Sensor Fault Isolation Research Articles

Aircraft Sensor and Actuator Fault Detection, Isolation, and Accommodation

The purpose of the paper is to present an approach to detect, isolate, and accommodate the aircraft sensor and actuator faults using unknown input observers (UIOs). Full-order observers, reduced-order observers, and UIOs are widely used in state estimations. After the estimation of states, fault detection can be provided by conducting residual analysis. Despite of the existence of unknown inputs, fault detection and isolation are implemented for a very large, four-engined, cargo jet aircraft model. Sensor accommodation is realized via switching under redundant sensor existence assumption. Actuator accommodation is provided by gain scheduling. Hence, if a fault occurs in an actuator corresponding to the control surfaces, the remainder (n−1) actuators are used to avoid hazardous flight. Sensor and actuator faults are detected by using residuals. Sensor faults are effective on the outputs, while actuator faults are effective on the state equations. Fault isolation is implemented by taking into account that each residual is sensitive to all of the other faults but one fault. Fault detection, isolation, and a recommended accommodation are shown through the simulations to be functional.

SFDIA of consecutive sensor faults using neural networks – demonstrated on a UAV

Neural network based sensor fault detection, isolation and accommodation (NN-SFDIA) is becoming a popular alternative to traditional linear time-invariant model-based sensor fault detection, isolation and accommodation (SFDIA) schemes, such as observer-based methods. Their online training capabilities and ability to model complex nonlinear systems have attracted much research interest in the applications area of neural networks. In this article, we design an NN-SFDIA scheme to detect multiple sensor faults in an unmanned air vehicle (UAV). Model-based SFDIA is a direction of development in particular with UAVs where sensor redundancy may not be an option due to weight, cost and space implications. In this article, a maximum of three consecutive faults are assumed in the pitch gyro, normal accelerometer and angle of attack sensor of a nonlinear UAV model. Furthermore, a novel residual generator which is designed to minimise the false alarm rates and missed faults, is implemented. After 33 separate SFDIA tests implemented on a 1.6 GHz Pentium processor, the NN-SFDIA scheme detected all but three faults with a fast execution time of 0.55 ms per flight data sample.

ANFIS based sensor fault detection for continuous stirred tank reactor

In this paper, an Adaptive Neuro-Fuzzy Inference System (ANFIS) based Sensor fault detection and isolation for Continuous Stirred Tank Reactor (CSTR) is proposed. CSTR is a highly nonlinear process exhibiting stable and unstable steady state at different operating regions. Fault detection (FD) of such a complicated CSTR process is a mind boggling problem. In this paper, an ANFIS based ‘dedicated observer’ scheme is dealt along with statistical methods for the detection of the fault. The result shows the feasibility of using the proposed method for the detection of sensor faults in CSTR.

Sensor and Actuator Fault Isolation Using Parameter Interval based Method for Nonlinear Dynamic Systems

Fault isolation problem for sensor and actuator in nonlinear dynamic systems is studied. Parame- ter interval based fault isolation method has fast isolation speed and fits many kinds of nonlinear dynamic systems. This method is extended to the isolation of sensor fault and actuator fault. The example shows good performance.

Sensor fault detection and isolation in three-phase systems using a signal-based approach

A fault detection and isolation system for monitoring voltage and current sensors in balanced three-phase systems is presented. It exploits the inherent redundancy of a model of the balanced three-phase signals (sinusoidal or not) through a residual generator based on the generalised observer scheme. A decision system made of a combination of vector cumulative sum (CUSUM) algorithms is used to process the residual vector and achieve detection and isolation of incipient (small magnitude) faults. The approach is validated using signals obtained from a test bench and from simulations.

Sensor fault isolation and detection of smart structures

This paper proposes a novel principal component analysis (PCA)-based sensor faultisolation and detection method for smart structures: detectability and isolability of eachsensor fault are analyzed using a PCA-based numeric residual generator and theprobability of errors in each sensor is also determined using the Bayesian probabilisticanalysis of these residuals. To demonstrate the performance of the proposed PCA-basedsensor fault isolation and detection methodology, a seismically excited three-story buildingstructure equipped with a magnetorheological damper that is operated by a semi-activenonlinear fuzzy control system is investigated. It is shown that the proposed PCA-basedsensor fault diagnosis approach is effective in identifying sensor faults of smartstructures for hazard mitigation of large structures as a model-free methodology.

Sensor fault detection and isolation for wind turbines based on subspace identification and Kalman filter techniques

AbstractThis paper aims at the blade root moment sensor fault detection and isolation issue for three‐bladed wind turbines with horizontal axis. The underlying problem is crucial to the successful application of the individual pitch control system, which plays a key role for reducing the blade loads of large offshore wind turbines. In this paper, a wind turbine model is built based on the closed loop identification technique, where the wind dynamics is included. The fault detection issue is investigated based on the residuals generated by dual Kalman filters. Both additive faults and multiplicative faults are considered in this paper. For the additive fault case, the mean value change detection of the residuals and the generalized likelihood ratio test are utilized respectively. For multiplicative faults, they are handled via the variance change detection of the residuals. The fault isolation issue is proceeded with the help of dual sensor redundancy. Simulation results show that the proposed approach can be successfully applied to the underlying issue. Copyright © 2009 John Wiley & Sons, Ltd.

Detection, isolation and identification of multiple actuator and sensor faults in nonlinear dynamic systems: Application to a waste water treatment process

The goal in many fault detection and isolation schemes is to increase the isolation and identification speed. This paper, presents a new approach of a nonlinear model based adaptive observer method, for detection, isolation and identification of actuator and sensor faults. Firstly, we will design a new method for the actuator fault problem where, after the fault detection and before the fault isolation, we will try to estimate the output of the instrument. The method is based on the formation of nonlinear observer banks where each bank isolates each actuator fault. Secondly, for the sensor problem we will reformulate the system by introducing a new state variable, so that an augmented system can be constructed to treat sensor faults as actuator faults. A method based on the design of an adaptive observers’ bank will be used for the fault treatment. These approaches use the system model and the outputs of the adaptive observers to generate residues. Residuals are defined in such way to isolate the faulty instrument after detecting the fault occurrence. The advantages of these methods are that we can treat not only single actuator and sensor faults but also multiple faults, more over the isolation time has been decreased. In this study, we consider that only abrupt faults in the system can occur. The validity of the methods will be tested firstly in simulation by using a nonlinear model of waste water treatment process with and without measurement noise and secondly with the same nonlinear model but by using this time real data.

An Adaptive Fault-Tolerant Multisensor Navigation Strategy for Automated Vehicles

This paper presents a novel adaptive fault-tolerant multisensor navigation strategy for automated vehicles on the automated highway system. To ensure safe and reliable running, the main concern is how we can realize sensor fault detection, isolation, and accommodation. The proposed strategy adopts a hybrid decentralized filter architecture that fuses multiple sensors, including the strapdown inertial navigation system, the carrier phase-differential Global Positioning System (GPS), the wheel encoder, and the electronic compass. Through the integration of multiple redundancy sensors, the navigation system has fault-tolerant potential. The integrated filter model is first established in detail. Then, a highly fault-tolerant navigation filter iterative algorithm is proposed. The proposed algorithm realizes fault tolerance by utilizing fuzzy logic to update the local filter based on a relative degree of mismatch by simultaneously considering sensor failure degree and to make fault isolation decisions. To compare and evaluate the fault-tolerant performance, several traditional navigation methods are also investigated. Simulation results demonstrate that the proposed integrated navigation method can effectively detect, isolate, and accommodate a variety of hard and soft sensor failures, which may even simultaneously occur.

A new fast converging Kalman filter for sensor fault detection and isolation

PurposeThe purpose of the paper is to present an approach to detect and isolate the sensor failures, using a bank of extended Kalman filters (EKF) using an innovative initialization of covariance matrix using system dynamics.Design/methodology/approachThe EKF is developed for nonlinear flight dynamic estimation of a spacecraft and the effects of the sensor failures using a bank of Kalman filters is investigated. The approach is to develop a fast convergence Kalman filter algorithm based on covariance matrix computation for rapid sensor fault detection. The proposed nonlinear filter has been tested and compared with the classical Kalman filter schemes via simulations performed on the model of a space vehicle; this simulation activity has shown the benefits of the novel approach.FindingsIn the simulations, the rotational dynamics of a spacecraft dynamic model are considered, and the sensor failures are detected and isolated.Research limitations/implicationsA novel fast convergence Kalman filter for detection and isolation of faulty sensors applied to the three‐axis spacecraft attitude control problem is examined and an effective approach to isolate the faulty sensor measurements is proposed. Advantages of using innovative initialization of covariance matrix are presented in the paper. The proposed scheme enhances the improvement in estimation accuracy. The proposed method takes advantage of both the fast convergence capability and the robustness of numerical stability. Quaternion‐based initialization of the covariance matrix is not considered in this paper.Originality/valueA new fast converging Kalman filter for sensor fault detection and isolation by innovative initialization of covariance matrix applied to a nonlinear spacecraft dynamic model is examined and an effective approach to isolate the measurements from failed sensors is proposed. An EKF is developed for the nonlinear dynamic estimation of an orbiting spacecraft. The proposed methodology detects and decides if and where a sensor fault has occurred, isolates the faulty sensor, and outputs the corresponding healthy sensor measurement.

An implementation of a novel vision‐based robotic tracking system

PurposeThe purpose of the paper is to present an approach to detect and isolate the sensor failures, using a bank of extended Kalman filters (EKFs) using an innovative initialization of covariance matrix using system dynamics.Design/methodology/approachThe EKF is developed for nonlinear flight dynamic estimation of a spacecraft and the effects of the sensor failures using a bank of Kalman filters in investigated. The approach is to develop fast convergence Kalman filter algorithm based on covariance matrix computation for rapid sensor fault detection. The proposed nonlinear filter has been tested and compared with the classical Kalman filter schemes via simulations performed on the model of a space vehicle; this simulation activity has shown the benefits of the novel approach.FindingsIn the simulations, the rotational dynamics of a spacecraft dynamic model are considered, and the sensor failures are detected and isolated.Research limitations/implicationsA novel fast convergence Kalman filter for detection and isolation of faulty sensors applied to the three axis spacecraft attitude control problem is examined and an effective approach to isolate the faulty sensor measurements is proposed. Advantages of using innovative initialization of covariance matrix are presented in the paper. The proposed scheme enhances the improvement in estimation accuracy. The proposed method takes advantage of both the fast convergence capability and the robustness of numerical stability. Quaternion‐based initialization of the covariance matrix is not considered in this paper.Originality/valueA new fast converging Kalman filter for sensor fault detection and isolation by innovative initialization of covariance matrix applied to a nonlinear spacecraft dynamic model is examined and an effective approach to isolate the measurements from failed sensors is proposed. An EKF has been developed for the nonlinear dynamic estimation of an orbiting spacecraft. The proposed methodology detects and decides if and where a sensor fault has occurred, isolates the faulty sensor, and outputs the corresponding healthy sensor measurement.

INTEGRATING FUZZY LOGIC PROCEDURE FOR SIGNAL VALIDATIONS: MALTA COLORING AS STUDY CASE

ABSTRACT In this study, the use of fuzzy logic for signal validation is introduced. This tool uses the sensor readings at the current time step to predict system state at the next time step using observers. The deviation between the sensor reading and the model prediction rises, leading to fault detection and isolation. The fault is accommodated by replacing the reading of the faulty sensor by the model prediction. The results presented in this article demonstrate that a simple scheme can carry out sensor fault detection, isolation and accommodation using a fuzzy rule‐based system based on experience of an operator into heuristics. Although the method lacks the mathematical basis that other analytical methods provide, this method is simple, effective and useful. PRACTICAL APPLICATIONSThe coloring process has to be controlled and monitored carefully because if the color of the MALTA is not adequate, then the MALTA has to be recycled to another unit (dilution unit) where the colorant is eliminated. This recycling process uses delay around 3 working days, and the economic impact is high. The validation of signal and reduction of fault sensor contribute to the reduction of the recycling process. In this study, the incorporation of fuzzy logic in the treatment of sensor signal represents a significant factor in the improvement of MALTA production units.

Multiple sensor fault detection and isolation of an air quality monitoring network using RBF-NLPCA model

This paper presents a data-driven method based on non-linear principal component analysis to detect and isolate multiple sensor faults. The RBF-NLPCA model is obtained by combining a principal curve algorithm and two three-layer radial basis function (RBF) networks. The reconstruction approach for multiple sensors is proposed in the non-linear case and successfully applied for multiple sensor fault detection and isolation of an air quality monitoring network. The proposed approach reduces considerably the number of reconstruction combinations and allows to determine replacement values for the faulty sensors.

Model-based fault detection and isolation for a diesel lean [formula omitted] trap aftertreatment system

The lean NOx trap (LNT) is an aftertreatment device used to reduce nitrogen oxides emissions on Diesel engines. To operate the LNT with high conversion efficiency, an optimized regeneration schedule is required, together with closed-loop control of the air/fuel ratio during regeneration. Furthermore, to comply with emissions regulations, diagnostic schemes are needed to detect and isolate faults, typically related to aging, sulfur poisoning and thermal deactivation.The paper describes a step-by-step methodology for the design and validation of model-based fault diagnosis for a LNT aftertreatment system. The approach is based on a control-oriented model of the LNT validated with experimental data.The proposed diagnostic approach is based on the generation of residuals using system models, through the comparison of the predicted and measured values of selected output variables. The paper focuses on the detection and isolation of sensor faults and LNT parametric faults. Different diagnostic methodologies are presented in relation to the detection of specific faults.Starting from sulfur poisoning detection in a laboratory environment which represents a preliminary validation of the approach, the diagnostic scheme is extended to detect various faults under different plant configurations and operating conditions, with a final application to on-board fault detection and isolation.

Current Sensor Fault Detection, Isolation, and Reconfiguration for Doubly Fed Induction Generators

Fault tolerance is gaining growing interest to increase the reliability and availability of distributed energy sources. Current sensor fault detection, isolation, and reconfiguration are presented for a voltage-oriented controlled doubly fed induction generator, which is mainly used in wind turbines. The focus of this analysis is on the isolation of the faulty sensor and the actual reconfiguration. During a short period of open-loop operation, the fault is isolated by looking at residuals calculated from observed and measured signals. Then, replacement signals from observers are used to reconfigure the drive and reenter closed-loop control. Laboratory measurement results are included to prove that the proposed concept leads to good results.

Doubly Fed Induction Generator Model-Based Sensor Fault Detection and Control Loop Reconfiguration

Fault tolerance is gaining interest as a means to increase the reliability and availability of distributed energy systems. In this paper, a voltage-oriented doubly fed induction generator, which is often used in wind turbines, is examined. Furthermore, current, voltage, and position sensor fault detection, isolation, and reconfiguration are presented. Machine operation is not interrupted. A bank of observers provides residuals for fault detection and replacement signals for the reconfiguration. Control is temporarily switched from closed loop into open-loop to decouple the drive from faulty sensor readings. During a short period of open-loop operation, the fault is isolated using parity equations. Replacement signals from observers are used to reconfigure the drive and reenter closed-loop control. There are no large transients in the current. Measurement results and stability analysis show good results.

Isolating incipient sensor faults and system reconfiguration in a flight control actuation system

This article presents the application of the additive form of the unscented Kalman filter (UKF-A) for the detection and isolation of incipient sensor faults in an electromechanical flight control actuation system. The detailed model of the actuator including mounting structure stiffness, load dynamics, compensators, power amplifier, and friction non-linearity is considered. The scheme utilizes the analytical redundancy that exists in the system between the linear actuator position, motor shaft angular velocity, and motor current for the diagnosis of incipient sensor faults in the system. Fault diagnosis is carried out using a single UKF-A, which is driven by the motor shaft velocity sensor. The onset of incipient sensor faults such as bias and scale factor faults in position and current sensor are detected and isolated using structured residuals that are generated using the UKF-A estimates. Bias and scale factor errors in the velocity sensor are detected using the statistical properties of the innovation sequence of the UKF-A. In the event of fault in the position sensor, real time reconfiguration using an estimate of the position from the UKF-A is used for continued operation of the system. Simulation results indicate that the isolation scheme formulated is capable of efficiently identifying incipient sensor faults and the reconfiguration scheme permits continued operation of the system in the presence of position sensor faults.

Subspace method aided data-driven design of fault detection and isolation systems

This paper deals with data-driven design of fault detection and isolation (FDI) systems. The basic idea is to identify a primary form of residual generators, instead of the process model, directly from test data and, based on it, to design advanced FDI systems. The proposed method can be applied for the parity space and observer based detection and isolation of sensor and actuator faults as well as the construction of soft-sensors. The application of the proposed method is illustrated by a simulation study on the Tennessee Eastman process.

Sensor fault identification and isolation for multivariate non-Gaussian processes

This paper addresses fault identification and isolation of multivariate processes for which the recorded variables follow non-Gaussian distributions. Recent work has demonstrated the effectiveness of independent component analysis to extract non-Gaussian source signal and support vector data description to determine control limits for associated monitoring statistics. This article extends this work by developing a fault reconstruction technique and introduces a fault identification index to diagnose abnormal process conditions. The utility of this work is demonstrated using a simulation example and the application to the Tennessee Eastman benchmark simulator.

Real-Time Model-Based Fault Detection and Isolation for UGVs

The paper presents a model-based sensor fault detection and isolation system applied in real-time to unmanned ground vehicles. Structural analysis is applied on the nonlinear model of the vehicle for building the residual generation module, followed by an ad-hoc residual evaluation module for detecting single and multiple sensor faults. The overall proposed diagnosis scheme has been tested in real-time on a real mobile robot in an outdoors environment and for different tasks. The obtained experimental results are satisfactory in terms of diagnosis performance and real-time implementation.