Analog Fault Identification in RF Circuits using Artificial Neural Networks and Constrained Parameter Extraction

The increase of analog and mixed-signal circuitry in modern RF and microwave integrated circuits demands for improved analog fault diagnosis methods. While digital fault diagnosis is well established, the analog counterpart is relatively much less mature due to the intrinsic complexity in analog faults and their corresponding identification. In this work, we present an artificial neural network (ANN) modeling approach to efficiently emulate the injection of analog faults in RF circuits. The resulting meta-model is used for fault identification by applying an optimization-based process using a constrained parameter extraction formulation. The proposed methodology is illustrated by a faulty analog CMOS RF circuit

Analog Gross Fault Identification in RF Circuits using Neural Models and Constrained Parameter Extraction

The demand and relevance of efficient analog fault diagnosis methods for modern RF and microwave integrated circuits increases with the growing need and complexity of analog and mixed-signal circuitry. The well-established digital fault diagnosis methods are insufficient for analog circuitry due to the intrinsic complexity in analog faults and their corresponding identification process. In this work, we present an artificial neural network (ANN) modeling approach to efficiently emulate the injection of analog faults in RF circuits. The resulting meta-model is used for fault identification by applying an optimization-based process using a constrained parameter extraction formulation. A generalized neural modeling formulation to include auxiliary measurements in the circuit is proposed. This generalized formulation significantly increases the uniqueness of the faults identification process. The proposed methodology is illustrated by two faulty analog circuits: a CMOS RF voltage amplifier and a reconfigurable bandpass microstrip filter

Blind fault detection using spectral signatures

This work studies a blind fault detection method, which only analyses a system\u27s output signal for any change in the characteristics from pre-fault to post-fault to identify the occurrence of faults. In our case the fault considered to develop the procedure is change in time constant of an aircraft\u27s aileron-actuator system and its simplified version - a position servo system. The method is studied as an alternative to conventional fault detection and identification methods. The output signal is passed through a filter bank to enhance the effect of a fault. The Short time Fourier transform is performed on the enhanced pre-fault and post-fault signals components to obtain indicators. Fault detection is approached as a clustering problem determining distances to fault signatures. This work presents two techniques to create signatures from the indicators. In the first method, the mean of the indicators is the signature. Tests on a position servo system show that the method effectively classifies the indicators by more than 85 % and can be used for online classification. A second method uses Principal Component Analysis and defines vector sub-space signatures. It is observed that for the position servo system, the pre-fault indicators had 14 % of false alarms and post-fault indicators the missed the faults by 17%. This second method was also applied to one axis model of an F-14 aircraft\u27s aileron-actuator system. The results obtained showed around 80 % of correctly identified pre-fault indicators and post-fault indicators. The blind fault detection method studies has potential but needs to be understood further by applying it to more varied cases of faults and systems

Adaptive and reconfigurable data fusion architectures in positioning navigation systems

Dans les systèmes de positionnement de véhicules, à tout moment, n'importe lequel des détecteurs peut, temporairement ou de manière permanente, tomber en panne ou cesser d'envoyer des informations. Il s'ensuit alors des répercussions sur la sécurité, la santé, ainsi que des informations financières ou même légales. Bien que les nouvelles pratiques de conception aient tendance à réduire au minimum les défaillances des détecteurs, il est reconnu que de tels évènements peuvent quand même souvenir. Dans un tel cas, le détecteur défectueux doit être identifié et isolé afin d'éviter de corrompre les évaluations globales et, finalement, le système doit être capable de se reconfigurer afin de surmonter le carence causée par la défaillance. En bref, un système de navigation doit être robuste et adaptatif. Cette thèse propose plusieurs architectures de fusion de données capables de s'adapter suite à des défaillances de détecteurs. Les diverses approches utilisent un filtre Kalman en combinaison avec la détection de défauts pour produire des modules de positionnement robuste. Les modules devront être capables de fonctionner dans des situations telles que l'entrée GPS est corrompue ou non disponible, ou bien qu'un plusieurs détecteurs de position sont défectueux ou bloqués. Le principe de travail vise la modification des gains du filtre Kalman en se basant sur les erreurs normalisées entre les états estimés et les observations. Pour évaluer l'architecture proposée, divers défauts de détecteurs et diverses dégradations de performance ont été mis en oeuvre et simulés. Les expériences démontrent que les solutions proposées peuvent compenser la plupart des erreurs associées aux défauts des détecteurs ou aux dégradations de performance, et que l'exactitude de positionnement qui en découle est améliorée significativement

Fault detection and diagnosis in HVAC systems using analytical models

Faults that develop in the heat exchanger subsystems in air-conditioning installations can lead to increased energy costs and jeopardise thermal comfort. The sensor and control signals associated with these systems contain potentially valuable information about the condition of the system, and energy management and control systems are able to monitor and store these signals. In practice, the only checks made are to verify set-points are being maintained and that certain critical variables remain within predetermined limits. This approach may allow the detection of certain abrupt or catastrophic faults, but degradation faults often remain undetected until their effects become quite severe. This thesis investigates the appropriateness of using mathematical models to track the development of degradation faults. An approach is developed, which is based on the use of analytical models in conjunction with a recursive parameter estimation algorithm. A subset of the parameters of the models, which are closely related to faults, is estimated recursively. Significant deviations in the values of the estimated parameters from nominal values, which represent `correct operation', are used as an indication that the system has developed a fault. The extent of the deviation from the nominal values is used as an estimate of the degree of fault. This thesis develops the theory and examines the robustness of the parameter estimator using simulation-based testing. Results are also presented from testing the fault detection and diagnosis scheme with data obtained from a simulated air-conditioning system and from a full size test installation

Pseudo Euler-Lagrange and Piecewise Affine Control Applied to Surge and Stall in Axial Compressors

This thesis addresses the control of the axial compressor surge and stall phenomena using Pseudo Euler-Lagrange and Piecewise Affine (PWA) controller synthesis techniques. These phenomena are considered as major gas turbine compressor instabilities that may result in failures such as the engine flame-out or severe mechanical damages caused by high blade vibration. The common approach towards the detection of the rotating stall and surge is to install various types of pressure sensors, hot wires and velocity probes. The inception of the rotating stall and surge is recognized by the presence of pressure fluctuation and velocity disturbances in the gas stream that are obtained through sensors. The necessary measure is then taken by applying proper stall and surge stabilizing control actions. The Lyapunov stability of pseudo Euler-Lagrange systems in the literature is extended to include additional nonlinear terms. Although Lyapunov stability theory is considered as the cornerstone of analysis of nonlinear systems, the generalization of this energy-based method poses a drawback that makes obtaining a Lyapunov function a difficult task. Therefore, proposing a method for generating a Lyapunov function for the control synthesis problem of a class of nonlinear systems is of potential importance. A systematic Lyapunov-based controller synthesis technique for a class of second order systems is addressed in this thesis. It is shown, in terms of stability characteristics, that the proposed technique provides a more robust solution to the compressor surge suppression problem as compared to the feedback linearization and the backstepping methods. The second contribution is a proposed new PWA approximation algorithm. Such an approximation is very important in reducing the complexity of nonlinear systems models while keeping the global validity of the models. The proposed method builds upon previous work on piecewise affine (PWA) approximation methods, which can be used to approximate continuous functions of n-variables by a PWA function. Having computed the PWA model of the stall and surge equations, the suppression problem is then solved by using PWA synthesis techniques. The proposed solution is shown to have higher damping characteristics as compared to the backstepping nonlinear method

An intelligent engine condition monitoring system

The main focus of the work reported here is in the design of an intelligent condition monitoring system for diesel engines. Mechanical systems in general and diesel engines in particular can develop faults if operated for any length of time. Condition monitoring is a method by which the performance of a diesel engine can be maintained at a high level, ensuring both continuous availability and design-level efficiency. A key element in a condition monitoring program is to acquire sensor information from the engine, and use this information to assess the condition of the engine, with an emphasis on monitoring causes of engine failure or reduced efficiency. A Ford 70PS 4-stroke diesel engine has been instrumented with a range of sensors and interfaced to a PC in order to facilitate computer controlled data acquisition and data storage. Data was analyzed to evaluate the optimum use of sensors to identify faults and to develop an intelligent algorithm for the engine condition monitoring and fault detection, and in particular faults affecting the combustion process in the engine. In order to investigate the fault-symptom relationships, two synthetic faults were introduced to the engine. Fuel and inlet air shortage were selected as the faults for their direct relationship to the combustion process quality. As a subtask the manually operated hydraulic brake was adapted to allow automatic control to improve its performance. Two modes of controlling were designed for the developed automatic control of the hydraulic brake system. A robust mathematical diesel engine model has been developed which can be used to predict the engine parameters related to the combustion process in the diesel engine, was constructed from the basic relationships of the diesel engine using the minimum number of empirical equations. The system equations of a single cylinder engine were initially developed, from which the multi-cylinder diesel engine model was validated against experimental test data. The model was then tuned to improve the predicted engine parameters for better matching with the available engine type. The final four-cylinder diesel engine model was verified and the results show an accurate match with the experimental results. Neural networks and fuzzification were used to develop and validate the intelligent condition monitoring and fault diagnosis algorithm, in order to satisfy the requirements of on-line operation, i. e. reliability, easily trained, minimum hardware and software requirements. The development process used a number of different neural network architecture and training techniques. To increase the number of the parameters used for the engine condition evaluation, the Multi-Net technique was used to satisfy accurate and fast decision making. Two neural networks are designed to operate in parallel to accommodate the different sampling rate of the key parameters without interference and with reduced data processing time. The two neural networks were trained and validated using part of the measured data set that represents the engine operating range. Another set of data, not utilized within the training stage, has been applied for validation. The results of validation process indicate the successful prediction of the faults using the key measured parameters, as well as a fast data processing algorithm. One of the main outcomes of this study is the development of a new technique to measure cylinder pressure and fuel pressure through the measurement of the strain in the injector body. The main advantage of this technique is that, it does not require any intrusive modification to the engine which might affect the engine actual performance. The developed sensor was tested and used to measure the cylinder and fuel pressure to verify the fuel fault effect on the combustion process quality. Due to high sampling rate required, the developed condition monitoring and fault diagnosis algorithm does not utilize this signal to reduce the required computational resources for practical applications.EThOS - Electronic Theses Online ServiceEgyptian GovernmentGBUnited Kingdo