abstract: With the growing importance of underground power systems and the need for greater reliability of the power supply, cable monitoring and accurate fault location detection has become an increasingly important issue. The presence of inherent random fluctuations in power system signals can be used to extract valuable information about the condition of system equipment. One such component is the power cable, which is the primary focus of this research. This thesis investigates a unique methodology that allows online monitoring of an underground power cable. The methodology analyzes conventional power signals in the frequency domain to monitor the condition of a power cable. First, the proposed approach is analyzed theoretically with the help of mathematical computations. Frequency domain analysis techniques are then used to compute the power spectral density (PSD) of the system signals. The importance of inherent noise in the system, a key requirement of this methodology, is also explained. The behavior of resonant frequencies, which are unique to every system, are then analyzed under different system conditions with the help of mathematical expressions. Another important aspect of this methodology is its ability to accurately estimate cable fault location. The process is online and hence does not require the system to be disconnected from the grid. A single line to ground fault case is considered and the trend followed by the resonant frequencies for different fault positions is observed. The approach is initially explained using theoretical calculations followed by simulations in MATLAB/Simulink. The validity of this technique is proved by comparing the results obtained from theory and simulation to actual measurement data. / Dissertation/Thesis / Masters Thesis Electrical Engineering 2016

The healthy condition of a rotating machine leads to safe and cheap operation of almost all industrial facilities and mechanical systems. To achieve such a goal, vibration-based condition monitoring has proved to be a well-accepted technique that detects incipient fault symptoms. The conventional way of On-Bearing Vibration Measurement (OBVM) captures symptoms of different faults, however, it requires a relatively expensive setup, an additional space for the auxiliary devices and cabling in addition to an experienced analyst. On-Shaft Vibration Measurement (OSVM) is an emerging method proposed to offer more reliable Faults Diagnosis (FD) tools with less number of sensors, minimal processing time and lower system and maintenance costs. The advancement in sensor and wireless communications technologies enables attaching a MEMS accelerometer with a miniaturised wireless data acquisition unit directly to the rotor without altering the machine dynamics. In this study, OSVM is analysed during constant speed and run-up operations of a test rig. The observations showed response modulation, hence, a Finite Element (FE) analysis has been carried out to help interpret the experimental observations. The FE analysis confirmed that the modulation is due to the rotary motion of the on-shaft sensor. A demodulation method has been developed to solve this problem. The FD capability of OSVM has been compared to that of OBVM using conventional analysis where the former provided more efficient diagnosis with less number of sensors. To incorporate more features, a method has been developed to diagnose faults based on Principal Component Analysis and Nearest Neighbour classifier. Furthermore, the method is enhanced using Linear Discriminant Analysis to do the diagnosis without the need for a classifier. Another faults diagnosis method has been developed that ensures the generalisation of extracted faults features from OSVM data of a specific machine to similar machines mounted on different foundations.

La gestion de la continuité des opérations est un cadre complet visant à éviter que les événements perturbateurs n’affectent les opérations commerciales, à rétablir rapidement les activités et à réduire les dommages potentiels correspondants pour les systèmes énergétiques, tels que les centrales nucléaires. Cette thèse propose des discussions sur les aspects suivants: développement de méthodes appropriées d'évaluation des risques afin d'intégrer les données de surveillance de l'état et les données d'inspection pour une mise à jour et des pronostics robustes et en temps réel du profil de risque. Pour tenir compte de l'incertitude des données de surveillance de l'état, un modèle de mélange gaussien de Markov caché est développé pour modéliser les données de surveillance de l'état. Un réseau bayésien est appliqué pour intégrer les deux sources de données. Pour améliorer l'applicabilité de la continuité des opérations dans la pratique, les variables variant dans le temps considèrent l'indice de continuité des opérations, par ex. la dégradation des composants, les revenus en fonction du temps, etc. sont pris en compte dans le processus de modélisation de la continuité des activités. Sur la base de l'indice de continuité d'activité proposé, une méthode d'optimisation conjointe prenant en compte toutes les mesures de sécurité dans le processus d'évolution des événements, y compris les étapes de prévention, d'atténuation, d'urgence et de récupération, est développée pour améliorer la continuité des opérations du système avec des ressources limitées. Les méthodologies proposées sont appliquées aux centrales nucléaires contre les événements perturbateurs. / Business continuity management is a comprehensive framework to prevent the disruptive events from impacting the business operations, quickly recovering business and reducing the corresponding potential damages for energy system, such as nuclear power plants (NPPs). This dissertation provides discussions on the following aspects: developing appropriate risk assessment methods in order to integrate condition monitoring data and inspection data for a robust and real-time risk profile updating and prognostics. To account for the uncertainty of condition monitoring data, a hidden Markov gaussian mixture model is developed to model the condition monitoring data. A Bayesian network is applied to integrate the two data sources. For improving applicability of business continuity in practice, time-variant variables regard business continuity index, e.g. component degradation, time-dependent revenue, etc are taken into consideration in the business continuity modelling process. Based on the proposed business continuity index, a joint optimization method considering all the safety measures in event evolvement process including prevention stage, mitigation stage, emergency stage and recovery stage is developed to enhance system business continuity under limited resources. The proposed methodologies are applied to NPP against disruptive event.

Bosch Rexroth in Mellansel is manufacturing hydraulic motors and constantly trying to improve their products to reduce downtime for their customers. An important thing to get a reliable system is to know the condition. In a hydraulic motor, it is crucial to determine the particle contamination of the oil to determine the condition. To do so many particle sensors have been tested by Bosch Rexroth but also other related companies during the past years. To this point, no sensor has been performing good enough to replace the ordinary magnetic plug for the laboratory tests at Bosch Rexroth.  The ordinary magnetic plug is based on an openable lid that has a magnet attached to it. The lid is opened to review the particle contamination of the system. To open the lid the motor has to be stopped and a competent person needs to be present to review the particles.To ease the work for the laboratory personal and also getting one step closer to a reliable condition monitoring solution a new idea was coined by employees at Bosch Rexroth. The idea was to use a magnet outside a glass disc and by that be able to detect the particles from outside the motor. Initial testing of the idea had been performed with promising results but more development was needed. The idea has therefore been investigated and developed further in this project. This has been done in parallel with an investigation of state-of-the-art techniques available on the market. The testing showed that the new type of magnet/glass solution was performing well and was able to detect particles of different sizes. Some other interesting options were also found during the investigation of other techniques but the new magnet/glass idea was the most prominent.

This paper deals with the application of speed variable pumps in industrial hydraulic systems. The benefit of the natural feedback of the load torque is investigated for the issue of condition monitoring as the development of losses can be taken as evidence of faults. A new approach is proposed to improve the fault detection capabilities by tracking the changes via machine learning techniques. The presented algorithm is an art of adaptive modeling of the torque balance over a range of steady operation in fault free behavior. The aim thereby is to form a numeric reference with acceptable accuracy of the unit used in particular, taking into consideration the manufacturing tolerances and other operation conditions differences. The learned model gives baseline for identification of major possible abnormalities and offers a fundament for fault isolation by continuously estimating and analyzing the deviations.

Coal-fired power plant boilers consist of several complex subsystems that all need to work together to ensure plant availability, efficiency and safety, while limiting emissions. Analysing this multi-objective problem requires a thermofluid process model that can simulate the water/steam cycle and the coal/air/flue gas cycle for steady-state and dynamic operational scenarios, in an integrated manner. The furnace flue gas side can be modelled using a suitable zero-dimensional model in a quasi-steady manner, but this will only provide an overall heat transfer rate and a single gas temperature. When more detail is required, CFD is the tool of choice. However, the solution times can be prohibitive. A need therefore exists for a computationally efficient model that captures the three-dimensional radiation effects, flue gas exit temperature profile, carbon burnout and O2 and CO2 concentrations, while integrated with the steam side process model for dynamic simulations. A thermofluid network-based methodology is proposed that combines the zonal method to model the radiation heat transfer in three dimensions with a one-dimensional burnout model for the heat generation, together with characteristic flow maps for the mass transfer. Direct exchange areas are calculated using a discrete numerical integration approximation together with a suitable smoothing technique. Models of Leckner and Yin are applied to determine the gas and particle radiation properties, respectively. For the heat sources the burnout model developed by the British Coal Utilisation Research Association is employed and the advection terms of the mass flow are accounted for by superimposing a mass flow map that is generated via an isothermal CFD solution. The model was first validated by comparing it with empirical data and other numerical models applied to the IFRF single-burner furnace. The full scale furnace model was then calibrated and validated via detailed CFD results for a wall-fired furnace operating at full load. The model was shown to scale well to other load conditions and real plant measurements. Consistent results were obtained for sensitivity studies involving coal quality, particle size distribution, furnace fouling and burner operating modes. The ability to do co-simulation with a steam-side process model in Flownex® was successfully demonstrated for steady-state and dynamic simulations.

No description available.

One way to tackle the climate change society is facing today is through the change to renewable energy sources, such as wind power. Today, a trend when it comes to technology is that products are evolving into becoming more cyber-physical systems (CPS) by integrating functions realized with mechanics, control and communication. One challenge for CPS is to find cost-effective and reliable sensor solutions.  The purpose of this project is to lay the foundations for an intelligent CPS with the help of sensors and condition monitoring methods that, with further development, can reduce the downtime of a wind turbine. Thus, the reliability of the wind turbine and the profitability of its investors increase. The aim of the work is to develop an overall concept for a sensor package with analysis methods that enable real-time diagnosis in the gearbox of a wind turbine. This sensor package should be able to monitor the most common problems that arise in the gearbox and it should also be able to be used as a basis for a possible development of a CPS in the future.  The work is based on an information search that enables the creation of a list of requirements. This then forms the basis for concept generation through the use of a function/means tree and concept evaluation through the use of elimination matrix, weight determination matrix and weighted criteria matrix.  The work concludes that there are four main types of failures that occur in the gearbox and that should be monitored. These are scuffing, micropitting, propagation of cracks and bearing failure. The final concept uses vibration analysis for monitoring of micropitting, crack propagation and bearing failure, oil analysis for monitoring of scuffing and micropitting and temperature measurement for monitoring of scuffing and bearing failure. For vibration analysis, piezoelectric sensors are used, for oil analysis electromagnetic sensors and for temperature measurement resistance thermometers are used.  The work finds that it is appropriate in this day and age to use well-established methods for condition monitoring in the gearbox of wind turbines. / Ett sätt att tackla de klimatförändringar samhället står inför idag är genom omställningen till förnybara energikällor, såsom vindkraft. Idag är en trend när det kommer till teknik att produkter utvecklas till att allt mer bli cyberfysiska system (CPS) genom att de integrerar funktioner som realiseras med mekanik, reglering och kommunikation. En utmaning för CPS är att hitta kostnadseffektiva och tillförlitliga sensorlösningar.  Syftet med detta projekt är att lägga grunden till ett intelligent CPS med hjälp av sensorer och tillståndsövervakningsmetoder som med vidare utveckling ska kunna minska stilleståndstiden hos ett vindkraftverk. Således ökar tillförlitligheten hos vindkraftverket samt räntabiliteten för investerarna av dessa. Arbetets mål är att utveckla ett övergripande koncept för ett sensorpaket med analysmetoder som möjliggör realtidsdiagnos i växellådan hos ett vindkraftverk. Detta sensorpaket ska kunna övervaka de vanligaste problemen som uppstår i växellådan och ska kunna användas som grund för eventuell utveckling av ett CPS i framtiden.  Arbetet grundas i en informationssökning som möjliggör skapandet av en kravspecifikation. Denna ligger sedan till grund för konceptgenerering genom användandet av funktions/medelträd samt konceptutvärdering genom användandet av elimineringsmatris, viktbestämningsmatris samt kriterieviktsmetoden.  I arbetet framkommer att det finns fyra huvudsakliga skador som uppkommer i växellådan och som bör övervakas. Dessa är scuffing, mikropitting, sprickpropagering och lagerhaveri. Det slutgiltiga konceptet använder vibrationsanalys för övervakning av mikropitting, sprickpropagering och lagerhaveri, oljeanalys för övervakning av scuffing och mikropitting samt temperaturmätning för övervakning av scuffing och lagerhaveri. Vid vibrationsanalys används piezoelektriska sensorer, vid oljeanalys elektromagnetiska sensorer och för temperaturmätning resistanstermometrar.  Arbetet konstaterar att det i dagsläget är lämpligt att använda väletablerade metoder för tillståndsövervakning i växellådan hos vindkraftverk.

Early failure detection has been an integral part of condition monitoring of critical systems, such as wind turbines and helicopter rotor drivetrains. An unexplored application of early failure detection is fatigue testing of driveline components. On many occasions, driveline components fail catastrophically, leaving no evidence of the root cause of failure and causing extensive damage to test equipment. This can be prevented by detecting failure in its early stages. Test specimen would be preserved, enabling correlation of test results with design predictions. In this thesis, a method for early failure detection of gear fatigue is proposed. The gears in questions are parts of driveline components undergoing fatigue tests. The proposed method includes generation of an autoregressive model from a healthy, time synchronously averaged vibration signal. The parameters of the generated model are then used to construct a filter, which predicts deviations from the healthy signal. The output of this filter is then processed to detect failure. Vibration data from four run to failure tests were analysed. While the proposed method detected failure in all four data sets, performance was better in tests carried out at high torque and low speed in comparison to tests carried out under low torque and high speeds. Finally, potential improvements in the proposed method to increase its effectiveness are proposed. / “Early Failure Detection” (tidig detektion av utmattningsbrott) har länge varit en viktig del av tillståndsövervakning av kritiska system, som till exempel vindkraftverk och drivsystem för rotorblad på helikoptrar. Ett mindre utforskat område av “Early Failure Detection” är utmattningstestning av komponenter för transmissionssystem. Ofta går komponenterna sönder på ett sådant sätt att grundorsaken till haveriet inte går att fastställa, och som riskerar att skada testriggarna. Detta kan förebyggas om haveriet kan upptäckas i ett tidigt skede innan komponenten gar sönder helt och hållet. Testobjeket kan då bevaras, vilket ger möjligheter att korrelera testresultatet till utmattningsberäkningar av konstruktionen.  I den här uppsatsen föreslås en metod för Early Failure Detection för drevsatser i växlar. Växlarna ingår i transmissionssystem som utmattningsprovas. Den föreslagna metoden innebär att en autoreggresiv modell skapas från en tids-synkron medelvärdesbildning på den uppmätta signalen för den oförstärda komponenten. Parametrarna från den modellen används sedan för att skapa ett filter som predikterar avvikelser mot den oförstörda komponenten. Slutligen behandlas utsignalen fran det filteret för att upptäcka utmattningsskador pa drevsatsen i växeln.  Vibrationsdata fran fyra utmattningsprov har analyserats. I samtliga prov har provet körts tills brott har konstaterats. Utmattningsskador kunde konstateras tidigt, innan brottet inträffade, i tre av de fyra fallen. Slutligen föreslås förslag på utveckling av den använda metoden for att förbättra predikteringarna.

A non-invasive, on-line method for detection of mechanical (rotor, bearings eccentricity) and stator winding faults in a 3-phase induction motors from observation of motor line current supply input. The main aim is to avoid the consequence of unexpected failure of critical equipment which results in extended process shutdown, costly machinery repair, and health and safety problems. This thesis looks into the possibility of utilizing machine learning techniques in the field of condition monitoring of electromechanical systems. Induction motors are chosen as an example for such application. Electrical motors play a vital role in our everyday life. Induction motors are kept in operation through monitoring its condition in a continuous manner in order to minimise their off times. The author proposes a model free sensor-less monitoring system, where the only monitored signal is the input to the induction motor. The thesis considers different methods available in literature for condition monitoring of induction motors and adopts a simple solution that is based on monitoring of the motor current. The method proposed use the feature extraction and Support Vector Machines (SVM) to set the limits for healthy and faulty data based on the statistical methods. After an extensive overview of the related literature and studies, the motor which is the virtual sensor in the drive system is analysed by considering its construction and principle of operation. The mathematical model of the motor is used for analysing the system. This is followed by laboratory testing of healthy motors and comparing their output signals with those of the same motors after being intentionally failed, concluding with the development of a full monitoring system. Finally, a monitoring system is proposed that can detect the presence of a fault in the monitored machine and diagnose the fault type and severity / Ministry of Higher Education, Libya; Switchgear & Instruments Ltd.