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Kalman-type filtering methods are mostly designed based on exact knowledge of the system’s model with known parameters. In real applications, there may be considerable amount of uncertainties about the model structure, physical parameters, level of noise, and initial conditions. In order to overcome such difficulties, robust state estimation techniques are recommended. This PhD thesis presents a novel robust state estimation method that is referred to as the 2nd-order smooth variable structure filter (2nd-order SVSF) and satisfies the first and second order sliding conditions. It is an extension to the 1st-order SVSF introduced in 2007. In the 1st-order SVSF chattering is reduced by using a smoothing boundary layer; however, the 2nd-order SVSF alleviates chattering by preserving the second order sliding condition. It reduces the estimation error and its first difference until the existence boundary layer is reached. Then after, it guarantees that the estimation error and its difference remain bounded given bounded noise and modeling uncertainties. As such, the 2nd-order SVSF produces more accurate and smoother state estimates under highly uncertain conditions than the 1st-order version. The main issue with the 2nd-order SVSF is that it is not optimal in the mean square error sense. In order to overcome this issue, the dynamic 2nd-order SVSF is initially presented based on a dynamic sliding mode manifold. This manifold introduces a variable cut-off frequency coefficient that adjusts the filter bandwidth. An optimal derivation of the 2nd-order SVSF is then obtained by minimizing the state error covariance matrix with respect to the cut-off frequency matrix. An experimental setup of an electro-hydrostatic actuator is used to compare the performance of the 2nd-order SVSF and its optimal version with other estimation methods such as the Kalman filter and the 1st-order SVSF. Experiments confirm the superior performance of the 2nd-order SVSF given modeling uncertainties. / Thesis / Doctor of Philosophy (PhD)

This research project is part of a wider project called Smart Fault Detection and Diagnosis for HeatPump Systems currently under development by the Royal Institute of Technology (KTH).Generally, maintenance, diagnosis and repair of heat pumps are manual operations. The qualityof the service relies almost exclusively on the skills, experience and motivation of the HVAC-Rtechnician. Moreover, professional technicians are only called up after a remarkable failure occursand not to perform routine follow up.The main objective of this master thesis will be to propose a method for fault detection of thebrine to water heat pump systems under operating conditions. It will be done by focusing into ninetests faults related to the first boundary level which represents the heat pump unit, the brine andwater loop. A model based approach was developed to generate features and parameters capableof reading the status of the system. The fault detection was done by imposing test faults in the model and evaluating the trend of the performance parameters. By comparing the predicted fault free values with the actual values (Residuals) from the model, several algorithms were proposed and conducted in order to obtain an online fault detection and diagnosis. It is concluded that the fault trend analysis can, in principle, provide a solution to detect faults in heat pump systems. The algorithms are considered user friendly tools, however more improvementsneeds to be done to include more faults and increase its resolution.

Induction motors (IM) find widespread use in modern industry and for this reason they have been subject to a significant amount of research interest in recent times. One particular aspect of this research is the fault detection and diagnosis (FDD) of induction motors for use in a condition based maintenance (CBM) strategy; by effectively tracking the condition of the motor, maintenance action need only be carried out when necessary. This type of maintenance strategy minimises maintenance costs and unplanned downtime. The benefits of an effective FDD for IM is clear and there have been numerous studies in this area but few which consider the problem in a practical sense with the aim of developing a single system that can be used to monitor motor condition under a range of different conditions, with different motor specifications and loads. This thesis aims to address some of these problems by developing a general FDD system for induction motor. The solution of this problem involved the development and testing of a new approach; the adaptive mixed-residual approach (AMRA). The main aim of the AMRA system is to avoid the vast majority of unplanned failures of the machine and therefore as opposed to tackling a single induction motor fault, the system is developed to detect all four of the most statistically prevalent induction motor fault types; rotor fault, stator fault, air-gap fault and bearing fault. The mixed-residual fault detection algorithm is used to detect these fault types which includes a combination of spectral and model-based techniques coupled with particle swarm optimisation (PSO) for automatic identification of motor parameters. The AMRA residuals are analysed by a fuzzy-logic classifier and the system requires only current and voltage inputs to operate. Validation results indicate that the system performs well under a range of load torques and different coupling methods proving it to have significant potential for use in industrial applications. / The full-text was made available at the end of the embargo period on 29th Sept 2017.

Fault Detection and Diagnosis (FDD) constitutes an essential aspect of modern life, with far-reaching implications spanning various domains such as healthcare, maintenance of industrial machinery, and cybersecurity. A comprehensive approach to FDD entails addressing facets related to detection, invariance, isolation, identification, and supervision. In FDD, there are two main perspectives: model-based and data-driven approaches. This thesis centers on model-based methodologies, particularly within the context of control and industrial applications. It introduces novel estimation strategies aimed at enhancing computational efficiency, addressing fault discretization, and considering robustness in fault detection strategies. In cases where the system's behavior can vary over time, particularly in contexts like fault detection, presenting multiple scenarios is essential for accurately describing the system. This forms the underlying principle in Multiple Model Adaptive Estimation (MMAE) like well-established Interacting Multiple Model (IMM) strategy. In this research, an exploration of an efficient version of the IMM framework, named Updated IMM (UIMM), is conducted. UIMM is applied for the identification of irreversible faults, such as leakage and friction faults, within an Electro-Hydraulic Actuator (EHA). It reduces computational complexity and enhances fault detection and isolation, which is very important in real-time applications such as Fault-Tolerant Control Systems (FTCS). Employing robust estimation strategies such as the Smooth Variable Structure Filter (SVSF) in the filter bank of this algorithm will significantly enhance its performance, particularly in the presence of system uncertainties. To relax the irreversible assumption used in the UIMM algorithm and thereby expanding its application to a broader range of problems, the thesis introduces the Moving Window Interacting Multiple Model (MWIMM) algorithm. MWIMM enhances efficiency by focusing on a subset of possible models, making it particularly valuable for fault intensity and Remaining Useful Life (RUL) estimation. Additionally, this thesis delves into exploring chattering signals generated by the SVSF filter as potential indicators of system faults. Chattering, arising from model mismatch or faults, is analyzed for spectral content, enabling the identification of anomalies. The efficacy of this framework is verified through case studies, including the detection and measurement of leakage and friction faults in an Electro-Hydraulic Actuator (EHA). / Thesis / Candidate in Philosophy / In everyday life, from doctors diagnosing illnesses to mechanics inspecting cars, we encounter the need for fault detection and diagnosis (FDD). Advances in technology, like powerful computers and sensors, are making it possible to automate fault diagnosis processes and take corrective actions in real-time when something goes wrong. The first step in fault detection and diagnosis is to precisely identify system faults, ensuring they can be properly separated from normal variations caused by uncertainties, disruptions, and measurement errors. This thesis explores model-based approaches, which utilize prior knowledge about how a normal system behaves, to detect abnormalities or faults in the system. New algorithms are introduced to enhance the efficiency and flexibility of this process. Additionally, a new strategy is proposed for extracting information from a robust filter, when used for identifying faults in the system.

<p>Unexpected failures in rotating machinery can result in production downtime, costly repairs and safety concerns. Electric motors are commonly used in rotating machinery and are critical to their operation. Therefore, fault detection and diagnosis of electric motors can play a very important role in increasing their reliability and operational safety. This is especially true for safety critical applications.</p> <p>This research aims to develop a Fault Detection and Diagnosis (FDD) strategy for detecting motor faults at their inception. Two FDD strategies were considered involving wavelets and state estimation. Bearing faults and stator winding faults, which are responsible for the majority of motor failures, are considered. These faults were physically simulated on a Permanent Magnet Brushless DC Motor (PMBLDC). Experimental results demonstrated that the proposed fault detection and diagnosis schemes were very effective in detecting bearing and winding faults in electric motors.</p> / Master of Applied Science (MASc)

Refrigeration is a fast-growing industry and has become an integral part of various industries such as food storage, pharmaceutical, residential, chemical, and data centers. Rising global temperatures have increased the need for more energy-efficient and environmentally conscious refrigeration systems. A consistent functioning refrigeration system requires good fault detection and diagnosis (FDD) system to detect faults before failures occur. A good FDD system can help reduce maintenance costs and increase energy savings. The refrigerant present in the chillers consists of greenhouse gases. Certain faults, such as refrigerant leakage, result in the release of refrigerant into the atmosphere, which has an environmental impact. Hence, an effective FDD system for chillers is important for accurately detecting and diagnosing faults. This thesis aims to build a data-driven FDD system for vapor-compression chillers. The purpose of this thesis is to address the variable operating conditions and fault conditions that occur during the operation of a chiller system. The operating conditions vary depending on the control logic, climate, and other factors that can occur in a system with multiple components. Faults can occur to varying degrees within a system. Early fault detection and diagnosis lead to prompt maintenance dispatching. It is imperative that an FDD system accommodates these conditions and accurately detect faults. This thesis uses two types of data to build an FDD model. The experimental data provided by ASHRAE RP-1043, which contained both normal and fault conditions, were used. The ASHRAE dataset contains normal conditions and seven fault conditions at the four severity levels. Other types of data used were simulated data generated using the ASHRAE RP-1043 model and a small chiller model developed by the author. Simulated data supplemented the experimental dataset with different normal operating conditions and fault severity levels. A hybrid architecture consisting of a dimensionality reduction method and classifier was proposed. This architecture facilitates the comparison of machine learning and deep learning techniques, and may be employed to develop a hybrid model that incorporates both approaches. Time series data was used to train and test this architecture. A deviation matrix method, which is a preprocessing method applied to the training and testing datasets, was proposed. This method was used with steady-state data to develop a 2D Convolutional Neural Network (CNN), Artificial Neural Network (ANN), and Support Vector Machine (SVM). The deviation matrix method proved effective for machine learning and deep learning models to detect and diagnose faults for different normal and fault severity levels. A study using steady-state time-series data and complete test cycle data was conducted to build and test the hybrid architecture. It was concluded that using steady-state time-series data yielded a higher classification accuracy for faults. The deviation matrix method was applied to the simulation and experimental data, and 2D CNN, ANN, and SVM were used to study these datasets. It was concluded that some parts of the training data could be substituted with the simulation data to obtain acceptable classification accuracy. The 2D CNN, ANN, and SVM were able to diagnose faults in the test data containing different severity levels from the training set and small-chiller data. The SVM was also effective in detecting near-normal operations. / Dissertation / Doctor of Philosophy (PhD)

This thesis presents an artificial neural network (ANN) approach to diagnose and detect faults in oil-filled power transformers based on dissolved gas-in-oil analysis. The goal of the research is to investigate the available transformer incipient fault diagnosis methods and then develop an ANN approach for this purpose. This ANN classifier should not only be able to detect the fault type, but also should be able to judge the cellulosic material breakdown. This classifier should also be able to accommodate more than one type of fault. This thesis describes a two-step ANN method that is used to detect faults with or without cellulose involved. Utilizing a feedforward artificial neural network, the classifier was trained with back-propagation, using training samples collected from different failed transformers. It is shown in the thesis that such a neural-net based approach can yield a high diagnosis accuracy. Several possible design alternatives and comparisons are also addressed in the thesis. The final system has been successfully tested, exhibiting a classification accuracy of 95% for major fault type and 90% for cellulose breakdown. / Master of Science

The High Redundancy Actuator (HRA) project investigates a novel approach to fault tolerant actuation, which uses a high number of small actuation elements, assembled in series and parallel in order to form a single intrinsically fault tolerant actuator. Element faults affect the maximum capability of the overall actuator, but through control techniques, the required performance can be maintained. This allows higher levels of reliability to be attained in exchange for less over-dimensioning in comparison to conventional redundancy techniques. In addition, the combination of both serial and parallel elements provides intrinsic accommodation of both lock-up and loose faults. Research to date has concentrated on HRAs based on electromechanical technology, of relatively low order, controlled through passive Fault Tolerant Control (FTC) methods. The objective of this thesis is to expand upon this work. HRA configurations of higher order, formed from electromagnetic actuators are considered. An element model for a moving coil actuator is derived from first principles and verified experimentally. This element model is then used to form high-order, non-linear HRA models for simulation, and reduced-order representations for control design. A simple, passive FTC law is designed for the HRA configurations, the results of which are compared to a decentralised, active FTC approach applied through a framework based upon multi-agent concepts. The results indicate that limited fault tolerance can be achieved through simple passive control, however, performance degradation occurs, and requirements are not met under theoretically tolerable fault levels. Active FTC offers substantial performance improvements, meeting the requirements of the system under the vast majority of theoretically tolerable fault scenarios. However, these improvements are made at the cost of increased system complexity and a reliance on fault detection. Fault Detection (FD) and health monitoring of the HRA is explored. A simple rule-based FD method, for use within the active FTC, is described and simulated. An interacting multiple model FD method is also examined, which is more suitable for health monitoring in a centralised control scheme. Both of these methods provide the required level of fault information for their respective purposes. However, they achieve this through the introduction of complexity. The rule-based method increases system complexity, requiring high levels of instrumentation, and conversely the interacting multiple model approach involves complexity of design and computation. Finally, the development of a software demonstrator is described. Experimental rigs at the current project phase are restricted to relatively low numbers of elements for practical reasons such as cost, space and technological limitations. Hence, a software demonstrator has been developed in Matlab/Simulink which provides a visual representation of HRAs with larger numbers of elements, and varied configuration for further demonstration of this concept.

<p>This research presents thermal image analysis for Fault Detection and Diagnosis (FDD) of Photovoltaic (PV) Systems. The traditional manual approach of PV inspection is generally more time-consuming, more dangerous, and less accurate than the modern approach of PV inspection using Aerial Thermography (AT). Thermal image analysis conducted in this research will contribute to utilizing thermography and UAVs for PV inspection by providing a more accurate and cost-efficient diagnosis of PV faults. In this research, PV module inspection was achieved through two steps: (i) PV monitoring and (ii) PV Fault Detection and Diagnosis (FDD). In the PV monitoring stage, PV cells were monitored by aerial thermography. In this stage, the thermal data was acquired for the next step. In the PV FDD stage, hot spot phenomenon and the condition of the PV modules were detected and measured. The proposed research will help with the problems of the modern PV inspection and, eventually, contribute to the performance of PV power generation.</p>