Search

This paper presents a data-driven fault detection and isolation (FDI) for a multirotor system using Koopman operator and Luenberger observer. Koopman operator is an infinite-dimensional linear operator that can transform nonlinear dynamical systems into linear ones. Using this transformation, our aim is to apply the linear fault detection method to the nonlinear system. Initially, a Koopman operator-based linear model is derived to represent the multirotor system, considering factors like non-diagonal inertial tensor, center of gravity variations, aerodynamic effects, and actuator dynamics. Various candidate lifting functions are evaluated for prediction performance and compared using the root mean square error to identify the most suitable one. Subsequently, a Koopman operator-based Luenberger observer is proposed using the lifted linear model to generate residuals for identifying faulty actuators. Simulation and experimental results demonstrate the effectiveness of the proposed observer in detecting actuator faults such as bias and loss of effectiveness, without the need for an explicitly defined fault dataset. [ABSTRACT FROM AUTHOR]

Synchronizing a network of robots in consensus is an important task for cooperative work. Detecting faults in a network of robots in consensus is a much more important task. In considering a formation of Wheeled Mobile Robots (WMRs) in a master–slave architecture modeled by graph theory, the main objective of this study was to detect and isolate a fault that appears on a robot of this formation in order to remove it from the formation and continue the execution of the assigned task. In this context, we exploit the extended Kalman filter (EKF) to estimate the state of each robot, generate a residual, and deduce whether a fault exists. The implementation of this technique was proven using a Matlab simulator. [ABSTRACT FROM AUTHOR]

The fault detection and isolation (FDI) procedure increases the assurance of quality, reliability, and safety of industrial systems. Several faults may appear simultaneously, and the purpose of multi-fault diagnosis is to identify and locate these multiple faults. This work is particularly interested in the diagnosis based on the structural analysis of the system; residuals can be generated and used as fault indicators by model-based fault detection techniques. The isolation is dependent on the structure of the fault signature matrix. A new fault signature that represents the superposition of the fault is produced by simultaneous fault effects, resulting in an additional column in an extended signature matrix. This remedy is rather combinatorial. This research focuses on two methods to isolate multiple faults: (1) A modified enumerative method; (2) A hybrid ant colony optimization algorithm-genetic algorithm (Hybrid ACO-GA) is adapted to the MFD problem which has the advantage of a better research as well as the hybridization with GA. [ABSTRACT FROM AUTHOR]

Synchronizing a network of robots in consensus is an important task for cooperative work. Detecting faults in a network of robots in consensus is a much more important task. In considering a formation of Wheeled Mobile Robots (WMRs) in a master–slave architecture modeled by graph theory, the main objective of this study was to detect and isolate a fault that appears on a robot of this formation in order to remove it from the formation and continue the execution of the assigned task. In this context, we exploit the extended Kalman filter (EKF) to estimate the state of each robot, generate a residual, and deduce whether a fault exists. The implementation of this technique was proven using a Matlab simulator.

This paper proposes a fault detection and isolation (FDI) scheme for a wind turbines subject to actuator faults in both the pitch system and the drive train system. The proposed scheme addresses fault detection and isolation problems using a fault estimation approach. The proposed approach considers the use of a particular class of sliding mode observers (SMOs) designed to maintain the sliding motion even in the presence of actuator faults. The fault detection problem is solved by reconstructing the actuator faults through an appropriate analysis of the nonlinear output error injection signal, which is required to keep the SMO in a sliding motion. To ensure accurate fault reconstruction, only two conditions are required, namely that the faults are bounded and they meet the matching condition. A scheme based on a bank of SMOs is proposed to solve the fault detection and isolation problem in the pitch system. For the drive train system, a scheme using only one SMO is proposed. The performance of the proposed FDI scheme is validated by using a wind turbine benchmark model subjected to several actuator faults. Normalized root mean square error (NRMSE) analysis is performed to evaluate the accuracy of the actuator fault estimations. [ABSTRACT FROM AUTHOR]

This article covers the latest fault-tolerant control system (FTCS) developments and applications. FTCSs aim to maintain stability, minimize performance degradation, and compensate for system component faults. These systems benefit from and mission-critical applications where service continuity is crucial. This article describes several sensor and actuator errors. Fault Tolerant Control (FTC) includes active, passive, and hybrid approaches and the latest design techniques. Finally, FTCS stability and reliability analysis and research gaps were reviewed. This study provides current and future FTCS researchers with the latest trends and applications. This study's contribution. System component failures and instability are two major causes of control performance decline. Fault-tolerant control, or FTC, was developed in recent decades to improve control system resiliency. Active and passive FTC techniques exist. This paper examines control system faults, failure causes, and the latest resilience solutions. Fault detection and isolation (FDI) and active fault tolerance control (FTC) advances were examined. Encouraging FTC and FDI research, a comprehensive comparison of several aspects is performed to understand the pros and cons of various FTC techniques.

Health monitoring of current complex systems significantly impacts the total cost of the system. Centralized fault diagnosis architectures are sometimes prohibitive for large-scale interconnected systems, such as distribution systems, telecommunication networks, water distribution networks, or fluid power systems. Confidentiality constraints are also an issue. This article presents a decentralized fault diagnosis method that only requires the knowledge of local models and limited knowledge of their neighboring subsystems. The method, implemented in the decentralized diagnoser design ($D^{3}$) algorithm, is based on structural analysis and can advantageously be applied to high-dimensional systems, linear or nonlinear. Using the concept of isolation on request, a hierarchy is built according to diagnostic objectives. The resulting diagnoser is based on analytical redundancy relations (ARRs) generated along the hierarchy. Their number is optimized via binary integer linear programming (BILP) while still guaranteeing maximal diagnosability at each level. $D^{3}$ proves of lower time complexity than its centralized equivalent. It is successfully applied to a nonlinear combined cycle gas-turbine power plant. [ABSTRACT FROM AUTHOR]

Data-driven fault detection and isolation (FDI) depends on complete, comprehensive, and accurate fault information. Optimal test selection can substantially improve information achievement for FDI and reduce the detecting cost and the maintenance cost of the engineering systems. Considerable efforts have been worked to model the test selection problem (TSP), but few of them considered the impact of the measurement uncertainty and the fault occurrence. In this article, a conditional joint distribution (CJD)-based test selection method is proposed to construct an accurate TSP model. In addition, we propose a deep copula function which can describe the dependency among the tests. Afterward, an improved discrete binary particle swarm optimization (IBPSO) algorithm is proposed to deal with TSP. Then, application to an electrical circuit is used to illustrate the efficiency of the proposed method over two available methods: 1) joint distribution-based IBPSO and 2) Bernoulli distribution-based IBPSO. [ABSTRACT FROM AUTHOR]

This article proposes an integrated approach of model-based and data-driven gap metric fault detection and isolation in a stochastic framework. For actuator and sensor faults, an adaptive Kalman filter combining with the generalized likelihood ratio method is suggested. For component faults, especially incipient faults, the model-based scheme maybe not a good choice due to the existence of disturbances or noises. Hence, a novel data-driven gap metric strategy is presented. The design of the appropriate fault cluster center model and radius via the gap metric technique is put forward to enhance the isolability of the incipient faults. Numerical simulation results are given to demonstrate the effectiveness of the proposed fault detection and isolation algorithm. [ABSTRACT FROM AUTHOR]

This article is concerned with the fault detection and isolation (FDI) problem for a class of nonlinear systems described by the T–S fuzzy models. Based on the concept of minimum unobservability subspace and geometric property of factor space, a set of FDI filters where each residual is only affected by one fault and completely decoupled from other faults is designed. Furthermore, in the decoupling space, the $H_{\infty }/H_{-}$ performance indexes are provided to enhance the sensitivity of residual to faults and robustness to disturbances. In particular, to solve the nonconvex filter design problem caused by introducing the $H_{-}$ index, the Gerschgorin theorem is first used to linearize the corresponding filter design conditions in the outer region of a ball. Then, the FDI filter design problem is converted into a convex optimization one, which is solved via the linear matrix inequality (LMI) control Toolbox, and the advantages and effectiveness of the proposed FDI method are verified through two simulation examples. [ABSTRACT FROM AUTHOR]

In this paper, a novel fault tolerant control (FTC) of the SAPF configuration is proposed and evaluated with balanced and unbalanced loads. The FTC operation is used in order to optimize the energy transfer and ensure the security and reliability of the shunt active power filter (SAPF) system. In healthy and failure modes and using detection algorithms, the proposed strategy is able to tolerate the unbalanced load problem and the inverter IGBT fault. To generate the reference harmonic currents, we used the instantaneous active and reactive power (p − q − r) method with the phase-locked loop and pulse width modulation techniques. Simulation results are presented in order to verify the effectiveness of the proposed FTC strategy of the SAPF. [ABSTRACT FROM AUTHOR]

Ideally, accurate sensor measurements are needed to achieve a good performance in the closed-loop control of mechatronic systems. As a consequence, sensor faults will prevent the system from working correctly, unless a fault-tolerant control (FTC) architecture is adopted. As model-based FTC algorithms for nonlinear systems are often challenging to design, this article focuses on a new method for FTC in the presence of sensor faults, based on deep learning. The considered approach replaces the phases of fault detection and isolation and controller design with a single recurrent neural network, which has the value of past sensor measurements in a given time window as input and the current values of the control variables as output. This end-to-end deep FTC method is applied to a mechatronic system composed of a spherical inverted pendulum, whose configuration is changed via reaction wheels, in turn actuated by electric motors. The simulation and experimental results show that the proposed method can handle abrupt faults occurring in link position/velocity sensors. [ABSTRACT FROM AUTHOR]

Process monitoring plays an essential role in safe and profitable operations. Various data-driven fault detection models have been suggested, but they cannot perform properly when the training data are insufficient or the information to construct the manifold is confined to a specific region. In this study, a process monitoring framework integrated with data augmentation is proposed to supplement rare but informative samples for the boundary regions of the normal state. To generate data for augmentation, a variational autoencoder was employed to exploit its advantage of stable convergence. For the construction of the process monitoring system, an autoencoder that can extract useful features in an unsupervised manner was used. To illustrate the efficacy of the proposed method, a case study for the Tennessee Eastman process was applied. The results show that the proposed method can improve the monitoring performance compared to the autoencoder without data augmentation in terms of fault detection accuracy and delay, particularly within the feature space. [ABSTRACT FROM AUTHOR]

We present a nonlinear geometric approach to fault detection and isolation (FDI) in a grid-connected inverter system. Open-switch faults in inverter power transistors together with faults in grid voltages and source current sensors are detected and isolated in the presence of disturbance. The detection process is based on dynamic model of an inverter, including a dc-bus capacitor and an output $RL$ filter. The proposed FDI system does not rely on balanced or zero-sum conditions in the grid point and is functional under nonsinusoidal voltages and currents. [ABSTRACT FROM AUTHOR]

This article investigates the memory adaptive event-triggered fault detection and isolation (FDI) problem for nonlinear networked control systems under periodic denial-of-service (DoS) attacks, where the nonlinear systems are described by Takagi–Sugeno (T–S) fuzzy models with unknown membership functions. First, a novel event-triggered mechanism is proposed to save communication resources. The triggering threshold is adaptively adjusted by multiple previous sampled data, not only depending on the latest triggering data. Second, taking DoS attacks, and event-triggered mechanism into consideration, a switching state-feedback controller is established, and the exponential stability is derived. Meanwhile, the controller, and the event-triggered mechanism are simultaneously developed based on a piecewise Lyapunov function. Then, a set of switching T–S fuzzy observers are constructed to realize FDI under DoS attacks. Besides, a switching variable method is introduced to address the asynchronous premise variables problem caused by the event-triggered mechanism. Finally, simulation cases are given to demonstrate the validity, and merit of the proposed FDI scheme. [ABSTRACT FROM AUTHOR]

This paper is concerned with the asynchronous fault detection and isolation (FDI) strategy in finite frequency domain for Markov jump systems (MJSs) with actuator failures under networked environment. A set of binary-valued Bernoulli distributed sequences is introduced to describe the transmission and the scheduling of the sensor nodes. Then, an MJS directing by two Markov process and with multiple stochastic parameters is derived to characterize the whole system dynamics. Subsequently, by means of the stochastic $\mathcal {H}_{-}$ index in finite frequency domain and the geometric mapping approach, an asynchronous FDI scheme is investigated, in which the filters complete the FDI mission with only partial information of the measurements. Meanwhile, each FDI filter is only sensitive to one possible actuator fault and decoupled from others. Then the existence of the desired filters is ensured by some novel sufficient conditions. Finally, an application example is presented to show the effectiveness of the proposed theoretical results. [ABSTRACT FROM AUTHOR]

This paper investigates the fault detection and isolation problem for Takagi–Sugeno fuzzy time-delay systems under the framework of geometric method. First, the geometric concept of unobservability subspace is introduced and the algorithm for constructing the subspace is presented. Then, based on the above algorithm, the faults are divided into the different unobservability subspaces, and the reduced-order subsystems set corresponding to the fault set is constructed according to the geometrical properties of factor space and canonical projection. Finally, a set of structured residuals which are sensitive to a single fault and decoupled from other faults is established by designing an observer for the subsystems. In addition, H ∞ performance is introduced to attenuate the effects of disturbances and error signals caused by a time-varying delay on the residual. Simulation example verifies the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

This work proposes a machine current sensor fault detection and isolation (FDI) strategy in permanent magnet synchronous machines (PMSMs) resilient to multiple faults. The fault detection is performed by comparing the measured and estimated DC link currents. The fault isolation is achieved according to machine phase signal estimation and the corresponding residual examination. Single sensor fault, multiple sensor faults and non-sensor fault are covered by the proposed FDI method. The proposed sensor FDI method is not influenced by machine imbalance, feasible for FDI of both single and multiple machine current sensor faults, and capable of distinguishing between machine current sensor fault and non-sensor fault. The proposed method is validated with simulation studies in MATLAB, and the studies demonstrate that the proposed machine current sensor FDI method, different from existing methods, has a unique feature of being able to handle multiple sensor faults with the consideration of non-sensor fault disturbance.

More Electric Aircraft (MEA) has a great development prospect in the aviation industry thanks to the progressive power electronics technology and digital systems. Nevertheless, any fault occurring in the power electric system of MEA could be a fatal risk for the safety of the aircraft. To deal with the real-time fault detection and isolation (FDI) on the MEA, we put forward an FPGA-based neural network method which includes two stages: off-line construction using TensorFlow and real-time monitoring on the FPGA. Long Short-Term Memory (LSTM) network is applied because of its capability of learning from the long-term historical information in time series. A comprehensive MEA model based on the Boeing 787 power system created in PSCAD/EMTDC® and a commercial electric aircraft model based on Airbus E-Fan in Simscape are simulated to validate the effectiveness and generality of our proposed method. Adequate contrast experiments are conducted to acquire the most applicable architecture and configurations of the network. The results illustrate that the evaluation of the real-time condition can achieve accuracy over 99.5% within one sampling time on FPGA and reasonable hardware resource utilization.

Fault detection and isolation (FDI) is considered as one of the most critical problems in biological processes. Therefore, in this paper, we consider a new FDI framework that aims to improve the monitoring of biological processes. To do that, a machine learning-based statistical hypothesis approach, which can identify the model, detect and isolate the faults, will be developed. In the developed approach, so-called partial Gaussian process regression (PGPR)-based generalized likelihood ratio test (GLRT), first, the GPR model that can accurately model biological processes is presented. Then, the fault detection phase is performed using the GLRT chart. Finally, the PGPR-based GLRT, which can effectively isolate the faults, is developed. The FDI performances of the developed PGPR-based GLRT approach are compared with partial support vector regression (SVR), extreme learning machines (ELM), Kernel ridge regression (KRR) and relevance vector machines (RVM)-based GLRT methods in terms of missed detection rate (MDR), false alarm rate (FAR), root mean square error (RMSE), execution time (ET) and isolation accuracy. The obtained results show that the proposed technique can reliably detect and isolate various faults using two examples: a synthetic data and a biological process representing a Cad System in E. coli (CSEC) model. [ABSTRACT FROM AUTHOR]

Vessels today are being fully monitored, thanks to the advance of sensor technology. The availability of data brings ship intelligence into great attention. As part of ship intelligence, the desire of using advanced data-driven methods to optimize operation also increases. Considering ship motion data reflects the dynamic positioning performance of the vessels and thruster failure might cause drift-offs, it is possible to detect and isolate potential thruster failure using motion data. In this article, thruster failure detection and isolation are considered as a time-series classification problem. A convolutional neural network (CNN) is introduced to learn the mapping from the logged motion sequence to the status of the thruster. CNN is expected to generate task-specific features from the original time series sensors data and then perform the classification. The data set is collected from a professional simulator in the Offshore Simulation Center AS. Experiments show that the proposed method can detect and isolate failed thrusters with up to 95% accuracy. The proposed model is further extended to deal with thruster failure in a real-time manner. [ABSTRACT FROM AUTHOR]

Reliable and comfortable high-speed railway (HSR) has skyrocketed in popularity as a transportation medium for traveling around the world. High-voltage direct current (HVDC) electrification system has been introduced to the HSR gradually. However, the coexistence of AC and DC systems will last for a long time because AC railway systems are still in the dominant position. A detailed HSR traction system transient model operating under the hybrid AC/DC grid was established in PSCAD/EMTDC. We proposed a real-time fault detection and isolation (FDI) method for the simulated model using neural network (NN). Hierarchical structure of the monitoring system has been employed. Low-level sub-monitors supervised the conditions of their local regions and the top-level monitor collected all the feedback from sub-monitors making the final evaluation of the entire HSR system based on a voting strategy. Both off-line and real-time experiments were conducted to validate the effectiveness of the proposed method. In the experiments, the sub-monitors were designed based on Gated Recurrent Unit (GRU) algorithm and implemented on the Xilinx VCU128 FPGA board. For the off-line experiment, the sub-monitors used the training and testing dataset both from PSCAD/EMTDC to construct the architecture of their individual GRU networks and to verify how great the networks can be. For the real-time task, the sub-monitors interfaced with a real-time HSR system emulator running on the Xilinx VCU118 FPGA board to test the performance in the real-time application. The results proved that our proposed FDI method has the capability of real-time detection and can achieve better accuracy within reasonable time and resource consumption than other NN-based methods. Moreover, the method was capable of standing against noises from measured signals to some extent. [ABSTRACT FROM AUTHOR]

In this article, a novel integral sliding-mode observer is proposed to estimate the external disturbance and velocity of Euler–Lagrangian systems. This method provides high bandwidth and precise estimation with only commanded input and position measurement. A system velocity measurement is not required to construct the sliding-mode manifold. The convergence of the estimation error to zero is theoretically in finite time, which is proven by a direct Lyapunov method utilizing the passivity property of Euler–Lagrangian systems. An integral sliding manifold is designed to reduce the reaching phase, such that the robustness of the estimation is enhanced. The method has been applied to a robot manipulator to estimate the joint velocity and external contact forces in a physical human–robot task. Simulations and experiments reveal that this novel method provides fast, precise, and robust estimation results and can be used to replace the measurement of an external force sensor. The successful application of this observer to a force-sensor-less admittance controller for a manipulator contributes to the implementation of a sensor-free safety framework for human–robot collaboration (HRC). [ABSTRACT FROM AUTHOR]

In this paper, the torus-event-based fault detection and isolation (FDI) problem is investigated for a class of time-varying multirate systems. An ellipsoidal constraint is first adopted to describe the fault in a more practical pattern, and a novel torus-event-triggering scheme is proposed to improve the unilateral triggering mechanism. The aim is to design the torus-event-based fault detection filter and fault isolation estimators such that both the prescribed variance constraint on the estimation error and the desired ${H}_{\infty }$ performance on the disturbance are guaranteed over the finite horizon. Especially, the residual evaluation function is employed to detect the fault, and the residual matching function is developed to isolate the fault. Furthermore, three optimization problems are provided to seek separately the minimal parameters on the ${H}_{\infty }$ performance level, the upper bound of the estimation error variance, and the triggering torus. Finally, two simulation examples are utilized to show the effectiveness of the FDI scheme proposed in this paper. [ABSTRACT FROM AUTHOR]

In this paper, a novel fault detection and isolation (FDI) framework based on kernel PCA (KPCA) and generalized likelihood ratio test (GLRT) that is capable of detecting and identifying faults is developed. Specifically, three main objectives are addressed. First, system model identification and residuals generation are addressed using KPCA model. Second, KPCA-based GLRT method is proposed to detect different types of faults in the systems. Third, partial KPCA (PKPCA)-based GLRT is developed for fault isolation. The proposed approach aims to apply a structured PKPCA -based GLRT to a set of sub-models. The fault detection and isolation performances using PKPCA-based GLRT are illustrated through two examples: a simulated continuous stirred tank reactor (CSTR) data and an air quality monitoring network data. The obtained results demonstrate the effectiveness of the partial KPCA-based GLRT method over the partial PCA-based GLRT method. [ABSTRACT FROM AUTHOR]

Proton exchange membrane fuel cells (PEMFCs) are considered as one of the most promising power sources for transportation in the near future. Proper and robust thermal management is a key issue in fuel cell applications. This paper mainly studies the sensor fault detection and isolation (FDI) and fault tolerant control (FTC) for the thermal management of PEMFC systems. The thermal model of fuel cell is established and analyzed by structure analysis, and the residual generator of sensor faults are designed by Dulmage–Mendelsohn decomposition to rearrange the biadjacency matrix of the thermal model. A sliding-mode-based active FTC (AFTC) strategy is proposed for the thermal management of fuel cell systems. The effectivenesses of the proposed FDI method and AFTC strategy are verified on a fuel cell test bench. The experimental results show that the temperature of the PEMFC stack can be maintained at the reference value with high accuracy even when sensor fails. [ABSTRACT FROM AUTHOR]

Data-driven fault diagnosis is a promising approach for the early detection and isolation of malfunctions in power generation plants deploying solid oxide fuel cells (SOFCs). Despite the supervised classifier used in a data-driven system is trained by samples gathered under one specific design-point operating condition, during real operation the plant can move to a new, unexpected off-design operating condition, reducing the performance of the diagnosis system. This Short Communication demonstrates that this reduction is heavily mitigated if the supervised classifier is adapted to the new condition through the domain adaptation statistical technique. The present study shows that a probability of correct classification between 85% and 94% can be achieved in off-design, when a probability of 95% is obtained at the design-point. • Data-driven fault diagnosis for solid oxide fuel cell plants is considered. • The fault classifier is trained by samples gathered at the design-point operating condition. • The fault diagnosis under unexpected off-design operating conditions is addressed. • The domain adaptation technique mitigates the reduction of the diagnosis performance. • High-level performance in a wide range of operating conditions and fault sizes is reported. [ABSTRACT FROM AUTHOR]

This paper deals with the problem of simultaneous concentration and faults estimations of a continuous stirred tank reactor (CSTR) subject to unknown inputs. We propose a combination of two robust nonlinear observers for state estimation and fault reconstruction without any use of a linear approximation of the CSTR dynamic model. Based on the high-gain observer, the proposed scheme can guarantee the asymptotic estimation of the concentration inside the reactor, while, a robust term is added to the nominal plant on the basis of the super-twisting algorithm for fault reconstruction. The stability analysis is proved mathematically using the Lyapunov theory. The effectiveness and robustness of the proposed scheme are illustrated via simulations. [ABSTRACT FROM AUTHOR]

Signals from sensors placed at different locations are used for control and protection purposes in a nuclear reactor. It also requires in-service fault detection and isolation (FDI) for its safe operation. The sensor signals are generally a superimposition of low-frequency components representative of true values of the variables being monitored, occasional high-frequency periodical oscillations due to disturbances and faults, and sensor faults. Techniques like principal component analysis (PCA) can be used for FDI; however, it would be more meaningful if the FDI technique can also help for predictive maintenance of reactor internals through vibration spectra. To address these issues, a multiscale PCA, integrating wavelet transform with PCA and aiming to reduce the modeling cost by using only a fewer scales that contribute to the monitoring, has been proposed in this paper for online FDI of an advanced heavy water reactor (AHWR). A new mathematical formulation of the generalized likelihood ratio test for its use with wavelet approximation coefficients has also been proposed for better sensor-FDI outcomes. The proposed approach detects and isolates sensor faults and process faults using the signals from neutron detectors. Efficacy of the proposed technique is established on the simulated ex-core ion chamber data of AHWR considering different scenarios that involve localized frequency contents representative of process faults, slowly developing (incipient) sensor faults, and the simultaneous presence of two or more of these scenarios. Simulation results validate the effectiveness of the proposed scheme for online FDI in the reactor. [ABSTRACT FROM AUTHOR]

This paper presents a real-time statistical technique for sensors incipient fault detection and isolation (FDI). The proposed approach comprises fault detection index and fault signature formulation using Kalman filter under relaxed assumption on the monitored system stability. A fault decision statistics is generated by combining the Kullback–Leibler divergence of considered hypotheses with an exponential weighted moving average. Furthermore, fault detection performance has been characterized using missed detection and false alarm probabilities. Fault-to-noise ratio (FNR) is acting as a comparative criterion between the fault and noise level for statistical characterization. Numerical results of single and multiple sensors incipient FDI for pressurized water reactors (PWRs) pressurizer illustrate the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

Abstract Reliability improvement of wind turbine power generation is the key issue that can turn the wind power into one of the main power sources to respond to the world power demands. The likelihood of fault occurrence on wind turbine components is unavoidable, especially for large rotor modern wind turbines, operating in harsh offshore environments. Accordingly, the maintenance need increases due to unanticipated faults, which in turn, leads to higher energy cost and less reliable power generation. In this regard, model-based fault detection and fault tolerant control techniques have been extensively investigated in the last decade, for achieving good performance. In this way, the reliability, availability and safety features of the wind turbine power generation are also enhanced. Thus, in this paper a comprehensive review of the most-recent model-based fault detection and fault tolerant control schemes for wind turbine power generation is presented, focusing on the advantages, capabilities and limitations. Note that the so-called data-driven or signal-based methodologies, which rely on the analysis of the signals directly generated from the monitored system, are not reviewed in this paper. This review is organized in a tutorial manner, to be a suitable reference for further research for the wind turbine's reliability improvement. Highlights • Wind turbine model representation for FDI purposes associated with all considered faults. • A Comprehensive review of all available FDI methods applicable to wind turbines. • Application of FDI methods on wind turbine components, comparing their advantage and disadvantages. • A comprehensive review of FTC techniques with wind turbine operational objectives point of view. • Future trends of wind turbine FDI and FTC approach towards the reliability improvement. [ABSTRACT FROM AUTHOR]

This paper proposes a real-time fault-tolerant estimation approach for combined sensor fault diagnosis and air data reconstruction. Due to simultaneous influence of winds and latent faults on monitored sensors, it is challenging to address the tradeoff between robustness to wind disturbances and sensitivity to sensor faults. As opposed to conventional fault-tolerant estimators that do not consider any constraints, we propose a constrained fault-tolerant estimator using moving horizon estimation (MHE). By exploiting wind bounds according to the weather or flight conditions, this approach improves fault sensitivity without sacrificing disturbance robustness. This improvement is attributed to active inequality constraints caused by faults, as shown in sensitivity analysis of the formulated MHE problem. The challenge of real-time nonlinear MHE is addressed by adopting an efficient structure-exploiting algorithm within a real-time iteration scheme. In order to facilitate the industrial validation and verification, the algorithm is implemented using an Airbus graphical symbol library to be compliant with the actual flight control computer, and its feasibility of real-time computation has been validated. The simulation results on the RECONFIGURE benchmark, which is a high-fidelity Airbus simulator, over a wide range of the flight envelop show the efficacy of the proposed approach. [ABSTRACT FROM AUTHOR]

This paper studies the problem of fault detection and isolation (FDI) for continuous-time semi-Markov jump systems (S-MJSs) with generally uncertain transition. Firstly, a class of residual signals and fault model of S-MJSs with generally uncertain transition rates are designed. Secondly, the parameters of residual generator are obtained by a geometric approach, such that each fault is affected by one fault and decoupled from other faults. The fault states can be determined by comparing the residual output signals with the threshold. Finally, simulation results of FDI based on geometric approach are provided to illustrate the effectiveness of the proposed theoretical results. [ABSTRACT FROM AUTHOR]

In this paper, the problem of distributed fault detection and isolation (FDI) for multi-agent systems (MAS) with complex-weights directed communication topology in the presence of actuator faults is studied. It is assumed that each agent only has access to local state information of neighboring agents. The communication network is also assumed to be a weights directed graph (digraph) whose edges are attributed with complex weights. First, a novel representation of the complex-weighted graph is proposed. More specifically, the difficulty with complex coefficient in the problem of FDI for MASs with complex-weights directed communication topology is indeed resolved. Then, using the novel representation, a bank of observers is constructed to compute residual vectors with an aim of allowing the FDI of actuator fault to occur on any agent of the MASs. It is proved that the proposed FDI scheme is capable of FDI in the MASs with complex-weights directed communication topology. Finally, some numerical example results are provided to demonstrate the effectiveness of the proposed scheme. [ABSTRACT FROM AUTHOR]

In this article, a fault-tolerant voltage source inverter (VSI) is proposed for a novel topology of stand-alone doubly fed induction generator (DFIG) feeding an isolated DC load. In this topology, stator and rotor sides of DFIG are connected to DC-bus through a diode rectifier and VSI, respectively. The fault-tolerant VSI includes a redundant leg connected by bidirectional switches in order to replace the faulted leg and improve the reliability of the proposed system. The field oriented control strategy is adopted to control the d and q-axis rotor currents in order to maintain the voltage and the frequency at the output of the generator constant. A novel, easy and fast approach for fault detection and isolation (FDI) of open-switch damage in insulated gate bipolar transistor-based VSI is proposed in this study. This approach is developed using mathematical transformations such as a hysteresis detector, an integrator and a trigger. The FDI algorithm proposed here does not require the knowledge of the system model and is independent from its complexity. Simulations results are illustrated for a 3.7 kW DFIG feeding a DC load with open-switch fault in VSI that confirm the concepts proposed in this study. [ABSTRACT FROM AUTHOR]

Air brake systems are crucial systems for safe and stable operation of electric multiple units (EMUs). The brake cylinder system, which includes brake cylinders, corresponding pressure sensors, and connection pipes, plays a vital role in the EMU air brake system. This is because brake cylinder pressures directly affect the brake operation. Currently, brake cylinder pressures are monitored by univariate control charts, i.e., the brake cylinder system will be considered as faulty if a certain pressure goes beyond its allowed range. Besides, serious sensor hardware faults such as open circuit or short circuit can also be detected by system self-inspection circuits. However, three kinds of faults, including brake cylinder component fault, soft sensor fault, as well as gas leakage fault, cannot be well handled by current monitoring methods if these faults are not very serious. In this paper, a fault detection index called intervariable variance (IVV) is first presented to perform fault detection for these faults. Fault detectability analysis is provided, and the IVV statistic is also compared with the univariate control chart approach. Then, a fault isolation strategy is proposed to distinguish different kinds of faults and determine the location of the occurred fault. Finally, the effectiveness of the proposed fault detection and isolation method is demonstrated via extensive experimental studies that are carried out on the EMU brake test bench of Qingdao Sifang Rolling Stock Research Institute Co., Ltd., China. [ABSTRACT FROM AUTHOR]

In this paper, sensor fault detection and isolation of time-varying nonlinear dynamical systems is studied by utilizing an adaptive kernel principal component analysis (KPCA) solution as a useful method to overcome the weaknesses of conventional KPCA approach in dealing with time-varying dynamical processes. Toward this goal, adaptive Hotelling's T 2 is used with KPCA to tackle the time-varying behavior of nonlinear systems. Moreover, for fault isolation, partial adaptive KPCA (AKPCA) is proposed where a set of residual signals is generated based on the structured residual set framework. The simulation studies demonstrate that using the proposed methodology, the occurrence of sensor faults in the nonlinear dynamic model of an aeroderivative gas turbine can be effectively detected and isolated in the presence of component degradation. [ABSTRACT FROM AUTHOR]

Electrical drives are more and more used in transportation applications and industrial process because of their effective cost and high efficiency. They have become critical components and therefore any fault or failure affecting them may seriously endanger the users and/or induce huge financial costs. As a consequence, early detection and isolation of these faults is recommended if not mandatory to enhance the safety, the availability, the reliability and improve the maintenance and the operational efficiency. Analytical Redundancy Relations (ARR) are used to compute to structured residuals on which the diagnosis is based. To smooth the residuals and enhance the fault detection capability, a sliding mode observer is developed to compute the derivatives. The analysis of the residuals reveals that they are Gaussian signals with a change in the mean. The comparison between the two-sigma and the two-sided CUSUM methods has shown that the latter is more efficient to compute the thresholds. Therefore with the two-sided CUSUM, the rotor resistance fault is detected with no false alarm. Moreover it is proved that the fault diagnosis is robust to the load torque variations. Beside, thanks to the residual, a load torque estimator is also developed. Extensive simulation results prove the validity of our approach. [ABSTRACT FROM AUTHOR]

Intermittent faults (IFs) have the properties of unpredictability, non-determinacy, inconsistency and repeatability, switching systems between faulty and healthy status. In this paper, the fault detection and isolation (FDI) problem of IFs in a class of linear stochastic systems is investigated. For the detection and isolation of IFs, it includes: (1) to detect all the appearing time and the disappearing time of an IF; (2) to detect each appearing (disappearing) time of the IF before the subsequent disappearing (appearing) time; (3) to determine where the IFs happen. Based on the outputs of the observers we designed, a novel set of residuals is constructed by using the sliding-time window technique, and two hypothesis tests are proposed to detect all the appearing time and disappearing time of IFs. The isolation problem of IFs is also considered. Furthermore, within a statistical framework, the definition of the diagnosability of IFs is proposed, and a sufficient condition is brought forward for the diagnosability of IFs. Quantitative performance analysis results for the false alarm rate and missing detection rate are discussed, and the influences of some key parameters of the proposed scheme on performance indices such as the false alarm rate and missing detection rate are analysed rigorously. The effectiveness of the proposed scheme is illustrated via a simulation example of an unmanned helicopter longitudinal control system. [ABSTRACT FROM PUBLISHER]

In this paper, a fault detection and isolation (FDI) methodology based on an immune system (IS) inspired mechanism known as the dendritic cell algorithm (DCA) is developed and implemented. Our proposed DCA-based FDI methodology is then applied to a well-known wind turbine test model. The proposed DCA-based scheme performs both detection as well as isolation of sensor faults given dual sensor redundancy, unlike other works in the literature that only address the fault detection problem and rely on analytical redundancy approach for accomplishing the fault isolation task. A nonparametric statistical comparison test is also performed to compare the performance of the DCA-based FDI scheme with another IS-based scheme known as the negative selection algorithm. Through extensive simulation case study scenarios the capabilities and performance of our proposed methodologies have been fully demonstrated and justified. [ABSTRACT FROM PUBLISHER]

This paper addresses the fault detection and isolation (FDI) problem for robotic assembly of electrical connectors in the framework of set-membership. Both the fault-free and faulty cases of assembly are modeled by different switched linear models with known switching sequences, bounded parameters, and external disturbances. The locations of switching points of each model are assumed to be inside some areas but the accurate positions are not clear. Given current input/output data, the feasible parameter set of fault-free switched linear model is obtained by sequentially calculating an optimal ellipsoid. If the pair of data is not consistent with any possible submodel, a fault is then detected. The isolation of fault is realized by checking the consistency between the data sequence and each possible fault model one by one. The robustness of the proposed FDI algorithms is proved. The effectiveness of these algorithms is verified by the robotic assembly experiments of mating electrical connectors.

Note to Practitioners—In modern robotic assembly tasks, the industrial robots often need to manipulate tiny objects with complex structure. Electrical connectors are a typical kind of these objects and widely used in many industrial fields. To avoid damaging the fragile connectors and accelerate the assembly process, it is required to promptly detect and isolate the certain assembly fault in real time so that the robot can immediately implement an error recovery procedure according to the identified fault. The proposed set-membership-based fault detection and isolation (FDI) methodology satisfies both the timing and fault-isolation requirements for this kind of robotic assembly task. In terms of the set-membership theory, no false alarm will occur if there are sufficient training data for the proposed method. In addition, it turns out that the proposed method can signal an alarm faster than conventional residual-based FDI method from plentiful experiments. Although only the robotic assembly of electrical connectors is investigated, our FDI method can also be applied in the assembly task of other small and complex parts. This is especially useful for increasing the productivity and promoting the automation level of electronic industries. [ABSTRACT FROM AUTHOR]

The fault and behavioral anomaly detection and isolation (FBADI) in programmable logic controller (PLC) controlled systems has been under an active study for several decades. In this paper, we present a tool, called the fault and behavior monitoring tool for PLC (FBMTP) that can solve the FBADI problem in PLC-controlled manufacturing systems effectively. FBMTP first creates a nominal deterministic finite-state automaton-based model of the PLC control process, and then utilizes that model to detect and isolate the faults and the behavioral anomalies. The key idea is to check whether the observed behavior is consistent with the modeled behavior or not. The control signal data stored in the PLC memory is used for this whole procedure. With growing size of the manufacturing systems, it becomes difficult to obtain all the control signal data accurately because of the limited data acquisition time in each PLC scan cycle. The proposed control process modeling and the FBADI methodology of FBMTP can cope with such data inaccuracy problems associated with large manufacturing systems efficiently. Our experiments show that FBMTP provides highly accurate FBADI results for both small and large manufacturing systems. [ABSTRACT FROM PUBLISHER]