K-means clustering and neural network for evaluating sound level vibration in vehicle cabin

Abstract

One of the characteristics that may influence customers in vehicle purchasing is the level of comfort of the vehicle’s sound vibration in the vehicle cabin. The basic principle suggests that the sound vibration discomfort level is affected by a few factors which are mainly based on magnitudes, frequencies, directions and also the exposed periods. Normally, the phenomenon of sound vibration disrupts the performance of the driver by affecting the driver’s vision and also inducing a certain degree of stress due to the sound and vibration to which the driver and his or her passengers are exposed. The sound vibration is generally contributed by a few sources originated from the transmission of the vehicle’s engine, tire interactions with the road surface and also the exposure of vehicle’s body vibration during the movement. The objective of this study is to propose an approach that clusters the level of sound and vibration into a few categories and classifies them into those categories without implementing the subjective test that normally involves human assessment. The study has observed the changes of the sound quality and the level of vibration at particular points in the vehicle cabin over the changes of engine speeds. In reference to the results, the study has successfully provided a technical procedure in order to cluster, and also to classify, the level of sound vibration by taking into account the correlation between experienced noise and exposed vibration in the vehicle cabin.