RFID-Based Smart Freezer

This paper presents a novel radio-frequency identification (RFID)-based smart freezer using a new inventory-management scheme for extremely low temperature environments. The proposed solution utilizes backpressure inventory control, systematic selection of antenna configuration, and antenna power control. The proposed distributed-inventory-control (DIC) scheme dictates the amount of items transferred through the supply chain. when a high item visibility is ensured, the control scheme maintains the desired level of inventory at each supply-chain echelon. The performance of the DIC scheme is guaranteed using a Lyapunov-based analysis. The proposed RFID antenna-configuration design methodology coupled with locally asymptotically stable distributed power control ensures a 99% read rate of items while minimizing the required number of RFID antennas in the confined cold chain environments with non-RF-friendly materials. The proposed RFID-based smart-freezer performance is verified through simulations of supply chain and experiments on an industrial freezer testbed operating at -100degF

Tolerância a falhas com base em comutação de controladores – implementação em autómatos programáveis

Dissertação para obtenção do Grau de Mestre em Engenharia Electrotécnica e de ComputadoresEsta Tese resulta do trabalho realizado na Faculdade de Ciências e Tecnologia da Universidade Nova de Lisboa, sendo o principal objectivo o desenvolvimento de um sistema de controlo tolerante a falhas. O projecto desenvolvido aborda algumas das metodologias já existentes para o desenvolvimento deste tipo de sistemas e apresenta algumas propostas para a sua implementação em autómatos programáveis. O sistema de controlo proposto tem por base a comutação de controladores, enquadrando-se por isso nos métodos activos de controlo tolerante a falhas. A abordagem proposta considera a existência de um sistema complementar de detecção, isolamento e identificação de falhas, fora do âmbito tese. De forma a garantir a independência entre o projecto de controlo e o de detecção e diagnóstico de falhas,é proposto que a implementação se realize em autómatos distintos, acrescentando ao projecto uma componente de comunicação entre dispositivos. A implementação proposta para as estruturas de controlo tem por base a utilização das linguagens de programação descritas na norma 61311-3 do IEC (Internacional Electrotechnical Commission) - Texto estruturado e Diagrama funcional de blocos. Para concluir sobre a aplicabilidade e desempenho das propostas sugeridas nesta tese, são apresentados os resultados do sistema de controlo quando aplicado ao processo laboratorial de dois tanques FBK 38-100, instalado no laboratório de automação da Faculdade de Ciências e Tecnologia da Universidade Nova de Lisbo

Model based fault diagnosis and prognosis of class of linear and nonlinear distributed parameter systems modeled by partial differential equations

With the rapid development of modern control systems, a significant number of industrial systems may suffer from component failures. An accurate yet faster fault prognosis and resilience can improve system availability and reduce unscheduled downtime. Therefore, in this dissertation, model-based prognosis and resilience control schemes have been developed for online prediction and accommodation of faults for distributed parameter systems (DPS). First, a novel fault detection, estimation and prediction framework is introduced utilizing a novel observer for a class of linear DPS with bounded disturbance by modeling the DPS as a set of partial differential equations. To relax the state measurability in DPS, filters are introduced to redesign the detection observer. Upon detecting a fault, an adaptive term is activated to estimate the multiplicative fault and a tuning law is derived to tune the fault parameter magnitude. Then based on this estimated fault parameter together with its failure limit, time-to-failure (TTF) is derived for prognosis. A novel fault accommodation scheme is developed to handle actuator and sensor faults with boundary measurements. Next, a fault isolation scheme is presented to differentiate actuator, sensor and state faults with a limited number of measurements for a class of linear and nonlinear DPS. Subsequently, actuator and sensor fault detection and prediction for a class of nonlinear DPS are considered with bounded disturbance by using a Luenberger observer. Finally, a novel resilient control scheme is proposed for nonlinear DPS once an actuator fault is detected by using an additional boundary measurement. In all the above methods, Lyapunov analysis is utilized to show the boundedness of the closed-loop signals during fault detection, prediction and resilience under mild assumptions --Abstract, page iv

Observer-based robust fault estimation for fault-tolerant control

A control system is fault-tolerant if it possesses the capability of optimizing the system stability and admissible performance subject to bounded faults, complexity and modeling uncertainty. Based on this definition this thesis is concerned with the theoretical developments of the combination of robust fault estimation (FE) and robust active fault tolerant control (AFTC) for systems with both faults and uncertainties.This thesis develops robust strategies for AFTC involving a joint problem of on-line robust FE and robust adaptive control. The disturbances and modeling uncertainty affect the FE and FTC performance. Hence, the proposed robust observer-based fault estimator schemes are combined with several control methods to achieve the desired system performance and robust active fault tolerance. The controller approaches involve concepts of output feedback control, adaptive control, robust observer-based state feedback control. A new robust FE method has been developed initially to take into account the joint effect of both fault and disturbance signals, thereby rejecting the disturbances and enhancing the accuracy of the fault estimation. This is then extended to encompass the robustness with respect to modeling uncertainty.As an extension to the robust FE and FTC scheme a further development is made for direct application to smooth non-linear systems via the use of linear parameter-varying systems (LPV) modeling.The main contributions of the research are thus:- The development of a robust observer-based FE method and integration design for the FE and AFTC systems with the bounded time derivative fault magnitudes, providing the solution based on linear matrix inequality (LMI) methodology. A stability proof for the integrated design of the robust FE within the FTC system.- An improvement is given to the proposed robust observer-based FE method and integrated design for FE and AFTC systems under the existence of different disturbance structures.- New guidance for the choice of learning rate of the robust FE algorithm.- Some improvement compared with the recent literature by considering the FTC problem in a more general way, for example by using LPV modeling

Sensor data-based decision making

Increasing globalization and growing industrial system complexity has amplified the interest in the use of information provided by sensors as a means of improving overall manufacturing system performance and maintainability. However, utilization of sensors can only be effective if the real-time data can be integrated into the necessary business processes, such as production planning, scheduling and execution systems. This integration requires the development of intelligent decision making models that can effectively process the sensor data into information and suggest appropriate actions. To be able to improve the performance of a system, the health of the system also needs to be maintained. In many cases a single sensor type cannot provide sufficient information for complex decision making including diagnostics and prognostics of a system. Therefore, a combination of sensors should be used in an integrated manner in order to achieve desired performance levels. Sensor generated data need to be processed into information through the use of appropriate decision making models in order to improve overall performance. In this dissertation, which is presented as a collection of five journal papers, several reactive and proactive decision making models that utilize data from single and multi-sensor environments are developed. The first paper presents a testbed architecture for Auto-ID systems. An adaptive inventory management model which utilizes real-time RFID data is developed in the second paper. In the third paper, a complete hardware and inventory management solution, which involves the integration of RFID sensors into an extremely low temperature industrial freezer, is presented. The last two papers in the dissertation deal with diagnostic and prognostic decision making models in order to assure the healthy operation of a manufacturing system and its components. In the fourth paper a Mahalanobis-Taguchi System (MTS) based prognostics tool is developed and it is used to estimate the remaining useful life of rolling element bearings using data acquired from vibration sensors. In the final paper, an MTS based prognostics tool is developed for a centrifugal water pump, which fuses information from multiple types of sensors in order to take diagnostic and prognostics decisions for the pump and its components --Abstract, page iv

Fault Diagnosis of Gas Turbine Engines by Using Multiple Model Approach

The field of fault detection and isolation (FDI) has attracted much attention in control theory during the last three decades which has resulted in development of sophisticated FDI algorithms. However, increasing the complexity of FDI algorithms is not necessarily feasible. Particularly for on-line FDI, the FDI unit must have the minimum possible computation cost to prevent any long delays in fault detection. In this research, we try to address the FDI problem of a single spool jet engine by using a modified linear multiple model (MM). We first develop a novel symbolic computation-based method for linearization purposes such that the obtained linear models are subjected to the symbolic fault variables. By substituting certain values for these symbolic variables, one can obtain different linear models, which describe mathematically the healthy and faulty models. In order to select the operating point, we use this fact that for a given constant fuel flow (W_f), the system reaches a steady state, that is varying for different values of W_f. Therefore, the operating points for linearization can be determined by the level of the Power Level Angel (PLA) (different values of W_f). These operating points are selected such that an observer, which is designed as a candidate for the healthy mode, can accurately estimates the states of the system in healthy scenario and the number of false alarm then would be kept to minimum. If the system works at different operating points one can then discretize the W_f into different intervals such that in each interval a linear model represents the behavior of the original system. By using the obtained models for different operating points, one designs the corresponding FDI units. Second, we provide a modified multiple model (MM) approach to investigate the FDI problem of a single spool jet engine. The main advantage of this method lies in the fact that the proposed MM consists of a certain set of linear Kalman filter banks rather than using nonlinear Kalman filters such as the Extended Kalman Filter which requires more computational cost. Moreover, a hierarchical structural multiple model is used to detect and isolate multiple faults. The simulation results show the capability of the proposed method when it is applied to a single spool jet engine model

Research and development of diagnostic algorithms to support fault accommodating control for emerging shipboard power system architectures

The U.S. Navy has proposed development of next generation warships utilising an increased amount of power electronics devices to improve flexibility and controllability. The high power density finite inertia network is envisioned to employ automated fault detection and diagnosis to aid timely remedial action. Integration of condition monitoring and fault diagnosis to form an intelligent power distribution system is anticipated to assist decision support for crew while enhancing security and mission availability. This broad research being in the conceptual stage has lack of benchmark systems to learn from. Thorough studies are required to successfully enable realising benefits offered by using increased power electronics and automation. Application of fundamental analysis techniques is necessary to meticulously understand dynamics of a novel system and familiarisation with associated risks and their effects. Additionally, it is vital to find ways of mitigating effects of identified risks. This thesis details the developing of a generalised methodology to help focus research into artificial intelligence (AI) based diagnostic techniques. Failure Mode and Effects Analysis (FMEA) is used in identifying critical parts of the architecture. Sneak Circuit Analysis (SCA) is modified to provide signals that differentiate faults at a component level of a dc-dc step down converter. These reliability analysis techniques combined with an appropriate AI-algorithm offer a potentially robust approach that can potentially be utilised for diagnosing faults within power electronic equipment anticipated to be used onboard the novel SPS. The proposed systematic methodology could be extended to other types of power electronic converters, as well as distinguishing subsystem level faults. The combination of FMEA, SCA with AI could also be used for providing enhanced decision support. This forms part of future research in this specific arena demonstrating the positives brought about by combining reliability analyses techniques with AI for next generation naval SPS.The U.S. Navy has proposed development of next generation warships utilising an increased amount of power electronics devices to improve flexibility and controllability. The high power density finite inertia network is envisioned to employ automated fault detection and diagnosis to aid timely remedial action. Integration of condition monitoring and fault diagnosis to form an intelligent power distribution system is anticipated to assist decision support for crew while enhancing security and mission availability. This broad research being in the conceptual stage has lack of benchmark systems to learn from. Thorough studies are required to successfully enable realising benefits offered by using increased power electronics and automation. Application of fundamental analysis techniques is necessary to meticulously understand dynamics of a novel system and familiarisation with associated risks and their effects. Additionally, it is vital to find ways of mitigating effects of identified risks. This thesis details the developing of a generalised methodology to help focus research into artificial intelligence (AI) based diagnostic techniques. Failure Mode and Effects Analysis (FMEA) is used in identifying critical parts of the architecture. Sneak Circuit Analysis (SCA) is modified to provide signals that differentiate faults at a component level of a dc-dc step down converter. These reliability analysis techniques combined with an appropriate AI-algorithm offer a potentially robust approach that can potentially be utilised for diagnosing faults within power electronic equipment anticipated to be used onboard the novel SPS. The proposed systematic methodology could be extended to other types of power electronic converters, as well as distinguishing subsystem level faults. The combination of FMEA, SCA with AI could also be used for providing enhanced decision support. This forms part of future research in this specific arena demonstrating the positives brought about by combining reliability analyses techniques with AI for next generation naval SPS

Active fault-tolerant control of nonlinear systems with wind turbine application

The thesis concerns the theoretical development of Active Fault-Tolerant Control (AFTC) methods for nonlinear system via T-S multiple-modelling approach. The thesis adopted the estimation and compensation approach to AFTC within a tracking control framework. In this framework, the thesis considers several approaches to robust T-S fuzzy control and T-S fuzzy estimation: T-S fuzzy proportional multiple integral observer (PMIO); T-S fuzzy proportional-proportional integral observer (PPIO); T-S fuzzy virtual sensor (VS) based AFTC; T-S fuzzy Dynamic Output Feedback Control TSDOFC; T-S observer-based feedback control; Sliding Mode Control (SMC). The theoretical concepts have been applied to an offshore wind turbine (OWT) application study. The key developments that present in this thesis are:• The development of three active Fault Tolerant Tracking Control (FTTC) strategies for nonlinear systems described via T-S fuzzy inference modelling. The proposals combine the use of Linear Reference Model Fuzzy Control (LRMFC) with either the estimation and compensation concept or the control reconfiguration concept.• The development of T-S fuzzy observer-based state estimate fuzzy control strategy for nonlinear systems. The developed strategy has the capability to tolerate simultaneous actuator and sensor faults within tracking and regulating control framework. Additionally, a proposal to recover the Separation Principle has also been developed via the use of TSDOFC within the FTTC framework.• The proposals of two FTTC strategies based on the estimation and compensation concept for sustainable OWTs control. The proposals have introduced a significant attribute to the literature of sustainable OWTs control via (1) Obviating the need for Fault Detection and Diagnosis (FDD) unit, (2) Providing useful information to evaluate fault severity via the fault estimation signals.• The development of FTTC architecture for OWTs that combines the use of TSDOFC and a form of cascaded observers (cascaded analytical redundancy). This architecture is proposed in order to ensure the robustness of both the TSDOFC and the EWS estimator against the generator and rotor speed sensor faults.• A sliding mode baseline controller has been proposed within three FTTC strategies for sustainable OWTs control. The proposals utilise the inherent robustness of the SMC to tolerate some matched faults without the need for analytical redundancy. Following this, the combination of SMC and estimation and compensation framework proposed to ensure the close-loop system robustness to various faults.• Within the framework of the developed T-S fuzzy based FTTC strategies, a new perspective to reduce the T-S fuzzy control design conservatism problem has been proposed via the use of different control techniques that demand less design constraints. Moreover, within the SMC based FTTC, an investigation is given to demonstrate the SMC robustness against a wider than usual set of faults is enhanced via designing the sliding surface with minimum dimension of the feedback signals