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Bearings are the essential components of modern rotating machines. Bearing faults can cause severe machine damages or even breakdowns. In recent years, artificial intelligence and deep learning have been successfully applied to fault detection. In this thesis, convolutional neural networks (CNN) are employed for bearing fault detection and classification. Computer simulations results demonstrate that the CNN based approach is advantageous over the conventional regression model, with an overall accuracy of 99.5%.

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.

Identifying faults in machinery before they cause critical failure is the core purpose of condition monitoring. This report gives a background to condition monitoring and outlines the current state of research in the field, and its most important theoretical components. It also describes parallels between sustainability goals and condition monitoring. Further, a method for creating a statistical model to predict faults in machines is described. The proposed model is machine specific and is evaluated on three cases. The model’s predictions is then compared to general limit values provided by an ISO-standard. The model successfully detected faults in time for repair in two of the three cases where the ISO-standard did not. The third case was a control and featured a machine with no issues. Neither our model nor the ISO-standard falsely predicted a fault on the control. From the results of the three cases it is concluded that the proposed machine specific approach is required for reliably predicting faults. / Syftet med tillståndsövervakning är att identifiera fel före de orsakar yterligare fel. Denna rapport ger en bakgrund till tillståndsövervakning samt redogör för den aktuella forskningen och de mest centrala teoretiska grunderna inom området. Rapporten beskriver även hur tillståndsövervakningen bidrar till de globala hållbarhetsmålen samt föreslås en konkret metod för tillämpning av tillståndsövervakning. Den föreslagna modellen är maskinspecifik och grundar sig på statistiska avvikelser av vibrationsdata som samlas från maskiner i ett välfungerande tillstånd. Modellen appliceras på tre olika maskiner och resultaten jämförs med ISO-standarden som har definierat generella gränsvärden för flera maskintyper. Den föreslagna modellen visar lovande resultat genom att upptäcka fel som ISO-standarden missade. Av resultaten från fallen dras slutsatsen att en generella gränsvärden inte är tillräckligt, utan en maskinspecifik metod krävs för att, på ett pålitligt sätt, detektera fel.

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