Parity Space Method Research Articles

Replay Attack Detection Based on Parity Space Method for Cyber-Physical Systems

Replay Attack Detection Based on Parity Space Method for Cyber-Physical Systems

Anomaly detection at the European X-ray Free Electron Laser using a parity-space-based method

A novel approach to detect anomalies in superconducting radio-frequency (rf) cavities is presented, based on the parity space method with the goal to detect quenches and distinguish them from other anomalies. The model-based parity space method relies on analytical redundancy and generates a residual signal computed from measurable rf waveforms. The residual is a sensitive indicator of deviation from the model and provides different signatures for different types of anomalies. This new method not only helps with detecting faults but also provides a catalog of unique signatures, based on the detected fault. The method was experimentally verified at the European X-ray Free Electron Laser (EuXFEL). Various types of anomalies incorrectly detected as quenches by the current quench detection system are analyzed using this new approach.9 MoreReceived 28 February 2022Accepted 5 December 2022DOI:https://doi.org/10.1103/PhysRevAccelBeams.26.012801Published by the American Physical Society under the terms of the Creative Commons Attribution 4.0 International license. Further distribution of this work must maintain attribution to the author(s) and the published article’s title, journal citation, and DOI.Published by the American Physical SocietyPhysics Subject Headings (PhySH)Research AreasMethods in electromagnetismRadio frequency calculationsAccelerators & Beams

Decoupling fault detection vectors for fault detection in the TS fuzzy system

ABSTRACT The parity space method can be applied to detect the fault of the time-invariant control system simply and effectively. However, the parity space method is greatly limited in the time-variant system. Therefore, in this paper, a TS fuzzy system is used to describe this kind of system. The system parameters are obtained based on TS fuzzy rules, and the decoupled fault detection vectors for different channels are designed. Using this vector, the residual representing fault information is extracted directly. Ideally, the fault detection of different channels is decoupled. However, sometimes the fully decoupled detection vector cannot be designed, then a suboptimal decoupled fault detection vector is proposed. Simulation and verification are carried out on a steady-state system and a TS fuzzy system.

Fault detection for linear discrete time-varying systems with multiplicative noise based on parity space method

This paper addresses the robust fault diagnosis problem for a class of linear discrete time-varying systems with multiplicative noise based on parity space method. A novel fault detection performance index, in terms of stochastic robustness/sensitivity ratio, is proposed to establish the residual generator. A computationally attractive recursive algorithm, is put forward to obtain the complex matrix involved in the aforementioned fault detection performance index. Drawing support of random matrix analysis and calculation, the corresponding solution is derived in an analytical form via solving a multi-objective optimization problem. By means of Randomized Algorithms, two fault detection threshold setting algorithms are provided subsequently to achieve residual performance assessment by taking into account the fault detection rate and false alarm rate in the probabilistic framework. Two illustrative examples are finally provided to illustrate the effectiveness of the proposed scheme.

Modulating Function Based Fault Diagnosis Using the Parity Space Method

A model-based method for the detection and estimation of faults in dynamic systems is proposed. The method is based on the combination of the parity space approach and the modulating function framework for estimation. The parity space method is employed as an efficient geometric procedure determining null subspaces for annihilating unknown terms and formulating residuals. With the modulating functions technique the dynamic relation from output differentiation is reformulated as an algebraic expression. This substantially reduces the noise sensitivity of the output derivatives required. The design allows for the robust fault detection and isolation also for some nonlinear systems. The robustness of the approach is demonstrated on a nonlinear model of a four-tank process.

An Anomaly Diagnosis Method for Redundant Inertial Measurement Unit and Its Application with Micro-Electro-Mechanical System Sensors

The application of the micro-electro-mechanical system inertial measurement unit has become a new research hotspot in the field of inertial navigation. In order to solve the problems of the poor accuracy and stability of micro-electro-mechanical system sensors, redundant design is an effective method under the restriction of current technology. Redundant data processing is the most important part in the micro-electro-mechanical system redundant inertial navigation system, which includes the processing of anomaly data and the fusion estimation of redundant data. To further improve the reliability of the micro-electro-mechanical system redundant inertial measurement unit, an anomaly detection, isolation, and recognition method for data anomalies is proposed. The relationship between the parity space method detection function and the deterioration degree of anomaly data is mathematically deduced. The parity space method detection functions of different anomalies are analyzed, and five indicators are designed to quantitatively analyze the detection function values. According to these indicators, the detection and recognition method are proposed. The new method is tested by a series of simulation experiments.

Parity Space Method for Mode Detection of a Nonlinear Switching System Using Takagi–Sugeno Modeling

This paper deals with the problem of the recognition of the active mode for nonlinear switching system and the determination of the switching instants. An extension of the parity space method is used for nonlinear modes that are represented by Takagi–Sugeno (TS) models which allows to model a widespread class of real systems. A set of residuals are generated to indicate the path online. The determination of the switching instants is from these residuals that are composed of different modes of the system which reduces the detection time. Tests of discernibility are calculated online to verify the uniqueness of the active path. The effectiveness of the proposed recognition approach has been demonstrated through simulations of examples. The considered cases are a combination of the system in a deterministic context with (or without) measurement noise or with (or without) uncertain parameters.

Optimal Fault Classification Using Fisher Discriminant Analysis in the Parity Space for Applications to NPPs

A parity space approach to monitoring and fault detection and identification of systems in nuclear power plants (NPPs) can be beneficial. However, if the number of fault classes exceeds the total independent residual signatures, the parity space method needs to be further enhanced to achieve the optimal fault classification. This situation happens frequently in NPP applications, where the safety and reliability are paramount. A possible enhancement proposed in this paper is to combine Fisher discriminant analysis with the parity space method to maximize the scatter among different fault classes, while minimizing the scatter within each class. Under identical conditions, the proposed technique can achieve optimal separation among different fault classes. Design, real-time implementation, and experimental evaluation of the proposed method are detailed in this paper. The implemented system has been validated on the Nuclear Power Control Test Facility to demonstrate the feasibility. The test results have revealed many salient features of the proposed method with potential applications in NPPs.

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.

Fault Detection and Isolation in Linear Heterogeneous Multi-Agent Networks

Detection and isolation of faulty behaviours in multi-agent dynamical networks under cooperative control laws is of paramount importance to secure that the network operation is not jeopardized by the failing of one or more of its nodes. The paper investigates under which conditions the fault detectability and isolability (FDI) properties of the individual agent are improved because of the information sharing with other agents in the network. Relying on the parity space method and focusing the analysis on linear heterogeneous dynamical systems two main contributions are achieved: (i) the sharing of information through the cooperative control law does not improve the FDI properties of the individual agents; (ii) the presence of relative measurements among the agents results in improved fault detection and isolation capabilities the multi-agent system under the assumption of a strongly connected network.

Diagnosis of Discrete-Time Singularly Perturbed Systems Based on Slow Subsystem

Abstract This paper deals with the diagnosis of discrete-time singularly perturbed systems presenting two time scales property. Parity space method is considered to generate the fault detection residual. The focus is in two directions. First, we discuss the residual illconditioning caused by the singular perturbation parameter. Then, the use of the slow subsystem is considered to make the fault diagnosis easier. It is shown that the designed diagnostic algorithm based on reduced order model is close to the one synthesized using the full order system. The developed approach aims at reducing the computational load and the ill-conditioning for stiff residual generation problem. Two examples of application are used to demonstrate the efficiency of the proposed method.

Fault detection based on fractional order models: Application to diagnosis of thermal systems

The aim of this paper is to propose diagnosis methods based on fractional order models and to validate their efficiency to detect faults occurring in thermal systems. Indeed, it is first shown that fractional operator allows to derive in a straightforward way fractional models for thermal phenomena. In order to apply classical diagnosis methods, such models could be approximated by integer order models, but at the expense of much higher involved parameters and reduced precision. Thus, two diagnosis methods initially developed for integer order models are here extended to handle fractional order models. The first one is the generalized dynamic parity space method and the second one is the Luenberger diagnosis observer. Proposed methods are then applied to a single-input multi-output thermal testing bench and demonstrate the methods efficiency for detecting faults affecting thermal systems.

Fault Detection and Isolation in Multiple MEMS-IMUs Configurations

This research presents methods for detecting and isolating faults in multiple micro-electro-mechanical system inertial measurement unit (MEMS-IMU) configurations. First, geometric configurations with n sensor triads are investigated. It is proved that the relative orientation between sensor triads is irrelevant to system optimality in the absence of failures. Then, the impact of sensor failure or decreased performance is investigated. Three fault detection and isolation (FDI) approaches (i.e., the parity space method, Mahalanobis distance method and its direct robustification) are reviewed theoretically and in the context of experiments using reference signals. It is shown that in the presence of multiple outliers the best performing detection algorithm is the robust version of the Mahalanobis distance.

Hybrid system diagnosis: Test of the diagnoser HYDIAG on a benchmark of the international diagnostic competition DXC'2011

Estimating the health status of the components forming a hybrid system means to account for interleaved continuous and discrete dynamics. It is the task of the diagnoser to infer this status from a partial set of observations and a mathematical model of the physical system. Whereas most of the approaches adopt a state estimation scheme, the HYDIAG diagnoser is based on an extension of the parity space method to hybrid systems together with an event based abstraction of the continuous dynamics that permits to cast the diagnosis problem in the discrete event systems framework. This paper aims at testing the HYDIAG diagnoser on the realistic test case ADAPT provided by NASA Ames in the form of a benchmark which is proposed for the diagnosis competition of the DX community.

Satellite Fault Detection and Isolation through Decoupling Parity Space

In order to detect and isolate orbiting satellite actuator faults, a decoupling parity space method was extended. The decoupling parity vector was obtained using this method by singular value decomposition. Sometimes this vector may not exist, then by singular value substitution or generalized Eigen value method was used for solving the optimal approximation. The vector can easily make satellite actuator fault detected and isolated. The simulation results showed the effectiveness of the proposed algorithm.

Experimental evaluation of fault diagnosis in a skew-configured UAV sensor system

This paper presents an experimental evaluation of a hybrid fault detection and isolation scheme against three successive faults in skew-configured inertial sensors of an unmanned aerial vehicle (UAV). An additional small and low-cost inertial measurement unit is installed with a skewed angle to a primary inertial measurement unit. A parity space method and an in-lane monitoring method are combined to increase system tolerance to the occurrence of multiple successive faults during flight. The first and second faults are detected and isolated by the parity space method. The third fault is detected by the parity space method and isolated by the in-lane monitoring method based on the discrete wavelet transform. Hardware in-the-loop tests and flight experiments with a fixed-wing UAV are performed to verify the performance of the proposed fault diagnosis scheme.

잔차 수평 투영 비교를 이용한 의사거리 비정상 증가 위성 식별

This paper can be used for GPS air navigation study on integrity monitoring algorithm as, the projected horizontal plane using GPS pseudorange residuals for fault detection satellites were suspected. Failure to remove the detected suspicious satellite, compare with threshold which is calculated using satellite deployment (PDOP) and determine whether the failure is presented. The theory that horizontal projection of the satellite failure residuals greater than residual of normal satellite is proved mathematically. Comparison with horizontal projection residuals are likely to malfunction in the satellite were presented. To evaulate the proposed algorithm, bias fault insert into GPS pseudorange, and compare with conventional parity space method about fault detection and isolation capability.

Adaptive process monitoring based on parity space methods

Motivated by data-driven design of fault detection system, a recursive algorithm is developed for updating the parity relation within the framework of subspace identification methods (SIM). The presented recursive algorithm benefits from the signal processing methods for noise subspace tracking to reduce the computationally burdensome updating of singular value decomposition (SVD), which is a crucial step in SIM. Based on the recursive updated parity relation, the residual generator for the fault detection purpose is constructed and the issues of residual evaluation and threshold computation are discussed further. The adaptive process monitoring scheme that integrates the aforementioned issues, is proposed and tested on the laboratory three-tank-system.

GPS fault detection and exclusion using moving average filters

A new approach based on the moving average (MA) is proposed to perform satellite failure detection and exclusion (FDE). By taking the average of the last few sums of the squares of the GPS range residual errors, the MA filter is applied to speed up failure detection. The detection threshold cannot be obtained directly because the cumulative distribution of a random process with an MA filter is unknown. Therefore, the Markov chain approach is applied to resolve the threshold. In addition, variations in the number of visible satellites may cause problems in data fusion. The probability integral transformation (PIT) method is adopted to overcome this. After a satellite failure is detected, the multivariate MA filter is used to reduce the incorrect exclusion rate (IER) by taking the average of the last few parity vectors. Simulation results show that, in comparison with the conventional least-squares-residuals method, the MA filter demonstrates higher performance in detecting small failures and a similar level of performance in detecting large failures. Moreover, simulation results also verify that the proposed method has lower IER than the conventional parity space method.

Adaptive fault tolerant control with adaptive residual generation

This paper presents an adaptive predictive fault tolerant controller and an adaptive residual generator in an integrated scheme applied to a non-linear process. A case study on the steam super-heating subsystem of a boiler, represented by a detailed non-linear physical based model, is described. The faults considered are additive, step like and occur on the actuator. The controller is a GPC, rendered adaptive by a RLS algorithm, and is designed as in the "classical" way, with the addition of the actuator fault estimate information. The adaptive residual generator is based on the parity space method, rendered adaptive through on-line estimation of the model parameters allowing residuals to stay near zero for different amplitudes of the set-point. Simulation results of this integrated scheme are presented highlighting the performance advantages of the adaptive residuals vs. the standard residuals and discussing the proposed modified GPC controller vs. the "classical" GPC.