Vehicles and machinery play a crucial role in our daily lives, contributing to our transportationneeds and supporting various industries. As society strives for sustainability, the advancementof technology and efficient resource allocation become paramount. However, vehicle faultscontinue to pose a significant challenge, leading to accidents and unfortunate consequences.In this thesis, we aim to address this issue by exploring the effectiveness of an ensemble ofdeep learning models for supervised classification. Specifically, we propose to evaluate the performance of 1D-CNN-Bi-LSTM and 1D-CNN-Bi-GRU models. The Bi-LSTM and Bi-GRUmodels incorporate a multi-head attention mechanism to capture intricate patterns in the data.The methodology involves initial feature extraction using 1D-CNN, followed by learning thetemporal dependencies in the time series data using Bi-LSTM and Bi-GRU. These models aretrained and evaluated on a labeled dataset, yielding promising results. The successful completion of this thesis has met the objectives and scope of the research, and it also paves the way forfuture investigations and further research in this domain.

The railway system is an important part of the sociotechnical society, as it enables efficient, reliable, and sustainable transportation of both people and goods. Despite increasing investments, the Swedish railway has encountered structural and technical problems due to worn-out infrastructure as a result of insufficient maintenance. Two important technical aspects of the rail are the stability and robustness. To prevent transversal and longitudinal deviations, the rail is attached to sleepers by fasteners. The fasteners’ conditions are therefore crucial for the stability of the track and the safeness of the railway. Automatic fastener inspections enable efficient and objective inspections which are a prerequisite for a more adequate maintenance of the railway. This master thesis aims to investigate how machine learning can be applied to the problem of automatic fastener fault detection. The master thesis includes the complete process of applying and evaluating machine learning algorithms to the given problem, including data gathering, data preprocessing, model training, and model evaluation. The chosen model was the state-of-the-art object detector YOLOv5s. To assess the model’s performance and robustness to the given problem, different settings regarding both the dataset and the model’s architecture in terms of transfer learning and hyperparameters were tested.  The results indicate that YOLOv5s is an appropriate machine learning algorithm for fastener fault detection. The models that achieved the highest performance reached an mAP[0.5:0.95] above 0.744 during training and 0.692 during testing. Furthermore, several combinations of different settings had a positive effect on the different models’ performances.  In conclusion, YOLOv5s is in general a suitable model for detecting fasteners. By closer analysis of the result, the models failed when both fasteners and missing fasteners were partly visible in the lower and upper parts of the image. These cases were not annotated in the dataset and therefore resulted in misclassification. In production, the cropped fasteners can be reduced by accurately synchronizing the frequency of capturing data with the distance between the sleepers, in such a way that only one sleeper and corresponding fasteners are visible per image leading to more accurate results. To conclude, machine learning can be applied as an effective and robust technique to the problem of automatic fastener fault detection.

<p>Cyber-physical systems (CPS) are a class of complicated systems integrating cyber components with physical components. Although such a cyber-physical interaction improves the system performance and intelligence, it increases the system complexity and makes the system vulnerable to various types of faults, failures, and cyber-attacks. To assure the security and improve the resiliency of CPS, it is found that the hybrid system model can be a powerful tool in the domain of fault detection and isolation, cyber-attack diagnosis and containment, as well as resilient control and reconfiguration. Several problems are concerned in this dissertation. For situational awareness, \textit{mode discernibility}, which stands for whether the discrete state of a hybrid system can be correctly identified, is characterized and discussed with potential applications to monitoring system design. For CPS vulnerability analysis, the problem of stealthy attack design for systems with switching structures is investigated, which is motivated by the recent literature. To further understand and remedy for the vulnerabilities, the detectability and identifiability for severe cyber-attacks are defined and characterized, which are followed by the discussions on the methodologies for cyber-attack detection and identification. Last but not least, based on the understanding of identifiability, a framework of resilient control design is proposed to mitigate the impact of cyber-attacks, which can be generalized in future to account for additional design criteria.</p>

The objective of this thesis work was to develop a method to detect early damage on small planetary gear sets that are installed in Atlas Copco nutrunners. The project has gone through several stages of product development, from idea to working product and signal analysis. Currently, Atlas Copco have a test rig for testing these planetary gears, this rig has been proven to be insufficient at detecting faults during an ongoing test. A new tailored test rig was therefore designed and manufactured. Low noise and low amount of vibration was of interest when designing the rig. Four concepts was thought of and evaluated through simulations using Matlab and Simulink. Most of the components of the rig were manufactured in the workshop at Atlas Copco in Nacka. Methods fo rmeasuring torsional, transverse and acoustic vibration was implemented and analyzed. There are many different parameters considering fault of fixed shaft gears. However, these are not easily applicable on a planetary gear because of the nature of its design. Therefore, special techniques are required. Two “new” parameters were tested (NSDS,FRMS [Lei. et al.]) with positive results. Pitting of individual gear members could befound.

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Condition monitoring techniques can be employed to enhance reliability of electric machinery. The stator winding fault is one of the dominant causes for the failure of induction machines. In this work, the condition monitoring of an inverter-fed induction machine using high-frequency signal injection based technique is investigated. Initially, an analytical model of the induction machine with a stator inter-turn fault is developed. Subsequently, the behaviour of the induction machine in the presence of stator inter-turn fault is analyzed using the symmetrical component theory. Because of their use for fault diagnosis purposes, the analytical expressions for the fundamental and high-frequency symmetrical component currents are derived. The high-frequency signal injection is performed by adding a balanced three-phase high-frequency low-magnitude voltage to the fundamental excitation voltage. The resulting high-frequency negative-sequence current component can be used as reliable fault indicator to detect stator inter-turn faults. The effectiveness of the high-frequency negative-sequence current as a fault indicator is compared with the fundamental negative-sequence current, which is one of the traditionally used fault indicators for detecting these faults. The high-frequency signal injection technique proposed in this work is tested experimentally on a prototype machine in a laboratory set-up. The use of the proposed fault indicator is found to be advantageous when compared to the use of the traditional fault indicator for variable-frequency drives. In particular, it is shown that the proposed fault indicator is less dependent from the drive operating conditions than the traditional fault indicator. / Tillståndsövervakning är en teknik som kan användas för att förbättra tillförlitligheten hos elektriska maskiner. För asynkronmaskiner är fel i statorlindningen en av de dominerande orsakerna som leder till problem. I detta arbete undersöks tillståndsövervakning av en omriktarmatad asynkronmotor med hjälp av en högfrekvent signalinjektionsbaserad teknik. Inledningsvis utvecklas en analytisk modell av en asynkronmaskin med korsslutningsfel mellan varven i statorn. Därefter analyseras beteendet hos maskinen med hjälp av teorin för symmetriska komponenter. Analytiska uttryck för både grund- och övertoner härleds för de symmetriska komponenterna. Den högfrekventa signalinjektionen utförs genom att addera en liten högfrekvent trefasspänning till den matningsspänningen. Den resulterande högfrekventa negativa strömkomponenten kan användas som en tillförlitlig indikator för att upptäcka eventuella kortslutningar i statorlindningen. Förmågan som felindikator hos den högfrekventa negativa sekvensströmmen jämförs med den grundläggande negativa strömkomponentens förmåga, vilken är den traditionella indikatorn för att detektera dessa fel. Den högfrekventa signalinjiceringsmetoden som föreslås i detta arbete undersöks experimentellt på en prototypmaskin. Den föreslagna felindikatorn har visat sig vara fördelaktig jämfört med användningen av den traditionella felindikatorn för frekvensomriktare. I synnerhet visas att den föreslagna felindikatorn är mindre beroende av frekvensomriktarens driftsförhållanden än den traditionella felindikatorn.

<p>This thesis considers the problem of fault diagnosis and fault-tolerant control (FTC) for chemical process systems with nonlinear dynamics. The primary objective of fault diagnosis discussed in this work is to identify the failed actuator or sensor by using the information embodied in a process model, as well as input and output data. To this end, an active fault isolation method is first proposed to identify actuator faults and process disturbances by utilizing control action and process nonlinearity. The key idea is to move the process to a region upon fault detection where the effect of each fault can be differentiated from others. The proposed method enables isolation of faults that may not be achievable under nominal operation. This work then investigates the problem of sensor fault isolation by exploiting model-based sensor redundancy through state observer design. Specifically, a high-gain observer is presented and the stability property of the closed-loop system is rigorously established. A method that uses a bank of high-gain observers is then proposed to isolate sensor faults, which explicitly accounts for process nonlinearity, and to continue nominal operation upon fault isolation. In addition to fault diagnosis, this work addresses the problem of handling severe actuator faults using a safe-parking approach and integrating fault diagnosis and safe-parking techniques in a unified fault-handling framework. In particular, several practical issues are considered for the design and implementation of safe-parking techniques, including changes in process dynamics, the network structure of a chemical plant, and actuators frozen at arbitrary positions. The advantage of this approach is that it enables stable process operation under faulty conditions, avoiding the partial or entire shutdown of a chemical plant and resulting economic losses. The efficacy of the proposed fault diagnosis and FTC methods is demonstrated through numerous simulations of chemical process examples.</p> / Doctor of Philosophy (PhD)

<p><h1>Abstract</h1></p> <p>As a compact, robust, and reliable power distribution method, hydraulic systems have been used for flight surface control for decades. Electro-hydrostatic Actuator (EHA) is increasingly replacing the conventional valve-controlled system for better performance, lighter weight and higher energy efficiency. The EHA is increasingly being used for flight control. As such its reliability is thereby critical important for flight safety. This research focuses on fault detection and diagnosis (FDD) for the EHA to enable predictive unscheduled maintenance when fault detected at its inception.</p> <p>An EHA prototype previously built at McMaster University is studied in this research and modified to physically simulate two faults conditions pertaining to leakage and friction. Nine different working conditions including normal running and eight fault conditions are simulated. Physical model has been derived mathematically capable of numerically simulating the fault conditions. Furthermore, for comparison, parametric model was obtained through system identification for each fault condition. This comparison revealed that parametric models are not suitable for fault detection and diagnosis due to the computation complexity.</p> <p>The FDD approach in this research uses model-based state estimation using filters. The filter based combined with the Interacting Multiple Model fault detection and diagnosis algorithm is introduced. Based on this algorithm, three FDD strategies are developed using a combination of the Extended Kalman Filter and IMM (IMM-EKF), the Smooth Variable Structure Filter with Varying Boundary and IMM (IMM-SVSF (VBL)), and the Smooth Variable Structure Filter with Fixed Boundary and IMM (IMM-SVSF (FBL)). All the three FDD strategies were implemented on the EHA prototype. Based on the results, the IMM-SVSF (VBL) provided the best performance. It detected and diagnosed faults correctly at high mode probabilities with excellent robustness to modeling uncertainties. It also was able to detect slow growing leakage fault, and predicted the changing trend of fault conditions.</p> / Master of Applied Science (MASc)

One of the most commonly used mechanical systems is the internal combustion engine. Internal combustion engines dominate the automotive industry, and have numerous other applications in generation, transportation, etc. This thesis presents the development of a fault detection and diagnosis (FDD) system for use with an internal combustion engine valve train. A FDD system was developed with a focus on the valve impact amplitudes. Engine cycle averaging and band-pass filtering methods were tuned and utilized for improving the signal to noise ratio. A novel feature extraction method was developed that included a local RMS sliding window method and an adaptive threshold. Faults were seeded in the form of deformed valve springs, as well as abnormal valve clearances. The engine’s manufacturer specifies that a valve spring with 3 mm or more of deformation should be replaced. This thesis investigated the detection of a relatively small 0.5mm spring deformation. Valve clearance values were adjusted 0.1mm above and below the nominal clearance value (0.15mm) to test large clearance faults (0.25mm) and small clearance faults (0.05mm). The performance of the FDD system was tested using an instrumented diesel engine test bed. A comparison of numerous signal processing techniques and classification methods was performed. / Master of Applied Science (MASc)