Airborne Performance Analysis by Multiparameter Sampling

The day-by-day increase in digital loads draws attention towards the need for an efficient and compatible distribution network. An LVDC distribution network has the capability to fulfill such digital load demands. However, the major challenge of an LVDC distribution network is its vulnerability during a fault. The need for a high-speed fault detection method is inevitable before it can be widely adopted. This paper proposes a new fault detection method which extracts the features of the current during a fault. The proposed fault detection method uses the merits of overcurrent, the first and second derivative of current, and signal processing techniques. Three different features are extracted from a time domain current signal through a sliding window. The extracted features are based upon the root squared zero, second, and fourth order moments. The features are then set with individual thresholds to discriminate low-, high-, and very high-resistance faults. Furthermore, a fault is located through the superimposed power flow. Moreover, this study proposes a new method based on the vector sum of positive and negative pole currents to identify the faulty pole. The proposed scheme is verified by using a modified IEEE 13 node distribution network, which is implemented in Matlab/Simulink. The simulation results confirm the effectiveness of the proposed fault detection and identification method. The simulation results also confirm that a fault having a resistance of 1 m Ω is detected and interrupted within 250 μ s for the test system used in this study.

Inter-turn short-circuit (ITSC) faults on the induction machine has received much attention in the recent years. Early detection of such faults in wind turbine systems would allow to avoid fluctuation on wind power output and maintain the reliability level. In this paper, Sliding Mode Observers (SMO)-based fault detection and isolation method is developed for induction generator (IG)-based variable-speed grid-connected wind turbines. Firstly, the dynamic model of the wind turbine and IG was given and then, the control was made based on Maximum Power Point Tracking (MPPT) method. The IG closed-loop via Indirect Rotor Flux Oriented Control (IRFOC) scheme was also described. Hence, the performance of the wind turbine system and the stability of injected power to the grid were analyzed under the ITSC fault conditions. The control schemes were proved to be inherently unstable under the faulty conditions. Then, robust SMO were investigated to design an ITSC fault detection and isolation scheme. Finally, simulation results of ITSC detection and isolation in the variable-speed grid-connected wind turbine with affected IG confirm the theoretical development.

In this paper, a diagnosis method for time-delay fractional systems is developed. The fault detection and isolation are ensured by the use of a bank of unknown input fractional observers. Theoretical development leads to the obtention of the observer parameters in order to engender structured residuals. A numerical example illustrating the validity of the method in fault detection and isolation is given.

This paper proposes a novel scheme combining support vector machines (SVM) and a residual-based method for wind turbine fault detection and isolation (FDI). SVMs with radius basis function kernels are used for detecting and identifying sensor stuck and offset faults, where binary codes of fault types are used as the outputs of the SVMs to minimize the number of SVMs being used. The same output of a SVM may correspond to different types of faults and the final decision is made by all SVMs instead of one SVM. Moreover, a residual-based fault detection method using a time-variant threshold is developed to identify the abrupt change and scaling faults. Monte Carlo simulations are carried out in MATLAB to test the effectiveness and robustness of the proposed FDI methods using a wind turbine FDI benchmark model. Results show that the proposed methods can always detect the faults successfully within the required time limits.

This paper deals with the fault detection, diagnosis and control problems of time delay systems by designing a disturbance observer (DOB). In proposed method, the DOB estimates the changes occurred as an input disturbance. In fact, the DOB is implemented as a residual generator for the system. Subsequently, the estimated changes can be used to compensate the effect of faults and plant alterations directly. In this method, the estimated disturbances can be used for the FDI purpose and the final system would be fault tolerant, without the need to have all other faults isolable and identifiable. It provides a simple, low cost and effective fault detection and isolation method which can be applied to more practical systems. The applicability and effectiveness of the proposed method is illustrated by the simulations provided for an industrial water pressure control system which is supposed to be subject to faults.

A method for fault detection and isolation is developed using the concatenated variances of the continuous wavelet transform (CWT) of plant outputs. These concatenated variances are projected onto the principal component space corresponding to the covariance matrix of the concatenated variances. Fisher and quadratic discriminant analyses are then performed in this space to classify the concatenated sample CWT variances of outputs in a given time window. The sample variance is a variance estimator obtained by taking the displacement average of the squared wavelet transforms of the current outputs. This method provides an alternative to the multimodel approach used for fault detection and identification, especially when system inputs are unmeasured stochastic processes, as is assumed in the case of the mechanical system example. The performance of the method is assessed using matrices having the percentage of correct condition identification in the diagonal and the percentages misclassified conditions in the off-diagonal elements. Considerable performance improvements may be obtained due to concatenation-when two or more outputs are available-and to discriminant analysis, as compared with other wavelet variance methods.

ABSTRACTQuick and timely fault detection is of great importance in control systems reliability. Undetected faulty sensors could result in irreparable damages. Although fault detection and isolation (FDI) methods in control systems have received much attention in the last decade, these techniques have not been applied for some classes of nonlinear systems yet. This paper deals with the issues of sensor fault detection and isolation for a class of Lipschitz uncertain nonlinear system. By introducing a coordinate transformation matrix for states and output, the original system is first divided into two subsystems. The first subsystem is affected by uncertainty and disturbance. The second subsystem just has sensor faults. The nonlinear term is separated to linear and pure nonlinear parts. For fault detection, two sliding mode observers (SMO) are designed for the two subsystems. The stability condition is obtained based on the Lyapunov approach. The necessary matrices and parameters are obtained by solving the linear matrix inequality (LMI) problem. Furthermore, two sliding mode observers are designed for fault isolation. Finally, the effectiveness of the proposed approach is illustrated by simulation examples.

Accurate and timely fault detection and isolation (FDI) improve the availability, safety, and reliability of target systems and enable cost-effective operations. In this study, a shared nearest neighbor (SNN)-based method is proposed to identify the fault variables of a circulating fluidized bed boiler. SNN is a derivative method of the k-nearest neighbor (kNN), which utilizes shared neighbor information. The distance information between these neighbors can be applied to FDI. In particular, the proposed method can effectively detect faults by weighing the distance values based on the number of neighbors they share, thereby readjusting the distance values based on the shared neighbors. Moreover, the data distribution is not constrained; therefore, it can be applied to various processes. Unlike principal component analysis and independent component analysis, which are widely used to identify fault variables, the main advantage of SNN is that it does not suffer from smearing effects, because it calculates the contributions from the original input space. The proposed method is applied to two case studies and to the failure case of a real circulating fluidized bed boiler to confirm its effectiveness. The results show that the proposed method can detect faults earlier (1 h 39 min 46 s) and identify fault variables more effectively than conventional methods.

A novel data-driven method for fault detection and isolation of control moment gyroscopes onboard satellites

Fault Detection and Isolation (FDI) methods have been developed over the past thirty years but only in the last several years they have been applied to complex real applications. This paper presents a synthesis of H∞ FDI filters for the longitudinal motion of the Boeing 747-100/200 aircraft. The filters are synthesized to detect and isolate faults in the elevator actuator and pitch rate sensor while attenuating disturbances and noise on the fault signals. They are synthesized for open-loop LTI models of the aircraft. Closed-loop simulations with a highfidelity nonlinear model in the presence of gust and noise are performed to validate the disturbance rejection and robust properties of the H∞ LTI FDI filters.

Fault diagnosis is critical for safe and reliable operations of quadrotor unmanned aerial vehicles (UAVs), so that timely remedial measures can be taken to reduce negative impacts of faults. This paper presents a real-time actuator fault detection and isolation (FDI) method for quadrotor UAV flight process. First, a linear parameter-varying model is established to describe quadrotor UAV dynamics, which considers measurement noises and external disturbances. Then, an FDI strategy is developed based on directional residuals, in which only one observer-based residual generator is required and H − / L ∞ indices are incorporated to balance fault sensitivity and disturbance robustness. A threshold is derived from the L ∞ performance condition, and actuator faults are detected through residual evaluation. Furthermore, by introducing fault feature vectors associated with the system model, the faulty actuator is isolated promptly by analyzing directional correlations between generated residual vectors and defined fault feature vectors. Finally, based on a quadrotor UAV platform, the effectiveness and superiority of the proposed FDI method are verified through online trajectory tracking processes.

Comparison Between Statistical and Observer-Based Approaches for Fault Detection and Isolation in a Chemical Process

This paper presents a Fault Detection and Isolation (FDI) method for stochastic nonlinear dynamic systems. Our contribution consists of showing another method of tackling the problem of the physical origin diagnosis of faults by combining the technique based on the innovations and the technique using the multiple Kalman filters for a nonlinear dynamic system strongly non-stationary. The usefulness of this combination is the implementation of all the faults dynamics if the decision threshold on the standardized innovation exceeds a fixed value. In the other case, one filter is enough to estimate the process state. An algorithm is described and applied to a perfectly stirred chemical reactor functioning in a semi-batch mode. In this paper, the chemical reaction used is an oxido reduction one, the oxidation of sodium thiosulfate by hydrogen peroxide.

In this paper, a recently introduced model-based method for precedent-free fault detection and isolation (FDI) is modified to deal with multiple input, multiple output (MIMO) systems and is applied to an automotive engine with exhaust gas recirculation (EGR) system. Using normal behavior data generated by a high fidelity engine simulation, the growing structure multiple model system (GSMMS) approach is used to construct dynamic models of normal behavior for the EGR system and its constituent subsystems. Using the GSMMS models as a foundation, anomalous behavior is detected whenever statistically significant departures of the most recent modeling residuals away from the modeling residuals displayed during normal behavior are observed. By reconnecting the anomaly detectors (ADs) to the constituent subsystems, EGR valve, cooler, and valve controller faults are isolated without the need for prior training using data corresponding to particular faulty system behaviors.

Method of fault detection and isolation in nonlinear electrical circuits