When released into the atmosphere, traditional refrigerants contribute to climate change several orders of magnitude more than a corresponding amount of carbon dioxide. For that reason, an increasing amount of interest has been paid to transcritical vapor compression systems in recent years, which use carbon dioxide as a refrigerant. Vapor compression systems also impact the environment through their consumption of energy. This can be greatly increased by faulty operation. Automated techniques for detecting and diagnosing faults have been widely tested for subcritical systems, but have not been applied to transcritical systems. These methods can involve either dynamic analysis of the vapor compression cycle or a variety of algorithms based on steady state behavior. In this thesis, the viability of dynamic fault detection is tested in relation to that of static fault detection for a transcritical refrigeration system. Step tests are used to determine that transient behavior does not give additional useful information. The same tests are performed on a subcritical air-conditioner showing little value in dynamic fault detection. A static component based method of fault detection which has been applied to subcritical systems is also tested for all pairings of four faults: over/undercharge, evaporator fouling, gas cooler fouling, and compressor valve leakage. This technique allows for low cost measurement and independent detection of individual faults even when multiple faults are present. Results of this method are promising and allow distinction between faulty and fault-free behavior.

An interval observer has been illustrated to be a suitable approach to detect and isolate faults affecting complex dynamical industrial systems. Concerning fault detection, interval observation is an appropriate passive robust strategy to generate an adaptive threshold to be used in residual evaluation when model uncertainty is located in parameters (interval model). In such approach, the observer gain is a key parameter since it determines the time evolution of the residual sensitivity to a fault and the minimum detectable fault. This thesis illustrates that the whole fault detection process is ruled by the dynamics of the fault residual sensitivity functions and by the time evolution of the adaptive threshold related to the interval observer. Besides, it must be taken into account that these two observer fault detection properties depend on the used observer gain. As a consequence, the observer gain becomes a tuning parameter which allows enhancing the observer fault detection performance while avoiding some drawbacks related to the analytical models, as the wrapping effect. In this thesis, the effect of the observer gain on fault detection and how this parameter can avoid some observer drawbacks (i.e. wrapping effect) are deeply analyzed. One of the results of this analysis is the determination of the minimum detectable fault function related to a given fault type. This function allows introducing a fault classification according to the fault detectability time evolution: permanently (strongly) detected, non-permanently (weakly) detected or just non-detected. In this fault detection part of this thesis, two examples have been used to illustrate the derived results: a mineral grinding-classification process and an industrial servo actuator. Concerning the interface between fault detection and fault isolation, this thesis shows that both modules can not be considered separately since the fault detection process has an important influence on the fault isolation result. This influence is not only due to the time evolution of the fault signals generated by the fault detection module but also to the fact that the fault residual sensitivity functions determines the faults which are affecting a given fault signal and the dynamics of this fault signal for each fault. This thesis illustrates this point suggesting that the interface between fault detection and fault isolation must consider a set of fault signals properties: binary property, sign property, fault residual sensitivity property, occurrence order property and occurrence time instant property. Moreover, as a result of the influence of the observer gain on the fault detection stage and on the fault residual sensitivity functions, this thesis demonstrates that the observer gain has also a key role in the fault isolation module which might allow enhancing its performance when this parameter is tuned properly (i.e. fault distinguishability may be increased). As a last point, this thesis analyzes the timed discrete-event nature of the fault signals generated by the fault detection module. As a consequence, it suggests using timed discrete-event models to model the fault isolation module. This thesis illustrates that this kind of models allow enhancing the fault isolation result. Moreover, as the monitored system is modelled using an interval observer, this thesis shows as this qualitative fault isolation model can be built up on the grounds of this system analytical model. Finally, the proposed fault isolation method is applied to detect and isolate faults of the Barcelona’s urban sewer system limnimeters. Keywords: Fault Detection, Fault Diagnosis, Robustness, Observers, Intervals, Discrete-event Systems. / En la presente tesis se demuestra que el uso de observadores intervalares para detectar y aislar fallos en sistemas dinámicos complejos constituye una estrategia apropiada. En la etapa de detección del fallo, dicha estrategia permite determinar el umbral adaptativo usado en la evaluación del residuo (robustez pasiva). Dicha metodología, responde a la consideración de modelos con parámetros inciertos (modelos intervalares). En dicho enfoque, la ganancia del observador es un parámetro clave que permite determinar la evolución temporal de la sensibilidad del residuo a un fallo y el mínimo fallo detectable para un tipo de fallo determinado. Esta tesis establece que todo el proceso de detección de fallos viene determinado por la dinámica de las funciones sensibilidad del residuo a los diferentes fallos considerados y por la evolución temporal del umbral adaptativo asociado al observador intervalar. Además, se debe tener en cuenta que estas dos propiedades del observador respecto la detección de fallos dependen de la ganancia del observador. En consecuencia, la ganancia del observador se convierte en el parámetro de diseño que permite mejorar las prestaciones de dicho modelo respecto la detección de fallos mientras que permite evitar algunos defectos asociados al uso de modelos intervalares, como el efecto wrapping. Uno de los resultados obtenidos es la determinación de la función fallo mínimo detectable para un tipo de fallo dado. Esta función permite introducir una clasificación de los fallos en función de la evolución temporal de su detectabilidad: fallos permanentemente detectados, fallos no permanentemente detectados y fallos no detectados. En la primera parte de la tesis centrada en la detección de fallos se utilizan dos ejemplos para ilustrar los resultados obtenidos: un proceso de trituración y separación de minerales y un servoactuador industrial. Respecto a la interfaz entre la etapa de detección de fallos y el proceso de aislamiento, esta tesis muestra que ambos módulos no pueden considerarse separadamente dado que el proceso de detección tiene una importante influencia en el resultado de la etapa de aislamiento. Esta influencia no es debida sólo a la evolución temporal de las señales de fallo generados por el módulo de detección sino también porque las funciones sensibilidad del residuo a los diferentes posibles fallos determinan los fallos que afectan a un determinado señal de fallo y la dinámica de éste para cada uno de los fallos. Esta tesis ilustra este punto sugiriendo que el interfaz entre detección y aislamiento del fallo debe considerar un conjunto de propiedades de dichos señales: propiedad binaria, propiedad del signo, propiedad de la sensibilidad del residuo a un fallo dado, propiedad del orden de aparición de las señales causados por los fallos y la propiedad del tiempo de aparición de estos. Además, como resultado de la influencia de la ganancia del observador en la etapa de detección y en las funciones sensibilidad asociadas a los residuos, esta tesis ilustra que la ganancia del observador tiene también un papel crucial en el módulo de aislamiento, el cual podría permitir mejorar el comportamiento de dicho módulo diseñando éste parámetro del observador de forma adecuada (Ej. Incrementar la distinción de los fallos para su mejor aislamiento). Como último punto, esta tesis analiza la naturaleza temporal de eventos discretos asociada a las señales de fallo generados por el módulo de detección. A consecuencia, se sugiere usar modelos de eventos discretos temporales para modelizar el módulo de aislamiento del fallo. Esta tesis muestra que este tipo de modelos permite mejorar el resultado de aislamiento del fallo. Además, dado que el sistema monitorizado es modelado usando un observador intervalar, esta tesis muestra como este modelo cualitativo de aislamiento puede ser construido usando dicho modelo analítico del sistema. Finalmente, el método propuesto de aislamiento del fallo es aplicado para detectar y aislar fallos en los limnimetros del sistema de alcantarillado de Barcelona. Palabras clave: Detección de Fallos, Diagnosis de Fallos, Robusteza, Observadores, Intervalos, Sistemas de Eventos Discretos.

Industrial robots make up an important part in today’s industry and are assigned to a range of different tasks. Needless to say, businesses need to rely on their machine park to function as planned, avoiding stops in production due to machine failures. This is where fault detection methods play a very important part. In this thesis a specific fault detection method based on signal analysis will be considered. When testing a robot for fault(s), a specific test cycle (trajectory) is executed in order to be able to compare test data from different test occasions. Furthermore, different test cycles yield different measurements to analyse, which may affect the performance of the analysis. The question posed is: Can we find an optimal test cycle so that the fault is best revealed in the test data? The goal of this thesis is to, using regression analysis, investigate how the presently executed test cycle in a specific diagnosis method relates to the faults that are monitored (in this case a so called friction fault) and decide if a different one should be recommended. The data also includes representations of two disturbances. The results from the regression show that the variation in the test quantities utilised in the diagnosis method are not explained by neither the friction fault or the test cycle. It showed that the disturbances had too large effect on the test quantities. This made it impossible to recommend a different (optimal) test cycle based on the analysis.

This master’s thesis work was performed at Volvo Powertrain in Köping, Sweden, which manufactures gearboxes and integrated transmission systems for heavy vehicles. The thesis is a continuation of a previous master’s thesis performed at the Köping factory in 2009. After manufacturing and assembly, each gearbox is manually validated to ensure the gearbox quality and functionality. When validating the gearbox gears, the operator shifts the gearbox in a predefined manner and listens for irregularities. If an error sound is heard the operator must then locate the source of error. With numerous of cog wheels rotating at the same time this task requires extensive knowledge and experience of the operator. The main objective is to develop an automatic diagnostic system for detection of cog errors and assist the operator in the process of locating the faulty component. The work consists of two parts. In the first part the automatic diagnostic system is developed and a database of gearbox recordings is stored. The amounts of logged non-faulty gearboxes are significantly much larger (50) than the logged faulty gearboxes (1). Therefore, when determining thresholds needed for the diagnosis, the data obtained from the non-faulty gearboxes are used. Two statistical methods are presented to extract the thresholds. The first method uses an extremevalue distribution and the other method a Gaussian distribution. When validated, both methods did successfully detect on cog faults. In the second part an investigation is made of how shaft imbalance can be detected and implemented in the developed system. Volvo Powertrain continually follows-up all faults found at the validation station to ensure the quality of their work and eliminate the sources of error. During system testing one logged gearbox was found faulty. The automatic diagnostic system did successfully detect and locate the faulty component which later also was confirmed when the gearbox was dismounted. With only one detected error it is difficult to conclude the system performance and further testing is required. However, during the testing no false detections were made.

When flying a fighter aircraft such as the JAS 39 Gripen, the pilot is exposed to high g-loads. In order to prevent the draining of blood from the brain during this stress an anti-g protection system is used. The system consists of a pair of trousers, called the anti-g trousers, with inflatable bladders. The bladders are filled with air, pressing tightly on to the legs in order to prevent the blood from leaving the upper part of the body. The purpose of this thesis is to detect if the pressure of the anti-g trousers is deviating from the desired value. This is done by developing a detection algorithm which gives two kinds of alarm. One is given during minor deviations using a CUSUM test, and one is given at grave deviations, based on different conditions including residual, derivative and time. The thresholds, in which between the pressure should lie in a faultless system, are calculated from the g-load value. The thresholds are based upon given static guidelines for the pressure tolerance area and are modified in order to adapt to the estimated dynamics of the system. The values of the input signals, pressure and g-load, were taken from real flight sessions. The validation has been performed using both faultless and faulty flight sequences, with low false alarm rate and no missed detections. All together the detection system is considered to work well.

Diagnosis means detection and isolation of faults. A model based diagnosis system is built on a mathematical model of the system. The difficulty when constructing the diagnosis system depends om how the model is formulated. In this report, a method is described that rewrites the model on such a form that the construction of the diagnosis algoritm is easy. The model is transformed by two state space transformations and the result will be a system on state space form where one part of the system becomes easy to supervise. The main part of the report describes the procedure to create these transformations, which can be done in seven steps, based on differential geometric methods. The aim of this masters thesis was to create an implementation in Mathematica (a computer tool for symbolic formula manipulation) of the creation of the two transformations and the system transformation. The created functions are described and examples of these are given. A further aim was to evaluate if Mathematica could be a good support to rewrit a model. This was done by studying examples, and on the basis of the examples, identify difficult and easy steps. The program has shown to be a good aid. Two of the seven steps have been identified as difficult and proposals for improvements have been given.

Within the next years, the amount of consumption data will increase rapidly as old meters will be exchanged in favor of meters with hourly remote reading. A new refined supervision system must be developed. The main objective of this thesis is to investigate mathematical methods that can be used to find incorrect hourly measurements in district heat and electricity consumption, for each consumer. A simulation model and a statistical model have been derived. The model parameters in the simulation model are estimated by using historical data of consumption and outdoor temperature. By using the outdoor temperature as input, the consumption can be simulated and compared to the actual consumption. Faults are detected by using a residual with a sliding window. The second model uses the fact that consumers with similar consumption patterns can be grouped into a collective. By studying the correlation between the consumers, incorrect measurements can be found. The performed simulations show that the simulation model is best suited for consumers whose consumption is mostly affected by the outdoor temperature. These consumers are district heat consumers and electricity consumers that use electricity for space heating. The fault detection performance of the statistical model is highly dependent on finding a collective that is well correlated. If these collectives can be found, the model can be used on district heat consumers as well as electricity consumers.

This thesis presents an on-line particle-filtering-based framework for fault diagnosis and failure prognosis in nonlinear, non-Gaussian systems. The methodology assumes the definition of a set of fault indicators, which are appropriate for monitoring purposes, the availability of real-time process measurements, and the existence of empirical knowledge (or historical data) to characterize both nominal and abnormal operating conditions. The incorporation of particle-filtering (PF) techniques in the proposed scheme not only allows for the implementation of real time algorithms, but also provides a solid theoretical framework to handle the problem of fault detection and isolation (FDI), fault identification, and failure prognosis. Founded on the concept of sequential importance sampling (SIS) and Bayesian theory, PF approximates the conditional state probability distribution by a swarm of points called particles and a set of weights representing discrete probability masses. Particles can be easily generated and recursively updated in real time, given a nonlinear process dynamic model and a measurement model that relates the states of the system with the observed fault indicators. Two autonomous modules have been considered in this research. On one hand, the fault diagnosis module uses a hybrid state-space model of the plant and a particle-filtering algorithm to (1) calculate the probability of any given fault condition in real time, (2) estimate the probability density function (pdf) of the continuous-valued states in the monitored system, and (3) provide information about type I and type II detection errors, as well as other critical statistics. Among the advantages offered by this diagnosis approach is the fact that the pdf state estimate may be used as the initial condition in prognostic modules after a particular fault mode is isolated, hence allowing swift transitions between FDI and prognostic routines. The failure prognosis module, on the other hand, computes (in real time) the pdf of the remaining useful life (RUL) of the faulty subsystem using a particle-filtering-based algorithm. This algorithm consecutively updates the current state estimate for a nonlinear state-space model (with unknown time-varying parameters) and predicts the evolution in time of the fault indicator pdf. The outcome of the prognosis module provides information about the precision and accuracy of long-term predictions, RUL expectations, 95% confidence intervals, and other hypothesis tests for the failure condition under study. Finally, inner and outer correction loops (learning schemes) are used to periodically improve the parameters that characterize the performance of FDI and/or prognosis algorithms. Illustrative theoretical examples and data from a seeded fault test for a UH-60 planetary carrier plate are used to validate all proposed approaches. Contributions of this research include: (1) the establishment of a general methodology for real time FDI and failure prognosis in nonlinear processes with unknown model parameters, (2) the definition of appropriate procedures to generate dependable statistics about fault conditions, and (3) a description of specific ways to utilize information from real time measurements to improve the precision and accuracy of the predictions for the state probability density function (pdf).

This thesis proposes an algorithm and develops a program for fault detection and system restoration in medium voltage distribution systems. In Turkey, TUBITAK-UZAY developed distribution automation system including fault detection and service restoration functions for Bogazici Electricity Distribution Company. By the time, expanding of distribution system with nonstandardized infrastructure (for example more than one circuit breaker in the feeder, mesh and closed loop feeder structure), developed automation system have not properly worked under these unplanned situations. Taking into consideration of previously utilized TUBITAK Distribution Automation System (TUDOSIS), fault isolation algorithm is improved to cope with practical problems as non-standardized infrastructure and selectivity issue in protection system, and the proposed isolation algorithm is simulated. Further system restoration solution for mesh distribution systems is analyzed for distribution system in Turkey and expert system based algorithm is proposed.

In this thesis work, the symptoms in Windows XP operating system for fault monitoring are investigated and a fault monitoring library is developed. A test GUI is implemented to examine this library. Performance tests including memory and CPU usage are done to see its overhead to the system and platform tests on the current version of Windows operating system series (Windows Vista) are done to see for compatibility. In this thesis, fault monitor-fault detector interface is also defined and implemented. To monitor a symptom that is not implemented in the monitoring library, projects can implement their own monitors. A monitoring framework is designed to control and coordinate these monitors with the main one. To create monitors for Java projects easily, a monitor creator library is developed.