The buzz, squeak, and rattle (BSR) problem is one of the most important issues affecting vehicle satisfaction related to unexpected noise and vibration and has been attracting increased attention because of the rise of electric and hybrid vehicles. However, until recently, the vehicle underbody BSR has not been studied as extensively as the upper-body or interior BSR owing to a number of complex issues, such as the lack of suitable methods for measuring or visualizing the underbody BSR. This study proposes a method for localizing the rattle noise source in the vehicle underbody using the accelerations measured during a vehicle driving test. The acceleration signals are denoised by considering the characteristics of rattle; then, a series of rattle-induced impulse signal units included in the denoised acceleration signals are identified via machine learning at the level of BSR experts. Finally, the accelerometer closer to the rattle noise source is quantitatively determined by analyzing the correlation indexes in the frequency domain based on the properties of the rattle-induced impulse signal units, which depend on their relative position from the rattle noise source. The proposed method is based only on the accelerations measured from the driving test and the rattle signal transmission characteristics, and does not require complex vehicle dynamics analysis; therefore, it can be widely used regardless of the vehicle type at low cost.
|Journal||Mechanical Systems and Signal Processing|
|Publication status||Published - 2022 Mar 1|
Bibliographical noteFunding Information:
This work was supported by the Materials and Components Technology Development Program ( 20011013 ) funded by the Ministry of Trade, Industry & Energy ( MOTIE , Korea) and by the Hyundai Motor Company, Korea.
© 2021 Elsevier Ltd
All Science Journal Classification (ASJC) codes
- Control and Systems Engineering
- Signal Processing
- Civil and Structural Engineering
- Aerospace Engineering
- Mechanical Engineering
- Computer Science Applications