Parity Space Approach Research Articles

Fault detection and isolation of PEM fuel cell system based on nonlinear analytical redundancy

This paper presents a procedure dealing with the issue of fault detection and isolation (FDI) using nonlinear analytical redundancy (NLAR) technique applied in a proton exchange membrane (PEM) fuel cell system based on its mathematic model. The model is proposed and simplified into a five orders state space representation. The transient phenomena captured in the model include the compressor dynamics, the flow characteristics, mass and energy conservation and manifold fluidic mechanics. Nonlinear analytical residuals are generated based on the elimination of the unknown variables of the system by an extended parity space approach to detect and isolate actuator and sensor faults. Finally, numerical simulation results are given corresponding to a faults signature matrix.

Simulink-Based FDI Simulator for Autonomous Low Earth Orbit Satellite

AbstractThe simulator development for fault detection and isolation (FDI) of a LEO satellite is presented. The simulator was constructed using satellite dynamics, FDI, sensors and actuators, flight software and a visualization block. Two FDI algorithms were applied. The first was an outlier detection method. The outlier detection method was based on the Kalman filter incorporating thresholds, data windows and average methods. This filter detected, isolated and recovered sudden abnormal data. The second FDI algorithm was a parity space approach for the gyro sensor FDI. The simulator was developed under the Matlab/Simulink environment. The results indicated that the simulator can be used for any LEO satellite.

확장 패리티 공간 기법의 이중고장 검출성능 향상 연구

We consider a double faults detection and isolation problem using modified extended parity space approach for inertial measurement unit which use redundant inertial sensors. A redundant IMU which has a hardware redundant is composed of the cone shape because it is good for fault detection and isolation. We analyze the type of double faults and the reason why fault isolation performance is low. We propose modified extended parity space approach method using EPSA and the difference of sensor data.

Subspace based Fault Detection and Identification for LTI Systems

This paper presents a new Fault detection and Identification approach Connected to Subspace Identification (FICSI) for LTI systems with additive faults. FICSI parameterizes the influence of initial states on residuals by past I/O measurements, and hence avoids projecting a residual vector onto the left null space of the extended observability matrix. It is proved that FICSI produces residuals more sensitive to faults than the classic parity space approach (PSA) does. Another novelty of this contribution is the unbiased fault estimation algorithm developed in the framework of FICSI, which is not possible in PSA without restricting the nature of faults. The difference of FICSI from the existing fault detection and estimation approaches based on PSA or moving window estimation (MHE) can also be attributed to the fact that FICSI does not require a state space model, but a sequence of Markov parameters mapping the I/O measurements to the residual, which can be estimated in closed-loop.

Subspace based Fault Detection and Identification for LPV Systems

This paper presents a new Fault detection and Identification approach Connected to Subspace Identification (FICSI) for a special class of nonlinear system casted to Linear but Parametrically Varying (LPV) form. The model based algorithm utilizes the specific nonlinear nature of the LPV system along the past horizon to construct the output predictor. Similar with its LTI counterpart in a companion paper ([Dong and Verhaegen (2008)]), the FICSI-LPV avoids projecting the residual vector onto the parity space of the extended observability matrix, and hence produces residuals more sensitive to faults than the parity space approach (PSA) for LPV systems does. Asymptotically unbiased condition and algorithm are also proposed for fault estimation. The difference of FICSI from the existing fault detection and estimation approaches based on PSA or moving horizon estimation (MHE) can also be attributed to the fact that FICSI does not require an LPV state space model, but a sequence of Markov parameters mapping the I/O measurements and the scheduling parameters to the residual, which can be estimated in closed-loop.

Fault detection based on uncertain models with bounded parameters and bounded parameter variations

This paper deals with a fault detection method taking into account model uncertainties described by bounded variables. A parity space approach is used for generating testable redundancy relations in which each uncertain parameter is defined by an interval containing all its feasible values. Consistency tests consist in evaluating these set-membership relations and lead to convex sets containing the feasible free-fault behaviours of the supervised system. The objective is to improve fault detection performance by taking into account constraints on variations of uncertain parameters, which do not randomly vary.

Fault Detection of Networked Control Systems with Packet Dropout

Fault detection of networked control systems with packet dropouts in both sensor-to-controller link and controller-to-actuator link is discussed in this paper. Two types of packet dropouts are considered. One is packet dropout characterized by a Bernoulli process, the other is characterized by a Markov chain. According to the system configuration, packet dropout in the sensor-to-controller link is known to the fault detection system, therefore a time varying parity space based residual generator which is fully decoupled from this influence is designed. Robustness to packet dropout in the controller-to-actuator link which is unknown is achieved by an adaptive threshold. Due to the known probability properties of the residual signal in parity space approach, upper bounds of false alarm rates of the designed thresholds are also given, which are very difficult to obtain in existent observer based fault detection approaches of networked control systems with packet dropout. It is verified that the proposed fault detection approach can also be realized by diagnostic observers by employing the relationship between parity vectors and observers.

Hybrid Systems Diagnosis by coupling Continuous and Discrete event Techniques

This paper deals with the problem of diagnosing systems that exhibit both continuous and discrete event dynamics. The proposed approach combines techniques from both continuous and discrete event diagnosis fields. On the on hand, an extension of the parity space approach is used to associate signatures to every operational mode of the system. On the other hand, signature switches arising from the transition from one mode to another are abstracted in the form of a set of events that capture the continuous dynamics. These events are merged into the original discrete dynamic model of the system, allowing us to apply the well-known discrete-event-systems diagnoser approach. This is illustrated on an example that shows the diagnosability improvement of the hybrid approach.

Fault detection of NCS based on eigendecomposition, adaptive evaluation and adaptive threshold

A method for fault detection (FD) of networked control systems with unknown network-induced delay and unknown input based on eigendecomposition, adaptive evaluation and adaptive threshold is proposed in this paper. In the method, a known structure matrix of network-induced delay which is supposed to be smaller than one sampling period is extracted by eigendecomposition and an observer based residual generator is constructed. Then an adaptive evaluation function is proposed to ensure the optimal robustness to network-induced delay and unknown input at the evaluation stage based on the Parity Space approach. Finally, based on the estimation of the maximal influence of unknown random delay and unknown input on the evaluation function, adaptive threshold is designed to enhance robustness of the FD system and reduce the miss detection rate as much as possible.

Soft Computing Algorithm to Data Validation in Aerospace Systems Using Parity Space Approach

This paper deals with the problem of data validation of an instrumentation system applied in the aerospace area. The fault diagnosis method used for the validation is based on the principle of the parity space approach. Residuals are generated thanks to the analytical redundancy relations given by the model and the important number of sensors. Indeed, we propose a procedure, which permits us to compute systematically all the redundancy equations for the residual generation phase. The additional concept, called residuals structuration, is necessary to isolate the detected faults. Finally, the data validation task consists in isolating the failing data and in sending only the valid information to a control system. An application to an aerospace system illustrates the proposed algorithm.

Statistical signal processing approaches to fault detection

The parity space approach to fault detection and isolation (FDI) has been developed during the last 20 years, and the focus here is to describe its application to stochastic systems. A mixed model with both stochastic inputs and deterministic disturbances and faults is formulated over a sliding window. Algorithms for detecting and isolating faults on-line and analyzing the probability for correct and incorrect decisions off-line are provided. A major part of the paper is devoted to discussing properties of this model-based approach and generalizations to cases of incomplete model knowledge, and non-linear non-Gaussian models. For this purpose, a simulation example is used throughout the paper for numerical illustrations, and real-life applications for motivations. The final section discusses the reverse problem: fault detection approaches to statistical signal processing. It is motivated by three applications that a simple CUSUM detector in feedback loop with an adaptive filter can mitigate the inherent trade-off between estimation accuracy and tracking speed in linear filters.

Robust Dynamic Sensor Fault Detection and Isolation of Helical Coil Steam Generator Systems Using a Subspace Identification Technique

A design approach to sensor fault detection and isolation (FDI) of helical coil steam generator (HCSG) systems of the International Reactor Innovation Secure (IRIS) reactor is presented. In the design phase, a physical model is first developed to provide a realistic simulation and generate data characterizing the system dynamics. A subspace identification technique is then used to extract a low-order linear state-space model from the data. Finally, a robust dynamic parity space approach is utilized to design residual generators for FDI. This design approach is able to achieve fault isolation following a predetermined logic without the need to use data during fault conditions, which is an unrealistic assumption of many FDI approaches studied for nuclear power plants. The results of the HCSG application show that the approach is robust to not only measurement and process noises but also operation condition changes and has the capability of correct FDI during reactor power transients and during the propagation of sensor faults in a control loop.

MULTIOBJECTIVE DESIGN OF ROBUST FAULT DETECTION SYSTEMS

This paper considers the multiobjective integrated design of fault detection systems for linear time-invariant systems with unknown inputs. First, the residual generator is designed in such a way that the residual is decoupled from the unknown inputs that mainly influence the system. Then, the rest freedom is used to improve the robustness of the residual generator to other unknown inputs and its sensitivity to faults. By virtue of the close relationship between the parity space approach and the observer based approach, the order of the residual generator can be kept at a low and flexible level.

STATISTICAL SIGNAL PROCESSING APPROACHES TO FAULT DETECTION

The parity space approach to fault detection and isolation (FDI) has been developed during the last twenty years, and the focus here is to describe its application to stochastic systems. A mixed model with both stochastic inputs and deterministic disturbances and faults is formulated over a sliding window. Algorithms for detecting and isolating faults on-line and analyzing the probability for correct and incorrect decisions off-line are provided. A major part of the paper is devoted to discussing properties of this model-based approach and generalizations to cases of incomplete model knowledge, and non-linear non-Gaussian models. For this purpose, a simulation example is used throughout the paper for numerical illustrations, and real-life applications for motivations. The final section discusses the reverse problem: fault detection approaches to statistical signal processing. It is motivated by three applications that a simple CUSUM detector in feedback loop with an adaptive filter can mitigate the inherent trade-off between estimation accuracy and tracking speed in linear filters.

On the relationship between parity space and [formula omitted] approaches to fault detection

Parity space approach and H 2 approach are two important fault detection approaches. This paper studies the relationship between these two approaches, which reveals frequency domain characteristics of the optimal solution of the parity space approach on the one side and provides a numerical solution of the H 2 -optimal design of residual generators on the other side.

GLR TESTS FOR FAULT DETECTION OVER SLIDING DATA WINDOWS

The Generalized Likelihood Ratio (GLR) test for fault detection as derived by Willsky and Jones is a recursive method to detect additive changes in linear systems in a Kalman filter framework. Here, we evaluate the GLR test on a sliding window and compare it to stochastic parity space approaches. Robust fault detection defined as being insensitive to faults in the signal space is also studied in the GLR framework.

Sensor Validation for Structural Systems with Additive Sensor Faults

Structures with a large number of sensors and actuators are becoming more common, and their applications vary from active control to damage location. This large amount of spatial information should be used to advantage to continuously monitor the correct functioning of the sensors during normal operation. Errors introduced by faulty sensors can cause a loss of performance and erroneous conclusions, and this paper analyses additive sensor faults. Two residual generation schemes are proposed to monitor sensor faults, namely the modal filtering approach and the so-called Parity Space approach. These residuals are then tested using a probabilistic approach using a 2 test to determine if there is a faulty sensor. These approaches are demonstrated on a simulated cantilevered beam excited at its tip and also on an experimental subframe structure.

Extended Parity Space Approach Considering Two-Faults

This paper presents a new fault detection and isolation method which is called EPSA(Extended Parity Space Approach). This method is particularly suitable for fault detection and isolation of the system with two faulty sensors. To analyze the situations of two-faults, the situations are classified into four types. Based on this analysis, the procedure to isolate the faulty sensors in each situation is suggested. Also how to decide the threshold value is suggested. The proposed FDI method is verified through Monte Carlo simulation.

A New Parity Space Approach for Fault Detection Based on Stationary Wavelet Transform

In the traditional parity relation based fault detection approach, a lower order parity vector means a simpler online realization but a poorer performance index, while a higher order parity vector brings a better performance index but leads to a higher computational load and a higher rate of misdetection. In this note, by introducing the so-called stationary wavelet transform into the residual signal, a new kind of parity relation based residual generator and the corresponding optimization approach are proposed. The residual generator of the new approach can ensure a good performance index, a satisfactory low misdetection rate and a suitable response speed to faults with a low order parity vector and a simple online implementation form. The method is compared with three existing parity relation based approaches.

관성센서의 이중 고장을 고려한 고장 검출 및 분리

Inertial navigation system with hardware redundancy must use FDI(Fault Detection and Isolation) method to remove the influence of faulty sensors. Until now, several FDI methods such as PSA(Parity Space Approach), GLT(Generalized Likelihood ratio Test) and OPT(Optimal Parity vector Test) method are generally used. However, because these FDI methods only consider the situation that the system has one faulty sensor, these methods cannot be directly adapted for the system with two faulty sensors. To solve this problem, in this paper, PSA method is analyzed and based on this result, new FDI method called EPSA is proposed to consider a detection and an isolation of two faulty sensors in inertial navigation system.