<p dir="ltr">Resilient engine operation hugely depends on proper functioning of the engine’s sensors, enabling efficient feedback control of the engine systems operation. When the sensors on the engine measure a physical quantity incorrectly, it leads the engine control system to determine that the sensor measuring the physical quantity has failed. This failure may be attributed to a sensor stick failure, bias failure, drift failure, or failure occurring due to physical wear and tear of the sensor. Failure of crucial engine sensors may have adverse effects on engine operation, and in most cases leading into a limp home mode or a torque limitation mode. This affects the engine performance and efficiency. The engine under study in this work is a medium duty marine engine with diesel fuel. Sensor failures in the middle of a marine operation can hugely impact its mission. Therefore, fault tolerant control systems are essential to counter these challenges occurring due to sensor failures. In this thesis, an advanced nonlinear fault detection and state estimation algorithm is developed and implemented on a GT-Power engine model, employing a sophisticated co-simulation approach. The focus is on a 6.7L Cummins diesel engine, for which a detailed nonlinear state space model is constructed. This model accurately replicates critical engine parameters, such as pressures, temperatures, and engine speed, by integrating various submodels. These sub-models estimate key parameters like cylinder inlet charge flow, valve flow, cylinder outlet temperature, turbocharger turbine flow, and charge air cooler flow. To assess the model’s accuracy and reliability, it is rigorously validated against a truth reference GT-Power engine model. The results demonstrate exceptional performance, with the nonlinear model exhibiting a minimal percentage performance error of less than 5% under steady-state conditions and less than 15% during transient conditions. The core of the Fault Detection and State Estimation (FDSE) modules consists of a bank of Extended Kalman Filters (EKF). These filters are meticulously designed to estimate vital engine states, generate residuals, and assess these residuals even in the presence of process and measurement noise. This approach enables the detection of sensor faults and facilitates controller reconfiguration, ensuring the engine’s robustness in the face of unexpected sensor failures. Crucially, the nonlinear physics-based model serves as the foundation for the state transition functions utilized in the design of the observer bank. Residuals generated by the EKFs are evaluated using both fixed and adaptive thresholding techniques masking the sensor faults at the time step at which it is detected, ensuring robust performance not only in steady-state conditions but also during varying transient load conditions. To comprehensively evaluate the system’s resilience in practical scenarios, multiple sensor stuck failures are introduced into the GT-Power model. A software-in-the-loop co-simulation strategy is meticulously established, employing both the GT-Power truth reference engine model and the nonlinear Fault Detection and State Estimation (FDSE) model within the Simulink environment. This unique co-simulation approach provides a platform to assess the FDSE performance and its effect on engine performance in simulated sensor fault scenarios. The FDSE module is able to detect sensor failures which deviate at least 5% from their actual values. The percentage estimation error is less than 10% under steady state conditions and less than 20% under transient load conditions. Ultimately, this process creates analytical redundancy, not only forming the basis of state estimation but also empowering the engine to maintain its performance in the presence of sensor faults.</p>

The popularity of MPI-based middleware and applications has led to their wide deployment. Such systems, however, are not inherently reliable and cannot tolerate external faults. This thesis presents a novel model-based approach for exploiting application features and other characteristics to categorize and create AEMs (Application Execution Model). This work realizes MPI/FT(tm), a middleware derived by selective incorporation of fault-tolerant features into MPI/Pro(tm) for two relevant AEMs. This thesis proves the following hypothesis: it is possible to successfully complete select MPI applications even in the presence of external faults, and such fault-tolerance can be achieved with acceptable performance overhead. This work defines parameters to measure the impact of this middleware on performance through faultree and fault-injected overheads. The hypothesis is validated through experimentation and measurement of sample MPI applications for two AEMs.

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Modern digital energy meters are installed between the distribution network and customers. Network operators and customers can use those meters to monitor electrical parameters, i.e., voltages and currents and to calculate statistics such as RMS value, fundamental Fourier component, etc. Observation of distribution network impedance can reveal problems in the system, such as broken neutral conductors. This project proposes a loop-theory method using variables from smart meters to estimate the network impedance, therefore giving the possibility of conductor fault detection. Specifically, loop theory uses the distribution network terminal’s voltage and current measured by smart meters and selects cases where the load appears to have varied significantly, so that there is a change of current together with a resulting change of voltage. For selected cases, the methods calculate different loop impedance and finally address single conductor impedance by the least-squares method. The project validates the proposed method by simulation, and with recorded data from a real house with varied known neutral-conductor impedance. Based on that, this method’s limitation and possible improvement are discussed for further study. / Moderna digitala energimätare används mellan distributionsnätet och kunderna. Sådana mätare kan användas för att övervaka elektriska parametrar, dvs. spänningar och strömmar och beräkna statistik som RMS-värde, grundläggande Fourier-komponent etc. Observation av distributionsnätets impedans kan avslöja problem i systemet, såsom neutrala ledare med brott eller hög impedans (’nollfel’). Detta projekt föreslår en loopteorimetod med hjälp av variabler från smarta mätare för att uppskatta nätverksimpedansen, vilket ger möjlighet till detektering av ledningsfel. Specifikt använder loopteorin spänning och ström mätt med genom smarta mätare, och väljer fall där belastningen verkar ha varierat avsevärt så att det sker en förändring av strömmen tillsammans med en resulterande spänningsförändring. För valda fall beräknar metoderna impedanser i olika slingor av nätets ledare, och slutligen gör en estimering av impedansen hos enskilda ledare genom minstakvadratmetoden. Projektet validerar den föreslagna metoden genom simulering, och med inspelade data från ett riktigt hus med varierad känd neutral ledare impedans. Baserat på detta diskuteras denna metods begränsning och möjliga förbättring för vidare studier.

Faulty manufactured alternators lead to commercial and safety concerns when installed in vehicles. Alternators have a major role in the Electrical Power Generation System (EPGS) of vehicles, and a defective alternator will lead to damaging of the battery and other important electric accessories. Therefore, fault detection and diagnosis of alternators can be implemented to quickly and accurately determine the health of an alternator during end of line testing, and not let faulty components leave the manufacturer. The focus of this research is to develop a Model Based Fault Detection and Diagnosis (FDD) strategy for detecting alternator faults during end of line testing. The proposed solution uses Extended Kalman Smooth Variable Structure Filter (EK-SVSF) to detect common alternator faults. A solution using the Dual Extended Kalman Filter (DEKF) is also discussed. The alternator faults were programmatically simulated on alternator measurements. The experimental results prove that both the EK-SVSF and DEKF strategies were very effective in alternator modeling and detecting open diode faults, shorted diode faults, and stator imbalance faults. / Thesis / Master of Applied Science (MASc)

Deep Learning Networks (DLN) is a relatively new artificial intelligence algorithm that gained popularity quickly due to its unprecedented performance. One of the key elements for this success is DL’s ability to extract a high-level of information from large amounts of raw data. This ability comes at the cost of high computational and memory requirements for the training process. Estimation algorithms such as the Extended Kalman Filter (EKF) and the Smooth Variable Structure Filter (SVSF) are used in literature to train small Neural Networks. However, they have failed to scale well with deep networks due to their excessive requirements for computation and memory size. In this thesis the concept of using EKF and SVSF for DLN training is revisited. A New family of filters that are efficient in memory and computational requirements are proposed and their performance is evaluated against the state-of-the-art algorithms. The new filters show competitive performance to existing algorithms and do not require fine tuning. These new findings change the scientific community’s perception that estimation theory methods such as EKF and SVSF are not practical for their application to large networks. A second contribution from this research is the application of DLN to Fault Detection and Diagnosis. The findings indicate that DL can analyze complex sound and vibration signals in testing of automotive starters to successfully detect and diagnose faults with 97.6% success rates. This proves that DLN can automate end-of-line testing of starters and replace operators who manually listen to sound signals to detect any deviation. Use of DLN in end-of-line testing could lead to significant economic benefits in manufacturing operations. In addition to starters, another application considered is the use of DLN in monitoring of the State-Of-Charge (SOC) of batteries in electric cars. The use of DLN for improving the SOC prediction accuracy is discussed. / Thesis / Doctor of Science (PhD) / There are two main ideas discussed in this thesis, both are related to Deep Learning (DL). The first investigates the use of estimation theory in DL network training. Training DL networks is challenging as it requires large amounts of data and it is computationally demanding. The thesis discusses the use of estimation theory for training of DL networks and its utility in information extraction. The thesis also presents the application of DL networks in an end-of-line Fault Detection and Diagnosis system for complex automotive components. Failure of appropriately testing automotive components can lead to shipping faulty components that can harm a manufacturer’s reputation as well as potentially jeopardizing safety. In this thesis, DL is used to detect and analyze complex fault patterns of automotive starters, complemented by sound and vibration measurements.

Unmanned Underwater Vehicles (UUVs) are widely used in commercial, scientific, and military missions for various purposes. What makes this technology challenging is the increasing mission duration and unknown environment. It is necessary to embed fault-tolerant control paradigms into UUVs to increase the reliability of the vehicles and enable them to execute and finalize complex missions. Specifically, fault-tolerant control (FTC) comprises fault detection, identification, and control reconfiguration for fault compensation. Literature review shows that there have been no systematic methods for fault-tolerant control of UUVs in earlier investigations. This study presents a hierarchical methodology of fault detection, identification and compensation (HFDIC) that integrates these functions systematically in different levels. The method uses adaptive finite-impulse-response (FIR) modeling and analysis in its first level to detect failure occurrences. Specifically, it incorporates a FIR filter for on-line adaptive modeling, and a least-mean-squares (LMS) algorithm to minimize the output error between the monitored system and the filter in the modeling process. By analyzing the resulting adaptive filter coefficients, we extract the information on the fault occurrence. The HFDIC also includes a two-stage design of parallel Kalman filters in levels two and three for fault identification using the multiple-model adaptive estimation (MMAE). The algorithm activates latter levels only when the failure is detected, and can return back to the monitoring loop in case of false failures. On the basis of MMAE, we use multiple sliding-mode controllers and reconfigure the control law with a probability-weighted average of all the elemental control signals, in order to compensate for the fault. We validate the HFDIC on the steering and diving subsystems of Naval Postgraduate School (NPS) UUVs for various simulated actuator and/or sensor failures, and test the hierarchical fault detection and identification (HFDI) with realistic data from at-sea experiment of the Florida Atlantic University (FAU) Autonomous Underwater Vehicles (AUVs). For both occasions, we model actuator and sensor failures as additive parameter changes in the observation matrix and the output equation, respectively. Simulation results demonstrate the ability of the HFDIC to detect failures in real time, identify failures accurately with a low computational overhead, and compensate actuator and sensor failures with control reconfiguration. In particular, verification of HFDI with FAU data confirms the performance of the fault detection and identification methodology, and provides important information on the vehicle performance. / Ph. D.

A non-invasive, on-line method for detection of mechanical (rotor, bearings eccentricity) and stator winding faults in a 3-phase induction motors from observation of motor line current supply input. The main aim is to avoid the consequence of unexpected failure of critical equipment which results in extended process shutdown, costly machinery repair, and health and safety problems. This thesis looks into the possibility of utilizing machine learning techniques in the field of condition monitoring of electromechanical systems. Induction motors are chosen as an example for such application. Electrical motors play a vital role in our everyday life. Induction motors are kept in operation through monitoring its condition in a continuous manner in order to minimise their off times. The author proposes a model free sensor-less monitoring system, where the only monitored signal is the input to the induction motor. The thesis considers different methods available in literature for condition monitoring of induction motors and adopts a simple solution that is based on monitoring of the motor current. The method proposed use the feature extraction and Support Vector Machines (SVM) to set the limits for healthy and faulty data based on the statistical methods. After an extensive overview of the related literature and studies, the motor which is the virtual sensor in the drive system is analysed by considering its construction and principle of operation. The mathematical model of the motor is used for analysing the system. This is followed by laboratory testing of healthy motors and comparing their output signals with those of the same motors after being intentionally failed, concluding with the development of a full monitoring system. Finally, a monitoring system is proposed that can detect the presence of a fault in the monitored machine and diagnose the fault type and severity / Ministry of Higher Education, Libya; Switchgear & Instruments Ltd.

The utilization of driverless forklifts necessitates stringent safety measures to prevent any harm to human or material involved in their operation. This thesis addresses the critical need for collision detection algorithms for driverless forklifts, particularly in scenarios where traditional sensors are obstructed during loading and unloading processes. Instead of relying on external sensors, this research focuses on utilizing the internal sensors already present in the forklift. Signals from the forklift were collected during various driving scenarios in a controlled lab environment. Five different algorithms were developed and evaluated, providing detailed insights into their strengths and limitations. These algorithms employ a range of techniques, including physical modeling, regression modeling, residual analysis, and machine learning classification. All five algorithms demonstrate notable accuracy and reliability in collision detection. The research contributes to the advancement of collision detection technology in industrial environments, offering practical insights for safer and more productive material handling operations.