Design Of Fault Detection System Research Articles

LPV model-based robust diagnosis of flight actuator faults

A linear parameter-varying (LPV) model-based synthesis, tuning and assessment methodology is developed and applied for the design of a robust fault detection and diagnosis (FDD) system for several types of flight actuator faults such as jamming, runaway, oscillatory failure, or loss of efficiency. The robust fault detection is achieved by using a synthesis approach based on an accurate approximation of the nonlinear actuator–control surface dynamics via an LPV model and an optimal tuning of the free parameters of the FDD system using multi-objective optimization techniques. Real-time signal processing is employed for identification of different fault types. The assessment of the FDD system robustness has been performed using both standard Monte-Carlo methods as well as advanced worst-case search based optimization-driven robustness analysis. A supplementary industrial validation performed on the AIRBUS actuator test bench for the monitoring of jamming, confirmed the satisfactory performance of the FDD system in a true industrial setting.

Modeling and Simulation of a Heat Recovery Steam Generator Using Partially Known Design Point Data

In a cogeneration or combined heat and power plant, a heat recovery steam generator (HRSG) helps achieve overall thermal efficiency as high as 80%. The purpose of this study is to model and simulate the HRSG given partial design point data. The pinch and approach temperatures are optimized within generally accepted range. In order to satisfy the energy conservation equation, tuning parameters are used for the overall heat transfer coefficients corresponding to the evaporator and economizer. For the off-design simulation, the values of pinch and approach temperatures are adjusted until the modeling error is within a set limit. The effect of mass flow rate on the heat transfer coefficient is accounted for & by employing empirical relations. A 12 Ton/hr natural circulation HRSG was considered as a case study. The validation test on inlet temperatures of the exhaust gas and feed water to the economizer demonstrated relative percentage errors of 0.4246% and 1.8776%, respectively. The model can be used for fault detection and diagnostic system design, performance optimization, and environmental load assessment.

Distributed Fault Detection System Design Based on the Communication Protocol

This article analyze the transmitting characteristics of information in the modern And than, we find the basic design criterion of fault detection system in the complicated electronic devices. Than we discuss the possibility of achieving fault detection through a communication protocol. So we describe the design project of achieving detecting fault function through the communication protocol. At last, we brief introduce simulative detection function about fault detection circuit.

A robust fault detection design for uncertain Takagi–Sugeno models with unknown inputs and time-varying delays

This paper investigates the problem of robust fault detection system design for a class of uncertain Takagi–Sugeno (T–S) models. The system under consideration is subject to unknown input and time-varying delay. The fault detection system is designed such that the unknown input is thoroughly decoupled from residual signals generated by the fault detection system. Furthermore, the residual signals show the maximum possible sensitivity to the faults and the minimum possible sensitivity to the external disturbances. The model matching approach is utilized to tackle the effects of parametric uncertainties in the model of the system. The design procedure is presented in terms of Linear Matrix Inequalities (LMIs). Some remarks are given to analyze the proposed method. Finally, a numerical example is presented to show the effectiveness of the proposed method.

Integrated trade‐off design of fault detection system for linear discrete time‐varying systems

In this study, the problem of fault detection (FD) system design for linear discrete time-varying systems is addressed. Different from most existing methods which separately handle residual generator and evaluator, this study focuses on integrated design of residual generator and evaluator. Instead of traditional trade-off design between sensitivity to faults and robustness to disturbances, this study aims at maximising FD rate given a predefined false alarm rate. The proposed approach in this study follows two steps: first, in the norm-based framework, the parity relation-based offline FD system achieves maximal FD rate by minimising the set of undetectable faults; then the optimal offline FD system is equivalently transformed into a recursive FD algorithm for online use. Relationship between the obtained solution in this study and the existing ones is analysed. The performance improvement of the proposed approach is illustrated by comparing against existing methods through Monte Carlo simulations.

Fault detection and fault tolerance methods for industrial robot manipulators based on hybrid intelligent approach

Fault tolerance is increasingly important in modern industrial robotic manipulators, especially those operated in remote and hazardous environment. Faults in robotic manipulator can cause economic and serious damages. So the robots need the ability to detect as well as tolerate failures, allow effectively coping with internal failures and continue performing designated tasks without the need for immediate human intervention. This saves time and cost involved in repairing the robot. This type of autonomous fault tolerance is also useful for industrial robots in that it decreases down-time by tolerating failures, identifies faulty components or subsystems to speed up the repair process and prevents the robot from damaging the products being manufactured. To support these fault tolerant capabilities, methods of detecting and tolerating failures must be perfected in robot manipulator. A number of researchers have proposed fault detection/tolerance architectures for robotic manipulators using the model based analytical, and redundancy approach. One of the main issues in the design of fault detection system is to model the rigid link robotic manipulators with modeling uncertainties. In this paper, a new approach hybrid intelligence based fault detection/tolerance for robot manipulators is discussed. A learning architecture, with neural network as on-line approximates the off-nominal system behavior, which is used for monitoring the robotic system for the faults. This generates the residual by comparing the actual output from robot. Fuzzy inference system is applied to identify and tolerate the faults which provide the adoptive threshold under the varying conditions. The new concepts discussed were validated through simulation study using a Scorbot ER 5plus manipulator robot mat lab toolbox.

Integrated fault detection system design for linear discrete time‐varying systems with bounded power disturbances

SUMMARYThis paper investigates the problem of an integrated fault detection system design for linear discrete time‐varying systems with bounded power disturbances. In the integrated design of residual generator and evaluator, an approximated energy constraint is first imposed on the bounded power disturbances, and then selected by solving a min–max problem to achieve minimal‐size set of undetectable faults under the condition of zero false alarms. To tackle the problem that the computational burden involved in solving the min–max optimization grows with time, the moving horizon method is proposed. The proposed approach in this paper has two advantages: (i) the approximated energy constraint on bounded power disturbances is explicitly selected as a min–max solution in the integrated design to improve fault detection rate; by contrast, when directly applying any existing fault detection method to the case of bounded power disturbances, a predefined approximated energy constraint is implicitly introduced without considering fault detection performance; (ii) the design objective of the proposed approach can choose to consider faults only in the recent time horizon rather than faults in the complete time horizon; this strategy enhances detection performance of recent faults and benefits early fault detection, but has not been considered by existing fault detection methods. Copyright © 2012 John Wiley & Sons, Ltd.

An approach for multimode dynamic process monitoring using Bayesian inference

The problem of data driven design of fault detection system for dynamic processes when the system is subjected to set-point changes as well as model changes due to different operation regimes is studied in this paper. To this end, a new monitoring approach is developed where the finite Gaussian mixture model is firstly identified directly from available historical data. The mixture model contains the mean and covariance information of the data for each component. Then an observer based residual generator is designed by identification of the parity vectors using covariance matrices of the mixture model. A Bayesian approach is further utilized for the purpose of fault detection. The performance and effectiveness of the proposed approach are finally demonstrated on a continuous stirred tank heater (CSTH) benchmark process.

Operator-based actuator fault detection system design of a thermal process

In this paper, an operator-based fault detection method for actuator fault of an aluminium plate thermal process is proposed by using operator-based robust right coprime factorisation (RCF) approach. In details, operator-based output tracking and fault detection system design for single input single output (SISO) system is proposed, then the theoretical analysis is extended to multi input multi output (MIMO) system. Based on the proposed design schemes, the output tracking performance can be realised and the actuator fault can be detected. After developing a mathematical model of the three-input three-output aluminium plate thermal process, the proposed design schemes are applied and the effectiveness of that is confirmed by the simulation results.

Data-Driven Design of Model-based Fault Diagnosis Systems

In this paper, recent development of data-driven design of fault detection and isolation (FDI) systems is presented. The major attention and focus are on the design schemes for observer-based FDI systems.

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

This paper deals with subspace method aided data-driven design of robust fault detection and isolation systems. The basic idea is to identify a primary form of residual generators directly from test data and then make use of performance indices to make uniform the design of different type robust residuals. Four algorithms are proposed to design fault detection, isolation and identification residual generators. Each of them can achieve robustness to unknown inputs and sensitivity to sensor or actuator faults. Their existence conditions and multi-fault identification problem are briefly analyzed as well and the application of the method proposed is illustrated by a simulation study on the vehicle lateral dynamic system.

Fuzzy model-based fault detection and diagnosis for a pilot heat exchanger

This article addresses the design and real-time implementation of a fuzzy model-based fault detection and diagnosis (FDD) system for a pilot co-current heat exchanger. The design method is based on a three-step procedure which involves the identification of data-driven fuzzy rule-based models, the design of a fuzzy residual generator and the evaluation of the residuals for fault diagnosis using statistical tests. The fuzzy FDD mechanism has been implemented and validated on the real co-current heat exchanger, and has been proven to be efficient in detecting and isolating process, sensor and actuator faults.

차단기 트립코일 이상감지 장치

The circuit breaker of power distribution board is essential part for the protection of electrical disaster from load short, trouble of power system. For the normal operation of circuit breaker, trip coil of the circuit breaker can cut the mechanical contact of circuit breaker from the detection of power system troubles.BR This paper presents a design and experimental results of trip coil fault detection system for the real time monitoring of the circuit breaker. The designed system is consisted by the trip coil fault detector which is connected to the each circuit breaker and remote monitoring unit. The trip coil fault detector can detect the impedance and operating voltage of the trip coil, and the detected values are compared with the normal state. And the remote monitoring unit can be connected to the 32 channels of trip coil fault detectors by serial communication. From the designed system, the fault and normal states of the trip coil can be remotely monitored in real time.BR The designed system is verified by the practical circuit breaker of power distribution board. And the results shows the effectiveness of the designed system.

Data-Driven Design of Observers and Its Applications

AbstractIn this paper, we address the data-driven design of observers for the process monitoring and control purposes. Instead of identifying a standard state space model, our design schemes are based on the identification of the so-called parity subspace. Two design schemes are developed, which allow a direct design of (a) observer-based fault detection systems (b) a full order observer for the estimation of the process state variables. The achieved results are illustrated by a benchmark example.

SUPERVISORY FAULT TOLERANT CONTROL BASED ON DWELL-TIME CONDITIONS

AbstractThe problem of the active fault tolerant control (FTC) with reconfiguration mechanism for linear systems with external disturbances is addressed with application of the supervisory control approach. Starting from the well known in FTC literature conditions for independent design of fault detection, isolation and fault compensation systems we propose new set of united conditions and the computational procedure providing mutual performance of the system. The efficiency of the approach is demonstrated on a flight system benchmark example.

Remote fault detection system design with online channel reliability information

In this article, the remote fault detection (FD) problem over unreliable communication channels encountering bit errors and quantisation errors is studied, where data transmissions can be centralised or decentralised. The online channel reliability information represented by the so-called bit error rate (BER) is first analysed in the view of control engineering and then transformed into time-varying parameters and stochastic signals in the control systems. By using the information of BERs provided by communication systems, we propose a new remote FD system which consists of an observer-based residual generator and a residual evaluator. The design of the residual generator is formulated as a model matching problem such that an optimal FD performance can be achieved. Then the residual signals are evaluated and compared with a threshold to detect the occurrences of faults. The false alarm rate is guaranteed to be below a given level. All the solutions are presented in terms of linear matrix inequalities. A numerical example is used to illustrate the effectiveness of the proposed methods.

Fault Detection System Design to an Actuator Fault of a Thermal Process Using Operater based Apprlach

Fault Detection System Design to an Actuator Fault of a Thermal Process Using Operater based Apprlach

Robust Fault Diagnosis for Atmospheric Reentry Vehicles: A Case Study

This paper deals with the design of robust model-based fault detection and isolation (FDI) systems for atmospheric reentry vehicles. This work draws expertise from actions undertaken within a project at the European level, which develops a collaborative effort between the University of Bordeaux, the European Space Agency, and European Aeronautic Defence and Space Company Astrium on innovative and robust strategies for reusable launch vehicles (RLVs) autonomy. Using an <formula formulatype="inline" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex Notation="TeX">${\cal H}_{\infty}/{\cal H}_{-}$</tex> </formula> setting, a robust residual-based scheme is developed to diagnose faults on the vehicle wing-flap actuators. This design stage is followed by an original and specific diagnosis-oriented analysis phase based on the calculation of the generalized structured singular value. The latter provides a necessary and sufficient condition for robustness and FDI fault sensitivity over the whole vehicle flight trajectory. A key feature of the proposed approach is that the coupling between the in-plane and out-of-plane vehicle motions, as well as the effects that faults could have on the guidance, navigation, and control performances, are explicitly taken into account within the design procedure. The faulty situations are selected by a prior trimmability analysis to determine those for which the remaining healthy control effectors are able to maintain the vehicle around its center of gravity. Finally, some performance indicators including detection time, required onboard computational effort, and CPU time consumption are assessed and discussed. Simulation results are based on a nonlinear benchmark of the HL-20 vehicle under realistic operational conditions during the autolanding phase. The Monte Carlo results are quite encouraging, illustrating clearly the effectiveness of the proposed technique and suggesting that this solution could be considered as a viable candidate for future RLV programs.

Fault Detection System Design for Actuator of a Thermal Process Using Operator Based Approach

Abstract This paper proposes an operator based fault detection method for an actuator fault of an aluminum plate thermal process with input constraints. Operator-based robust right coprime factorization (RCF) approach is utilized in this method. After developing a mathematical model of the thermal process, a robust tracking operator system is designed for the process with input constraints. Following this, design of the fault detection system is given by using operator-based robust RCF approach. Finally, experiment is conducted to support the proposed design method.

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.