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.

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In this thesis, I/O signal recognizers, called VIRTUALBLOCKS, are synthesized to interface with a SYSTEM UNDER TEST (SUT). Methods for automated synthesis of virtualblocks allow us to simulate environment interfaces with SUT and also perform fault detection on SUT. Such methods must be able to recognize incoming sequences of signals from SUT, and upon the signal recognition determine the proper outgoing sequences of signals to SUT. We characterize our systems into four distinctive systems: system under test, AUXILIARY SYSTEM, controller and external environment. The auxiliary system is represented as a form of condition system Petri net (virtualblocks) and interacts with SUT along with the interaction among the controller and the external environment. Fault detection is performed by subsystems called DETECTBLOCKS synthesized from the virtualblocks. We present construction procedures for virtualblocks andamp; detectblocks and discuss the notion of LEGALITY and DETECTABILITY. Finally, we illustrate our approach using a model of a scanner control unit.

Fault detection and location algorithms have allowed for the power industry to alter the power grid from the traditional model to becoming a smart grid. This thesis implements an already established algorithm for detecting faults, as well as an impedance-based algorithm for detecting where on the line the fault has occurred and develops a smart algorithm for future HDL conversion using Simulink. Using the algorithms, the ways in which this implementation can be used to create a smarter grid are the fundamental basis for this research. Simulink was used to create a two-bus power system, create environment variables, and then Matlab was used to program the algorithm such that it could be FPGA-implementable, where the ways in which one can retrieve the data from a power line has been theorized. This novel approach to creating a smarter grid was theorized and created such that real-world applications may be further implemented in the future.

Model-based diagnosis compares observations from a system with predictions using a mathematical model to detect and isolate faulty components. Analyzing which faults that can be detected and isolated given the model gives useful information when designing a diagnosis system. This information can be used, for example, to determine which residual generators can be generated or to select a sufficient set of sensors that can be used to detect and isolate the faults. With more information about the system taken into consideration during such an analysis, more accurate estimations can be computed of how good fault detectability and isolability that can be achieved. Model uncertainties and measurement noise are the main reasons for reduced fault detection and isolation performance and can make it difficult to design a diagnosis system that fulfills given performance requirements. By taking information about different uncertainties into consideration early in the development process of a diagnosis system, it is possible to predict how good performance can be achieved by a diagnosis system and avoid bad design choices. This thesis deals with quantitative analysis of fault detectability and isolability performance when taking model uncertainties and measurement noise into consideration. The goal is to analyze fault detectability and isolability performance given a mathematical model of the monitored system before a diagnosis system is developed. A quantitative measure of fault detectability and isolability performance for a given model, called distinguishability, is proposed based on the Kullback-Leibler divergence. The distinguishability measure answers questions like "How difficult is it to isolate a fault fi from another fault fj?. Different properties of the distinguishability measure are analyzed. It is shown for example, that for linear descriptor models with Gaussian noise, distinguishability gives an upper limit for the fault to noise ratio of any linear residual generator. The proposed measure is used for quantitative analysis of a nonlinear mean value model of gas flows in a heavy-duty diesel engine to analyze how fault diagnosability performance varies for different operating points. It is also used to formulate the sensor selection problem, i.e., to find a cheapest set of available sensors that should be used in a system to achieve required fault diagnosability performance. As a case study, quantitative fault diagnosability analysis is used during the design of an engine misfire detection algorithm based on the crankshaft angular velocity measured at the flywheel. Decisions during the development of the misfire detection algorithm are motivated using quantitative analysis of the misfire detectability performance showing, for example, varying detection performance at different operating points and for different cylinders to identify when it is more difficult to detect misfires. This thesis presents a framework for quantitative fault detectability and isolability analysis that is a useful tool during the design of a diagnosis system. The different applications show examples of how quantitate analysis can be applied during a design process either as feedback to an engineer or when formulating different design steps as optimization problems to assure that required performance can be achieved.

Diagnosis of faults associated with hydraulic actuators is essential to avoid accidents or loss of system functionality. This thesis focuses on internal leakage fault diagnosis in valve controlled hydraulic actuation systems (VCA) as well as electrohydrostatic actuation systems (EHA). For the VCA, the hydraulic actuator is driven in a closed loop mode to track a pseudorandom input signal whereas for the EHA, an actuator is driven in an open loop mode to track a sinusoidal input. Motivated by developing a method that does not rely on the model of the system or type of fault, signal processing techniques based on the ratio of metric lengths of pressure signals, autocorrelation of pressure signal, cross correlation between chamber pressure signals, and cross correlation between control signal and piston displacement is employed for internal leakage diagnosis. For the VCA, autocorrelation of pressure signals performed well at lower lags (less than 4) and at a window size of 200 data points; both cross correlation between pressure signals and cross correlation between control signal and piston displacement performed well at higher lags (higher than 8) and at a window size of 100 data points; ratio of metric lengths of pressure signals was found to be more effective at higher lag ratios (more than 16:3). All methods were sensitive to the lowest simulated leakage of 0.047 L/min, though with different level of success; ratio of metric lengths produced 84% sensitivity, autocorrelation 19% sensitivity, cross correlation between pressure signals 25% sensitivity and cross correlation between piston displacement and control signal 20% sensitivity. For the EHA, all methods were capable of identifying small leakage of 0.98 L/min. The ratio of metric lengths produced 6.7% sensitivity, autocorrelation 2.59% sensitivity, cross correlation between pressure signals 9.4% sensitivity and cross correlation between piston displacement and control signal 31.9% sensitivity. The low leakage detection achieved without requiring a model of the actuator or leakage type make these methods very attractive for industrial implementation

Transformers form a very important part of the power transmissions and distribution network; as they are responsible for the transfer of electrical energy from the power generation sites onto the transmission lines and finally to the distribution stage. Dry-type and oil-filled single-phase transformers, either alone or as a part of three-phase banks, are used extensively in the power distribution network, ultimately providing power to the domestic consumers. Any faults in the single-phase transformers leading to power outages or catastrophic power systems failures cause huge loss of capital, property and in some cases even human casualties. Gradual deterioration of the electrical winding insulation ultimately leads to inter-turn winding short circuit faults; which account for a significant proportion of all transformer failures. Incipient stages of inter-turn winding faults have negligible impact on the terminal voltages and currents of transformers; thus these faults often go undetected by the traditional differential relay based protection mechanisms. By the time, the faults manifest themselves into severe winding short-circuit faults consequently forcing the differential relays to operate for tripping the circuit breakers; a significant part of the transformer windings and core may get extensively damaged. Over the years, various techniques have been developed for detecting and studying inter-turn winding faults; however their practical implementation involves quite a few challenges such as high cost, lack of reliability, low accuracy and need for mounting additional equipment inside the transformer casing. Additionally, none of the existing techniques are suitable for online and real-time condition monitoring of the transformers. This absence of any proven technique to detect incipient levels of inter-turn winding faults in single-phase transformers has motivated the research of this thesis. In the thesis, firstly, a non-invasive technique for modeling single-phase transformers has been developed which is based solely on the terminal measurements of voltages and currents. The effects of transformer core saturation, non-linearity, hysteresis are incorporated in the model by considering a time-varying magnetizing inductance comprising of any desired number of harmonic components. The coefficients of the magnetizing inductance are computed from the instantaneous values of flux linkage and magnetizing current over one complete cycle. The model is found to replicate the behaviour of the single-phase transformer with an extremely high level of accuracy, under any load conditions for healthy as well as faulty operations. Detailed simulation and experiment based studies have been performed for corroborating the effectiveness of the proposed terminal measurement based modeling technique not only in detecting incipient stages of inter-turn winding faults (involving less than 1% of the turns) but also in estimating fault severity. Also, a non-invasive, online and real-time implementation of the proposed inter-turn winding fault detection technique for continuous monitoring of the transformer health has been suggested. Firstly, with the experimentally acquired primary line voltage and line current data of the healthy transformer, a healthy no-load model of the transformer is generated. Next, a healthy estimated indicator value, computed from this model under the given input voltage condition, is compared with the actual indicator value for detecting the presence of an inter-turn winding fault. It involves minimum hardware (only two current sensors and one voltage sensor), low memory requirements and low computational complexity and thus holds a good promise for practical applications. Further discussion is made on the possible challenges for realizing the proposed fault diagnostic technique in the industry and suitable recommendations have been made for further improvement. / Graduate / 0544 / bhowmick@uvic.ca

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.

Η μελέτη αυτή αποσκοπεί να εμβαθύνει εμπεριστατωμένα στο ζήτημα του ελέγχου βλαβών αυτοκινήτου με την χρήση πιεζοκρυστάλλων. Οι κρύσταλλοι αυτοί είναι τοποθετημένοι στο σώμα της μηχανής και λαμβάνουν δονήσεις από το περιβάλλον τους τις οποίες μετατρέπουν σε ηλεκτρικά σήματα. Τα σήματα αυτά επεξεργάζονται κατόπιν από ένα μικροελεγκτή και συγκεκριμένα το AduC 7026 της Analog Devices. Η διάταξη μας δηλαδή είναι ικανή να ανιχνεύσει οποιαδήποτε βλάβη υπάρχει στο αυτοκίνητο η οποία επηρεάζει συστήματα εντός της εμβέλειας των πιεζοηλεκτρικών αισθητήρων οι οποίοι βρίσκονται πάνω στο σώμα της μηχανής. Η μελέτη χωρίζεται σε έξι μέρη. Στο πρώτο αναλύεται εξαρχής η κατασκευή , η λέιτουργία και τα μέρη της μηχανής του αυτοκινήτου. Στο δεύτερο αναλύεται η φύση , η λειτουργία των πιεζοκρυστάλλων και οι σχέσεις που διέπουν τη λειτουργία τους. Στο τρίτο μέρος ακολουθεί η μέθοδος με την οποία αναλύονται οι δονήσεις και εκμαιεύεται συμπέρασμα για την ορθή ή εσφαλμένη λειτουργία της μηχανής. Στο τέταρτο εξετάζεται η δομή και η λειτουργία του μικροελεγκτή, καθώς και παρουσιάζεται το λογισμικό Keil-Uvision. Στο πέμπτο παρατίθενται τμήματα του κώδικα που αναπτύχθηκε και εξηγείται η λειτουργία τους. Στο έκτο γίνεται η προσομοίωση του κώδικα. Ο κώδικας γράφτηκε σε γλώσσα προγραμματισμού C. / This thesis aims to investigate the subject of fault detection of automobile vehicles by the use of piezoelectric transducers that are placed upon the engine of the vehicle. The transducers receive the vibrations and turn them into electric signals. These signals are being processed by a microcontroller and specifically the Aduc7026 built by Analog Devices. The ordinance is capable of detecting faults in the vehicle affecting systems within the range of vibration receiving capability of piezoelectric transducers The thesis is devided into 6 parts. The 1st section deals with the construction and the operation of the internal combustion engine. In the 2nd section the nature, the operation and the mathematical formulations that define the operation of piezoelectric transducers are being analyzed. The 3rd part deals with the method by which the vibrations are analyzed so as to decide whether the engine operates normally. The 4th section deals with the structure and operation of the microcontroller and with the Keil-Uvision software. In the 5th part sections of the code are referenced and their operation is being explained. In the 6th and final part the code is being simulated. The code is written in C programming language.