# Noise Identification for an Automotive Wheel Bearing  
[PERSON]  
1 Smart Manufacturing Technology R&D Group, Daegyeong Division, Korea Institute of Industrial Technology, 88-42, Yongiu-ro, Jangsu-myeon, Yeongju-i 36144, Gyeongsangbuk-do, Korea; [EMAIL_ADDRESS] (J.K.); [EMAIL_ADDRESS] (J.J.) 1  
[PERSON]  
2 Department of Mechanical Engineering, Hanyanyany University, 55 Hanyangdehaha-ro, Sangnok-gu, Ansan 15588, Gyeongong-iQ, Korea; [EMAIL_ADDRESS] (S.K. ); [EMAIL_ADDRESS] (S.R.) 2  
[PERSON]  
2 Department of Mechanical Engineering, Hanyanyany University, 55 Hanyangdehaha-ro, Sangnok-gu, Ansan 15588, Gyeongong-iQ, Korea; [EMAIL_ADDRESS] (S.K. ); [EMAIL_ADDRESS] (S.R.) 2  
[PERSON]  
3 Department of Mechanical Engineering, H  
Furthermore, wheel bearings are a type of rolling bearing, and studies have been conducted to predict and improve the life of wheel bearings ([PERSON] et al., 2018; [PERSON] et al., 2019; [PERSON] et al., 2019; [PERSON] et al., 2019; [PERSON] et al., 2019; [PERSON] et al., 2019). [PERSON] et al. (2019) expressed the relationship between contact stress and life in a wheel bearing using a formula based on finite element analysis. [PERSON] et al. (2019) calculated the internal stress distribution of a wheel bearing using simulations and predicted the failure that can occur in wheel bearings by analyzing the results. [PERSON] et al. (2019) proposed a life prediction method for multi-axis wheel loads to predict the wheel bearing life under realistic load conditions. Conversely, [PERSON] and [PERSON] (2019) and [PERSON] et al. (2020) improved the fatigue life of wheel bearings by developing the material of the rolling element and outer ring, respectively. Furthermore, [PERSON] (2019) suggested guidelines for design, testing, quality, and production to improve the durability of wheel bearings by investigating the various causes of failure. Rolling bearings, including wheel bearings, lead to various defects in the raceway surface during operation. These defects significantly affect bearing life; therefore, studies are being conducted on identifying bearing defects. Defect diagnosis of a rolling bearing is usually analyzed based on the vibration and noise signals generated when the bearing rotates. Given that the defect diagnosis analyzes the changes in the small noise/vibration due to a slight defect on the bearing surface, the accuracy of the defect diagnosis varies based on the signal processing method. Therefore, various signal processing methods have been proposed to detect and identify defects in rolling bearings ([PERSON] and [PERSON], 2019; [PERSON] et al., 2020; [PERSON] et al., 2020). [PERSON] and [PERSON] (2019) explained the suitability and limitations of each method by applying and comparing various signal-processing methods to the defect diagnosis of wheel bearings. [PERSON] et al. (2019) proposed a method for diagnosing wheel-bearing defects via road-surface acoustic detection. A study was also performed to predict the remaining useful life of the wheel bearing based on the fault signals ([PERSON] et al., 2020). Recently, a noise problem occurred in a wheel bearing applied to an electric vehicle while it was being driven. As electric vehicles are relatively quieter than internal combustion vehicles, new noise issues that have not been a problem in existing internal combustion vehicles are emerging. Specifically, wheel-bearing noise is a new issue. Many cases of wheel-bearing noise are due to surface defects of the balls, inner ring, and outer ring. However, the noise-generating wheel bearings used in this study do not exhibit surface defects. Therefore, it is necessary to investigate the cause and mechanism of the wheel bearing noise, which is a new problem. The purpose of this study involves identifying the noise generated by automotive wheel bearings. In this study, we analyzed an assembled wheel bearing in which the wheel bearing, knuckle, and dust shield are bolted together.