Industrial fault detection and diagnosis using Bayesian belief network

Rapid development in industry have contributed to more complex systems that are prone to failure. In applications where the presence of faults may lead to premature failure, fault detection and diagnostics tools are often implemented. The goal of this research is to improve the diagnostic ability of existing FDD methods. Kernel Principal Component Analysis has good fault detection capability, however it can only detect the fault and identify few variables that have contribution on occurrence of fault and thus not precise in diagnosing. Hence, KPCA was used to detect abnormal events and the most contributed variables were taken out for more analysis in diagnosis phase. The diagnosis phase was done in both qualitative and quantitative manner. In qualitative mode, a networked-base causality analysis method was developed to show the causal effect between the most contributing variables in occurrence of the fault. In order to have more quantitative diagnosis, a Bayesian network was constructed to analyze the problem in probabilistic perspective.

An Interval Intelligent-based Approach for Fault Detection and Modelling

Not considered in the analytical model of the plant, uncertainties always dramatically decrease the performance of the fault detection task in the practice. To cope better with this prevalent problem, in this paper we develop a methodology using Modal Interval Analysis which takes into account those uncertainties in the plant model. A fault detection method is developed based on this model which is quite robust to uncertainty and results in no false alarm. As soon as a fault is detected, an ANFIS model is trained in online to capture the major behavior of the occurred fault which can be used for fault accommodation. The simulation results understandably demonstrate the capability of the proposed method for accomplishing both tasks appropriately

An Interval Intelligent-based Approach for Fault Detection and Modelling

Not considered in the analytical model of the plant, uncertainties always dramatically decrease the performance of the fault detection task in the practice. To cope better with this prevalent problem, in this paper we develop a methodology using Modal Interval Analysis which takes into account those uncertainties in the plant model. A fault detection method is developed based on this model which is quite robust to uncertainty and results in no false alarm. As soon as a fault is detected, an ANFIS model is trained in online to capture the major behavior of the occurred fault which can be used for fault accommodation. The simulation results understandably demonstrate the capability of the proposed method for accomplishing both tasks appropriately

Time misalignments in fault detection and diagnosis

El desalineamiento temporal es la incorrespondencia de dos señales debido a una distorsión en el eje temporal. La Detección y Diagnóstico de Fallas (Fault Detection and Diagnosis-FDD) permite la detección, el diagnóstico y la corrección de fallos en un proceso. La metodología usada en FDD está dividida en dos categorías: técnicas basadas en modelos y no basadas en modelos. Esta tesis doctoral trata sobre el estudio del efecto del desalineamiento temporal en FDD. Nuestra atención se enfoca en el análisis y el diseño de sistemas FDD en caso de problemas de comunicación de datos, como retardos y pérdidas. Se proponen dos técnicas para reducir estos problemas: una basada en programación dinámica y la otra en optimización. Los métodos propuestos han sido validados sobre diferentes sistemas dinámicos: control de posición de un motor de corriente continua, una planta de laboratorio y un problema de sistemas eléctricos conocido como hueco de tensión.

Hybrid fault detection and isolation in an heater bank of an HVAC system

In this work a hybrid technique that includes probabilistic and optimization based methods is presented. The method is applied, both in simulation and by means of real-time experiments, to the heating unit of a Heating, Ventilation Air Conditioning (HVAC) system. It is shown that the addition of the probabilistic approach improves the fault diagnosis accuracy.

Fault detection and isolation on a noisy nonlinear circuit

In this paper fault detection and isolation (FDI) schemes are applied in the context of the surveillance of emerging faults in an electrical circuit. The FDI problem is studied on a noisy nonlinear circuit, where both abrupt and incipient faults in the voltage source are considered. A rigorous analysis of fault detectability precedes the application of the fault detection (FD) scheme; then, the fault isolation (FI) phase is accomplished with two alternative FI approaches, proposed as new extensions of that FD approach. Numerical simulations illustrate the applicability of the mentioned schemes.

Development and improvement of protection-system algorithms for ground-fault detection and location in synchronous machines

Uno de los defectos más frecuentes en los generadores síncronos son los defectos a tierra tanto en el devanado estatórico, como de excitación. Se produce un defecto cuando el aislamiento eléctrico entre las partes activas de cualquiera de estos devanados y tierra se reduce considerablemente o desaparece. La detección de los defectos a tierra en ambos devanados es un tema ampliamente estudiado a nivel industrial. Tras la detección y confirmación de la existencia del defecto, dicha falta debe ser localizada a lo largo del devanado para su reparación, para lo que habitualmente el rotor debe ser extraído del estator. Esta operación resulta especialmente compleja y cara. Además, el hecho de limitar la corriente de defecto en ambos devanados provoca que el defecto no sea localizable visualmente, pues apenas existe daño en el generador. Por ello, se deben aplicar técnicas muy laboriosas para localizar exactamente el defecto y poder así reparar el devanado. De cara a reducir el tiempo de reparación, y con ello el tiempo en que el generador esta fuera de servicio, cualquier información por parte del relé de protección acerca de la localización del defecto resultaría de gran utilidad. El principal objetivo de esta tesis doctoral ha sido el desarrollo de nuevos algoritmos que permitan la estimación de la localización de los defectos a tierra tanto en el devanado rotórico como estatórico de máquinas síncronas. Respecto al devanado de excitación, se ha presentado un nuevo método de localización de defectos a tierra para generadores con excitación estática. Este método permite incluso distinguir si el defecto se ha producido en el devanado de excitación, o en cualquiera de los componentes del sistema de excitación, esto es, transformador de excitación, conductores de alimentación del rectificador controlado, etc. En caso de defecto a tierra en del devanado rotórico, este método proporciona una estimación de su localización. Sin embargo, para poder obtener la localización del defecto, se precisa conocer el valor de resistencia de defecto. Por ello, en este trabajo se presenta además un nuevo método para la estimación de este parámetro de forma precisa. Finalmente, se presenta un nuevo método de detección de defectos a tierra, basado en el criterio direccional, que complementa el método de localización, permitiendo tener en cuenta la influencia de las capacidades a tierra del sistema. Estas capacidades resultan determinantes a la hora de localizar el defecto de forma adecuada. En relación con el devanado estatórico, en esta tesis doctoral se presenta un nuevo algoritmo de localización de defectos a tierra para generadores que dispongan de la protección de faltas a tierra basada en la inyección de baja frecuencia. Se ha propuesto un método general, que tiene en cuenta todos los parámetros del sistema, así como una versión simplificada del método para generadores con capacidades a tierra muy reducida, que podría resultar de fácil implementación en relés de protección comercial. Los algoritmos y métodos presentados se han validado mediante ensayos experimentales en un generador de laboratorio de 5 kVA, así como en un generador comercial de 106 MVA con resultados satisfactorios y prometedores. ABSTRACT One of the most common faults in synchronous generators is the ground fault in both the stator winding and the excitation winding. In case of fault, the insulation level between the active part of any of these windings and ground lowers considerably, or even disappears. The detection of ground faults in both windings is a very researched topic. The fault current is typically limited intentionally to a reduced level. This allows to detect easily the ground faults, and therefore to avoid damage in the generator. After the detection and confirmation of the existence of a ground fault, it should be located along the winding in order to repair of the machine. Then, the rotor has to be extracted, which is a very complex and expensive operation. Moreover, the fact of limiting the fault current makes that the insulation failure is not visually detectable, because there is no visible damage in the generator. Therefore, some laborious techniques have to apply to locate accurately the fault. In order to reduce the repair time, and therefore the time that the generator is out of service, any information about the approximate location of the fault would be very useful. The main objective of this doctoral thesis has been the development of new algorithms and methods to estimate the location of ground faults in the stator and in the rotor winding of synchronous generators. Regarding the excitation winding, a new location method of ground faults in excitation winding of synchronous machines with static excitation has been presented. This method allows even to detect if the fault is at the excitation winding, or in any other component of the excitation system: controlled rectifier, excitation transformer, etc. In case of ground fault in the rotor winding, this method provides an estimation of the fault location. However, in order to calculate the location, the value of fault resistance is necessary. Therefore, a new fault-resistance estimation algorithm is presented in this text. Finally, a new fault detection algorithm based on directional criterion is described to complement the fault location method. This algorithm takes into account the influence of the capacitance-to-ground of the system, which has a remarkable impact in the accuracy of the fault location. Regarding the stator winding, a new fault-location algorithm has been presented for stator winding of synchronous generators. This algorithm is applicable to generators with ground-fault protection based in low-frequency injection. A general algorithm, which takes every parameter of the system into account, has been presented. Moreover, a simplified version of the algorithm has been proposed for generators with especially low value of capacitance to ground. This simplified algorithm might be easily implementable in protective relays. The proposed methods and algorithms have been tested in a 5 kVA laboratory generator, as well as in a 106 MVA synchronous generator with satisfactory and promising results.

Detection and diagnosis solutions for fault-tolerant VSI

This paper presents solutions for fault detection and diagnosis of two-level, three phase voltage-source inverter (VSI) topologies with IGBT devices. The proposed solutions combine redundant standby VSI structures and contactors (or relays) to improve the fault-tolerant capabilities of power electronics in applications with safety requirements. The suitable combination of these elements gives the inverter the ability to maintain energy processing in the occurrence of several failure modes, including short-circuit in IGBT devices, thus extending its reliability and availability. A survey of previously developed fault-tolerant VSI structures and several aspects of failure modes, detection and isolation mechanisms within VSI is first discussed. Hardware solutions for the protection of power semiconductors with fault detection and diagnosis mechanisms are then proposed to provide conditions to isolate and replace damaged power devices (or branches) in real time. Experimental results from a prototype are included to validate the proposed solutions.

Sliding mode for detection and accommodation of computation time delay fault

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

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.

Robust fault detection using consistency techniques for uncertainty handling

Often practical performance of analytical redundancy for fault detection and diagnosis is decreased by uncertainties prevailing not only in the system model, but also in the measurements. In this paper, the problem of fault detection is stated as a constraint satisfaction problem over continuous domains with a big number of variables and constraints. This problem can be solved using modal interval analysis and consistency techniques. Consistency techniques are then shown to be particularly efficient to check the consistency of the analytical redundancy relations (ARRs), dealing with uncertain measurements and parameters. Through the work presented in this paper, it can be observed that consistency techniques can be used to increase the performance of a robust fault detection tool, which is based on interval arithmetic. The proposed method is illustrated using a nonlinear dynamic model of a hydraulic system

Robust fault detection using consistency techniques for uncertainty handling

Often practical performance of analytical redundancy for fault detection and diagnosis is decreased by uncertainties prevailing not only in the system model, but also in the measurements. In this paper, the problem of fault detection is stated as a constraint satisfaction problem over continuous domains with a big number of variables and constraints. This problem can be solved using modal interval analysis and consistency techniques. Consistency techniques are then shown to be particularly efficient to check the consistency of the analytical redundancy relations (ARRs), dealing with uncertain measurements and parameters. Through the work presented in this paper, it can be observed that consistency techniques can be used to increase the performance of a robust fault detection tool, which is based on interval arithmetic. The proposed method is illustrated using a nonlinear dynamic model of a hydraulic system

Leakage Detection And Localization Method Based On A Hybrid Inverse/Direct Modelling Approach Suitable For Handling Multiple-Leak Scenarios

When an accurate hydraulic network model is available, direct modeling techniques are very straightforward and reliable for on-line leakage detection and localization applied to large class of water distribution networks. In general, this type of techniques based on analytical models can be seen as an application of the well-known fault detection and isolation theory for complex industrial systems. Nonetheless, the assumption of single leak scenarios is usually made considering a certain leak size pattern which may not hold in real applications. Upgrading a leak detection and localization method based on a direct modeling approach to handle multiple-leak scenarios can be, on one hand, quite straightforward but, on the other hand, highly computational demanding for large class of water distribution networks given the huge number of potential water loss hotspots. This paper presents a leakage detection and localization method suitable for multiple-leak scenarios and large class of water distribution networks. This method can be seen as an upgrade of the above mentioned method based on a direct modeling approach in which a global search method based on genetic algorithms has been integrated in order to estimate those network water loss hotspots and the size of the leaks. This is an inverse / direct modeling method which tries to take benefit from both approaches: on one hand, the exploration capability of genetic algorithms to estimate network water loss hotspots and the size of the leaks and on the other hand, the straightforwardness and reliability offered by the availability of an accurate hydraulic model to assess those close network areas around the estimated hotspots. The application of the resulting method in a DMA of the Barcelona water distribution network is provided and discussed. The obtained results show that leakage detection and localization under multiple-leak scenarios may be performed efficiently following an easy procedure.