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.