Search

In this paper, a novel adaptive fault-tolerant controller is proposed for a typical electro-hydraulic rotary actuator in the presence of disturbances, internal leakage fault, and sensor fault simultaneously. To construct the suggested controller, a nonlinear unknown input observer is developed to effectively identify the sensor fault, which is unaffected by not only internal leakage fault but also mismatched disturbances/uncertainties. Furthermore, a radial basis function neural network is designed to compensate for the mismatched disturbances/uncertainties caused by payload variation and unknown friction nonlinearities. Besides, an adaptive law based on the projection mapping function is applied to tackle the effect of the internal leakage fault. The integration of the above-mentioned techniques into the adaptive backstepping terminal sliding mode is investigated to obtain high tracking performance, robustness as well as fast convergence. The stability of the closed-loop system is proven by the Lyapunov theory. Finally, the capability and effectiveness of the proposed approach are validated via simulation results under various faulty scenarios.

The aim of this paper is to develop robust three-stage extended Kalman filter for a model based on a fault detection and identification for nonlinear hover mode system of helicopter unmanned aerial vehicle. In addition, we show that, in considered systems, the actuator faults are affected by each other motivated in five scenarios simulation results. More precisely, the proposed approach estimates and decouples actuator faults in the presence of external disturbances in nonlinear mathematical model. Moreover, we analyze and identify various faults such as bias fault and also catastrophic faults such as stuck and floating faults. Finally, the simulation results show effectiveness of the proposed robust method for detection and isolation of various actuator faults and differentiating bias and stuck faults.

Condition monitoring of the stator and rotor problems such as interturn short fault (ITSF) and partial irreversible demagnetization fault (PIDF) in an interior permanent magnet synchronous motor (IPMSM) is essential for achieving high efficiency and reliability. This article presents a torque angle-based inverter-embedded technique for the online detection and identification of ITSF and PIDF. This technique monitors the deviation in the torque angle caused due to ITSF and PIDF at a certain operating point. In steady-state operation, the torque angle decreases from its original value (healthy state) due to ITSF while increases in the case of PIDF. This particular trend of torque angle is used for the detection and identification of ITSF and PIDF. In this article, the torque angle for a healthy IPMSM is first obtained using multivariate regression analysis over the entire operating region and then compared with the torque angle during operation for any potential variation. The proposed method is verified using a finite-element method-based simulation and experiment on a 400-W IPMSM. The results suggest that the proposed method can be a viable candidate for efficient ITSF and PIDF detection and identification in real time.

Faults in sensor channels as a result of calibration errors or a bias in transmitters can lead to various safety and operational issues in nuclear power plants, such as reduced trip margins, jeopardized performance, or creating a false sense of security regarding the health of the plant operation. Unfortunately, such faults are difficult to detect, specifically when the sensors are part of a closed-loop system, which has a tendency to reduce or to mask the effects of the fault through feedback control actions during a dynamic transient process. Detecting these types of faults within a steam generator (SG) level control system is investigated in this paper. It is shown that the tools based on analytical redundancy can be very effective in this case. Using a typical SG level control system as a reference plant, specific analytical redundancy formulations are derived. Three sensor fault scenarios have been considered in detail. It has been shown, by simulation and analysis, that the proposed technique can effectively detect the sensor faults that are otherwise difficult to detect.

This paper designs an active fault-tolerant control system for spacecraft attitude control in the presence of actuator faults, fault estimation errors, and control input constraints. The developed fault-tolerant control system is able to detect the actuator fault without false alarms caused by external disturbances, and also estimate the total fault effects accurately through an indirect fault identification approach, in which an auxiliary variable is utilized to build the relation between fault and system states. Once the fault identification is completed with certain degree of reconstruction accuracy, a fault-tolerant backstepping controller using the nonlinear virtual control input is reconfigured to accommodate the detected actuator faults effectively, in spite of actuator saturation limitations and fault estimation errors. Numerical simulation is carried out to demonstrate that the proposed active fault-tolerant control system is successful in fault detection, identification, and controller reconfiguration for handling actuator faults in attitude control systems.

Designing an effective procedure for fault detection and identification (FDI) is necessary to maintain the healthy and safe operation of robot manipulators. The complexities of nonlinear parameters inherent in a robot manipulator make it challenging to detect and identify faults. To address this issue, a powerful, robust, hybrid fault identification method based on the fuzzy extended ARX-Laguerre proportional integral (PI) observer for perturbation robot manipulators is presented. Accurate fault estimation is an essential challenge in classical extended ARX-Laguerre PI observers. The Takagi-Sugeno (T-S) fuzzy algorithm is applied to the sliding mode extended ARX-Laguerre PI observer to modify the performance of fault estimation. Moreover, using the ARX-Laguerre algorithm, PI observation technique, sliding mode estimation method, and T-S fuzzy procedure, the system’s performance showed fast convergence and high accuracy. A PUMA robot manipulator was used to test the effectiveness of the proposed method. Results indicated that the proposed algorithm outperforms the ARX-Laguerre PI observer performance.

A fault detection and identification (FDI) method based on mixed logic dynamic(MLD) model for three-phase inverter using single current sensor is proposed in this paper. Single current sensor is used to measure and reconstruct the output currents of a three-phase inverter. A MLD model of the inverter is further established to estimate the output current in real-time under different fault conditions. The estimated currents and the measured currents can be compared and residuals between them can also be calculated in real-time to detect and locate the faults. Compared with existing FDI methods, method proposed in this paper can reduce the uncertainty brought by current sensors by using single current sensor, and improve the accuracy of fault diagnosis by using mixed logic model of the inverter. Simulation results show the effectiveness of the FDI method.

The anti-lock braking system (ABS) is an essential part in ensuring safe driving in vehicles. The Security of onboard safety systems is very important. In order to monitor the functions of ABS and avoid any malfunction, a model-based methodology with respect to structural analysis is employed in this paper to achieve an efficient fault detection and identification (FDI) system design. The analysis involves five essential steps of SA applied to ABS, which includes critical faults analysis, fault modelling, fault detectability analysis and fault isolability analysis, Minimal Structural Over-determined (MSO) sets selection, and MSO-based residual design. In terms of the four faults in the ABS, they are evaluated to be detectable through performing a structural representation and making the Dulmage-Mendelsohn decomposition with respect to the fault modelling, and then they are proved to be isolable based on the fault isolability matrix via SA. After that, four corresponding residuals are generated directly by a series of suggested equation combinations resulting from four MSO sets. The results generated by numerical simulations show that the proposed FDI system can detect and isolate all the injected faults, which is consistent with the theoretical analysis by SA, and also eventually validated by experimental testing on the vehicle (EcoCAR2) ABS.

In this paper, we present a novel fault detection and identification (FDI) scheme for a class of nonlinear systems with model uncertainty. At the heart of this approach is an on-line approximator, referred to as fault tracking approximator (FTA). Differently from the other approximators, the FTA uses iterative algorithms to detect and identify nonlinear system faults, even in the presence of model uncertainty, which is motivated by predictive control theory and iterative learning control theory. The FTA can simultaneously detect and identify the shape and magnitude of the faults. The rigorous stability analysis and fault tracking properties of the FTA are also proved. Finally, two examples are given to illustrate the feasibility and effectiveness of the proposed approach.

Different kinds of faults in the motor can occur, which causes various and independent variations in the magnetic flux distribution of machine and consequently causes different changes in the behavior of the motor and its performance. These changes reflect in the machine flux linkage, which can be determined by the machine voltage. A novel fault detection and identification technique for inter-turn-short fault and irreversible demagnetization fault in interior permanent magnet synchronous motor are presented in this paper. The deviation caused by the inter-turn short fault and irreversible demagnetization fault in the angle between $d$ -axis voltage and $q$ -axis voltage, which is termed as voltage or load angle is analyzed. It is observed that in case of demagnetization fault the voltage angle increases, whereas in case of inter-turns short fault it decreases. In this study, we used this unique phenomenon for the detection and identification of these faults. The proposed method is verified by simulation and experiments.

A successful fault detection (FD) procedure depends on correct sensory measurements which may suffer from different sensory soft faults in the form of bias, drift, scaling factor and hard faults which cannot be identified and detected in a standalone use but in combination with other sensors. Thus in this paper the problem of sensory fault detection is considered fusing sensory information. The sensory soft faults are modeled and augmented to electro-pump state space model. Nonlinear model of induction motor is linearized and a state space model for pump subsystem is developed using electrical analogy approach. Both system states and augmented sensory soft faults are then estimated employing a Kalman filter. The efficiency of the method in finally evaluated through simulation.

In This study, we present a new sensor fault detection approach based on nonlinear parity technique in presence of sensor noise. Conventionally analytical redundancy (AR) was used to fault detection and isolation in linear systems. The proposed parity space approach with nonlinear analytical redundancy (NLAR) technique can be applied to detect sensor fault in the nonlinear affine systems with mentioned class. The proposed approach will be implemented in pH neutralization system. At the end nonlinear fault detection and identification algorithm will be successfully implemented, examined and reported.

Fault detection and identification (FDI) are important tasks in most modern industrial and mechanical systems and processes. Many of these systems are most naturally modeled by differential-algebraic equations (DAE). This paper addresses active fault detection in DAE. A technique is presented to calculate an auxiliary test signal guaranteeing detection, assuming bounded additive noise. An efficient real time detection algorithm is also provided as are example simulations. The extension to model uncertainty is discussed.

Geometric control theory is used to investigate the problem of fault detection and isolation for 3D linear systems described by Fornasini---Marchesini models with the aim using these results in applications areas such as wireless sensor networks. Necessary and sufficient conditions for the existence of a solution to this problem are established together with constructive methods for the design of observers for fault detection and identification.

This paper deals with the real time Fault Detection and Identification (FDI) process of inverter Open Switch Fault (OSF) and Open Phase Fault (OPF) in five-phase PMSM designed for aerospace applications in which the electric drive system has particular operating characteristics either in healthy states or in the faulty ones. Two original contributions are considered in this paper. They consist in normalizing the input variables of the FDI process applied to multiphase system and compensating the noise (switching and sensors noises) and dccomponent resulting from the fault occurrence. The proposed strategy uses multiple normalized criterias derived from the measured phase currents and the references currents obtained from the outputs of the speed controllers. The FDI shows independence with respect to transient states and to switching and measurement noises. Moreover, it can be easily included in an existing software without any additional sensors. The validity of the proposed method is verified by Matlab/Simulink simulation tests. Projet "3MToletance" financé par la Région Hauts de France

As part of a European Framework project, FAST20XX, well known and advanced fault detection, identification and recovery techniques are evaluated for its use on a future low-energy suborbital transportation concept. In this article, an on-board FDIR scheme for faults in the Angle of Attack and True Airspeed sensors is presented. The FDIR logic is based on the Unscented Kalman Filter theory and the resulting design performs on-line sensor evaluation and compensation of the Navigation component of the vehicle's GNC system. A Monte Carlo campaign using a high-fidelity model of the vehicle is used to demonstrate the performance and robustness of the scheme.

A hydraulic torque converter (HTC) is a key component in an automatic transmission. To monitor its operating status and to detect and locate faults, and considering the high-efficiency fault detection and identification (FDI) scheme design by the methodology of structural analysis (SA), this paper presents an SA-based FDI system design and validation for the HTC. By the technique of fault mode and effect analysis (FMEA), eight critical faults are obtained, and then two fault variables are chosen to delegate them. Fault detectability and isolability, coupled with different sensor placements, are analyzed, and as a result, two speed sensors and two torque sensors of pump and turbine are selected to realize the maximal fault detectability and fault isolability: all six faults are detectable, four faults are uniquely isolable, and two faults are isolated from the other faults, but not from each other. Then five minimal structurally overdetermined (MSO) sets are easily acquired by SA to generate five corresponding residuals. The proposed FDI scheme of the HTC by SA is first validated by a theoretical model, then by an offline experiment in a commercial SUV, and the testing results indicate a consistent conclusion with the simulations and theory analysis.

WOS: 000260262300008 This article presents a new scheme for incipient fault detection and its identification in transformers. The new approach is actually based on adaptive modeling of transformers using the transmission line method (TLM) obtained from the hysteresis model. The adaptive TLM observer representing no-load, quarter-load, half-load, and rated-load conditions is used for faults detection. The continuous wavelet transform (CWT) is performed on residuals that are obtained by comparing real system currents and calculated TLM observer currents in order to extract the features for fault identification. An adaptive fuzzy reasoning technique is used to identify incipient faults in the transformer. The sum of CWT coefficients of residuals is applied to the adaptive fuzzy rule-based decision-making unit to indicate the type of faults. The main advantage of the suggested scheme is that different types of incipient faults in the transformer can be correctly identified. The test results verify the effectiveness of the suggested method.

This paper introduces a statistical fault detection and identification (FDI) scheme for aircraft systems, which uses flight attitude data rather than information from purposely developed physical or virtual sensors. The scheme is based on the modelling of relationships among the considered data via stochastic Time-dependent Functionally Pooled Non-linear AutoRegressive with Exogenous excitation (TFP-NARX) representations. These are globally valid inside a flight regime and under various considered environmental conditions, thanks to the pooling technique used for their identification. Moreover, due to the TFP-NARX coefficients being a function of time-dependent quantities, high modelling accuracy is achieved. The scheme's operation involves identifying nominal TFP-NARX models of relationships among the attitude data from an aircraft operating in a healthy state. Owing to fault occurrence, these relationships may change. Then, an in-flight comparison of the nominal and the current aircraft dynamics provides fault-related information, which is statistically evaluated for FDI purposes. The scheme's performance and robustness are assessed with numerous flights conducted throughout a flight regime under various manoeuvring settings and turbulence levels.

In this paper, binary Hall-effect sensor faults are investigated in rectangular-current-fed brushless dc (BLDC) drives and a very effective methodology for their detection, identification, and compensation is explored. It is shown that these faults cause erroneous commutation, generally leading to unstable operation. Using a fault detection and identification technique proposed by the authors in a recent paper on low-cost field-oriented drives, it is possible to pinpoint the faulty sensors. In this paper, it is demonstrated that the destabilizing effect of these faults on motion-state estimation can be compensated for in any position and speed estimation algorithm, as long as it is properly readapted. To this end, it is shown how to incorporate such fault-compensation in three state-of-the-art estimation algorithms: 1) the zeroth-order algorithm (ZOA); 2) the hybrid observer (HO); and 3) the vector-tracking observer (VTO). Comparative experimental tests are performed and it is verified that stable operation is achieved with three, two, or only a single Hall-effect sensor functioning correctly. These results show that the classical BLDC drive with three Hall-effect sensors has an inherent double redundancy to position-sensor faults. With the proposed method, this property can be exploited in systems that require very high reliability, such as in aerospace and automotive applications. Redundancy can be increased, using more than three Hall-effect sensors; reduced using two sensors; or eliminated using a single sensor, in ultra low-cost applications where redundancy is not a requirement.

International audience; This paper focuses on the voltage dip diagnosis in a Wind Generator Turbine Farm. The proposed approach is based on the analysis of the grid currents vector trajectory in the stationary reference frame. Two features are used; the diameter variation for fault detection and the angle deviation for fault isolation. The approach is validated through simulation results of a two Doubly Fed Induction Generator and generalized for N Machines.

The conventional receiver autonomous integrity monitoring (RAIM) algorithm of pseudo-range residual causes large residual error without a relatively accurate priori receiver position, which may lead to fault detection and identification. Therefore, measurements with gross errors may be used in positioning instead of high quality ones, especially when there are more than one measurement being distorted by gross errors. Meanwhile, the accuracy of the positioning result will be seriously deteriorated. In order to solve this problem, a new robust RAIM algorithm with positioning result based on the proportion of visible satellites is proposed in this paper. Using the proportion of visible satellites, the new algorithm produces a coarse position by the least squares method to calculate the pseudo-range residual. Accordingly, a robust approach to evaluate the accuracy of the coarse position based on the least median square (LMedS) is also proposed to make sure the position can meet the requirements of the following fault detection. In addition, the threshold mentioned in the new algorithm is derived theoretically. Finally, the performance of the new algorithm is good in simulations and real-data-tests, compared with conventional approaches. Results show that under the complicated scenarios the new algorithm can provide a considerable reduction of the probability of miss detection (7.89 % in this test), and improve the positioning accuracy remarkably.

A multiple fault detection and identification method based on fully decoupled parity equations for dynamic systems with known linear and unknown nonlinear terms is presented. The fully decoupled parity equation vectors is derived and it is shown that the residuals generated from it are decoupled from other faults and the unknown nonlinear term and are sensitive only to specific actuator or sensor faults. The condition for the existence of the equation is also given. From the residuals generated from the fully decoupled parity equation, the faults are estimated using the recursive least-squares method. The performance of the proposed method is illustrated by applying it to detect, isolate, and identify faults in a simulated dc motor

Control systems that utilize switched linear controllers have proven to be useful (and, in some cases, essential) for accomplishing certain control objectives in particular classes of plants. These controllers are often digital in nature and, as such, are subject to internal hardware malfunctions (faults). In this paper, we present a systematic methodology for constructing embeddings to protect switched linear controllers against hardware faults that corrupt their internal state. Our methodology is based on replacing the original controller with a redundant (higher dimensional) controller that preserves the functionality of the original controller while enabling error detection and correction. More importantly, this methodology allows an external mechanism to detect and identify transient state-transition faults through non-concurrent (e.g. periodic) parity checks. The resulting error detection and correction procedures can then be performed periodically, thereby relaxing the reliability requirements and overhead associated with the checking mechanism. © 2005 Elsevier Ltd. All rights reserved. 42 3 383 391

This paper describes a fault detection and identification method, based on the parity equations approach, to be applied to nonlinear systems. The input-output nonlinear model of the plant, used in the method, has been obtained by a neural fuzzy inference system. The method proposed is able to detect both abrupt and incipient faults. This method has been applied with good performance to a real industrial pilot plant for fault detection in a level sensor.

A method is proposed for estimating both the weights and the state of a multiple model system with one common state vector. In this system, the weights are related to the activation of each individual model. For the resulting nonlinear estimation problem a method is developed that efficiently decomposes the total problem into two separate parts, one for estimating the model weights and one for estimating the state. The method has been validated on a component, actuator and sensor fault detection and identification problem for a linearized model of an aircraft.

This paper presents a sensor fault detection and identification scheme for uncertain linear time-invariant neutral delay systems. Modeling uncertainty disturbance and noise are represented as unknown bounded inputs appearing in the state and output equations. An adaptive observer scheme extending Vemuri and Polycarpou (1997)'s work is utilized to detect and identify the fault. Robustness to the unknown inputs is analysed rigorously. It is proved that the fault estimate and the errors of state and output estimation are uniformly bounded. A numerical example is included for illustration.

A method is proposed to detect and identify two common classes of actuator faults in nonlinear systems. The two fault classes are total and partial actuator faults. This is accomplished by representing the nonlinear system by a Linear Parameter Varying (LPV) model, which is derived from experimental input-output data. The LPV model is used in a Kalman filter to estimate augmented states, which are directly related to the faults. Decision logic has been developed to determine the fault class from the estimated augmented states. The proposed method has been validated on a nonlinear simulation model of a small commercial aircraft.

A unique fault detection and identification algorithm using measurements for engine control use is presented. The algorithm detects an engine fault and identifies the associated component, using a gas path analysis technique with a detailed nonlinear engine model. The algorithm is intended to detect step-like changes in component performance rather than gradual change of all components. Since simultaneous multiple faults are unlikely, a single component fault is assumed, which reduces the number of unknown parameters to less than two. By setting the number of adjustable parameters to that of the available measurements, the parameters are computed using an engine model. After computing all of the six possible combinations of adjustable parameters, the average magnitude of the parameter deviation vectors is used to detect an engine fault. Component performance deviation (efficiency and flow rate) is represented by a magnitude and a phase. The phase is selected to minimize the error of matrices consisting of normalized adjustable parameter deviation vectors. Then the magnitude is computed by the average magnitude ratio of the vectors. Since the algorithm is simple, it is easily applied to newly developed engines. A fault detection and identification program was specifically developed for IM270 engine, a single shaft gas turbine with 2MW output capacity. By utilizing operational data obtained at a remote monitoring center, the algorithm was able to quantitatively identify the compressor and the turbine performance deviation. Although the algorithm correctly identifies the turbine as the faulty component, there remains some ambiguity. Analysis of linear dependency of the measurement deviation vectors shows that identification capability varies with phase. There are several phases where identification is impossible in the current IM270 sensor system.Copyright © 2003 by ASME

An automobile fault detection and diagnosis method is described for an active vehicle suspension system. The method is based on mathematical models of the suspension system. It is shown how the unknown parameters of these mathematical models can be obtained experimentally by parameter estimation. Furthermore, the semi-physical approach of the local linear neuronal network LOLIMOT is applied for the generation of parity equations. Both, parameter estimation and parity equations are then used for model based fault detection and identification. Various faults are simulated with test rig data and it is shown, how these faults are detected.

After a short introduction into the topic of active vehicle suspension systems, a mathematical model of the considered active vehicle suspension, which is presented in a test rig, is derived. It is shown how the unknown parameters can be obtained experimentally by parameter estimation. The results of parameter estimation are used for model based fault detection and identification, in order to obtain reliable knowledge of the system's state. All results are shown for measurements from an active suspension on a test rig.

This paper illustrates analysis of a fault tolerant control (FTC) system with false fault detections. The possible false fault detection from a fault detection and identification (FDI) module may cause a FTC system to become linearly unstable for very short time. Using the LPV analysis with brief instabilities, the upper bound of an induced-L 2 norm and asymptotic instability ratio of a closed-loop system can be calculated, which indicate performance degradation and tolerance of instabilities of the FTC system. Analysis of a FTC system is demonstrated for a HiMAT FTC system.

In this paper the Fault Detection and Identification (FDI) filter which identifies the different actuator and sensor failures is designed. The FDI filter design is based on the weighted H ∞ filtering. It uses the Linear Parameter Varying (LPV) model of the vehicle, in which the forward velocity is selected as a scheduling parameter. The LPV based FDI filter design is demonstrated in varies vehicle manoeuvres.

In this paper, a reconfigurable control protocol for a linear multi-agent system seeking consensus in presence of actuator faults and saturations and environmental disturbances is investigated. Two controllers, namely the healthy controller and the reconfigured controller, are proposed for the healthy system and the system with faulty agents, respectively. The healthy controller is designed off-line and guarantees that the team achieves consensus in presence of environmental disturbances. On the other hand, the reconfigured/recovered controller is designed on-line subject to constrained control signals and based on the information that the fault detection and identification (FDI) module has provided. The FDI information are assumed to be inaccurate, however the bounds on the uncertainties are assumed to be known. 2014 EUCA. Qatar National Research Fund Scopus

Maintenance of process equipment is one of the inescapable tasks associated with the operation of process plants and, until relatively recently, it was implemented either on a routine basis or after the failure of equipment. Attitudes are changing and now many organizations are adopting methods for identifying incipient faults, so that maintenance can be scheduled before there has been a failure which would lead to loss of production and spoilage of raw material. These methods include schemes in which all the measurement signals in a plant are gathered and, in addition to providing the input to the various control systems, they are compared with model-based values for individual sections throughout the entire process, so that a warning or an alarm can be raised, according to the seriousness of the observed discrepancy. Although these methods represent a considerable advance on ‘routine maintenance’ and ‘repair after failure’, their effectiveness is limited by the quality and detail of the information that is gathered. Each process measurement should be regarded as a ‘window’ through which the operation of the process can be observed. However, the majority of existing process measurement systems have been designed to provide the input signal to a process control system, for which the primary requirement is a good average value. Typically, this is obtained by filtering out all the components of the sensor signal having frequencies greater than about 5 Hz, with the result that the ‘window’ is only translucent, rather than transparent, and much of the detailed information which is available at the interface between the process and the sensor is lost. If, on the other hand, the measurement system has a wide frequency response, then much of the information that is available at the interface between the process and the sensor can be gathered and analysed to provide a clear view of the operation and status of both the process and the measurement system. This concept involves two significant departures from the conventional approach to process measurements and to the utilization of measurement information. In the first place, the measurement systems should be designed to have the widest possible frequency response, so that the maximum information regarding the measured process parameter that is present at the interface between the process and the sensor is gathered. Secondly, instead of conditioning the primary sensor signal to provide a good average value for process control, it is analysed using well established techniques to provide not only the signal for process control but also information on which predictive maintenance procedures can be based.

This paper deals with model based Fault Detection and Identification in non-linear systems. The first step of the FDI procedure is to design a set of residuals which reflect the discrepancy between the actual behaviour of the system and the expected behaviour given by its model. In order to design these residuals, two approaches are classically distinguished in the literature. The first one rests on Analytic Redundancy Relations (ARR) while the second one uses non-linear observers (extended Luenberger observers, high gain observers, sliding mode observers). The comparison between the two approaches has been widely developed in the linear case and some equivalence results have been given. This paper provides a contribution which extends those results to a class of non-linear MISO systems.

A number of different fundamental problems in fault detection and fault identification are formulated in this paper. The fundamental problems include exact, almost, generic and class-wise fault detection and identification. Necessary and sufficient conditions for the solvability of the fundamental problems are derived. These conditions are weaker than the ones found in the literature since we do not assume any particular structure for the residual generator. At the end of the paper, a time domain synthesis procedure based on state-space methods to construct appropriate residual generators is given. Copyright © 2000 John Wiley & Sons, Ltd.

In a former paper, the problem of monitoring systems subject to both equality and inequality constraints has been introduced and solved in the static case, using both parity space and state estimation approaches, which have been shown to be equivalent. The conditions under which the inequality constraints may be supervised in the non faulty as well as in the faulty case have been provided. The present paper extends this study to the case of dynamic systems, for which arises the specific problem of minimizing the detection delay.

Fault detection and identification (FDI) are important tasks in most modern industrial and mechanical systems and processes. Many of these systems are most naturally modeled by differential algebraic equations. One approach to FDI is based on the use of observers and filters to detect and identify faults. The method presented here uses the least squares completion to compute an ODE that contains the solution of the DAE and applies the observer directly to this ODE. Robustness with respect to disturbances is also addressed by a frequency filtering technique.Copyright © 2013 by ASME

Observability of decomposed systems consisting of algebraic and integrating parts is investigated in the presence of unknown inputs. Conditions for the existence and construction of integrating subsystems which, when connected to the algebraic system, give rise to an unknown-input, unknown-state observable and unknown-input, zero-state observable composite system are established. Application of the results to fault detection and identification in algebraic systems is discussed.

The present paper deals with fault tolerant control for linear dynamics with additive disturbances. The control action is generated based on information collected from a redundant, multi-sensors network. Delays that may appear during plant measurements transmission through real communication channels are considered as faults. Depending on presence of delay in feedback loop, different invariant sets can be computed. We show that fault tolerant control can be achieved through invariant sets separation with respect to different delay values. Sets separation is accomplished for specific values of the reference signal. Therefore, we introduce in the loop a reference governor which is designed by a receding horizon technique. Thus, we provide reference signals which practically guarantee fault detection and identification in real time.

The last decade has seen the emergence of set-theoretic methods in fault detection and identification mechanisms. These techniques are seen as restrictive and mathematically challenging due to the strict conditions (e.g. signal boundedness) imposed for reactivity to faults by means of set separation. The present paper aims at implementing such methods to a practical application proposed by a wind turbine benchmark setup. It is shown that strict boundedness conditions can be adjusted in order to obtain robust fault detection.

We combine a previously developed strategy for Fault Detection and Identification (FDI) with a supervisory controller in closed loop. The combined method is applied to a model of a pilot-scale cooling loop of a nuclear plant, which includes Kalman filters and a model-based predictive controller as part of normal operation. The system has two valves available for flow control meaning that some redundancy is available. The FDI method is based on likelihood ratios for different fault scenarios which in turn are derived from the application of the Kalman filter. A previously introduced extension of the FDI method is used here to enable detection and identification of non-linear faults like stuck valve problems and proper accounting of the time of fault introduction. The supervisory control system is designed so to take different kinds of actions depending on the status of the fault diagnosis task and on the type of identified fault once diagnosis is complete. Some faults, like sensor bias and drift, are parametric in nature and can be adjusted without need for reconfiguration of the regulatory control system. Other faults, like a stuck valve problem, require reconfiguration of the regulatory control system. The whole strategy is demonstrated for several scenarios.

This paper is concerned with the application of Kalman filter based methods for Fault Detection and Identification (FDI). The original Kalman based method, formulated for bias faults only, is extended for three more fault types, namely the actuator or sensor being stuck, sticky or drifting. To benchmark the proposed method, a nonlinear buffer tank system is simulated as well as its linearized version. This method based on the Kalman filter delivers good results for the linear version of the system and much worse for the nonlinear version, as expected. To alleviate this problem, the Extended Kalman Filter (EKF) is investigated as a better alternative to the Kalman filter. Next to the evaluation of detection and diagnosis performance for several faults, the effect of dynamics on fault identification and diagnosis as well as the effect of including the time of fault occurrence as a parameter in the diagnosis task are investigated. � 2011 Elsevier Ltd.

In this paper we combine an invariant-set approach to fault detection and identification (FDI), recently proposed by the authors, with the virtual actuator approach to controller reconfiguration of Steffen, Lunze and co-authors. The FDI approach is based on the separation of invariant sets that characterise healthy system operation from invariant sets that characterise faulty operation. The derivation of these sets takes into account the closed-loop system reconfigured by means of the virtual actuator under all considered fault situations. We provide analytic conditions, in terms of closed-loop system parameters and bounds on external signals, which guarantee the aforementioned set separation and, thus, closed-loop stability of the scheme under all considered fault situations.

A fault detection and identification method for nonlinear systems based on fuzzy parity equations is presented. The proposed technique involves approximating first the nonlinear system by a T-S model, and then fully-decoupled parity equations are derived for the local linear models. Residuals generated from the fuzzy parity equations in the T-S model are fused by another T-S model. As the residual generated by the fuzzy parity equation is sensitive to a specific actuator or sensor fault, and insensitive to other faults, system states and disturbance inputs, faults can be detected and/or identified. A simulation example involving a nonlinear airplane model is presented to illustrate the performance of the proposed technique.

This paper describes a fault detection and identification method, based on the parity equations approach, to be applied to nonlinear systems. The input-output nonlinear model of the plant, used in the method, has been obtained by a neural fuzzy inference system. The method proposed is able to detect both abrupt and incipient faults. This method has been applied with good performance to a real industrial pilot plant for fault detection in a level sensor.

This paper deals with continuous linear time-invariant systems and is focused on the model-based failure detection and identification problem, and more precisely, on the so-called residual generation problem. Our purpose is to present some new results concerning the practical situation arising when the problem cannot be solved using conventional tools for a given system. Thus, the residual generation must be done by choosing a new measured output matrix, that is, we are concerned with the development of sensor location methodologies that are oriented to guarantee the solution of the failure detection problem. Solvability conditions are presented and procedures for sensor location are developed. When the solvability conditions hold, the minimality of the associated residual generator is ensured. A geometric approach and the notions of fundamental and extended residual generation problems as stated by Massoumia are followed.