then generate analytic lower, and upper bounds on the confusion matrix elements. This approach allows for the generation of optimal sensor sets without numerical simulations. The confusion matrix bounds are verified with simulated aircraft engine data.

Model—based fault diagnosis of a pump-displacement-controlled actuator with a multidisciplinary approach using bond graph

In this chapter, firstly, the pump-displacement-controlled actuator system with applications in aerospace industries is modeled using the bond graph methodology. Secondly, an approach is developed towards simplification and model order reduction for bond graph models that can usually use in conceptual representation or design procedures. The model order reduction process indicates which system components have the most bearing on the frequency response, and the final model retains structural information. Finally, the state space form of mathematical model of the system based on the bond graph model is presented. By associating bond graph model, it becomes possible to design fault detection and isolation (FDI) algorithms, i.e. the generation of fault indicators, and to improve monitoring of the actuator.

Neural network-based approach for early detection of cascading events in electric power systems

This study proposes neural modelling and fault diagnosis methods for the early detection of cascading events in electric power systems. A neural-fuzzy network is used to model the dynamics of the power transmission system in fault-free conditions. The output of the neural-fuzzy network is compared to measurements from the power system and the obtained residuals undergo statistical processing according to a fault detection and isolation algorithm. If a fault threshold, defined by the fault detection and isolation (FDI) algorithm, is exceeded then deviation from normal operation can be detected at its early stages and an alarm can be launched. In several cases fault isolation can be also performed, that is the sources of fault in the power transmission system can be also identified. The performance of the proposed methodology is tested through simulation experiments.

An indoor localization system in a multiblock workspace

As service robots and other ubiquitous technology have evolved, an increasing need for the autonomous navigation of mobile objects has arisen. In a large number of localization schemes, the absolute-position estimation method, which relies on navigation beacons or landmarks, has been widely used as it has the advantages of being economical and accurate. However, only a few of these schemes have expanded their application to complicated workspaces, or those that have many rooms or blocks. As the navigation of mobile objects in complicated workspaces is vital for ubiquitous technology, multiblock navigation is necessary. This article presents methodologies and techniques for the multiblock navigation of the indoor localization system with active beacon sensors. This new indoor localization system design includes ultrasonic attenuation compensation, dilution-of-precision analysis, and a fault detection and isolation algorithm using redundant measurements.

Fault Detection and Isolation Based on the Constrained GLR Test

The problem of fault diagnosis (detection and isolation) in safety-critical manufacturing systems is addressed in the paper. Such an application necessitates to carefully define the notion of “failure” for a safety-critical manufacturing system and to design a fault detection and isolation algorithm by explicitly taking into account this definition. To integrate the information on the system failures in decision-making algorithm, it is proposed to use the constrained generalized likelihood ratio test. It is shown that due to a more accurate definition of the failure the statistical properties of such a diagnosis algorithm can be improved.

Distributed Fault Detection and Isolation Algorithm Using Modified Gath-Geva Clustering Technique

This paper presents a set of new fault detection and isolation (FDI) approaches based on a Modified Gath-Geva (MGG) Clustering approach. The proposed approaches are formulated in the forms of a combined principal component analysis (PCA)-MGG or nonlinear PCA (NPCA)-MGG schemes. The PCA and NPCA perform as a data preprocessing step on the measured industrial multivariate time series data to enhance their informative richness, capsulated in a more compact form. Then, the MGG clustering approach is used to detect and isolate the faults by organizing the PCA/NPCA-transformed data in different clusters. A distributed-MGG scheme is also proposed in this paper as a new FDI approach which is based on a distributed monitoring configuration. The main idea is to divide the overall monitoring task into a series of local distributed monitoring sub-tasks so as to track and capture the process faults locally. The diagnostic performances of the proposed FDI approaches are evaluated by a set of comparative test studies on the Tennessee Eastman process plant as a large-scale benchmark problem.

Fault Detection, Isolation, and Accommodation for LTI Systems Based on GIMC Structure

In this contribution, an active fault‐tolerant scheme that achieves fault detection, isolation, and accommodation is developed for LTI systems. Faults and perturbations are considered as additive signals that modify the state or output equations. The accommodation scheme is based on the generalized internal model control architecture recently proposed for fault‐tolerant control. In order to improve the performance after a fault, the compensation is considered in two steps according with a fault detection and isolation algorithm. After a fault scenario is detected, a general fault compensator is activated. Finally, once the fault is isolated, a specific compensator is introduced. In this setup, multiple faults could be treated simultaneously since their effect is additive. Design strategies for a nominal condition and under model uncertainty are presented in the paper. In addition, performance indices are also introduced to evaluate the resulting fault‐tolerant scheme for detection, isolation, and accommodation. Hard thresholds are suggested for detection and isolation purposes, meanwhile, adaptive ones are considered under model uncertainty to reduce the conservativeness. A complete simulation evaluation is carried out for a DC motor setup.

An Analytical Redundancy-Based Fault Detection and Isolation Algorithm for a Road-Wheel Control Subsystem in a Steer-By-Wire System

This paper presents a novel observer-based analytical redundancy for a steer-by-wire (SBW) system. An analytical redundancy methodology was utilized to reduce the total number of redundant road-wheel angle (RWA) sensors in a triply redundant RWA-based SBW system while maintaining a high level of reliability. A full-state observer was designed using the combined model of the vehicle and SBW system to estimate the vehicle-body sideslip angle. The steering angle was then estimated from the observed and measured states of the vehicle (body sideslip angle and yaw rate) as well as the current input to the SBW electric motor(s). A fault detection and isolation (FDI) algorithm was developed using a majority voting scheme, which was then used to detect faulty sensor(s) to maintain safe drivability. The proposed analytical redundancy-based FDI algorithms and the linearized vehicle model were modeled in SIMULINK. Simulation results show that the proposed analytical redundancy-based FDI algorithm provides the same level of fault tolerance as in an SBW system with full hardware redundancy against single-point failures.

Distributed Diagnosis in Formations of Mobile Robots

Multirobot systems are being increasingly used for a variety of tasks in manufacturing, surveillance, and space exploration. These systems can degrade or develop faults during operation, and, therefore, require online diagnosis algorithms to ensure safe operation. Centralized approaches to online diagnosis of robot formations do not scale well for two primary reasons: 1) the computational complexity of the algorithm grows significantly with the number of robots, and 2) the individual robots must communicate a large number of measurements to a central diagnoser. To overcome these problems, we present a distributed, model-based, qualitative fault-diagnosis approach for formations of mobile robots. The approach is based on a bond-graph modeling framework that can deal with multiple sensor types and isolate process, sensor, and actuator faults. The diagnosis scheme employs relative measurement orderings to discriminate among faults by exploiting the temporal order of measurement deviations. This increases the discriminatory power of the measurement set and produces a more efficient fault-isolation algorithm. We describe a distributed diagnoser design algorithm applied to robot formations. Experimental results demonstrate the improvement in both the discriminatory power of the measurements produced by the relative measurement orderings, and the computational efficiency achieved by the distributed-diagnosis approach

An unknown input Kalman filter based component FDI algorithm and its application in automobiles

An Unknown Input Kalman Filter (UIKF) based Component Fault Detection and Isolation (CFDI) technique for a dynamical system, affected by both plant and measurement noise, is presented. The Fault Detection and Isolation (FDI) algorithm, which consists of two steps, is developed with the assumption that the fault occurs in a single component of the system. In step 1, the detection of the fault and the isolation of the faulty region are achieved. In the next step, the faulty parameter is isolated from the faulty region. The method is applied on a road vehicle model to show the effectiveness of the algorithm.

SENSOR FAULT DIAGNOSIS OF AUTONOMOUS UNDERWATER VEHICLE BASED ON NONLINEAR PRINCIPALCOMPONENT ANALYSIS

An approach of sensor fault diagnosis based on a NLPCA (Nonlinear Principal Component Analysis) model is described in this paper. Firstly, in order to perform NLPCA, a double RBF(Radial Basis Function) neural network is introduced; Secondly, NLPCA fault detection and isolation algorithm is presented; Finally, A simulation example of sensor fault diagnosis of autonomous underwater vehicle is given to show the performances of the proposed approach.

Hybrid Fault Detection and Isolation Techniques for Aircraft Inertial Measurement Sensors

In this paper, a redundancy management system for aircraft is studied, and fault detection and isolation algorithms of inertial sensor system are proposed. Contrary to the conventional aircraft systems, UAV system cannot allow triple or quadruple hardware redundancy due to the limitations on space and weight. In the UAV system with dual sensors, it is very difficult to identify the faulty sensor. Also, conventional fault detection and isolation (FDI) method cannot isolate multiple faults in a triple redundancy system. In this paper, two FDI techniques are proposed. First, hardware based FDI technique is proposed, which combines a parity equation approach with a wavelet based technique. Second, analytic FDI technique based on the Kalman filter is proposed, which is a model-based FDI method utilizing the threshold value and the confirmation time. To provide the reference value for detecting the fault, residuals are calculated using the extended Kalman filter. To verify the effectiveness of the proposed FDI methods, numerical simulations are performed.

REAL-TIME FAULT DETECTION AND ISOLATION WITH SUPERVISED TRAINING

The paper is focused on fault detection and isolation (FDI) in a stochastic nonlinear system affected by noise. The substance of the presented Bayesian identification-based FDI methodology is a probabilistic model determined by the fault probability table. The methodology consists in probabilistic mapping of the measured data into a fault variable. Necessary computations are very simple and heuristic knowledge about the fault can also be easily included in real time. The practical aspects of the proposed FDI algorithm have been tested in real time on a laboratory heating system. The results obtained proved suitability of the designed algorithm for fault detection and isolation in real plants.

Design and implementation of a fault tolerant controller for EMS systems

Conventional EMS (electromagnetic suspension) systems are susceptible to instability problems, and could even break down upon failures of the air-gap sensor or the accelerometer. Therefore, in order to improve EMS performance, a fault tolerant controller and a fault detection and isolation (FDI) algorithm are presented in this work. The fault tolerant controller is an extended version of the linear fault tolerant controller designed for known actuator or sensor failures, and it adopts the LMI-based H ∞ control for a class of nonlinear systems. The fault detection algorithm employs fuzzy inference. The merits of the proposed control scheme have been verified by the experiments with a single-axis two-magnet suspension system subjected to failures of the actuator or the sensors.