Improved RBF network application in analog circuit fault isolation

One kind of steepest descent incremental projection learning algorithm for improving the training of radial basis function(RBF)neural network is proposed,which is applied to analog circuit fault isolation.This algorithm simplified the structure of network through optimum output layer coefficient with incremental projection learning(IPL)algorithm,and adjusted the parameters of the neural activation function to control the network scale and improve the network approximation ability.Compared to the traditional algorithm,the improved algorithm has quicker convergence rate and higher isolation precision.Simulation results show that this improved RBF network has much better performance,which can be used in analog circuit fault isolation field.

Study of Test Flow Optimization Method in Radar Fault Isolation

In order to optimize test flow after the default flow is modified by a tester, a new software framework for the radar fault isolation is illustrated. This framework separates all mapping algorithms from test flows so as to modify flow and to insert mapping algorithm dynamically in testing process. Based on this framework, a kind of optimization method of test flow is proposed and studied. By defining an objective function, we can evaluate all candidate test flows so as to get an optimized flow. An example explains how to search the flow from candidate flows.

ASCS Online Fault Detection and Isolation Based on an Improved MPCA

Multi-way principal component analysis（MPCA）has received considerable attention and been widely used in process monitoring.A traditional MPCA algorithm unfolds multiple batches of historical data into a two-dimensional matrix and cut the matrix along the time axis to form subspaces.However,low efficiency of subspaces and difficult fault isolation are the common disadvantages for the principal component model.This paper presents a new subspace construction method based on kernel density estimation function that can effectively reduce the storage amount of the subspace information.The MPCA model and the knowledge base are built based on the new subspace.Then,fault detection and isolation with the squared prediction error（SPE）statistic and the Hotelling（T2）statistic are also realized in process monitoring.When a fault occurs,fault isolation based on the SPE statistic is achieved by residual contribution analysis of different variables.For fault isolation of subspace based on the T2 statistic,the relationship between the statistic indicator and state variables is constructed,and the constraint conditions are presented to check the validity of fault isolation.Then,to improve the robustness of fault isolation to unexpected disturbances,the statistic method is adopted to set the relation between single subspace and multiple subspaces to increase the corrective rate of fault isolation.Finally fault detection and isolation based on the improved MPCA is used to monitor the automatic shift control system（ASCS）to prove the correctness and effectiveness of the algorithm.The research proposes a new subspace construction method to reduce the required storage capacity and to prove the robustness of the principal component model,and sets the relationship between the state variables and fault detection indicators for fault isolation.

Robust Nonlinear Analytic Redundancy for Fault Detection and Isolation in Mobile Robot

A robust nonlinear analytical redundancy （RNLAR） technique is presented to detect and isolate actuator and sensor faults in a mobile robot. Both model-plant-mismatch （MPM） and process disturbance are considered during fault detection. The RNLAR is used to design primary residual vectors （PRV）, which are highly sensitive to the faults and less sensitive to MPM and process disturbance, for sensor and actuator fault detection. The PRVs are then transformed into a set of structured residual vectors （SRV） for fault isolation. Experimental results on a Pioneer 3-DX mobile robot are presented to justify the effectiveness of the RNLAR scheme.

Sensor Fault Detection, Isolation and Reconstruction Using Nonlinear Principal Component Analysis

State reconstruction approach is very useful for sensor fault isolation, reconstruction of faulty measurement and the determination of the number of components retained in the principal components analysis （PCA） model. An extension of this approach based on a Nonlinear PCA （NLPCA） model is described in this paper. The NLPCA model is obtained using five layer neural network. A simulation example is given to show the performances of the proposed approach.

Multiobjective fault detection and isolation for flexible air-breathing hypersonic vehicle

An application of the multiobjective fault detection and isolation（FDI） approach to an air-breathing hypersonic vehicle（HSV） longitudinal dynamics subject to disturbances is presented.Maintaining sustainable and safe flight of HSV is a challenging task due to its strong coupling effects,variable operating conditions and possible failures of system components.A common type of system faults for aircraft including HSV is the loss of effectiveness of its actuators and sensors.To detect and isolate multiple actuator/sensor failures,a faulty linear parameter-varying（LPV） model of HSV is derived by converting actuator/system component faults into equivalent sensor faults.Then a bank of LPV FDI observers is designed to track individual fault with minimum error and suppress the effects of disturbances and other fault signals.The simulation results based on the nonlinear flexible HSV model and a nominal LPV controller demonstrate the effectiveness of the fault estimation technique for HSV.

Diagnosis of process faults and sensor faults in a class of nonlinear uncertain systems

This paper presents a fault diagnosis method for process faults and sensor faults in a class of nonlinear uncertain systems.The fault detection and isolation architecture consists of a fault detection estimator and a bank of adaptive isolation estimators,each corresponding to a particular fault type.Adaptive thresholds for fault detection and isolation are presented.Fault detectability conditions characterizing the class of process faults and sensor faults that are detectable by the presented method are derived.A simulation example of robotic arm is used to illustrate the effectiveness of the fault diagnosis method.

Hybrid robust fault detection and isolation of satellite reaction wheel actuators

In this paper,a combined robust fault detection and isolation scheme is studied for satellite system subject to actuator faults,external disturbances,and parametric uncertainties.The proposed methodology incorporates a residual generation module,including a bank of filters,into an intelligent residual evaluation module.First,residual filters are designed based on an improved nonlinear differential algebraic approach so that they are not affected by external disturbances.The residual evaluation module is developed based on the suggested series and parallel forms.Further,a new ensemble classification scheme defined as blended learning integrates heterogeneous classifiers to enhance the performance.A wide range of simulations is carried out in a high-fidelity satellite simulator subject to the constant and time-varying actuator faults in the presence of disturbances,manoeuvres,uncertainties,and noises.The obtained results demonstrate the effectiveness of the proposed robust fault detection and isolation method compared to the traditional nonlinear differential algebraic approach.

Ellipsoidal bounding set-membership identification approach for robust fault diagnosis with application to mobile robots

A robust fault diagnosis approach is developed by incorporating a set-membership identification (SMI) method. A class of systems with linear models in the form of fault related parameters is investigated, with model uncertainties and parameter variations taken into account explicitly and treated as bounded errors. An ellipsoid bounding set-membership identification algorithm is proposed to propagate bounded uncertainties rigorously and the guaranteed feasible set of faults parameters enveloping true parameter values is given. Faults arised from abrupt parameter variations can be detected and isolated on-line by consistency check between predicted and observed parameter sets obtained in the identification procedure. The proposed approach provides the improved robustness with its ability to distinguish real faults from model uncertainties, which comes with the inherent guaranteed robustness of the set-membership framework. Efforts are also made in this work to balance between conservativeness and computation complexity of the overall algorithm. Simulation results for the mobile robot with several slipping faults scenarios demonstrate the correctness of the proposed approach for faults detection and isolation (FDI).

Optimal State Estimation and Fault Diagnosis for a Class of Nonlinear Systems

This study proposes a scheme for state estimation and,consequently,fault diagnosis in nonlinear systems.Initially,an optimal nonlinear observer is designed for nonlinear systems subject to an actuator or plant fault.By utilizing Lyapunov's direct method,the observer is proved to be optimal with respect to a performance function,including the magnitude of the observer gain and the convergence time.The observer gain is obtained by using approximation of Hamilton-Jacobi-Bellman(HJB)equation.The approximation is determined via an online trained neural network(NN).Next a class of affine nonlinear systems is considered which is subject to unknown disturbances in addition to fault signals.In this case,for each fault the original system is transformed to a new form in which the proposed optimal observer can be applied for state estimation and fault detection and isolation(FDI).Simulation results of a singlelink flexible joint robot(SLFJR)electric drive system show the effectiveness of the proposed methodology.

A gain-varying UIO approach with adaptive threshold for FDI of nonlinear F16 systems

A discrete gain-varying unknown input observer （UIO） method is presented for actuator fault detection and isolation （FDI） problems in this paper. A novel residual scheme together with a moving horizon threshold is proposed. This design methodology is applied to a nonlinear F16 system with polynomial aerodynamics coefficient expressions, where the coefficient expressions for the F16 system and UIOs may be slightly different. The simulation results illustrate that a satisfactory FDI performance can be achieved even when the F16 system is under the environment of model uncertainties, exogenous noise and measurement errors.

The Study of RAIM Performance by Simulation

Receiver Autonomous Integrity Monitoring （RAIM） is a software algorithm available in some GPS receivers which gives an indication if the position solution given by the GPS receiver is suitable to use. The detail algorithm of the parity space method of RAIM technique is presented. Using FDI and FDE methods, the simulations of RAIM performance have been done in three different phases independently with respect to the bias of the fault satellite. Case study of simulation results is discussed and each performance of RAIM is analyzed. According to the analysis of simulation results, the parity space method of RAIM can meet the integrity requirements for nonprecision, terminal and enroute flight phase. It also indicates that the results of performance of FDE are better than that of FDI.

A novel fault tolerant permanent magnet synchronous motor with improved optimal torque control for aerospace application

Improving fault tolerant performance of permanent magnet synchronous motor has always been the central issue of the electrically supplied actuator for aerospace application. In this paper, a novel fault tolerant permanent magnet synchronous motor is proposed, which is character- ized by two stators and two rotors on the same shaft with a circumferential displacement of mechanical angle of 4.5°. It helps to reduce the cogging torque. Each segment of the stator and the rotor can be considered as an 8-pole/10-slot five-phase permanent magnet synchronous motor with concentrated, single-layer and alternate teeth wound winding, which enhance the fault isola- tion capacity of the motor. Furthermore, the motor has high phase inductance to restrain the short-circuit current. In addition, an improved optimal torque control strategy is proposed to make the motor work well under the open-circuit fault and short-circuit fault conditions. Simulation and experiment results show that the proposed fault tolerant motor system has excellent fault tolerant capacity, which is able to operate continuously under the third open-circuit fault and second short- circuit fault condition without system performance degradation, which was not available earlier.

A comprehensive review of DC fault protection methods in HVDC transmission systems

High voltage direct current (HVDC) transmission is an economical option for transmitting a large amount of power over long distances. Initially, HVDC was developed using thyristor-based current source converters (CSC). With the development of semiconductor devices, a voltage source converter (VSC)-based HVDC system was introduced, and has been widely applied to integrate large-scale renewables and network interconnection. However, the VSC-based HVDC system is vulnerable to DC faults and its protection becomes ever more important with the fast growth in number of installations. In this paper, detailed characteristics of DC faults in the VSC-HVDC system are presented. The DC fault current has a large peak and steady values within a few milliseconds and thus high-speed fault detection and isolation methods are required in an HVDC grid. Therefore, development of the protection scheme for a multi-terminal VSC-based HVDC system is challenging. Various methods have been developed and this paper presents a comprehensive review of the different techniques for DC fault detection, location and isolation in both CSC and VSC-based HVDC transmission systems in two-terminal and multi-terminal network configurations.

Review of protection and fault handling for a flexible DC grid

With the development of power electronics technology,the flexible DC grid will play a significant role in promoting the transformation and reformation of the power grid.It is immune to commutation failure and has high flexibility in power control and renewable energy grid integration.However,the protection and fault handling technology for a flexible DC grid is a big challenge because of the limited overcurrent capability of the converters.This paper summarizes the development of the flexible DC grid,and analyzes the fault characteristics in detail.Next,the applicability,advantages and disadvantages of the existing protection principle,fault isolation and recovery schemes are reviewed.Finally,the key problems and development trend of the future flexible DC grid are pointed out and forecasted respectively.

Combination of Model-based Observer and Support Vector Machines for Fault Detection of Wind Turbines

Support vector machines and a Kalman-like observer are used for fault detection and isolation in a variable speed horizontalaxis wind turbine composed of three blades and a full converter. The support vector approach is data-based and is therefore robust to process knowledge. It is based on structural risk minimization which enhances generalization even with small training data set and it allows for process nonlinearity by using flexible kernels. In this work, a radial basis function is used as the kernel. Different parts of the process are investigated including actuators and sensors faults. With duplicated sensors, sensor faults in blade pitch positions,generator and rotor speeds can be detected. Faults of type stuck measurements can be detected in 2 sampling periods. The detection time of offset/scaled measurements depends on the severity of the fault and on the process dynamics when the fault occurs. The converter torque actuator fault can be detected within 2 sampling periods. Faults in the actuators of the pitch systems represents a higher difficulty for fault detection which is due to the fact that such faults only affect the transitory state(which is very fast) but not the final stationary state. Therefore, two methods are considered and compared for fault detection and isolation of this fault: support vector machines and a Kalman-like observer. Advantages and disadvantages of each method are discussed. On one hand, support vector machines training of transitory states would require a big amount of data in different situations, but the fault detection and isolation results are robust to variations in the input/operating point. On the other hand, the observer is model-based, and therefore does not require training, and it allows identification of the fault level, which is interesting for fault reconfiguration. But the observability of the system is ensured under specific conditions, related to the dynamics of the inputs and outputs. The whole fault detection and isolation scheme is evaluated using a wind turbine benchmark with a real sequence of wind speed.

On-Line Fast Motor Fault Diagnostics Based on Fuzzy Neural Networks

An on-line method was developed to improve diagnostic accuracy and speed for analyzing run- ning motors on site. On-line pre-measured data was used as the basis for constructing the membership functions used in a fuzzy neural network （FNN） as well as for network training to reduce the effects of various static factors, such as unbalanced input power and asymmetrical motor alignment, to increase accuracy. The preprocessed data and fuzzy logic were used to find the nonlinear mapping relationships between the data and the conclusions, The FNN was then constructed to carry motor fault diagnostics, which gives fast accurate diagnostics. The on-line fast motor fault diagnostics clearly indicate the fault type, location, and severity in running motors. This approach can also be extended to other applications.

Circuit breakers in HVDC systems: state-of-the-art review and future trends

High voltage direct current(HVDC)systems are efficient solutions for the integration of large-scale renewable energy sources with the main power grids.The rapid development of the HVDC grid has resulted in a growing interest in DC circuit breakers(DCCBs).A fast and reliable circuit breaker is a necessary requirement in the development of large scale HVDC grids.This paper provides a comprehensive review and survey of the HVDC CBs and discusses potential research directions.Operational principles and the main features of various DCCBs are described and their merits and shortcomings are also highlighted.

Multi‑level autonomous integrity monitoring method for multi‑source PNT resilient fusion navigation

For the integrity monitoring of a multi-source PNT(Positioning,Navigation,and Timing)resilient fusion navigation system,a theoretical framework of multi-level autonomous integrity monitoring is proposed.According to the mode of multi-source fusion navigation,the framework adopts the top-down logic structure and establishes the navigation source fault detection model based on the multi-combination separation residual method to detect and isolate the fault source at the system level and subsystem level.For isolated non-redundant navigation sources,the system level recovery verification model is used.For the isolated multi-redundant navigation sources,the sensor fault detection model optimized with the dimension-expanding matrix is used to detect and isolate the fault sensors,and the isolated fault sensors are verified in real-time.Finally,according to the fault detection and verification results at each level,the observed information in the fusion navigation solution is dynamically adjusted.On this basis,the integrity risk dynamic monitoring tree is established to calculate the Protection Level(PL)and evaluate the integrity of the multi-source integrated navigation system.The autonomous integrity monitoring method proposed in this paper is tested using a multi-source navigation system integrated with Inertial Navigation System(INS),Global Navigation Satellite System(GNSS),Long Baseline Location(LBL),and Ultra Short Baseline Location(USBL).The test results show that the proposed method can effectively isolate the fault source within 5 s,and can quickly detect multiple faulty sensors,ensuring that the positioning accuracy of the fusion navigation system is within 5 m,effectively improving the resilience and reliability of the multi-source fusion navigation system.

Electronictization-A Foundation for Grid Modernization

Smart grid is the flag under which the US DoE has been mobilizing efforts to modernize the grid.Electronictization is the first step towards a smart modern grid.It is a process that transforms the grid from electrical and electromechanical(EE)to electronic,electrical and electromechanical(EEE),laying down the very basic foundation for the modern grid.All things grid connected(ATGC)has five groups of essential hardware:1)Grid interface(smart)inverters;2)Hardware for flexible AC transmissions;3)Intelligent electronic power transformers(grid scale);4)Solid-state circuit breaker,current limiters,smart fuses and sensors;and 5)Multi-port bidirectional power&control units.Development and deployment of ATGC will be a grassroots drive to transform the grid from an old passive technology to a new active technology based on electronic power transmission,distribution,processing and protection.Grid modernization represents a win-win-win situation for the environment(Government),consumers,and grid owners/operators.