Enhanced Methodologies for predicting Automotive Wheel Bearing life and damage using RLDA

The durability of wheel bearings is assessed in terms of raceway life and flange life. Raceway life focuses on the performance and damage tolerance of rolling elements, while flange life evaluates the structural integrity of wheel flanges under operational stresses. Traditionally, durability predictions relied on conventional design methods and analytic formulas for raceway spalling, as well as static load assumptions for flange fatigue analysis. Recently, integrating design of experiments (DOE) with traditional approaches has enhanced these methods, enabling systematic evaluation of design variables and loading conditions. This paper introduces a methodology for analysing raceway life and damage in automotive wheel bearings using RLDA (Road Load Data Acquisition) data. The process involves acquiring raw deterministic load data, filtering it to preserve high-peaked signals, and transforming the filtered data into block cycles derived from load time histories. Each block cycle contains load values and their frequency of application, providing a structured representation of dynamic loading scenarios. Raceway life evaluation emphasizes the cumulative effects of dynamic loads over time through techniques like load-cycle transformation. By incorporating road load data and equivalent load computations, damage mechanisms can be predicted. Simulating real-world conditions allows for numerical estimation of raceway life, offering insights into bearing longevity and reliability. A formula for calculating the equivalent load (P) is employed, using an exponent (e) to weigh and aggregate load values raised to its power, then normalizing by the total number of cycles. This approach simplifies complex load cases for faster, efficient evaluation. The methodology provides a systematic framework for assessing dynamic load impacts on raceways, aiding in life prediction and durability improvement.