For Fault Detection And Isolation Research Articles

Redundant MEMS-Based Inertial Navigation Using Nonlinear Observers

We present two alternative methods for fault detection and isolation (FDI) with redundant Microelectromechanical system (MEMS) inertial measurement units (IMUs) in inertial navigation systems (INS) based on nonlinear observers (NLOs). The first alternative is based on the parity space method, while the second is expanded with quaternion-based averaging and FDI. Both alternatives are implemented and validated using data gathered in a full-scale experiment on an offshore vessel. Data from three identical MEMS IMUs and the vessel's own industrial sensors are used to verify the methods' FDI capabilities. The results reveal that when it comes to FDI of the IMUs' angular rate sensors, there are differences between the two methods. The navigation algorithm based on quaternion weighting is essentially unaffected by the failure of an angular rate sensor, while the parity-space-method-based alternative experiences a perturbation.

Heating, ventilation and air conditioning systems: Fault detection and isolation and safe parking

This work presents an integrated framework for fault detection and isolation (FDI) and fault tolerant control (FTC) of variable air volume (VAV) boxes, a common component of heating, ventilation and air conditioning (HVAC) systems. To this end, first a statistical model based FDI framework is designed using existing techniques such as principal component analysis (PCA) and joint angle analysis as a benchmark for comparison. Then a novel linear causal model based framework for FDI of multiple actuator and multiple sensor faults is designed and implemented and shown to possess superior FDI capabilities compared to the statistical model based framework. Finally, a safe parking strategy is designed and the ensuing energy savings for the case of stuck dampers demonstrated.

Interval Observer Approach to Output Stabilization of Linear Impulsive Systems

The problem of output stabilization is studied for a class of linear hybrid systems subject to signal uncertainties: linear impulsive systems under dwell-time constraints. Two problems are considered. First, an interval observer estimating the set of admissible values for the state is designed. Next, an output stabilizing feedback design problem is studied where the stability is checked using linear matrix inequalities (LMIs). To the best of our knowledge, interval observer approach has never been proposed for the stabilization of this class of hybrid systems. Efficiency of the proposed approach is demonstrated by computer experiments for Fault Detection and Isolation (FDI) and Fault-Tolerant Control (FTC) of a power split device with clutch for heavy-duty military vehicles.

Sensor Fault Detection and Isolation Based on Artificial Neural Networks and Fuzzy Logic Applicated on Induction Motor for Electrical Vehicle

<p>Recently, research has picked up a fervent pace in the area of fault diagnosis of electrical vehicle. Like failures of a position sensor, a voltage sensor, and current sensors. Three-phase induction motors are the “workhorses” of industry and are the most widely used electrical machines. This paper presents a scheme for Fault Detection and Isolation (FDI). The proposed approach is a sensor-based technique using the mains current measurement. Current sensors are widespread in power converters control and in electrical drives. Thus, to ensure continuous operation with reconfiguration control, a fast sensor fault detection and isolation is required. In this paper, a new and fast faulty current sensor detection and isolation is presented. It is derived from intelligent techniques. The main interest of field programmable gate array is the extremely fast computation capabilities. That allows a fast residual generation when a sensor fault occurs. Using of Xilinx System Generator in Matlab / Simulink allows the real-time simulation and implemented on a field programmable gate array chip without any VHSIC Hardware Description Language coding. The sensor fault detection and isolation algorithm was implemented targeting a Virtex5. Simulation results are given to demonstrate the efficiency of this FDI approach.</p>

Fault Diagnosis for Electric Drive Systems of Electrified Vehicles Based on Structural Analysis

Functional safety is of great importance for electric and hybrid electric vehicles (EV/HEVs). One way to improve functional safety of EV/HEVs is to develop reliable and robust onboard diagnostics (OBD) so that, once a component fault is detected, effective remedial actions are taken to avoid system failures. In this paper, we develop a systematic model-based diagnostic approach based on structural analysis for electric drive systems that can form the basis of OBD design for EVs. The structural analysis approach for fault detection and isolation (FDI) evaluates a system's structural model, using the mathematical model of the system in matrix form, from which it is possible to determine the analytic redundancy and to design the structured residuals. In this paper, we demonstrate the application of this methodology by carrying out the design of a diagnostic approach for permanent-magnet synchronous machine (PMSM) drive systems of EVs, with specific focus on sensor fault diagnosis. The diagnostic approach is first verified through a simulation study on the EcoCAR2 vehicle, which is a plug-in HEV developed by the Center for Automotive Research at The Ohio State University, and is then further validated through an experimental study on a test bench consisting of a three-phase inverter enabled by the TMS320F28035 digital signal processor and a PMSM.

Robust fault detection and isolation in bond graph modelled processes with Bayesian networks

The main objective of this paper is to present a new method for Fault Detection and Isolation (FDI) of non-linear uncertain parameters systems modelled by bond graphs (BGs) with Bayesian networks (BN). From the BG model of a process, residuals, which are fault detectors, are determined directly from the Diagnostic Bond Graph (DBG). In ideal conditions, those residuals are equal to zero. But in practice, owing to uncertainties, perturbations and measurement noises, residuals are different from zero. Classical approaches used thresholds to deduce whether a process is in normal operating mode or in faulty mode. In our approach, we generate a statistical decision procedure to detect the operating mode. For isolation, a Bayesian network is generated by covering the causal paths of the DBG, and the method proposed by Weber et al. is exploited. A simulation example on a three tanks system is provided to show the efficiency of the proposed FDI procedure.

Sensor fault detection and isolation over wireless sensor network based on hardware redundancy

In order to diagnose sensor faults with small magnitude in wireless sensor networks, distinguishability measures are defined to indicate the performance for fault detection and isolation (FDI) at each node. A systematic method is then proposed to determine the information to be exchanged between nodes to achieve FDI specifications while limiting the computation complexity and communication cost.

Robust fault detection and isolation in bond graph modelled processes with Bayesian networks

The main objective of this paper is to present a new method for Fault Detection and Isolation (FDI) of non-linear uncertain parameters systems modelled by bond graphs (BGs) with Bayesian networks (BN). From the BG model of a process, residuals, which are fault detectors, are determined directly from the Diagnostic Bond Graph (DBG). In ideal conditions, those residuals are equal to zero. But in practice, owing to uncertainties, perturbations and measurement noises, residuals are different from zero. Classical approaches used thresholds to deduce whether a process is in normal operating mode or in faulty mode. In our approach, we generate a statistical decision procedure to detect the operating mode. For isolation, a Bayesian network is generated by covering the causal paths of the DBG, and the method proposed by Weber et al. is exploited. A simulation example on a three tanks system is provided to show the efficiency of the proposed FDI procedure.

An ensemble of dynamic neural network identifiers for fault detection and isolation of gas turbine engines

In this paper, a new approach for Fault Detection and Isolation (FDI) of gas turbine engines is proposed by developing an ensemble of dynamic neural network identifiers. For health monitoring of the gas turbine engine, its dynamics is first identified by constructing three separate or individual dynamic neural network architectures. Specifically, a dynamic multi-layer perceptron (MLP), a dynamic radial-basis function (RBF) neural network, and a dynamic support vector machine (SVM) are trained to individually identify and represent the gas turbine engine dynamics. Next, three ensemble-based techniques are developed to represent the gas turbine engine dynamics, namely, two heterogeneous ensemble models and one homogeneous ensemble model. It is first shown that all ensemble approaches do significantly improve the overall performance and accuracy of the developed system identification scheme when compared to each of the stand-alone solutions. The best selected stand-alone model (i.e., the dynamic RBF network) and the best selected ensemble architecture (i.e., the heterogeneous ensemble) in terms of their performances in achieving an accurate system identification are then selected for solving the FDI task. The required residual signals are generated by using both a single model-based solution and an ensemble-based solution under various gas turbine engine health conditions. Our extensive simulation studies demonstrate that the fault detection and isolation task achieved by using the residuals that are obtained from the dynamic ensemble scheme results in a significantly more accurate and reliable performance as illustrated through detailed quantitative confusion matrix analysis and comparative studies.

Functional Hybrid Bond Graph for Operating Mode Management

This paper proposes the Operating Modes Management (OMM) of the Hybrid Dynamical Systems (HDS) using a generic tool named Hybrid Bond Graph (HBG) driven by an automaton. This tool is used for modeling and for fault detection and isolation (FDI) as well. The innovative interest is the use of only one tool (the HBG) for the behavioral and functional modeling and the OMM taking into account the FDI results. A hybrid hydraulic example is presented to demonstrate the effectiveness of this technique.

Built-in Test Design for Fault Detection and Isolation in an Aircraft Environmental Control System

A novel built-in test (BIT) design method for fault detection and isolation (FDI) is presented, in which the test information extracted is maximized using parametric sensitivities derived by a system model. Two case studies are presented to demonstrate this approach. The first test focuses on fouling identification in an aircraft heat exchanger, in the presence of uncertain system inputs. The second example expands this method to a subsystem of an aircraft environmental control system (ECS) to calculate optimal conditions for component FDI.

Fault Diagnosis of a Wind Turbine Benchmark via Identified Fuzzy Models

In order to improve the availability of wind turbines and to avoid catastrophic consequences, the detection of faults in their earlier occurrence is fundamental. This paper proposes the development of a fault diagnosis scheme relying on identified fuzzy models. The fuzzy theory is exploited since it allows approximating uncertain models and managing noisy data. These fuzzy models, in the form of Takagi-Sugeno prototypes, represent the residual generators used for fault detection and isolation (FDI). A wind turbine benchmark is used to validate the achieved performances of the designed FDI scheme. Finally, extensive comparisons with different fault diagnosis methods highlight the features of the suggested solution.

SLAM Bayesian Network Model For Robot Behavior

In this paper, a bayesian framework for fault detection and isolation (FDI) based on kalman filtering is developed. Furthermore, in order to detect the faults affecting on the covariance matrix of the kalman filter, a real-time approach is presented. This proposed framework extracts the proper behavior of a mobile robot besides simultaneously localization and map building (SLAM). Actually the framework is a combination of the kalman filter and bayesian networks. Learning the model of the world is difficult. In particular when the system dynamics become nondeterministic, all aspects of the system cannot be directly observed and the sensors are subjected to noise. In many situations, learning a complete model is not possible. Therefore, only probabilistic models which are capable of taking uncertainty of sensors and environment can be employed. In this paper, we describe a framework as a composition between model-free and model-based systems. Model learning is perfectly based on bayesian network (BN) and fault detection is done by kalman filter. Experimental results show that the learned model outperforms the traditional BN. We demonstrate how the resulting algorithm can be used to detect faults in a complex system. Proposed method is not very sensitive to changing the map of robot. However, the Bayesian network and dynamic Bayesian network are very sensitive to changing the map and in the presence of the fault. The proposed method is tested in a real home environment with a mobile robot. Keywords: Bayesian network, behavior, fault detection, kalman filter, mobile robot, SLAM.

Sensor and Actuator Fault Diagnosis Based on Soft Computing Techniques

AbstractComputational intelligence techniques are being investigated as an extension of the traditional fault diagnosis methods. This article presents, for the first time, a scheme for fault detection and isolation (FDI) via artificial neural networks and fuzzy logic. It deals with the sensor fault of a three-link selective compliance assembly robot arm (SCARA) robot. A second scheme is proposed for fault detection and accommodation via analytical redundancy, and it deals with the sensor fault of a three-link SCARA robot. These proposed FDI approaches are implemented on Matlab/Simulink software and tested under several types of faults. The results show the importance of this process. Then, the sensor faults are detected and isolated successfully. Also, the actuator faults are detected and a fault tolerance strategy is used for reconfigurable control using a sliding-mode observer.

FITBOX - A Fault Isolation Toolbox

In this paper, we present the theoretical background for the implementation of FITBOX - a new, freely available fault isolation toolbox for MATLAB that makes use of novel set-membership methods for Fault Detection and Isolation (FDI). We apply the proposed methods to the FDI of a wind turbine and evaluate their performance in a simulation setting.

Vibration-based Fault Diagnosis Approach for Permanent Magnet Synchronous Motors

The aim of this paper is to propose a fault detection and diagnosis approach for Permanent Magnet Synchronous Machines (PMSMs) based on vibration signals analysis. First, an improved multi-physical model of the machine has been simulated to generate healthy and faulty vibration displacements, by considering the entire stator structure. The presented model has been validated, in previous papers, by a Finite Element Analysis (FEA). Then, a detailed state-of-the-art on the most encountered machine faults, with their electromagnetic and mechanical signatures, is presented. Both Rotor Eccentricity and DeMagnetization faults are simulated by changing some model's parameters and then, they are detected by a spectral and modal analysis of vibration displacements. Finally, a global diagnosis approach is investigated for Fault Detection and Isolation (FDI) in the considered machine. Therefore, the proposed multi-physical model could provide healthy and faulty characteristics and could be suitably used for vibration monitoring and fault diagnosis tasks.

LRE Fault Detection and Isolation Based on Fuzzy Direction Neural Network

A fuzzy directions neural network used for fault detection and isolation (FDI) of a liquid rocket engine (LRE) is presented in this paper. Neural network utilizes fuzzy sets as engine fault classes. Each fuzzy set is an aggregate of fuzzy direction bodies. A fuzzy direction body is described by a direction vector, an included angle and two radii. FDI simulation of the turbo-pump fed liquid rocket engine demonstrates the strong qualities of the fuzzy direction neural network.

Fault detection and isolation in hybrid process systems using a combined data‐driven and observer‐design methodology

A combined data‐driven and observer‐design methodology for fault detection and isolation (FDI) in hybrid process systems with switching operating modes is proposed. The main contribution is to construct a unified framework for FDI by integrating Gaussian mixture models (GMM), subspace model identification (SMI), and results from unknown input observer (UIO) theory. Initially, a GMM is built to identify and describe the multimodality of hybrid systems using the recorded input/output process data. A state‐space model is then obtained for each specific operating mode based on SMI if the system matrices are unknown. An UIO is designed to estimate the system states robustly, based on which the fault detection is laid out through a multivariate analysis of the residuals. Finally, by designing a set of unknown input matrices for specific fault scenarios, fault isolation is performed through the disturbance‐decoupling principle from the UIO theory. A significant benefit of the developed framework is to overcome some of the limitations associated with individual model‐based and data‐based approaches in dealing with the problem of FDI in hybrid systems. Finally, the validity and effectiveness of the proposed monitoring framework are demonstrated using a numerical example, a simulated continuous stirred tank heater process, and the Tennessee Eastman benchmark process. © 2014 American Institute of Chemical Engineers AIChE J, 60: 2805–2814, 2014

Designing fault detection observers for linear systems with mismatched unknown inputs

Algebraic unknown input observers (UIOs) that have been previously reported in the literature can be constructed under the assumption that linear systems with unknown inputs satisfy the so-called observer matching condition. This condition restricts practical applications of UIOs for fault detection and isolation (FDI). We present an algebraic design for fault detection observers (FDOs) for the case in which the observer matching condition is not satisfied. To loosen the restriction imposed by the observer matching condition, the UIO design method combined with the unknown input modeling technique is proposed to design an FDO that decouples the effect of mismatched unknown inputs. To do this, first, unknown inputs that denote the faults of no interest and process disturbances are decomposed into algebraically rejectable unknown inputs and modeled unknown inputs such that the observer matching condition is satisfied. Under the assumption that mismatched unknown inputs are deterministic and can be expressed as the responses of fictitious autonomous dynamical systems, an augmented system is obtained by combining the original system model with the unknown input model. Finally, through the design technique of a UIO for the augmented system, a reduced-order FDO is constructed to estimate an augmented state vector that consists of both the original state variables and the augmentative state variables. The estimated state is then used to generate the residual, which should be designed to be insensitive to unknown inputs while being sensitive to the faults of interest. Two numerical examples are provided to show the usefulness and the feasibility of the presented approach.

A Method for Fault Detection and Isolation Based on the Processing of Multiple Diagnostic Indices: Application to Inverter Faults in AC Drives

A general method for fault detection and isolation (FDI) is proposed and applied to inverter faults in drives of electric vehicles (EVs). This method is based on a change detection algorithm, which allows multiple fault indices (FIs) to be combined to retrieve the most likely state of the drive. The drive topology under study is a six-leg inverter associated with a three-phase open-end winding machine. Due to the inherent fault-tolerant topology, the conventional FIs are no longer effective. Therefore, an analysis of simulations under faulty conditions leads to the derivation of suitable FIs. These are based on the envelope of the phase currents, as well as their instantaneous frequency. Specific operating conditions related to the EV environment are taken into account, such as the flux-weakening region and energy recovery. In these modes of operation, FDI can be affected by uncontrolled currents circulating through the free-wheeling diodes. Finally, the performances of the FDI scheme are evaluated under steady-state and nonstationary conditions through simulations and experimental results.