Kernel-based fault diagnosis of inertial sensors using analytical redundancy

Kernel methods are able to exploit high-dimensional spaces for representational advantage, while only operating implicitly in such spaces, thus incurring none of the computational cost of doing so. They appear to have the potential to advance the state of the art in control and signal processing applications and are increasingly seeing adoption across these domains. Applications of kernel methods to fault detection and isolation (FDI) have been reported, but few in aerospace research, though they offer a promising way to perform or enhance fault detection. It is mostly in process monitoring, in the chemical processing industry for example, that these techniques have found broader application. This research work explores the use of kernel-based solutions in model-based fault diagnosis for aerospace systems. Specifically, it investigates the application of these techniques to the detection and isolation of IMU/INS sensor faults – a canonical open problem in the aerospace field. Kernel PCA, a kernelised non-linear extension of the well-known principal component analysis (PCA) algorithm, is implemented to tackle IMU fault monitoring. An isolation scheme is extrapolated based on the strong duality known to exist between probably the most widely practiced method of FDI in the aerospace domain – the parity space technique – and linear principal component analysis. The algorithm, termed partial kernel PCA, benefits from the isolation properties of the parity space method as well as the non-linear approximation ability of kernel PCA. Further, a number of unscented non-linear filters for FDI are implemented, equipped with data-driven transition models based on Gaussian processes - a non-parametric Bayesian kernel method. A distributed estimation architecture is proposed, which besides fault diagnosis can contemporaneously perform sensor fusion. It also allows for decoupling faulty sensors from the navigation solution

Simultaneous state and actuator fault estimation for satellite attitude control systems

AbstractIn this paper, a new nonlinear augmented observer is proposed and applied to satellite attitude control systems. The observer can estimate system state and actuator fault simultaneously. It can enhance the performances of rapidly-varying faults estimation. Only original system matrices are adopted in the parameter design. The considered faults can be unbounded, and the proposed augmented observer can estimate a large class of faults. Systems without disturbances and the fault whose finite times derivatives are zero piecewise are initially considered, followed by a discussion of a general situation where the system is subject to disturbances and the finite times derivatives of the faults are not null but bounded. For the considered nonlinear system, convergence conditions of the observer are provided and the stability analysis is performed using Lyapunov direct method. Then a feasible algorithm is explored to compute the observer parameters using linear matrix inequalities (LMIs). Finally, the effectiveness of the proposed approach is illustrated by considering an example of a closed-loop satellite attitude control system. The simulation results show satisfactory performance in estimating states and actuator faults. It also shows that multiple faults can be estimated successfully

Model-based fault diagnosis for aerospace systems: a survey

http://pig.sagepub.com/content/early/2012/01/06/0954410011421717International audienceThis survey of model-based fault diagnosis focuses on those methods that are applicable to aerospace systems. To highlight the characteristics of aerospace models, generic nonlinear dynamical modeling from flight mechanics is recalled and a unifying representation of sensor and actuator faults is presented. An extensive bibliographical review supports a description of the key points of fault detection methods that rely on analytical redundancy. The approaches that best suit the constraints of the field are emphasized and recommendations for future developments in in-flight fault diagnosis are provided

Fault detection, isolation, and identification for nonlinear systems using a hybrid approach

This thesis presents a novel integrated hybrid approach for fault diagnosis (FD) of nonlinear systems; taking advantage of both system's mathematical model and the adaptive nonlinear approximation capability of computational intelligence techniques. Unlike most FD techniques, the proposed solution simultaneously accomplishes fault detection, isolation, and identification (FDII) within a unified diagnostic module. At the core of this solution are a bank of adaptive neural parameter estimators (NPE) and a set of single-parameterized fault models. The NPEs continuously estimate unknown fault parameters (FP) that are indicators of faults in the system. In view of the availability of full-state measurements, two NPE structures, namely series-parallel and parallel, are developed with their exclusive set of desirable attributes. The parallel scheme is extremely robust to measurement noise and possesses a simpler, yet more solid, fault isolation logic. On the contrary, the series-parallel scheme displays short FD delays and is robust to closed-loop system transients due to changes in control commands. Simple neural network architecture and update laws make both schemes suitable for real-time implementations. A fault tolerant observer (FTO) is then designed to extend the FDII schemes to systems with partial-state measurement. The proposed FTO is a neural state estimator that can estimate unmeasured states even in presence of faults. The estimated and the measured states then comprise the inputs to the FDII schemes. Simulation results for FDII of reaction wheels of a 3-axis stabilized satellite in presence of disturbances and noise demonstrate the effectiveness of the proposed FDII solution under both full and partial-state measurements

Fault detection, diagnosis and active fault tolerant control for a satellite attitude control system

Modern control systems are becoming more and more complex and control algorithms more and more sophisticated. Consequently, Fault Detection and Diagnosis (FDD) and Fault Tolerant Control (FTC) have gained central importance over the past decades, due to the increasing requirements of availability, cost efficiency, reliability and operating safety. This thesis deals with the FDD and FTC problems in a spacecraft Attitude Determination and Control System (ADCS). Firstly, the detailed nonlinear models of the spacecraft attitude dynamics and kinematics are described, along with the dynamic models of the actuators and main external disturbance sources. The considered ADCS is composed of an array of four redundant reaction wheels. A set of sensors provides satellite angular velocity, attitude and flywheel spin rate information. Then, general overviews of the Fault Detection and Isolation (FDI), Fault Estimation (FE) and Fault Tolerant Control (FTC) problems are presented, and the design and implementation of a novel diagnosis system is described. The system consists of a FDI module composed of properly organized model-based residual filters, exploiting the available input and output information for the detection and localization of an occurred fault. A proper fault mapping procedure and the nonlinear geometric approach are exploited to design residual filters explicitly decoupled from the external aerodynamic disturbance and sensitive to specific sets of faults. The subsequent use of suitable adaptive FE algorithms, based on the exploitation of radial basis function neural networks, allows to obtain accurate fault estimations. Finally, this estimation is actively exploited in a FTC scheme to achieve a suitable fault accommodation and guarantee the desired control performances. A standard sliding mode controller is implemented for attitude stabilization and control. Several simulation results are given to highlight the performances of the overall designed system in case of different types of faults affecting the ADCS actuators and sensors

Fault Detection, Isolation and Identification of Formation Flying Satellites using Wavelet-Entropy and Neural Networks

The main objective of this thesis is to develop a fault detection, isolation and identification (FDII) scheme based on Wavelet Entropy (WE) and Artificial Neural Network (ANN) for reaction wheels (RW) that are employed as actuators in the attitude control subsystem (ACS) of a satellites to perform the formation flying (FF) missions. In this thesis two FDII approaches are proposed, i) Spacecraft-level fault diagnosis and ii) Formation-level fault diagnosis. In the "spacecraft-level" fault diagnosis scheme in order to analysis faults, absolute attitude and angular measurements from a satellite are considered as diagnostic signals. In order to detect the fault, the wavelet-entropy technique is employed on diagnostic signals and the sum of the absolute wavelet entropies (SAWE) of the diagnostic signals are obtained and compared with an appropriately selected threshold. If the SAWE passes the threshold the faulty condition is established. In order to isolate the fault in a satellite the angular velocity measurements in a satellite are considered as diagnostic signals and the relative wavelet energy (RWE) of these signals are obtained and compared to a threshold. In our proposed fault identification scheme, the attitude measurements in a satellite are considered and the detail and approximation coefficients of the wavelet signals are obtained and these coefficients are used as inputs to an artificial neural network to identify the type of the fault in a satellite. Using a confusion matrix evaluation system we demonstrate that our spacecraft-level FDII can detect, isolate and identify the high severity faults in a satellite however this scheme cannot detect low severity faults in a satellite. Our proposed "formation-level" FDII scheme utilizes data collected from the relative attitudes and relative angular velocity measurements of the formation flying satellites. In this fault diagnosis scheme, the relative attitude and relative angular velocity measurements in a satellite with respect to each its neighbor's in a formation are considered as diagnostic signals. In order to detect the fault, the relative attitude measurements in a satellite are considered as diagnostic signals. The wavelet-entropy technique is utilized on diagnostic signals and the SAWEs with respect to each satellite's neighbor are obtained. These SAWEs are then compared with an appropriately selected threshold. The faulty satellite is determined if these SAWEs pass the thresholds. In order to isolate the fault in a faulty satellite, the relative angular velocity measurements are considered as diagnostic signals. The RWE of these signals are obtained and compared to a threshold. In our proposed fault identification scheme, the relative attitude measurements in a satellite are considered as diagnostic signals. In this scheme, the RWEs of the diagnostic signals are obtained and used as inputs to an artificial neural network to identify the type of the fault in a satellite. According to the simulation results, our proposed FDII scheme can detect, isolate and identified both low severity and high severity faults in the reaction wheels of satellite