In the search for green energy to combat climatechange, a shift from conventional energy sources such as coal,oil, and nuclear towards Renewable Energy Sources (RES) isneeded. This shift poses a threat to the stability of the powergrids as RES do not contribute with rotating mass in the system.A lack of rotating mass, or in other words inertia, jeopardizesthe ability of power systems to counteract large disturbances.Frequency Containment Reserves (FCR) units are responsiblefor controlling the frequency in power systems by regulatingthe balance between the generated and consumed power. If thefrequency deviates outside of the defined range from the nominalvalue, it can lead to system separation, blackouts, and systemequipment damage. The frequency deviations are faster in lowinertia systems, making it more difficult for FCR to keep thefrequency within accepted ranges. Hydro turbines are often usedas FCR units, but additional means of support could be neededfor low inertia systems. Viable support could be battery systems.This project investigates the change towards low inertia and thepossible implementation of a battery system as fast step-wisepower support with a frequency trigger. The investigation is donethrough case studies of simulated system models in Matlab andSimulink. / I jakten på grön energi för att bekämpa klimatförändringarna behövs en övergång från konventionella energikällor som kol, olja och kärnkraft mot förnyelsebara energikällor. Denna övergång utgör ett hot mot kraftnätens stabilitet då förnyelsebara energikällor inte bidrar med roterande massa. Brist på roterande massa eller med andra ord tröghet äventyrar kraftsystemens förmåga att motverka stora störningar. Frequency Containment Reserves (FCR) är system som aktivt arbetar med att styra frekvensen i kraftsystemet genom att reglera balansen mellan den producerade och konsumerade effekten. Om detta misslyckas och frekvensen avviker för mycket från den nominella frekvensen kan detta leda till systemseparation, strömavbrott eller skada hos systemkomponenter. I ett system med låg tröghet blir frekvensavvikelserna snabbare. Detta gör det svårare att använda sig av FCR för att hålla frekvensen inom accepterade intervall. Vattenkraftverk används ofta som FCR enheter, men för system med låg tröghet kan ytterliggare stöd behövas. Ett möjligt effektstöd kan vara batterisystem. Detta projekt undersöker förändringen till lägre tröghet i ett kraftsystem och möjlig implementering av ett batterisystem med ett snabbt stegsvar för effektstöd, vilket aktiveras vid en förbestämd frekvens. Undersökningen görs genom studier av specifika fall med en linjäriserad modell av ett kraftsystemet, lerade i Matlab och Simulink. / Kandidatexjobb i elektroteknik 2021, KTH, Stockholm

In this thesis, the main focus is to formulate and test a suitable model forexogenous fault detection in swarms containing unmanned aerial vehicles(UAVs), which are aerial autonomous systems. FOI Swedish DefenseResearch Agency provided the thesis project and research question. Inspiredby previous work, the implementation use behavioral feature vectors (BFVs)to simulate the movements of the UAVs and to identify anomalies in theirbehaviors. The chosen algorithm for fault detection is the density-based cluster analysismethod known as the Local Outlier Factor (LOF). This method is built on thek-Nearest Neighbor(kNN) algorithm and employs densities to detect outliers.In this thesis, it is implemented to detect faulty agents within the swarm basedon their behavior. A confusion matrix and some associated equations are usedto evaluate the accuracy of the method. Six features are selected for examination in the LOF algorithm. The firsttwo features assess the number of neighbors in a circle around the agent,while the others consider traversed distance, height, velocity, and rotation.Three different fault types are implemented and induced in one of the agentswithin the swarm. The first two faults are motor failures, and the last oneis a sensor failure. The algorithm is successfully implemented, and theevaluation of the faults is conducted using three different metrics. Several setsof experiments are performed to assess the optimal value for the LOF thresholdand to understand the model’s performance. The thesis work results in a strongLOF value which yields an acceptable F1 score, signifying the accuracy of theimplementation is at a satisfactory level. / I denna uppsats är huvudfokuset att formulera och testa en lämplig modellför detektion av exogena fel i svärmar som innehåller obemannade flygfordon(UAV:er), vilka utgör autonoma luftburna system. Examensarbetet ochforskningsfrågan tillhandahölls av FOI, Totalförsvarets forskningsinstitut.Inspirerad av tidigare arbete används beteendemässiga egenskapsvektorer(BFV:er) för att simulera rörelserna hos UAV:erna och för att identifieraavvikelser i deras beteenden. Den valda algoritmen för felavkänning är en densitetsbaserad klusterana-lysmetod som kallas Local Outlier Factor (LOF). Denna metod byggerpå k-Nearest Neighbor-algoritmen och använder densiteter för att upptäckaavvikande datapunkter. I denna uppsats implementeras den för att detekterafelaktiga agenter inom svärmen baserat på deras beteende. En förväxlings-matris(Confusion Matrix) och dess tillhörande ekvationer används för attutvärdera metodens noggrannhet. Sex egenskaper valdes för undersökning i LOF-algoritmen. De första tvåegenskaperna bedömer antalet grannar i en cirkel runt agenter, medande andra beaktar avstånd, höjd, hastighet och rotation. Tre olika feltyperimplementeras och framkallas hos en av agenterna inom svärmen. De förstatvå felen är motorfel, och det sista är ett sensorfel. Algoritmen implementerasframgångsrikt och utvärderingen av felen genomförs med hjälp av treolika mått. Ett antal uppsättningar av experiment utförs för att hitta detoptimala värdet för LOF-gränsen och för att förstå modellens prestanda.Examensarbetet resultat är ett optimalt LOF-värde som genererar ettacceptabelt F1-score, vilket innebär att noggrannheten för implementationennår en tillfredsställande nivå.

[pt] Os métodos de aprendizado de máquina para monitoramento de pás deturbinas eólicas dependem principalmente de conjuntos de dados completos erotulados, que são caros e muitas vezes impraticáveis de obter. Além disso, naciência dos materiais, a maioria dos métodos depende de dados experimentaiscaros, com exploração limitada de técnicas de aumento de dados para reduziro custo do treinamento de modelos. O objetivo desta pesquisa é abordar essaslacunas significativas explorando a classificação de uma classe para a detecçãode anomalias em pás de turbinas eólicas e desenvolvendo métodos para aumentar os dados existentes para um treinamento de modelos mais econômicona ciência dos materiais. Os métodos aplicados neste trabalho para a detecçãode anomalias incluem One Class Support Vector Machine (OCSVM), SupportVector Data Description (SVDD) e autoencoders Long Short-Term Memory(LSTM). Para aumentar os dados experimentais de materiais, foram utilizadas técnicas de adição de ruído e manipulação de imagens. Para a detecçãode anomalias, o autoencoder LSTM atingiu uma precisão de 97.4 por cento com aproximadamente 100 por cento de recall, enquanto o OCSVM atingiu 89 por cento de precisão e 97 por cento de recall. O OCSVM foi considerado mais adequado devido aos custos de treinamento mais baixos e desempenho semelhante. Os resultados para aumento de dados experimentais mostraram uma melhoria de 20 por cento em relaçãoaos modelos previamente treinados, com a técnica de aumento melhorando significativamente o desempenho, especialmente em modelos treinados com dadosde amostras experimentais diferentes. Em conclusão, esta pesquisa demonstraa eficácia da classificação de uma classe para a detecção de anomalias em pásde turbinas eólicas e destaca os benefícios das técnicas de aumento de dadospara o treinamento de modelos econômicos na ciência dos materiais. / [en] The background machine learning approaches for wind turbine blade monitoring rely mostly on complete and labeled datasets, which are costly and often impractical to obtain. Additionally, in material science, most methods depend on expensive experimental data, with limited exploration of data augmentation techniques to reduce the cost and effort of model training. The objective of this research addresses these significant gaps by exploring one-class classification for anomaly detection in wind turbine blades and by developing methods to augment existing data for more cost-effective model training in material science. The methods applied in this work for anomaly detection include One Class Support Vector Machine (OCSVM), Support Vector Data Descriptio (SVDD), and Long Short-Term Memory (LSTM) autoencoders. For augmenting experimental material data, noise addition and image manipulation techniques were used. For anomaly detection, the LSTM autoencoder achieved an accuracy of 97.4 percent with approximately 100 percent recall, while OCSVM achieved 89 percent accuracy and 97 percent recall. OCSVM was deemed more suitable due to lower training costs and similar performance. The results for experimental data augmentation showed a 20 percent improvement over previously trained models, with the augmentation technique significantly enhancing performance, especially in models trained with data from different experimental samples. In conclusion, this research demonstrates the effectiveness of one-class classification for anomaly detection in wind turbine blades and highlights the benefits of data augmentation techniques for cost-effective model training in material science.

Das Hauptmerkmal eines fehlertoleranten Regelungssystems ist die Aufrechterhaltung der Gesamtsystemstabilität und einer akzeptablen Leistung angesichts von Fehlern und Ausfällen innerhalb des Systems. In dieser Arbeit wird ein Verfahren zur Fehlererkennung und Ansätze der fehlertoleranten Regelung (FTR) mit einer Anwendung gegen Aktorfehler von Regelungssystemen, die aus sowohl einem einzelnen Aktor als auch mehreren Aktoren (z.B. Softrobotik) bestehen, vorgestellt. Diese Methode beruht hauptsächlich auf einem Index, genannt Fitnessindex (FI. Der FI wird durch den Vergleich der aktuellen Parameter des Aktors und die im Normalzustand mittels eines Modells geschätzt. Die Ergebnisse des Fitnessindex werden mit Hilfe des Performanceindex verwendet zur Bewertung des Schweregrades eines Aktorfehlers während des Betriebes in einem geschlossenen Regelkreis und sind die entscheidende Grundlage zur automatischen Fehlerdetektion, Fehlerdiagnose und der anschließenden FTR. Diese Methode wird in einem Simulationsmodell getestet, das dem Versuchsaufbau eines Formgedächtnislegierungs-Aktors entspricht. Die Simulationsergebnisse zeigen die Berechnungsergebnisse über den FI und anschließend die Verwendung der Ergebnisse des FIs zur Durchführung der FTR, um die Leistung zu gewährleisten und die Zuverlässigkeit der Regelungssysteme zu verbessern, wenn ein Fehler im Aktor auftritt. / The main feature of a fault tolerant control system is to maintain overall system stability and acceptable performance in the face of errors and failures within the system. In this thesis, a method for fault detection and approaches of the fault tolerant control (FTC) with an application against actuator errors of control systems, which consist of a single actuator as well as several actuators (soft robotics), is presented. This method is mainly based on an index called the fitness index (FI). The FI is estimated by comparing the current parameters of the actuator and in the normal state, based on a model. The results of the fitness index are used with the help of the performance index to evaluate the severity of an actuator fault during an operation in a control loop and are the decisive basis for automatic fault detection, fault diagnosis and subsequent the FTC. This method is tested in a simulation model that corresponds to the experimental setup of a shape memory alloy actuator. The simulation results show the calculation results about the FI and in addition the use the results of the FI to perform the FTC in order to ensure the performance and improve the reliability of the control systems when a fault occurs in the actuator.

The aim of this work is to develop algorithms for analyzing time series obtained from the digital process bus streams of a digital substation. The paper addresses issues related to enhancing the observability of digital substation equipment and identifying faults through non-destructive testing methods. During the research, models were created to simulate typical faults. Based on the obtained data, the most informative parameters were selected and formulated, which serve as the input data for the developed algorithms. The developed analysis algorithms were tested on data obtained during the simulation. / Целью настоящей работы является разработка алгоритмов анализа временных рядов, получаемых в составе цифровых потоков шины процесса цифровой подстанции. В работе рассматриваются вопросы повышения наблюдаемости оборудования цифровой подстанции и определения неисправностей посредством методов неразрушающего контроля. В ходе работы созданы модели для симуляции характерных неисправностей. На основании полученных данных, выбраны и сформированы наиболее информативные параметры, которые служат исходными данными для разработанных алгоритмов. Разработанные алгоритмы анализа параметров работы протестированы на данных, полученных в ходе моделирования.

The area of systems biology evolved in an attempt to introduce mathematical systems theory principles in biology. Although we believe that all biological processes are essentially chemical reactions, describing those using precise mathematical rules is not easy, primarily due to the complexity and enormity of biological systems. Here we introduce a formal approach for modeling biological dynamical relationships and diseases such as cancer. The immediate motivation behind this research is the urgency to find a practicable cure of cancer, the emperor of all maladies. Unlike other deadly endemic diseases such as plague, dengue and AIDS, cancer is characteristically heterogenic and hence requires a closer look into the genesis of the disease. The actual cause of cancer lies within our physiology. The process of cell division holds the clue to unravel the mysteries surrounding this disease. In normal scenario, all control mechanisms work in tandem and cell divides only when the division is required, for instance, to heal a wound platelet derived growth factor triggers cell division. The control mechanism is tightly regulated by several biochemical interactions commonly known as signal transduction pathways. However, from mathematical point of view, these pathways are marginal in nature and unable to cope with the multi-variability of a heterogenic disease like cancer. The present research is possibly one first attempt towards unraveling the mysteries surrounding the dynamics of a proliferating cell. A novel yet simple methodology is developed to bring all the marginal knowledge of the signaling pathways together to form the simplest mathematical abstract known as the Boolean Network. The malfunctioning in the cell by genetic mutations is formally modeled as stuck-at faults in the underlying Network. Finally a mathematical methodology is discovered to optimally find out the possible best combination drug therapy which can drive the cell from an undesirable condition of proliferation to a desirable condition of quiescence or apoptosis. Although, the complete biological validation was beyond the scope of the current research, the process of in-vitro validation has been already initiated by our collaborators. Once validated, this research will lead to a bright future in the field on personalized cancer therapy.

The main goal of this thesis is to develop practical digital controller architectures for multi-phase dc-dc converters utilized in low power (up to few hundred watts) and cost-sensitive applications. The proposed controllers are suitable for on-chip integration while being capable of providing advanced features, such as dynamic efficiency optimization, inductor current estimation, converter component identification, as well as combined dynamic current sharing and fast transient response. The first part of this thesis addresses challenges related to the practical implementation of digital controllers for low-power multi-phase dc-dc converters. As a possible solution, a multi-use high-frequency digital PWM controller IC that can regulate up to four switching converters (either interleaved or standalone) is presented. Due to its configurability, low current consumption (90.25 μA/MHz per phase), fault-tolerant work, and ability to operate at high switching frequencies (programmable, up to 10 MHz), the IC is suitable to control various dc-dc converters. The applications range from dc-dc converters used in miniature battery-powered electronic devices consuming a fraction of watt to multi-phase dedicated supplies for communication systems, consuming hundreds of watts. A controller for multi-phase converters with unequal current sharing is introduced and an efficiency optimization method based on logarithmic current sharing is proposed in the second part. By forcing converters to operate at their peak efficiencies and dynamically adjusting the number of active converter phases based on the output load current, a significant improvement in efficiency over the full range of operation is obtained (up to 25%). The stability and inductor current transition problems related to this mode of operation are also resolved. At last, two reconfigurable digital controller architectures with multi-parameter estimation are introduced. Both controllers eliminate the need for external analog current/temperature sensing circuits by accurately estimating phase inductor currents and identifying critical phase parameters such as equivalent resistances, inductances and output capacitance. A sensorless non-linear, average current-mode controller is introduced to provide fast transient response (under 5 μs), small voltage deviation and dynamic current sharing with multi-phase converters. To equalize the thermal stress of phase components, a conduction loss-based current sharing scheme is proposed and implemented.

Heutzutage gewinnt die Simulation von Gebäuden und Anlagen zunehmend an Bedeutung, um die Betriebsweise der Anlagen zu diagnostizieren bzw. zu bewerten und den Energiebedarf vorherzusagen. Dabei hängt die erzielte Genauigkeit von dem Kompliziertheitsgrad des angewendeten Simulationsprogramms ab. Deshalb ist Modellbildung und -validierung ein sehr wichtiger Bestandteil eines Softwareentwicklungsprozesses, um die Zuverlässigkeit zu sichern. Am Institut für Thermodynamik und Technische Gebäudeausrüstung liegen zahlreiche Simulationsmodelle vor. Im Rahmen dieser vorliegenden Arbeit wurden weitere benötigte Modelle (hygrisches Verhalten der Wände (vereinfachtes Verfahren), Rippenrohrwärmeüberträger, Wärmeregenerator et al.) entwickelt und in das Programm TRNSYS eingefügt sowie die vorhandenen Modelle an ihre Genauigkeit angepasst. Insbesondere sind dies die Modelle für Splitsysteme bei stetiger und nichtstetiger Regelung mit der detaillierten Betrachtung des Anlagenverhaltens sowohl beim Voll- als auch beim Teillastbetrieb. Damit ist es erstmals gelungen, das gesamte Anlagensystem der Splittechnik ausführlich zu beschreiben. Um die analytische Validierung durchführen zu können, wurden die analytischen Modelle für eine Splitanlage bei stetiger und nichtstetiger Regelung unter den vordefinierten Randbedingungen entwickelt. Zur analytischen Validierung finden auch die vorhandenen Simulationsmodelle Anwendung, so dass sich die meisten Komponenten und das Simulationsprogramm TRNSYS verifizieren ließen. Diese Validierung erfolgte im Rahmen des IEA-SHC/HVAC BESTEST TASK 22. Da an diesem TASK verschiedene Forschungsinstitutionen mit jeweils unterschiedlichen Simulationsprogrammen teilnahmen, ergab sich die beste Möglichkeit, vergleichende Tests durchzuführen. Wenn dabei ein Programm signifikante Unterschiede zu den anderen liefert, liegt dies nicht immer an Programmfehlern. Aber kollektive Erfahrungen aus diesem TASK zeigen, dass bei Abweichungen meistens Fehler bzw. fragwürdige Algorithmen gefunden wurden. Nachdem das Simulationsprogramm TRNSYS validiert war, erfolgte die Erstellung eines Konzeptes zur Fehlererkennung und Diagnose der Regelstrategien von RLTA. Das Verfahren erlaubt sowohl die Beseitigung der möglichen Fehler in der Planungsphase beim Entwurf der Regelstrategien als auch den Test der vorhandenen Regelstrategien. Dies erhöht die Zuverlässigkeit und damit die Sicherheit beim Anlagenbetrieb. Schließlich dient das Verfahren als Werkzeug zur Optimierung der Betriebsweise von RLTA. Das Regelverhalten wurde anhand typischer Fälle vorgestellt und diskutiert. Mit Hilfe des Verfahrens zur Fehlererkennung und Diagnose der Betriebsweise von RLTA ließen sich vorhandene Regelstrategien testen und verbessern.

The main goal of this thesis is to develop practical digital controller architectures for multi-phase dc-dc converters utilized in low power (up to few hundred watts) and cost-sensitive applications. The proposed controllers are suitable for on-chip integration while being capable of providing advanced features, such as dynamic efficiency optimization, inductor current estimation, converter component identification, as well as combined dynamic current sharing and fast transient response. The first part of this thesis addresses challenges related to the practical implementation of digital controllers for low-power multi-phase dc-dc converters. As a possible solution, a multi-use high-frequency digital PWM controller IC that can regulate up to four switching converters (either interleaved or standalone) is presented. Due to its configurability, low current consumption (90.25 μA/MHz per phase), fault-tolerant work, and ability to operate at high switching frequencies (programmable, up to 10 MHz), the IC is suitable to control various dc-dc converters. The applications range from dc-dc converters used in miniature battery-powered electronic devices consuming a fraction of watt to multi-phase dedicated supplies for communication systems, consuming hundreds of watts. A controller for multi-phase converters with unequal current sharing is introduced and an efficiency optimization method based on logarithmic current sharing is proposed in the second part. By forcing converters to operate at their peak efficiencies and dynamically adjusting the number of active converter phases based on the output load current, a significant improvement in efficiency over the full range of operation is obtained (up to 25%). The stability and inductor current transition problems related to this mode of operation are also resolved. At last, two reconfigurable digital controller architectures with multi-parameter estimation are introduced. Both controllers eliminate the need for external analog current/temperature sensing circuits by accurately estimating phase inductor currents and identifying critical phase parameters such as equivalent resistances, inductances and output capacitance. A sensorless non-linear, average current-mode controller is introduced to provide fast transient response (under 5 μs), small voltage deviation and dynamic current sharing with multi-phase converters. To equalize the thermal stress of phase components, a conduction loss-based current sharing scheme is proposed and implemented.

This thesis presents the development of a fault detection and diagnosis (FDD) procedure capable of capturing, isolating and identifying multiple abrupt parametric faults. The proposed method relies on parameter estimation deployed in a set membership framework. This approach presupposes the utilization of a linearly parametrizable model and the a priori knowledge of bounded noise errors and parameter perturbations. Under these assumptions, a data-hyperspace is generated at every time instant. The goal of set membership identification (SMI) is the determination of the parametric set, formed as an orthotope or ellipsoid, within which the nominal parameter vector resides and intersects with the data-hyperspace. The fault detection mechanism is activated when the normal operation of the SMI procedure is interrupted due to an empty intersection of the data-hyperspace and the estimated parametric set. At the detection instant, a resetting procedure is performed in order to compute the parameter set and the data-hyperspace that contain the varied nominal parameter vector, allowing the SMI algorithm to continue its operation. During the fault isolation, consistency tests are executed, relying on the projections of the worst case parametric sets and the ones arisen from the normal operation of SMI. A faulty component is indicated when these projections do not intersect, while the distance of their centers is used for fault identification. In case of the ellipsoidal SMI-based FDD and under the assumption of a time invariant parameter vector, a new fault detection criterion is defined based on the intersection of support orthotopes of ellipsoids. A more accurate estimation of the time instant of fault occurrence is proposed based on the application of a backward-in-time procedure starting from the fault detection instant, while the conditions under which a fault will never be detected by the orthotopic and ellipsoidal SMI based FDD are provided. This dissertation explores the efficiency of the proposed FDD methodology for capturing failure modes of two microelectromechanical systems; an electrostatic parallel-plate microactuator and a torsionally resonant atomic force microscope. From an engineering point of view, failure modes appeared in the microcomponents of the microactuator and the TR-AFM are encountered as parameter variations and are captured, isolated and identified by the proposed FDD methodology. / Σε αυτή την διατριβή, παρουσιάζεται η ανάπτυξη μιας διαδικασίας Ανίχνευσης και Διάγνωσης Σφαλμάτων, η οποία είναι ικανή να εντοπίζει, απομονώνει και αναγνωρίζει πολλαπλά, απότομα παραμετρικά σφάλματα. H προτεινόμενη μέθοδος βασίζεται στην αναγνώριση του συνόλου συμμετοχής των παραμέτρων. Ο στόχος της Αναγνώρισης Συνόλου Συμμετοχής είναι ο καθορισμός του παραμετρικού συνόλου εντός του οποίου κείται το ονομαστικό διάνυσμα παραμέτρων, δεδομένου ότι το ονομαστικό διάνυσμα παραμέτρων ανήκει επίσης σε έναν υπερχώρο δεδομένων. Το παραμετρικό σύνολο απεικονίζεται ως ένα ορθότοπο ή ένα ελλειψοειδές, λόγω της εύκολης μαθηματικής τους περιγραφής. Έτσι, η διαδικασία Αναγνώρισης Συνόλου Συμμετοχής αντιστοιχεί σε ένα πρόβλημα βελτιστοποίησης, το οποίο αποσκοπεί στον υπολογισμό του ορθοτόπου ή ελλειψοειδούς το οποίο περιέχει το ονομαστικό διάνυσμα παραμέτρων και τέμνεται με τον υπερχώρο δεδομένων. Ο μηχανισμός Ανίχνευσης Σφαλμάτων ενεργοποιείται όταν διακόπτεται η φυσιολογική λειτουργία της Αναγνώρισης Συνόλου Συμμετοχής, λόγω της κενής τομής μεταξύ των εκτιμώμενου παραμετρικού συνόλου και του υπερχώρου δεδομένων. Τη χρονική στιγμή ανίχνευσης ενός σφάλματος, εφαρμόζεται μια διαδικασία επαναρύθμισης που σκοπεύει στον υπολογισμό του νέου παραμετρικού συνόλου, το οποίο περιέχει το μεταβεβλημένο ονομαστικό διάνυσμα παραμέτρων και τέμνεται με το υπερχώρο δεδομένων. Κατά τη διάρκεια της διαδικασίας απομόνωσης του σφάλματος, εκτελούνται τεστ συμβατότητας, τα οποία βασίζονται στις προβολές των νέων παραμετρικών συνόλων και στις προβολές των παραμετρικών συνόλων χείριστης περίπτωσης, ενώ η απόσταση των κέντρων των προβολών χρησιμοποιείται για αναγνώριση σφάλματος. Στην περίπτωση που η Ανίχνευση και Διάγνωση Σφαλμάτων πραγματοποιείται βασιζόμενη στην Αναγνώριση Συνόλου Συμμετοχής με ελλειψοειδή και θεωρώντας το ονομαστικό διάνυσμα παραμέτρων χρονικά αμετάβλητο, ορίζεται ένα νέο κριτήριο ανίχνευσης σφαλμάτων, χρησιμοποιώντας την τομή των περιβαλλόντων ορθοτόπων των ελλειψοειδών. Σε αυτή την περίπτωση, ένα σφάλμα ανιχνεύεται όταν η τομή αυτή είναι κενή. Ακόμη, προτείνεται μια πιο ακριβής εκτίμηση της χρονικής στιγμής εμφάνισης του σφάλματος, ενώ παρατίθενται οι συνθήκες υπό τις οποίες ένα σφάλμα μπορεί να μην ανιχνευθεί ποτέ με την εφαρμογή των προτεινόμενων μεθόδων. Η συγκεκριμένη διατριβή επίσης ερευνά την αποτελεσματικότητα της προτεινόμενης μεθοδολογίας Ανίχνευσης και Διάγνωσης Σφαλμάτων για τον εντοπισμό των τρόπων εκδήλωσης σφαλμάτων σε δύο μικροηλεκτρομηχανικά συστήματα (ΜΗΜΣ), έναν ηλεκτροστατικό μικροεπενεργητή παράλληλων πλακών και ένα ατομικό μικροσκόπιο συντονισμού στρέψης. Από πλευράς μηχανικής, οι τρόποι εκδήλωσης σφαλμάτων στα δομικά στοιχεία του μικροεπενεργητή ή του ατομικού μικροσκοποίου αντιμετωπίζονται ως απότομες παραμετρικές, οι οποίες εντοπίζονται και διαγιγνώσκονται από τις προτεινόμενες μεθόδους.