Failure Detection And Isolation Research Articles

Hierarchical Combination Reliability Modeling Method for Fault Tolerant Sensor System

According to the architecture feature of the fault tolerant sensor system, a hierarchical combination reliability modeling method was proposed based on the dynamic fault tree analysis and the Markov process. Temporal operators were applied to represent the sequential logic, and the analytic formula for the failure probability of the top event was derived. The FDI (failure detection and isolation) behavior was modeled by the Markov process. The resultant model was compared to the PFCM (perfect fault coverage model). The impact of the parameters on the system reliability was evaluated. The simulation results demonstrate the modeling method is feasible, efficient and more accurate.

A New Diagnostic Model for Identifying Parametric Faults

This paper presents a new approach to failure detection and isolation (FDI) for systems modeled as an interconnection of subsystems that are each subject to parametric faults. This paper develops the concept of a diagnostic model and the concept of a fault emulator which are used to model and parameterize subsystem faults. There are two stages to the FDI scheme. In the first stage there is a requirement to identify the diagnostic model. Once identified, the diagnostic model is used in the second stage to generate a residual. Artifacts within the measured residual are then used as a basis for identifying parametric faults. The scheme is distinct from others as it does not require an online recursive least squares type identifier.

Failure detection and isolation in robotic manipulators using joint torque sensors

SUMMARYReliability of any model-based failure detection and isolation (FDI) method depends on the amount of uncertainty in a system model. Recently, it has been shown that the use of joint torque sensing results in a simplified manipulator model that excludes hardly identifiable link dynamics and other nonlinearities such as friction, backlash, and flexibilities. In this paper, we show that the application of the simplified model in a fault detection algorithm increases reliability of fault monitoring system against modeling uncertainty. The proposed FDI filter is based on a smooth velocity observer of degree 2n where n stands for the number of manipulator joints. No velocity measurement and assumptions on smoothness of faults are used in the fault detection process. The paper focuses on actuator faults and investigates the effect of torque sensor noise on threshold selection. The FDI filter is further improved to become robust against an unknown bias in torque sensor reading. The effect of position sensor noise together with position sensor faults are also investigated. Simulation example on a 6-degrees of freedom manipulator is carried out to illustrate the performance of the proposed FDI method.

Detection of Component Failures for Smart Structure Control Systems

Uncertainties in the dynamics model of a smart structure are often of significance due to model errors caused by parameter identification errors and reduced-order modeling of the system. Design of a model-based Failure Detection and Isolation (FDI) system for smart structures, therefore, needs careful consideration regarding robustness with respect to such model uncertainties. In this paper, we proposes a new method of robust fault detection that is insensitive to the disturbances caused by unknown modeling errors while it is highly sensitive to the component failures. The capability of the robust detection algorithm is examined for the sensor failure of a flexible smart beam control system. It is shown by numerical simulations that the proposed method suppresses the disturbances due to model errors and markedly improves the detection performance.

DETECTION OF COMPONENT FAILURES FOR SMART STRUCTURE CONTROL SYSTEMS

Uncertainties in the dynamics model of a smart structure are often of significance due to model errors caused by parameter identification errors and reduced-order modeling of the system. Design of a model-based Failure Detection and Isolation (FDI) system for smart structures, therefore, needs careful consideration regarding robustness with respect to such model uncertainties. In this paper, we proposes a new method of robust fault detection that is insensitive to the disturbances caused by unknown modeling errors while it is highly sensitive to the component failures. The capability of the robust detection algorithm is examined for the sensor failure of a flexible smart beam control system. It is shown by numerical simulations that the proposed method suppresses the disturbances due to model errors and markedly improves the detection performance.

Reconfigurable NDI controller using inertial sensor failure detection & isolation

A modified derivation of nonlinear dynamic inversion provides the theoretical underpinnings for a reconfigurable control law for aircraft that have suffered combinations of actuator failures, missing effector surfaces, and aerodynamic changes. The approach makes use of acceleration feedback to extract information pertaining to any aerodynamic change and thus does not require a complete aerodynamic model of the aircraft. The control law does require feedback of effector positions to accommodate actuator dynamics. Both accelerometer and rate gyro failure detection and isolation (FDI) systems are implemented, allowing up to three independent failures for each FDI system as long as they are in different axes. Nonlinear simulation results show that the FDI systems improve the robustness to accelerometer/rate gyro uncertainties. An advanced tailless aircraft model is used to demonstrate the concepts. The simulation includes accelerometer and rate gyro noise and bias, failures due to accelerometers, rate gyros, and actuators, and modeled missing surfaces that cause airplane aerodynamic changes.

Detection of interference/jamming and spoofing in a DGPS-aided inertial system

Previous research at the Air Force Institute of Technology (AFIT) has resulted in the design of a differential Global Positioning System (DGPS) aided INS-based (inertial navigation system) precision landing system (PLS) capable of meeting the FAA precision requirements for instrument landings. The susceptibility of DGPS transmissions to both intentional and nonintentional interference/jamming and spoofing must be addressed before DGPS may be safely used as a major component of such a critical navigational device. This research applies multiple model adaptive estimation (MMAE) techniques to the problem of detecting and identifying interference/jamming and spoofing in the DGPS signal. Such an MMAE is composed of a bank of parallel filters, each hypothesizing a different failure status, along with an evaluation of the current probability of each hypothesis being correct, to form a probability-weighted average state estimate as an output. For interference/jamming degradation represented as increased measurement noise variance, simulation results show that, because of the good failure detection and isolation (FDI) performance using MMAE, the blended navigation performance is essentially that of a single extended Kalman filter (EKF) artificially informed of the actual interference noise variance. However, a standard MMAE is completely unable to detect spoofing failures (modeled as a bias or ramp offset signal directly added to the measurement). This work describes a moving-bank pseudoresidual MMAE (PRMMAE) to detect and identify such spoofing. Using the PRMMAE algorithm, spoofing is very effectively detected and isolated; the resulting navigation performance is equivalent to that of an EKF operating in an environment without spoofing.

Designed inputs for detection and isolation of failures in the state transition matrices of dynamic systems

A new failure detection and isolation algorithm is presented for linear time-invariant deterministic systems. In particular, the technique exploits the system inputs to distinguish between a change in the state transition matrix and actuator failures, including output equivalent events. It is assumed that a failure has been detected previously by some other method, such as by a failure detection filter. Previous methods that attempted to isolate changes in the state transition matrix did not use the input in any way and required explicit knowledge of the changed parameter values (e.g., the multiple model method). The designed input method presented in this paper requires only the knowledge of the null spaces of the changed parameter matrices, not of the explicit values of the changed parameters. This feature allows the failure detection and isolation (FDI) algorithm based on designed inputs to isolate partially unknown changes in the state transition matrix, a distinct advantage over other methods in terms of computational efficiency. Detailed design procedures are accompanied by an example that illustrates the input design method. The example uses designed inputs to distinguish between changes in the state transition parameters of an AFTI/F-16 model from nearly output equivalent actuator calibration errors.

State observation, failure detection and isolation (FDI) in bilinear systems

This paper explores the design of a reduced order observer with unknown inputs for the purpose of fault detection and isolation (FDI) in a class of bilinear systems. An approach for sensor and actuator failure detection and isolation (FDI), based on the proposed observer is presented. Finally, the applicability and effectiveness of the proposed FDI scheme is illustrated on an electrohydraulic system.

Sensitivity Analysis of the State Chi-Square Test

In this paper, a sensitivity analysis algorithm was developed which provides a unified tool for statistical analysis of the impact of modeling errors on the performance of Failure Detection and Isolation (FDI) systems which use the state chi-square test. Under the assumption that there exists an underlying state variable truth model of fairly high dimension that completely describes the detailed error evolution of the system, the sensitivity analysis algorithm can be used to calculate the actual mean and covariance matrix of the state estimation difference between the Kalman filter and the state propagator. It can also find the truth distribution of the statistics of the state chi-square test. Then the impact of modeling errors on failure decision can be ascertained by comparing the true statistical distribution of the test statistics with the one with no modeling errors. The sensitivity analysis results can be further grouped, by using intelligent data classification techniques into robust FDI decision regions. This minimizes the impact of modeling errors and maximizes the detection sensitivity. The sensitivity analysis algorithm is currently being applied to analyze the effects of modeling errors on GPS integrity monitoring systems.

Failure detection and isolation structure for global positioning system autonomous integrity monitoring

In this paper, a new failure detection and isolation (FDI) structure is investigated for global positioning system (GPS) integrity monitoring in an integrated inertial navigation system (INS)/GPS navigation system. In the structure, a bank of auxiliary integrated INS/GPS Kalman filters, each of them processing a subset of the GPS measurements, are applied to provide high-accuracy detection references. The failure detection is then undertaken by checking the consistency between the state estimate of the main integrated INS/GPS Kalman filter and the auxiliaries. Two state propagators are also included in the FDI structure to reliably isolate the failed GPS measurement. The two propagators are alternatively reset with the data of the Kalman filter to increase their accuracy and thereby the failure isolation performance. The failure isolation is realized by testing the consistency between the state estimates of the auxiliary Kalman filters and the state propagator. The simulation results show that the FDI structure is capable of not only detecting the presence of a malfunctioning satellite that results in a large range error, but also identifying which satellite is malfunctioning.

Parity Vector Compensation Using Non-Linear Filtering

The performance of a failure detection and isolation(FDI) algorithm applied to a redundant strapdown inertial measurement unit (IMU) is limited by sensor errors such as input misalignments, scale factor errors, and biases. Non-linear filtering is proposed to estimate the combinations of sensor errors which affect the parity vector, the estimates are used to form a compensated parity vector. The compensated parity vector is used to make FDI decision for improving FDI performance. Simulation results prove that the FDI performance can be improved greatly by using the proposed method

Some Extension to the Generalized Parity Space Method for FDI

This paper describes some new development in the method of failure detection and isolation (FDI) in uncertain systems. The new development is based on the generalized parity space method, however in a closed-loop setting. Residual signals generated by parity checks under this setting are shown to have enhanced robustness with respect to modeling errors and disturbances. An iterative algorithm is developed for optimizing the design of residual signal generators.

On the Use of Descriptor Systems for Failure Detection and Isolation

In this paper we investigate the problem of failure detection and isolation (FDI) for noisy dynamical systems. We follow a statistical approach, so that performance criteria are related to false alarm rate and detection delay. Using a simple and powerful re-formulation of maximum likelihood estimation in linear Gaussian systems, we introduce a new approach based on a descriptor systems formulation. We show that a reduction in computational cost as well as a significant improvement in robustness against uncertainty in the dynamics result.

Generation of Optimal Structured Residuals in the Parity Space

This paper proposes an approach for the improvement of the performances of Failure Detection and Isolation (FDI) systemsStructural specifications of the expected results lead to optimize the robustness of the system with respect to the modelling uncertainties and unknown inputs. Most of the solutions of this optimization problem have been developed through the transformation of an observer - based or a parity space approachWe show in this paper how the parity space approach allows the direct generation of residuals which respond to the given specifications.

Detection of sensor failures in automotive engines

The real-time application of detection filters to the diagnosis of sensor failures in automotive engine control systems is presented. The detection filter is the embodiment of a model-based failure detection and isolation (FDI) methodology, which utilizes analytical redundancy within a dynamical system to isolate the cause and location of abnormal behavior. The philosophy and essential features of FDI theory are presented, and the practical application of the method to the diagnosis of faults in some key sensors in an electronically controlled internal combustion engine is described. The experimental results presented here have been obtained on a production vehicle, and demonstrate that the real-time implementation of such detection filters is feasible, opening the way to a new generation of diagnostic strategies. >

Failure detection and isolation of ultrasonic ranging sensors for robotic applications

A failure detection and isolation (FDI) method for validation of ultrasonic ranging sensor (URS) signals in robot position control systems is presented. The technique builds on the concepts of parity space and analytic redundancy, where integration of analytic and sensor redundancy provides a direct, reliable method for measuring the end-effector position of a robot relative to the world coordinates. These measurements are not influenced by deflections caused by the payload, accumulated joint measurement errors in a serial mechanism, or computational errors in executing kinematic relationships. The position control system's insensitivity to structural deflections allows the robot to handle larger payloads. Simulation results are presented to demonstrate how the FDI technique can be applied. >

Failure detection and isolation by model cost analysis

A new concept for solving failure detection and isolation (FDI) problems in a linear system, called the parity-cost algorithm, is developed. A parity function is used to generate the residual of the measurement system by projecting the redundancy data from the measurement domain onto an orthogonal space. The decision-making process is done by using model cost analysis through the energy concept. The concept developed has three merits: (a) both direct and analytical redundancy systems can be handled easily; (b) FDI problems of combined sensors and actuators can be solved simultaneously; and (c) the parity-cost process can be quantified and multiple failures isolated quickly.

Instrument failure detection and estimation methodology for the nuclear power plant

To detect instrument failures in a nuclear power plant, a failure detection and isolation (FDI) method based on the Kalman filter is developed. Each filter is designed to be insensitive to the failed measurements by decreasing the Kalman gain artificially. Since it is mainly dependent upon the dynamic model and averaged outputs, it can exactly indicate the direction of failures. Even though this concept minimizes the number of filters, it performs the role of analytic redundancy for estimation. As soon as the residual exceeds the predetermined bound, the Kalman filter indicates the possibility of failures. However, since the measurement may show false indication owing to abrupt noises, it must be confirmed several times by the multiple consecutive miscomparison counter, which is strongly dependent on measurement history. Then, if it is not in accordance with other measurements, detailed information on the status of the measurements is provided to help the operator's decision. Various simulations were performed to verify and validate the FDI logic in detecting steam generator and pressurizer instrument failures. It is shown that the FDI technique can detect not only a single failure but also simultaneous common-mode and sequential multiple failures of several direct redundancies. It can correctly estimate the physical states in real time, and the remaining time may be used for control with signal validation.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>

Detection delays, false alarm rates and the reconfiguration of control systems

Abstract Systems like unstable aircraft or large space structures are inherently vulnerable to failure of components and their reliability has to be improved through fault-tolerant control: given a set of redundant components, the failed element is diagnosed by a failure detection and isolation (FDI) device and its influence is removed through a reconfiguration of the control algorithms. Performance requirements are then usually expressed in terms of a short detection delay and a low false alarm rate for the FDI function and, for example, stability of the reconfigured system. Linear systems subject to random jumps in parameter values are used to analyse the dynamic behaviour of a fault-tolerant control system and it is found that even a short detection delay and/or a low false alarm rate, when fedback in a reconfigurable control loop, significantly affect stability.