Hitachi Energy is a world leading company in the production and installation of power transformers and the quality of their services is of the utmost importance. The majority of the company's current quality control methods are performed manually, the purpose of this study is to identify potential areas where the use of 3D-scanning technology as a method for quality control has potential, making use of interviews, literature studies and document analysis to do so. Further the study wishes to outline the specific methods that could be used to increase the efficiency of the production and present the steps that would be required for these methods to be implemented effectively.  The results revealed that both the active core and final assembly processes currently face issues. The active core assembly with specific aluminum pipes that often differ from the specified tolerances. Most of these problems come from external suppliers and get noticeable when they arrive at the active core assembly. The final assembly faces issues with the lowering down of the active core into its container. A process that can vary drastically in the time it takes while simultaneously being a very time sensitive process. The study proposes three methods as solutions to these problems. An initial control of the aluminum pipes after delivery with FARO:s Quantum Max ScanArm and CAM2 Software. As well as a simulated lowering down of the active core, making use of FARO:s Focus Laser Scanner and the software program SCENE. Lastly, a more general use of 3D-scanning for quality control. It is concluded that committing to an investment is required to reach further results.

Vibrations are everywhere around us all the time and we often recognise them as sounds that we can hear and analyse with our brain. In this thesis, data that has been gathered from a diverter switch (DS) in a controlled environment, is analysed. This data consists of vibroacoustic measurements and information to indicate what is happening inside the DS as the vibroacoustic data is gathered. The frequency properties of vibroacoustic data from the DS gathered before this thesis are displayed using a wavelet transformation model. This means the frequency properties of the signal can be approximated for all times in the operation with a certain accuracy. As the DS is built from many different components, the frequency properties of these components are compared to the time-frequency picture of the full DS operation. This sort of comparison ends up not being feasible as the complexity of the DS frequency pattern is much more than that of a sum of its component’s frequency pattern. A second approach of analysing the gathered vibroacoustic data is by using a classification model. The information about what is happening inside of the DS is used to train a logistic regression model on different defined regions of the vibroacoustic data. Before the training is preformed though, the different defined regions are transformed into frequency space with help of the fast Fourier transform. With this, a classification model is produced, where vibroacoustic data of any time region can be fed into the model and the model will classify which defined region this vibroacoustic data belongs to. The results are promising, and the model can be used both for classification of the defined regions and potentially used to determine if the vibroacoustic properties of the DS has changed due to wear of the mechanical components or transformer oil.