Uppsatsen är ett examensarbete på 22,5 hp, som kom till på uppdrag av SONOinredningar och Tranås skolmöbler och har utförts av Sara Storm, student påProduktdesignprogrammet vid Malmö högskola, under våren 2012.Arbetet behandlar klassrummens ljudmiljö i låg- och mellanstadieskolor samthur elever sitter i sina stolar. Kan en elevstol bidra till en bättre ljudmiljö i klassrumoch erbjuda en bra ergonomisk sittkomfort för eleven?Resultatet blev en stol, där material och konstruktionslösningar reducerar bulleroch med sin vippfunktion möjliggör ett flexibelt sittande. / This paper is a thesis work of 22,5 hp, that was made on behalf of SONOinredningar and Tranås skolmöbler. It was executed by Sara Storm, a student at theprogram of Product design at Malmö University, during the spring 2012.The thesis is focused on classroom acoustics in Primary schools but is alsolooking at how pupils use their chairs while in class. Can a school chair contributeto a better sound environment in class rooms and offer a good ergonomic seatingcomfort for the pupil?The result was a chair, where materials and construction techniques reducesnoise and with its tilt function allows for a flexible seating.

This thesis aims to use vibration and acoustic noise analysis to help a production line of a certain motor type to ensure good quality. Noise from the gearbox is sometimes present and the way it is detected is with a human listening to it. This type of error detection is subjective, and it is possible for human error to be present. Therefore, an automatic test that pass or fail the produced Brush Less Direct Current (BLDC)-motors is wanted. Two measurement setups were used. One was based on an accelerometer which was used for vibration measurements, and the other based on a microphone for acoustic sound measurements. The acquisition and analysis of the measurements were implemented using the data acquisition device, compactDAQ NI 9171, and the graphical programming software, NI LabVIEW. Two methods, i.e., power spectrum analysis and machine learning, were used for the analyzing of vibration and acoustic signals, and identifying faults in the gearbox. The first method based on the Fast Fourier transform (FFT) was used to the recorded sound from the BLDC-motor with the integrated planetary gearbox to identify the peaks of the sound signals. The source of the acoustic sound is from a faulty planet gear, in which a flank of a tooth had an indentation. Which could be measured and analyzed. It sounded like noise, which can be used as the indications of faults in gears. The second method was based on the BLDC-motors vibration characteristics and uses supervised machine learning to separate healthy motors from the faulty ones. Support Vector Machine (SVM) is the suggested machine learning algorithm and 23 different features are used. The best performing model was a Coarse Gaussian SVM, with an overall accuracy of 92.25 % on the validation data.