Parity Space Approach Research Articles

Sensor fault detection and isolation for smart irrigation wireless sensor network based on parity space

<p>In the recent years, wireless sensor network technology (WSN) has been widely adopted in precision agriculture for determining the needs of the soil in term of water by monitoring some environmental parameters. To do this, WSN is constructed using several sensor nodes; these small sensing devices are prone to failure and may produce erroneous measurements. To ensure good management of freshwater, the network service quality is necessary. To avoid the degradation of service, the detection of the faulty sensor in WSN is required. In this paper, a fault detection and isolation (FDI) algorithm derived from a parity space approach and based on direct redundancy is proposed toward detecting and isolating sensor fault in WSN. In laboratory experiments, the proposed FDI algorithm proved its effectiveness.</p>

Design of Stealthy Deception Attacks With Partial System Knowledge

This article investigates the design of stealthy deception attacks with the aim of destroying the estimate performance without knowing the filter gain. The residual used to detect attacks is generated by the parity space approach and the Kullback–Leibler divergence is adopted as the metric of stealthiness. We first give the necessary and sufficient condition for the inexistence of the strictly stealthy attack, which does not change the residual and can lead to unbounded estimate error. When the strictly stealthy attack does not exist, a lower bound of the secondary moment of the estimate error is then derived via the posterior Cramér–Rao bound. The zero-mean Gaussian attack that maximizes this lower bound is obtained by solving a convex optimization problem. The proposed method can also be applied to design stealthy attacks with the aim of destroying the control performance. Finally, a numerical example of longitudinal flight control system is illustrated to demonstrate the effectiveness of the proposed attack.

Event-Triggered Parity Space Approach to Fault Detection for Linear Discrete-Time Systems

This article is concerned with the development of a new event-triggered parity space fault detection (FD) scheme. A linear discrete-time system model with varying sampling periods is presented for handling the problem of event-triggered FD and a new parity relation is established. Based on this, an event-triggered residual generator is constructed and the generated residual is completely decoupled from event-triggered transmission error. The design of the parity matrix is formulated into an optimization problem and an optimal solution of the parity matrix is obtained by using singular value decomposition. The issue of residual evaluation is also considered in the event-triggering implementation. The novelties of this article are twofold. First, a new event-triggered parity relation is obtained and the parity space-based residual signal achieves complete decoupling with the event-triggered transmission error. Second, the calculation of the parity matrix is independent of event parameters. So the design of the parity space-based residual generator and event generator can be carried out independently. Finally, a simulation example is considered to demonstrate the effectiveness of the proposed method.

Fast interval estimation for discrete-time linear systems: An [formula omitted] optimization method

This paper studies interval estimation for discrete-time linear systems with unknown but bounded disturbances. Inspired by the parity space approach, we propose a point estimator with fixed-time convergence property. The estimator is combined with the zonotope-based interval analysis to achieve fast interval estimation. The parameter matrix in the estimator is optimized by minimizing the length of the edges of the outer box of the error zonotope. It is formulated as L1 optimization problem and can be efficiently solved by linear programming. Comparison studies illustrate the superiority of the proposed method over existing techniques.

Exploitation of multi-models identification with decoupled states in twin shaft gas turbine variables for its diagnosis based on parity space approach

Exploitation of multi-models identification with decoupled states in twin shaft gas turbine variables for its diagnosis based on parity space approach

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.

Model-based active fault-tolerant control for a cryogenic combustion test bench

In this paper a method is proposed to design a fault detection and isolation scheme based on quantitative physics-based models, as well as fault-tolerant control strategy to improve the reliability of a cryogenic combustion bench operation. The detection and isolation scheme is composed of an extended observer, a cumulative sum algorithm and an exponentially weighted moving average chart. In the case of interdependent parts, a dynamic parity space approach is proposed to isolate one or two simultaneous faults with constraints based on the mass flow rate continuity and the energy conservation for the overall system. The method allows settling adaptive thresholds that avoid pessimistic decision about the continuation of tests while detecting and isolating faults in the system. Then a fault-tolerant system reconfiguration mechanism is provided with a control law which compensates for an estimated actuator additive fault to maintain the overall system stability or allows converging to a reference state in order to overcome instabilities and take into account actuator saturations. For that purpose, the fault-tolerant control algorithm comprises a linear quadratic regulator, an unknown input observer to estimate the fault and an anti-windup scheme. The model and the estimation part were validated on real data from the ONERA/CNES MASCOTTE test bench, and the reconfiguration control law was validated in realistic simulations of the same system.

Integrated model predictive fault-tolerant control, and fault detection based on the parity space approach for a reverse osmosis desalination unit

Actuator faults are inevitable in small reverse osmosis desalination plants. It may cause energy losses and reduce the quality of the freshwater, which may endanger human life. This paper focuses on the integrated fault detection and fault-tolerant control approach. The primary motivation of this paper is to propose a novel integrated fault detection and fault-tolerant control approach. The actuator fault is estimated using the concept of parity space approach. Then the system model is updated in the fault-tolerant control block using the information of the estimated fault parameter. Moreover, the proposed approach uses the receding-horizon predictive control-bounded data uncertainties controller, which is the robust and stable variant of generalized predictive control. The remaining uncertainty caused by the model and observer is compensated by this controller. The structure of a small reverse osmosis desalination plant is deployed. In this plant, the permeate flow rate and conductivity are controlled by a retentate valve and a bypass valve, which add a small amount of inlet to the outlet. The performances of three predictive model controllers are evaluated, and a comparison is made between their computational costs, stability, and robustness. The plant is considered to be linear time-invariant and subject to model uncertainties, measurement noise, and actuator fault in the retentate valve as efficiency dropping. The results reveal the robustness of the proposed approach concerning noise and matched uncertainties as well as its accommodation to actuator fault up to 90%.

Sensor Fault Detection for Salient PMSM based on Parity-Space Residual Generation and Robust Exact Differentiation

An online model-based fault detection and isolation method for salient permanent magnet synchronous motors is proposed using the parity-space approach. Given the polynomial model equations, Buchberger’s algorithm is used to eliminate the unknown variables (e.g. states, unmeasured inputs) resulting in analytic redundancy relations for residual generation. Furthermore, in order to obtain the derivatives of measured signals needed by such a residual generator, robust exact differentiators are used. The fault detection and isolation method is demonstrated using simulation of various fault scenarios for a speed controlled salient motor showing the effectiveness of the presented approach.

Fast interval estimation for discrete-time systems based on fixed-time convergence

This paper studies interval estimation for discrete-time linear systems with unknown but bounded disturbance and measurement noise. Inspired by the well-known parity space approach in the field of fault diagnosis, we propose a fast interval estimation method with fixed-time convergence property. A singular value decomposition-based parameter optimization algorithm is used to attenuate the effect of uncertainties on the estimation error. Comparison study illustrates the superiority of the proposed method over existing technique.

Residual-signal generation for vehicle lateral dynamics disturbances: a switched-observers approach

Residual signal generation is a fundamental step in fault diagnosis and fault tolerant control systems. This paper proposes a switched observer approach to generate a road-adaptive vehicle dynamics residual signal. The main contribution in this paper is to generate a residual signal with low false alarm rate. Lateral vehicle dynamics suffers from presence of two types of disturbances, a stochastic white-noise disturbance and a deterministic lateral dynamics one. Kalman filter and parity-space approach are used to generate the residual signals that must feed the fault tolerant control system. The observers were modeled and simulated in Matlab/Simulink environment, they were then applied to a vehicle dynamics scenarios with lateral disturbances using virtual vehicle simulator (CarMaker).

Fault Detection and Isolation with Fluid Mechanics Constraints For Cryogenic Combustion Bench Cooling Circuit

This paper presents the design of a Fault Detection and Isolation scheme to improve the reliability of a cryogenic engine test bench operation, focusing specifically on its cooling circuit. The proposed fault detection consists in an extended unknown input observer, a cumulative sum algorithm and an exponentially moving average chart. A dynamic parity space approach is then proposed to isolate one or two simultaneous faults in the cooling circuit. The initial system model, for each line composing the cooling circuit, is augmented with constraints based on the mass flow rate continuity and the energy conservation for the overall system. Time delays in the transients are accounted for by recursive equations over a sliding window. The method allows settling adaptive thresholds that avoid pessimistic decision about the continuation of tests while detecting and isolating faults in the transient and permanent states of the system. The model structure and the estimation method were validated on the real Mascotte test bench (ONERA/CNES) data. The fault detection and isolation scheme was validated in realistic simulations.

Fault Detection and Isolation for Redundant Inertial Measurement Unit under Quantization

Fault detection and isolation with redundant strapdown inertial measurement unit is critical for ensuring the reliability of the guidance or navigation system in the fields of both aeronautics and astronautics. Although the parity space approach is used widely, it cannot detect the soft fault which affects navigation performance under pulse quantization. This paper develops the three-channel filters to detect the soft fault and conducts theoretical implementation. The constraint conditions of their parameters are explored and the influence of the weight of different ratios is analyzed. The Monte Carlo simulation is carried out in order to verify the validity of the fault detection and isolation method. The simulation results and their analysis provide a theoretical reference for fault detection and isolation with redundant strapdown inertial measurement unit.

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.

A Wavelet-based Parity Space Approach to Fault Detection of Linear Discrete Time-varying Systems

This paper deals with the problem of parity space-based fault detection (FD) for linear discrete time-varying systems. In order to simultaneously ensure the performance of FD and avoid the heavy computational burden of high dimensional parity space, the techniques of stationary wavelet transform (SWT) on residual signals are applied in the paper. Based on this, the design of residual generators can be formulated as generalized optimization problems and a recursive algorithm is developed to a feasible solution of parity vectors. Furthermore, a bank of residual signals are generated at multiscales. The significant advantage of the SWT based parity space approach is the improvement of FD performance with low parity space order and an easy online realization. Finally, a numerical example is given to demonstrate the effectiveness of the proposed approach.

Fault estimation for a quadrotor unmanned aerial vehicle by integrating the parity space approach with recursive least squares

This paper studies actuator fault detection and estimation for a quadrotor unmanned aerial vehicle. By combining a parity space approach and a recursive least squares algorithm, we propose a novel fault detection and estimation method strategy for a quadrotor unmanned aerial vehicle, which is described by a linear time-varying system. Specifically, the parity space approach is used to generate a residual in fault detection, and then the magnitude of the fault is estimated by a recursive least squares algorithm with a variable forgetting factor based on the parity. Numerical simulations of a quadrotor unmanned aerial vehicle are conducted to verify the effectiveness of the proposed method.

Detecting intermittent sensor faults for linear stochastic systems subject to unknown disturbance

Intermittency of sensor faults leads to wrong control commands, fault propagation and overmuch maintenance, which complicates the detection task for characterizations of unpredictability, discontinuity, non-determinacy and repeatability. In this paper, we present a novel robust detection method for intermittent sensor faults in linear stochastic systems corrupted by unknown disturbance. In order to detect all the appearing time and the disappearing time of an intermittent sensor fault, a scalar robust residual is designed by utilizing the parity space approach such that the residual gets decoupled from unknown disturbance but most sensitive to the intermittent senor fault. Based on the analysis of characteristics of the scalar residual, two hypothesis tests are proposed to detect all the appearing time and the disappearing time, respectively. Within this framework, the detectability of an intermittent sensor fault is further defined and a sufficient condition is obtained to ensure that (i) all the appearing time and the disappearing time are detected; (ii) each appearing (disappearing) time of the intermittent sensor fault is determined before the subsequent disappearing (appearing) time in a probabilistic sense. In addition, capacities of detection speed, false alarm rates and missed detection rates are theoretically analyzed. Finally, efficiency of the proposed scheme is verified through a simulation on a simplified vertical dynamic model of an F-16 aircraft.

Outlier detection by using fault detection and isolation techniques in geodetic networks

Fault detection and isolation (FDI) techniques, which are called standard parity space approach (SPSA) and optimal parity vector approach (OPVA), have been presented in literature extensively for engineering sensor systems or sensor networks. This paper demonstrates the abilities of these approaches to detect and isolate outliers in geodetic networks. The ability to detect and isolate outliers has been measured by computing the mean success rate (MSR) for some given probability of significance levels. These approaches have been applied to a levelling network and a Global Navigation Satellite System (GNSS) network. Different matrix decomposition techniques have been used as an alternative way to the Potter algorithm, which is used in SPSA and OPVA. It has been proven that the abilities of FDI techniques, i.e. the MSRs of OPVA, increase with regard to the ones of SPSA in the levelling network and the GNSS network especially if the significance level α is chosen as 0.001 by using Monte–Carlo simulation.

Advanced control for fault-tolerant dynamic positioning of an offshore supply vessel

The paper presents a solution to guarantee a fault-tolerant robust control for the dynamic positioning of an over-actuated offshore supply vessel. Fault detection is obtained by a combination of two model-based techniques: the parity space approach and the Luenberger observer. The dynamic positioning system is provided by a bank of reconfigurable Discrete-Time Variable-Structure Controllers (DTVSC), selected by a supervisor, based on a fault isolation logic. The control system is combined with a wave compensation based on a Multi-rate Extended Kalman Filter (MREKF). The proposed solution is compared with a standard Proportional-Integral-Derivative (PID) control system and a passive nonlinear wave filter to assess its robustness to input disturbances and uncertainties in the model parameters. The simulation tests developed for a scale model of an offshore supply vessel show that, in the case of actuators faults, dynamic positioning is guaranteed by the proposed solution.

Diagnosing a solar volumetric receiver combining NN-based modelling with online parameter identification and rule-based techniques

The lack of detectability when model-based techniques are applied to intelligent fault detection and isolation tasks, which mainly occur in most non-linear processes so far is an unresolved problem. Generally, all types of non-linear processes will also suffer from lack of detectability due to the inherent ambiguity in discerning faults in the process, sensors and/or actuators. The aim of this work is to develop and apply a strategy to detect and isolate process and/or sensor faults by combining a neural network-based functional approximation procedure associated with an online identification algorithm, both processed by recursive rule-based techniques using parity space approaches. A case study dealing with the supervision of a solar volumetric receiver was performed using the proposed intelligent techniques. The conducted study produced reliable and acceptable intelligent fault detection and isolation results on the basis of heuristic knowledge-based rules.