If a fault occurs in a technical system, for example in an airplane, it is important to beable to detect that there is a fault and to find what in the system that is faulty. Theprocedure of determining, given certain observations, if faults are present and if so thelocation of faults is called a diagnosis. For achieving diagnosis we can use computersoftware that takes observations of a system as input and that generates a diagnosis asoutput. This is called a diagnostic system. To build a diagnostic system we needanother piece of computer software which is called a diagnostic tool. This thesis willpresent a market survey for diagnostic tools as well as an analysis of three of the toolsfound in the survey. The analysis can be seen as constituted by two different aspects,one focusing on the diagnostic methods with which each tool creates diagnosticsystems, the other focusing on practical details that determine the usability of eachtool. The analysis found that the largest differences were between the methods used increating the diagnostic systems.

<p>If a fault occurs in a technical system, for example in an airplane, it is important to beable to detect that there is a fault and to find what in the system that is faulty. Theprocedure of determining, given certain observations, if faults are present and if so thelocation of faults is called a diagnosis. For achieving diagnosis we can use computersoftware that takes observations of a system as input and that generates a diagnosis asoutput. This is called a diagnostic system. To build a diagnostic system we needanother piece of computer software which is called a diagnostic tool. This thesis willpresent a market survey for diagnostic tools as well as an analysis of three of the toolsfound in the survey. The analysis can be seen as constituted by two different aspects,one focusing on the diagnostic methods with which each tool creates diagnosticsystems, the other focusing on practical details that determine the usability of eachtool. The analysis found that the largest differences were between the methods used increating the diagnostic systems.</p>

Model based fault detection techniques utilize functional redundancies in the static and dynamic relationships among system inputs and outputs for fault detection and isolation. Analytical models based on the underlying physics of the system can capture the dependencies between different measured signals in terms of system states and parameters. These physical models of the system can be used as a tool to detect and isolate system faults. As a machine degrades, system outputs deviate from desired outputs, generating residuals defined by the error between sensor measurements and corresponding model simulated signals. These error residuals contain valuable information to interpret system states and parameters. Setting up the measurements from a faulty system as baseline, the parameters of the idealistic model can be varied to minimize these residuals. This process is called “Parameter Tuning”. A framework to automate this “Parameter Tuning” process is presented with a focus on DC motors and 3-phase induction motors. The parameter tuning module presented is a multi-tier module which is designed to operate on real system models that are highly non-linear. The tuning module combines artificial intelligence techniques like Quasi-Monte Carlo (QMC) sampling (Hammersley sequencing) and Genetic Algorithm (Non Dominated Sorting Genetic Algorithm) with an Extended Kalman filter (EKF), which utilizes the system dynamics information available via the physical models of the system. A tentative Graphical User Interface (GUI) was developed to simplify the interaction between a machine operator and the module. The tuning module was tested with real measurements from a DC motor. A simulation study was performed on a 3-phase induction motor by suitably adjusting parameters in an analytical model. The QMC sampling and genetic algorithm stages worked well even on measurement data with the system operating in steady state condition. But the downside was computational expense and inability to estimate the parameters online – ‘batch estimator’. The EKF module enabled online estimation where update was made based on incoming measurements. But observability of the system based on incoming measurements posed a major challenge while dealing with state estimation filters. Implementation details and results are included with plots comparing real and faulty systems. / text

Post-implantation facial nerve stimulation is a common side-effect of cochlear electrical stimulation. Facial nerve stimulation can often be resolved through adjustments in speech processor fitting but, in some instances, exhibit limited benefit or may have a detrimental effect on speech perception. In this study, the apical reference stimulation mode was investigated as a potential intervention to facial nerve stimulation. Firstly, a model refinement software tool was developed to improve the accuracy of models created by an automated workflow. Secondly, the refined model of the human cochlea, facial nerve and electrode array, coupled with a neural model, was used to predict excitations of auditory and facial nerve fibres. Finally, psychoacoustic tests were used to determine auditory comfort and threshold levels for the apical reference stimulation mode while simultaneously capturing electromyography data. The refinement tool illustrated an improved accuracy compared to measured data. Models predicted a desirable outcome for apical reference stimulation, as facial nerve fibre thresholds were higher and auditory thresholds were lower, in direct comparison to conventional monopolar stimulation. Psychoacoustic tests illustrated decreased auditory thresholds and increased dynamic range during apical reference stimulation. Furthermore, apical reference stimulation resulted in lower electromyography energy levels, compared to conventional monopolar stimulation, which suggests a reduction in facial nerve stimulation. Subjective feedback corroborated that apical reference stimulation alleviated facial nerve stimulation. This suggests that apical reference stimulation may be a viable strategy to alleviate facial nerve stimulation considering the improvements in dynamic range and auditory thresholds, complemented with a reduction in facial nerve stimulation / Dissertation (MEng (Bioengineering))--University of Pretoria, 2019. / NRF / Electrical, Electronic and Computer Engineering / MEng (Bioengineering) / Unrestricted

The purpose of this project was to perform model-based diagnosis on Li-ion batteries using real-world data and sensor fusion algorithms. The data used in this project was collected and distributed by NASA and mainly consists of voltage and current measurements collected on numerous batteries that were repeatedly charged and discharged from their beginning of life, and until surpassing their end of life. The health of the batteries was decided by estimating their state of health through the increase in internal resistance. To validate the results, the increase in resistance was later compared with the decrease in charge capacity. Firstly an attempt was made to parameterize equivalent circuit models by fitting the model parameters to the data with a forgetting-factor recursive least squares filter, and a batch-wise recursive least squares filter. The forgetting-factor recursive least squares filter proved unreliable and unable to consistently be able to parameterize the models. The batch-wise recursive least squares filter was able to consistently parameterize the models providing a root mean squared error of around 0.35V in simulated voltage response tests. In total 20 equivalent circuit models were parameterized for four batteries. The models were then used in conjunction with a standard Kalman filter and an unscented Kalman filter to fur-ther estimate the increase in internal resistance of the batteries throughout their lifetime. In addition, the state of charge of the batteries was tracked through Coulomb counting and later attempted to refine via the Kalman filters. The results were partly successful as both the standard and unscented Kalman filters were able to track the state of health of the batteries, albeit to a varying degree. However, the Kalman filters were unable to improve the state of charge estimation, thus highlighting a limitation in their application. While the Kalman filters showcased limitations in tracking the state of charge, their effectiveness intracking the state of health thus proved that model-based approaches and sensorfusion algorithms can be utilized with both the standard Kalman filter and the unscented Kalman filter for meaningful health tracking.

Maintenance and diagnostics play a vital role in the aviation sector. This is especially true for the engines, being one of the most vital components. Lack of maintenance, or poor knowledge of the current health status of the engines, may lead to unforeseen disruptions and possibly catastrophic effects. To keep track of the health status, and thereby supporting maintenance planning, model based diagnostics is a key factor.  In the work going into this thesis, various aspects of model based gas turbine diagnostics, focused on aero engines, are covered. First, the importance of knowing what health parameters may be derived from a set of measurements is addressed. The selected combination is herein denoted as a matching scheme. A framework is proposed where the most suitable matching scheme is selected for a numerically robust diagnostic system. If a sensor malfunction is detected, the system automatically adapts. The second subject is a system for detecting a burn-through of an afterburner inner liner. This kind of burn-through event has a very small impact on available on-board measurements, making it difficult to detect numerically. A method is proposed performing back-to-back testing after each engine start. The method has shown potential to detect major burn-through events under the preconditions, regarding data collection time and frequency. Increasing these will allow for more accurate estimations. The third subject covers the importance of knowing the airplane installation effects. These are generally the intake pressure recovery, bleed and shaft power extraction. Just like inaccurate measurements affect diagnostic results, so does erroneous installation effects. A method for estimating said effects in the presence of gradual degradation has been proposed by using neural networks. By retraining the networks throughout the degradation process, the estimation errors is reduced, ensuring relevant estimations even at severe degradations. Finally, an issue related to the general lack of on-board measurements for diagnostics is addressed. Due to lack of measurements, the diagnostic model tend to be underdetermined. A least square solver working without a priori information has been implemented and evaluated. Results from the solver is very much dependent on available instrumentation. In well instrumented components, such as the compressors, good diagnostic accuracy was achieved while the turbine health estimations suffer from smeared out results due to poor instrumentation.

Modeling, fault assessment, and diagnostics of rolling element bearings and induction motors were studied. Dynamic model of rolling element bearings with faults were developed using vector bond graphs. The model incorporates gyroscopic and centrifugal effects, contact deflections and forces, contact slip and separations, and localized faults. Dents and pits on inner race, outer race and balls were modeled through surface profile changes. Experiments with healthy and faulty bearings validated the model. Bearing load zones under various radial loads and clearances were simulated. The model was used to study dynamics of faulty bearings. Effects of type, size and shape of faults on the vibration response and on dynamics of contacts in presence of localized faults were studied. A signal processing algorithm, called feature plot, based on variable window averaging and time feature extraction was proposed for diagnostics of rolling element bearings. Conducting experiments, faults such as dents, pits, and rough surfaces on inner race, balls, and outer race were detected and isolated using the feature plot technique. Time features such as shape factor, skewness, Kurtosis, peak value, crest factor, impulse factor and mean absolute deviation were used in feature plots. Performance of feature plots in bearing fault detection when finite numbers of samples are available was shown. Results suggest that the feature plot technique can detect and isolate localized faults and rough surface defects in rolling element bearings. The proposed diagnostic algorithm has the potential for other applications such as gearbox. A model-based diagnostic framework consisting of modeling, non-linear observability analysis, and parameter tuning was developed for three-phase induction motors. A bond graph model was developed and verified with experiments. Nonlinear observability based on Lie derivatives identified the most observable configuration of sensors and parameters. Continuous-discrete Extended Kalman Filter (EKF) technique was used for parameter tuning to detect stator and rotor faults, bearing friction, and mechanical loads from currents and speed signals. A dynamic process noise technique based on the validation index was implemented for EKF. Complex step Jacobian technique improved computational performance of EKF and observability analysis. Results suggest that motor faults, bearing rotational friction, and mechanical load of induction motors can be detected using model-based diagnostics as long as the configuration of sensors and parameters is observable. / text

The project explores the domain of tactical sensor systems, focusing on SAAB Gripen’s sensor technologies such as radar, RWR (Radar Warning Receiver), and IRST (InfraRed Search and Track). The study employs structured hypothesis testing and model based diagnostics to examine the effectiveness of identifying and isolating deviations within these systems. The central question addressed is whether structured hypothesis testing reliably detects and isolates anomalies in a tactical sensor system. The research employs a framework involving sensor modeling of radar, RWR, and IRST, alongside a sensor fusion model, applied on a linear target tracking model as well as a real target flight track obtained from SAAB Test Flight and Verification. Test quantities are derived from the modeled data, and synthetic faults are intentionally introduced into the system. These test quantities are then compared to predefined thresholds, thereby facilitating structured hypothesis testing. The robustness and reliability of the diagnostics model are established through a series of simulations. Multiple scenarios with varied fault introductions across different sensor measurements are examined. Key results include the successful creation of a tactical sensor model and sensor fusion environment, showcasing the ability to introduce and detect faults. The thesis provides arguments supporting the advantages of model based diagnosis through structured hypothesis testing for assessing sensor fusion data. The results of this research are applicable beyond this specific context, facilitating improved sensor data analysis across diverse tracking scenarios, including applications beyond SAAB Gripen. As sensor technologies continue to evolve, the insights gained from this thesis could offer guidance for refining sensor models and hypothesis testing techniques, ultimately enhancing the efficiency and accuracy of sensor data analysis in various domains. / Denna rapport undersöker området inom taktiska sensorsystem och fokuserar på SAAB Gripens sensorteknik, såsom radar, RWR (Radar Warning Receiver) och IRST (InfraRed Search- and Track). Studien använder strukturerad hypotesprövning och modellbaserad diagnostik för att undersöka effektiviteten av att identifiera och isolera avvikelser inom dessa system. Den centrala frågan som behandlas är om strukturerad hypotesprövning tillförlitligt upptäcker och isolerar avvikelser i ett taktiskt sensorsystem. För att tackla denna utmaning används sensormodellering av radar, RWR och IRST, tillsammans med en sensorfusionsmodell som appliceras på en linjär målspårningsmodell samt verklig målflygbana erhållen från SAAB. Testkvantiteter härleds från den resulterande datan, och syntetiska fel introduceras avsiktligt i systemet. Dessa testskvantiteter jämförs sedan med fördefinierade trösklar vilket lägger grunden för strukturerad hypotesprövning. Tillförlitligheten och pålitligheten hos diagnostikmodellen etableras genom en serie av simuleringar bestående av flera scenarier med varierade felintroduktioner över olika sensorinmätningar. Huvudresultat inkluderar skapandet av en taktisk sensormodell och en sensorfusionsmiljö, som visar förmågan att introducera och upptäcka fel på ett effektivt sätt. Avhandlingen ger argument som stödjer fördelarna med modellbaserad diagnostik genom strukturerad hypotestestning för bedömning av sensorfusionsdata. Resultaten av denna forskning är tillämpliga utanför detta specifika sammanhang, vilket underlättar förbättrad sensordataanalys över olika spårningsscenarier, inklusive applikationer bortom SAAB Gripen. I takt med att sensorteknologier fortsätter att utvecklas kan insikterna från denna avhandling ge vägledning för att förbättra sensormodeller och hypotestestningstekniker, vilket i slutändan förbättrar effektiviteten och noggrannheten för sensordataanalys inom olika områden.