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 while wheel bearings do fail due to curb strikes and contamination ingress, the failure rates are typically very low in traditional internal combustion engine vehicles with 200 – 300k mile lifespans. Rapid advancements in battery technology are expected to open the door for vehicles with million-mile lifespans, exceeding the reliable life of existing 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 a low-cost fault detection system on the vehicle.
To develop such a system, data collected 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 limitations of each are explored, and the specific challenges of developing an approach for wheel bearing fault detection are discussed in detail. A method for injecting brinell dent failures is developed, and the results of injecting 27 faulty wheel bearings are presented. Metrics to measure and summarize the ground-truth health of a wheel bearing using vibration signals recorded on a test bench are explored. We discuss the results and challenges of the fault injection process in detail and outline the future work for developing a fault detection algorithm using data collected on these bearings.
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bearing, fault injection, brinell dent, brinell, fault monitoring, condition monitoring
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