<p>Users of heavy trucks, as well as legislation, put increasing demands on heavy trucks. The vehicles should be more comfortable, reliable and safe. Furthermore, they should consume less fuel and be more environmentally friendly. For example, this means that faults that cause the emissions to increase must be detected early. To meet these requirements on comfort and performance, advanced sensor-based computer control-systems are used. However, the increased complexity makes the vehicles more difficult for the workshop mechanic to maintain and repair. A diagnosis system that detects and localizes faults is thus needed, both as an aid in the repair process and for detecting and isolating (localizing) faults on-board, to guarantee that safety and environmental goals are satisfied.</p><p>Reliable fault isolation is often a challenging task. Noise, disturbances and model errors can cause problems. Also, two different faults may lead to the same observed behavior of the system under diagnosis. This means that there are several faults, which could possibly explain the observed behavior of the vehicle.</p><p>In this thesis, a Bayesian approach to fault isolation is proposed. The idea is to compute the probabilities, given ``all information at hand'', that certain faults are present in the system under diagnosis. By ``all information at hand'' we mean qualitative and quantitative information about how probable different faults are, and possibly also data which is collected during test drives with the vehicle when faults are present. The information may also include knowledge about which observed behavior that is to be expected when certain faults are present.</p><p>The advantage of the Bayesian approach is the possibility to combine information of different characteristics, and also to facilitate isolation of previously unknown faults as well as faults from which only vague information is available. Furthermore, Bayesian probability theory combined with decision theory provide methods for determining the best action to perform to reduce the effects from faults.</p><p>Using the Bayesian approach to fault isolation to diagnose large and complex systems may lead to computational and complexity problems. In this thesis, these problems are solved in three different ways. First, equivalence classes are introduced for different faults with equal probability distributions. Second, by using the structure of the computations, efficient storage methods can be used. Finally, if the previous two simplifications are not sufficient, it is shown how the problem can be approximated by partitioning it into a set of sub problems, which each can be efficiently solved using the presented methods.</p><p>The Bayesian approach to fault isolation is applied to the diagnosis of the gas flow of an automotive diesel engine. Data collected from real driving situations with implemented faults, is used in the evaluation of the methods. Furthermore, the influences of important design parameters are investigated.</p><p>The experiments show that the proposed Bayesian approach has promising potentials for vehicle diagnosis, and performs well on this real problem. Compared with more classical methods, e.g. structured residuals, the Bayesian approach used here gives higher probability of detection and isolation of the true underlying fault.</p> / <p>Både användare och lagstiftare ställer idag ökande krav på prestanda hos tunga lastbilar. Fordonen ska var bekväma, tillförlitliga och säkra. Dessutom ska de ha bättre bränsleekonomi vara mer miljövänliga. Detta betyder till exempel att fel som orsakar förhöjda emissioner måste upptäckas i ett tidigt stadium.</p><p>För att möta dessa krav på komfort och prestanda används avancerade sensorbaserade reglersystem.</p><p>Emellertid leder den ökade komplexiteten till att fordonen blir mer komplicerade för en mekaniker att underhålla, felsöka och reparera.</p><p>Därför krävs det ett diagnossystem som detekterar och lokaliserar felen, både som ett hjälpmedel i reparationsprocessen, och för att kunna detektera och lokalisera (isolera) felen ombord för att garantera att säkerhetskrav och miljömål är uppfyllda.</p><p>Tillförlitlig felisolering är ofta en utmanande uppgift. Brus, störningar och modellfel kan orsaka problem. Det kan också det faktum två olika fel kan leda till samma observerade beteende hos systemet som diagnosticeras. Detta betyder att det finns flera fel som möjligen skulle kunna förklara det observerade beteendet hos fordonet.</p><p>I den här avhandlingen föreslås användandet av en Bayesianska ansats till felisolering. I metoden beräknas sannolikheten för att ett visst fel är närvarande i det diagnosticerade systemet, givet ''all tillgänglig information''. Med ''all tillgänglig information'' menas både kvalitativ och kvantitativ information om hur troliga fel är och möjligen även data som samlats in under testkörningar med fordonet, då olika fel finns närvarande. Informationen kan även innehålla kunskap om vilket beteende som kan förväntas observeras då ett särskilt fel finns närvarande.</p><p>Fördelarna med den Bayesianska metoden är möjligheten att kombinera information av olika karaktär, men också att att den möjliggör isolering av tidigare okända fel och fel från vilka det endast finns vag information tillgänglig. Vidare kan Bayesiansk sannolikhetslära kombineras med beslutsteori för att erhålla metoder för att bestämma nästa bästa åtgärd för att minska effekten från fel.</p><p>Användandet av den Bayesianska metoden kan leda till beräknings- och komplexitetsproblem. I den här avhandlingen hanteras dessa problem på tre olika sätt. För det första så introduceras ekvivalensklasser för fel med likadana sannolikhetsfördelningar. För det andra, genom att använda strukturen på beräkningarna kan effektiva lagringsmetoder användas. Slutligen, om de två tidigare förenklingarna inte är tillräckliga, visas det hur problemet kan approximeras med ett antal delproblem, som vart och ett kan lösas effektivt med de presenterade metoderna.</p><p>Den Bayesianska ansatsen till felisolering har applicerats på diagnosen av gasflödet på en dieselmotor. Data som har samlats från riktiga körsituationer med fel implementerade används i evalueringen av metoderna. Vidare har påverkan av viktiga parametrar på isoleringsprestandan undersökts.</p><p>Experimenten visar att den föreslagna Bayesianska ansatsen har god potential för fordonsdiagnos, och prestandan är bra på detta reella problem. Jämfört med mer klassiska metoder baserade på strukturerade residualer ger den Bayesianska metoden högre sannolikhet för detektion och isolering av det sanna, underliggande, felet.</p>

Process control and monitoring is a common problem in high value added chemical manufacturing industries where batch processes are used to produce wide range of products on the same piece of equipment. This results in frequent adjustments on control and monitoring schemes. A chemical toner manufacturing process is representative of an industrial case which is used in this thesis. Process control and monitoring problem of batch processes have been researched, mostly through the simulation, and published in the past . However, the concept of applying the subject to chemical toner manufacturing process or to use a single indicator for multiple pieces of equipment have never been visited previously. In the case study of this research, there are many different factors that may affect the final quality of the products including reactor batch temperature, jacket temperature, impeller speed, rate of the addition of material to the reactor, or process variable associated with the pre-weight tank. One of the challenging tasks for engineers is monitoring of these process variables and to make necessary adjustments during the progression of a batch and change controls strategy of future batches upon completion of an existing batch. Another objective of the proposed research is the establishment of the operational boundaries to monitor the process through the usage of process trajectories of the history of the past successful batches. In this research, process measurements and product quality values of the past successful batches were collected and projected into matrix of data; and preprocessed through time alignment, centering, and scaling. Then the preprocessed data was projected into lower dimensions (latent variables) to produce latent variables and their trajectories during successful batches. Following the identification of latent variables, an empirical model was built through a 4-fold cross validation that can represent the operation of a successful batch. The behavior of two abnormal batches, batch 517 and 629, is then compared to the model by testing its statistical properties. Once the abnormal batches were flagged, their data set were folded back to original dimension to form a localization path for the time of abnormality and process variables that contributed to the abnormality. In each case the process measurement were used to establish operational boundaries on the latent variable space.

It is well known that the optimal nonblocking supervisory control problem of timed discrete event systems is NP-hard, subject in particular to state space explosion that is exponential in the number of system components. In this thesis, we propose to manage complexity by organizing the system as a Timed State Tree Structure (TSTS). TSTS are an adaptation of STS to timed Supervisory Control Theory (SCT). Based on TSTS we present an e±cient recursive symbolic algorithm that can perform nonblocking supervisory control design for systems of state size 10^12 and higher. Failure diagnosis is the process of detecting and identifying deviations of a system from its normal behavior using the information available through sensors. A method for fault diagnosis of the TSTS model is proposed. A state based diagnoser is constructed for each timed holon of TSTS. Fault diagnosis is accomplished using the state estimates provided by the timed holon diagnosers. The diagnosers may communicate among each other in order to update their state estimates. At any given time, only a subset of the diagnosers are operational, and as a result, instead of the entire model of the system, only the models of the timed holons associated with the operational diagnosers are used. It is shown that the computational complexity of constructing and storing the transition systems required for diagnosis in the proposed approach is polynomial in the number of system components, whereas in the original monolithic approach the computational complexity is exponential.

In this thesis, two problems are studied. The first problem is to find a technique to generate a particular type of failure information in real time for large flexible space structures (LFSSs). This problem is solved by using structured residuals. The failure information is then incorporated into an existing fault tolerant control scheme. The second problem is a ``spin-off'' from the first. Although the H-infinity sliding mode observer (SMO) cannot be applied to the colocated LFSS , its ability to do robust state and fault estimation of the SMO makes it suitable to be used in an integrated fault tolerant control (IFTC) scheme. We propose to combine the H-infinity SMO with a linear fault accommodation controller. Our IFTC scheme is closed loop stable, suppresses the effects of faults and enjoys enhanced robustness to disturbances. The effectiveness of the IFTC is illustrated through the control of a permanent magnet synchronous motor under actuator fault.

It is well known that the optimal nonblocking supervisory control problem of timed discrete event systems is NP-hard, subject in particular to state space explosion that is exponential in the number of system components. In this thesis, we propose to manage complexity by organizing the system as a Timed State Tree Structure (TSTS). TSTS are an adaptation of STS to timed Supervisory Control Theory (SCT). Based on TSTS we present an e±cient recursive symbolic algorithm that can perform nonblocking supervisory control design for systems of state size 10^12 and higher. Failure diagnosis is the process of detecting and identifying deviations of a system from its normal behavior using the information available through sensors. A method for fault diagnosis of the TSTS model is proposed. A state based diagnoser is constructed for each timed holon of TSTS. Fault diagnosis is accomplished using the state estimates provided by the timed holon diagnosers. The diagnosers may communicate among each other in order to update their state estimates. At any given time, only a subset of the diagnosers are operational, and as a result, instead of the entire model of the system, only the models of the timed holons associated with the operational diagnosers are used. It is shown that the computational complexity of constructing and storing the transition systems required for diagnosis in the proposed approach is polynomial in the number of system components, whereas in the original monolithic approach the computational complexity is exponential.

In this thesis, two problems are studied. The first problem is to find a technique to generate a particular type of failure information in real time for large flexible space structures (LFSSs). This problem is solved by using structured residuals. The failure information is then incorporated into an existing fault tolerant control scheme. The second problem is a ``spin-off'' from the first. Although the H-infinity sliding mode observer (SMO) cannot be applied to the colocated LFSS , its ability to do robust state and fault estimation of the SMO makes it suitable to be used in an integrated fault tolerant control (IFTC) scheme. We propose to combine the H-infinity SMO with a linear fault accommodation controller. Our IFTC scheme is closed loop stable, suppresses the effects of faults and enjoys enhanced robustness to disturbances. The effectiveness of the IFTC is illustrated through the control of a permanent magnet synchronous motor under actuator fault.

<p>Advances in semiconductor technology and design automation methods have introduced a new era for electronic products. With design sizes in millions of logic gates and operating frequencies in GHz, defects-per-million rates continue to increase, and defects are manifesting themselves in subtle ways. Traditional test methods are not sufficient to guarantee product quality and diagnostic programs cannot rapidly locate the root cause of failure in large systems. Therefore, there is a need for efficient fault diagnosis methods that can provide quality assurance, accelerate new product release, reduce manufacturing cost, and increase product yield.</p><p>This thesis research is focused on fault-insertion test (FIT) and fault diagnosis at the board and system levels. FIT is a promising technique to evaluate system reliability and facilitate fault diagnosis. The error-handling mechanism and system reliability can be assessed in the presence of intentionally inserted faults, and artificial faulty scenarios can be used as references for fault diagnosis. However, FIT needs to be deployed under constraints of silicon area, design effort, availability of equipment, and what is actually possible to test from one design to the next. In this research, physical defect modeling is developed to provide an efficient solution for fault-insertion test. Artificial faults at the pin level are created to represent physical defects inside devices. One pin-level fault is able to mimic the erroneous behaviors caused by multiple internal defects. Therefore, system reliability can be evaluated in a more efficient way.</p><p>Fault diagnosis is a major concern in the semiconductor industry. As the density and complexity of systems increase relentlessly and the subtle effects of defects in nanometer technologies become more pronounced, fault diagnosis becomes difficult, time-consuming, and ineffective. Diagnosis of functional failure is especially challenging. Moreover, the cost associated with board-level diagnosis is escalating rapidly. Therefore, this thesis presents a multi-pronged approach to improve the efficiency and accuracy of fault diagnosis, including the construction of a diagnostic framework with FIT and Bayesian inference, the extraction of an effective fault syndrome (error flow), the selection of diagnosis-oriented fault-insertion points, and the application of machine learning for intelligent diagnosis.</p><p>First, in the inference-based diagnosis framework, FIT is used to create a large number of faulty samples and derive the probabilities needed for the application of Bayes' theorem; next the probability of a fault candidate being the root cause can be inferred based on the given fault syndromes. Results on a case study using an open-source RISC system-on-chip demonstrate the feasibility and effectiveness of the proposed approach. Second, the concept of error flow is proposed to mimic actual data propagation in a circuit, and thus it reflects the logic functionality and timing behavior of circuits. With this additional information, more fault syndromes are distinguishable. Third, diagnosis-oriented fault-insertion points are defined and selected to create the representative and distinguishable syndromes. Finally, machine learning approaches are used to facilitate the debug and repair process. Without requiring the need to understand the complex functionality of the boards, an intelligent diagnostic system is designed to automatically exploit the diagnostic knowledge available from past cases and make decisions on new cases.</p><p>In summary, this research has investigated efficient means to perform fault-insertion test and developed automated and intelligent diagnosis methods targeting functional failures at the board level. For a complex circuit board currently in production, the first-time success rate for diagnosis has been increased from 35.63% to 72.64%. It is expected to contribute to quality assurance, product release acceleration, and manufacturing-cost reduction in the semiconductor industry.</p> / Dissertation

Process control and monitoring is a common problem in high value added chemical manufacturing industries where batch processes are used to produce wide range of products on the same piece of equipment. This results in frequent adjustments on control and monitoring schemes. A chemical toner manufacturing process is representative of an industrial case which is used in this thesis. Process control and monitoring problem of batch processes have been researched, mostly through the simulation, and published in the past . However, the concept of applying the subject to chemical toner manufacturing process or to use a single indicator for multiple pieces of equipment have never been visited previously. In the case study of this research, there are many different factors that may affect the final quality of the products including reactor batch temperature, jacket temperature, impeller speed, rate of the addition of material to the reactor, or process variable associated with the pre-weight tank. One of the challenging tasks for engineers is monitoring of these process variables and to make necessary adjustments during the progression of a batch and change controls strategy of future batches upon completion of an existing batch. Another objective of the proposed research is the establishment of the operational boundaries to monitor the process through the usage of process trajectories of the history of the past successful batches. In this research, process measurements and product quality values of the past successful batches were collected and projected into matrix of data; and preprocessed through time alignment, centering, and scaling. Then the preprocessed data was projected into lower dimensions (latent variables) to produce latent variables and their trajectories during successful batches. Following the identification of latent variables, an empirical model was built through a 4-fold cross validation that can represent the operation of a successful batch. The behavior of two abnormal batches, batch 517 and 629, is then compared to the model by testing its statistical properties. Once the abnormal batches were flagged, their data set were folded back to original dimension to form a localization path for the time of abnormality and process variables that contributed to the abnormality. In each case the process measurement were used to establish operational boundaries on the latent variable space.

The aim of this study is to solve Fault Detection and Diagnosis (FDD) problems occurring in nonlinear dynamical systems by using model and knowledge-based FDD methods and to give a priority and a degree about faults. For this purpose, three model-based FDD approaches, called FDD by utilizing principal component analysis (PCA), system identification based FDD and inverse model based FDD are introduced. Performances of these approaches are tested on different nonlinear dynamical systems starting from simple to more complex. New fuzzy discrete event system (FDES) and fuzzy discrete event dynamical system (FDEDS) concepts are introduced and their applicability to an FDD problem is investigated. Two knowledge-based FDD methods based on FDES and FDEDS structures using a fuzzy rule-base are introduced and they are tested on nonlinear dynamical systems. New properties related to FDES and FDEDS such as fuzzy observability and diagnosibility concepts and a relation between them are illustrated. A dynamical rule-base extraction method with classification techniques and a dynamical and a static diagnoser design methods are also introduced. A nonlinear and event based extension of the Luenberger observer and its application as a diagnoser to isolate faults are illustrated. Finally, comparisons between the proposed model and knowledge-based FDD methods are made.

Η παρούσα διδακτορική διατριβή πραγματεύεται την ανάπτυξη μεθόδων διάγνωσης σφαλμάτων σε ηλεκτρικά κινητήρια συστήματα που αποτελούνται από ασύγχρονη μηχανή βραχυκυκλωμένου κλωβού και ηλεκτρονικούς μετατροπείς ισχύος στο στάτη αυτής. Εστιάστηκε κυρίως στον εντοπισμό των σφαλμάτων του δρομέα της ασύγχρονης μηχανής μέσω της ανάλυσης των ρευμάτων του αντιστροφέα σε μόνιμες και μεταβατικές καταστάσεις του κινητηρίου συστήματος. Προτάθηκε μέθοδος εντοπισμού των σφαλμάτων αναλύοντας το ρεύμα του στάτη της μηχανής χρησιμοποιώντας το Συνεχή Μετασχηματισμό με κυματίδια για μεταβαλλόμενες τιμές του μηχανικού φορτίου ή της συχνότητας λειτουργίας του αντιστροφέα. Μελετήθηκε το αρμονικό περιεχόμενο του ρεύματος στον κλάδο συνεχούς ρεύματος του αντιστροφέα για αρμονικές συνιστώσες λόγω σφαλμάτων στην ασύγχρονη μηχανή και προτάθηκε μέθοδος ανίχνευσης σφαλμάτων με ανάλυση του ρεύματος αυτού. Επιπλέον, προτάθηκε άλλη μια μέθοδος διάγνωσης των σφαλμάτων που βασίζεται στη διαμόρφωση κατά πλάτος των ρευμάτων του στάτη της ασύγχρονης μηχανής. Τα αποτελέσματα της προσομοίωσης του κινητηρίου συστήματος και των πειραμάτων στο Εργαστήριο επιβεβαιώνουν την ικανότητα των προτεινόμενων μεθόδων ως προς τη διάγνωση των σφαλμάτων. / This thesis deals with the development of novel methods for fault diagnosis in electric drive systems consisting of squirrel cage asynchronous motor and power electronic converters. It is focused mainly on identifying faults in the asynchronous machine by analyzing the currents of the inverter in steady state and transient operation. The Continuous Wavelet Transform (CWT) of motor stator current has been proposed for successful motor fault diagnosis during changing values of the mechanical load or the operating frequency of the voltage source inverter. Furthermore, the harmonic content of the inverter dc link current has been studied for harmonic components due to faults in the asynchronous machine and a novel diagnostic method has been proposed. In addition, another method based on the amplitude modulation of the stator currents has been investigated. The proposed methods have been validated by simulation and experimental results.