Automotive Wheel Bearings Research Articles

Construction of bearing health indicator under time-varying operating conditions based on Isolation Forest

While there have been extensive studies in the health assessment of the bearings using the vibration signal, most have focused on the constant operating conditions. The field, however, operates often under time-varying conditions as is the case of the automotive wheel bearing. In this study, a novel health indicator (HI) is proposed to address this problem based on the Isolation Forest algorithm, which was originally developed for anomaly detection. The method is advantageous in two aspects: the HI is not influenced by the type of operating conditions whether it is constant or time-varying. Only the data under normal condition are used to construct the HI without the need of run-to-failure data. The method is demonstrated by the three cases with different types of bearing and operating conditions ranging from constant to the highly variable conditions. As a result, monotonic trends are obtained for the HI in all cases, which may be useful for the prognostic monitoring. Furthermore, in comparison with the HI constructed by the run-to-failure data in the previous literature, it is found that the trends of HI agree reasonably well with each other, which supports the validity of the method.

Fatigue Analysis for Automotive Wheel Bearing Flanges

The automotive wheel bearing is an integral component of automobiles that transfers rotation and supports the weight of the vehicle. The life of automotive wheel bearings can be categorized into the raceway and flange lives. The raceway life is based on the spalling that occurs in the raceway and can be determined analytically using the basic rating life calculation method for the rolling bearing.The flange life is based on failure involving the flange and can be numerically predicted through fatigue analysis. In particular, the fatigue analysis for predicting the flange life must consider rotation owing to the bearing characteristics, unlike the general fatigue analysis. In this study, we examined the fatigue analysis of the automotive wheel bearing flange while considering rotation. We performed sequential stress and fatigue analyses using commercial software. As the fatigue material properties are required for fatigue analysis, we obtained the properties by conducting a rotary bending fatigue test on the bearing materials. To verify the results of the analysis, we compared the analysis results with the test results. The validity of the proposed fatigue analysis was confirmed.

Noise Identification for an Automotive Wheel Bearing

In this study, we identified the noise generated from automotive wheel bearings, which has recently emerged as a new problem in electric vehicles. The wheel bearing assembly considered in this study consists of a wheel bearing, dust shield, and knuckle, which are fastened with bolts. To obtain the noise characteristics of the wheel bearing, the noise and vibration were experimentally measured when the bearing rotated. Additionally, the natural frequencies and mode shapes of the main components of the bearing were acquired via modal testing. By comparing the obtained natural frequencies with the peak frequencies of the measured noise and vibration signals, we identified where the noise radiated. To specifically identify bearing defects, a finite element analysis model was established, and the deformation of the bearing under load was analyzed. Based on the analysis, we determined that the deformation of the outer ring in an outboard row, which resulted from bolt fastening, leads to noise and vibration in the wheel bearing.

Brinell Fault Injection to Enable Development of a Low-Cost Wheel Bearing Fault Monitoring System for Automobiles

Although bearing condition monitoring and fault diagnosis is a widely studied and mature field, applications to automotive wheel bearings have received little attention. This is likely due to the lack of business case, as the vehicle’s four wheel bearings are typically designed to last the vehicle life with low failure rates. Rapid advancements in battery technology are expected to open the door for EVs with million-mile lifespans, exceeding the reliable life of existing low-cost wheel bearing designs. Vehicle designers and fleet owners must choose between paying a higher price for bearings with a longer life or replacing wheel bearings periodically throughout the vehicle life. The latter strategy can be implemented most effectively with the implementation of a low-cost fault detection system on the vehicle.
 
 To develop such a system, data from systems with healthy and faulty wheel bearings is needed. This paper discusses the options for generating this data, such as simulation, bench tests, and vehicle-level tests. The challenges and limitations of each are explored, and the specific challenges of developing an approach for a wheel bearing fault detection system are discussed in detail. A method for injecting Brinell Dent failures is developed, and the results of injecting a total of 40 faulty wheel bearings are presented. Metrics of measuring and summarizing the ground-truth health of a wheel bearing using vibration signals recorded on a test bench are explored. These wheel bearings are used to collect preliminary vehicle data, and some initial analysis is shared highlighting the differences between healthy and faulty wheel bearings, setting the stage for future work to develop a low-cost wheel bearing fault detection system.

Orbital Forming of Automotive Wheel Bearings

<div class="section abstract"><div class="htmlview paragraph">Orbital forming is considered an optimal assembly process for many designs of automotive wheel bearing assemblies. More specifically, this process involves forming the spindle over the inner race of the wheel bearing to maximize surface area contact on the inner ring for retention, and to maintain bearing preload. The unique forming properties and precise controls allow orbital forming to produce an ideal finished retention feature and form for these types of bearings. This paper discusses the specific benefits of orbital forming of automotive wheel bearings. Forming basics, the physics behind the benefits of orbital forming, as well as pre-form lip geometry and other part design considerations are presented. Advanced machine control methodologies and historical machine process improvements are also reviewed concluding with next steps in process control and in-machine quality assurance.</div></div>

Performance Prediction of Automotive Wheel Bearing Seals

Performance Prediction of Automotive Wheel Bearing Seals

The Effect of Outer Ring Flange Concavity on Automotive Wheel Bearings Performance

The Effect of Outer Ring Flange Concavity on Automotive Wheel Bearings Performance

작동하중과 회전속도를 고려한 자동차용 휠 베어링의 수명평가

자동차용 휠 베어링은 회전운동을 전달하고 차량의 무게를 지지해주는 중요한 부품이다. 최근 COSUB2/SUB 배출가스 저감과 연비 규제에 따라서 자동차 경량화 및 소형화 요구는 점점 증가하고 있고, 이에 부응하여 베어링의 수명 평가기술은 더욱 그 필요성이 증가하고 있다. 휠 베어링의 내구수명은 고려해야 할 인자들이 매우 다양하며 이들이 서로 복잡하게 연관되어 있으므로 수명을 예측하는 것이 쉽지 않다. 본 논문에서는 자동차용 휠 베어링의 수명에 영향을 끼치는 많은 인자들 중에서 작동하중과 회전속도를 변화시키면서 수명을 시험하였고, 이를 ISO 국제표준에서 제안하는 이론수명인 기본정격수명과 보정정격수명 결과와 비교하였다. 이들 수명을 비교한 결과, 기본정격수명과 보정정격수명 모두 시험수명과 차이가 존재하며, 기본정격수명 보다 보정정격수명이 시험수명을 상대적으로 잘 예측함을 알 수 있었다.

Statistical quality control through overall vibration analysis

The present study introduces the concept of statistical quality control in automotive wheel bearings manufacturing processes. Defects on products under analysis can have a direct influence on passengers’ safety and comfort. At present, the use of vibration analysis on machine tools for quality control purposes is not very extensive in manufacturing facilities. Noise and vibration are common quality problems in bearings. These failure modes likely occur under certain operating conditions and do not require high vibration amplitudes but relate to certain vibration frequencies. The vibration frequencies are affected by the type of surface problems (chattering) of ball races that are generated through grinding processes. The purpose of this paper is to identify grinding process variables that affect the quality of bearings by using statistical principles in the field of machine tools. In addition, an evaluation of the quality results of the finished parts under different combinations of process variables is assessed. This paper intends to establish the foundations to predict the quality of the products through the analysis of self-induced vibrations during the contact between the grinding wheel and the parts. To achieve this goal, the overall self-induced vibration readings under different combinations of process variables are analysed using statistical tools. The analysis of data and design of experiments follows a classical approach, considering all potential interactions between variables. The analysis of data is conducted through analysis of variance (ANOVA) for data sets that meet normality and homoscedasticity criteria. This paper utilizes different statistical tools to support the conclusions such as chi squared, Shapiro–Wilks, symmetry, Kurtosis, Cochran, Hartlett, and Hartley and Krushal–Wallis. The analysis presented is the starting point to extend the use of predictive techniques (vibration analysis) for quality control. This paper demonstrates the existence of predictive variables (high-frequency vibration displacements) that are sensible to the processes setup and the quality of the products obtained. Based on the result of this overall vibration analysis, a second paper will analyse self-induced vibration spectrums in order to define limit vibration bands, controllable every cycle or connected to permanent vibration-monitoring systems able to adjust sensible process variables identified by ANOVA, once the vibration readings exceed established quality limits.

A Design Method of an Automotive Wheel-Bearing Unit With Discrete Design Variables Using Genetic Algorithms

In order to improve the efficiency of the design process and the quality of the resulting design, this study proposes a design method for determining design variables of an automotive wheel-bearing unit of double-row angular-contact ball bearing type by using a genetic algorithm. The desired performance of the wheel-bearing unit is to maximize system life while satisfying geometrical and operational constraints without enlarging mounting space. The design variables selected are number of balls, initial contact angle, standard ball diameter, pitch circle diameter, preload, distance between ball centers, and wheel offset, which must be selected at the preliminary design stage. The use of gradient-based optimization methods for the design of the unit is restricted because this design problem is characterized by the presence of discrete design variables such as the number of balls and standard ball diameter. Therefore, the design problem of rolling element bearings is a constrained discrete optimization problem. A genetic algorithm using real coding and dynamic mutation rate is used to efficiently find the optimum discrete design values. To effectively deal with the design constraints, a ranking method is suggested for constructing a fitness function in the genetic algorithm. A computer program is developed and applied to the design of a real wheel-bearing unit model to evaluate the proposed design method. Optimum design results demonstrate the effectiveness of the design method suggested in this study by showing that the system life of an optimally designed wheel-bearing unit is enhanced in comparison with that of the current design without any constraint violations. It is believed that the proposed methodology can be applied to other rolling element bearing design applications.

A Design of an Automotive Wheel Bearing Unit for Long Life

A Design of an Automotive Wheel Bearing Unit for Long Life