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.

Development of an Additional System for Wheel Bearing Protection

The main goal of the article is to describe the development and production of a prototype of an additional system for wheel motor bearing protection using additive technologies. These technologies include rapid prototyping and vacuum casting. These technologies greatly facilitate and speed up the whole process of developing a new component. Before the development itself, it was necessary to digitize the wheel hub using reverse engineering technology. Using a 3D scanner, a part model was created, based on which an additional system for wheel bearing protection was designed for the CAD system. The dimensional analysis of the produced prototypes was performed. Prototypes of the protection system made of different materials will be tested in common operation conditions.

Intelligent Fault Diagnosis of Planetary Wheel Bearing Using Constrained Independent Component Analysis and Stacked Sparse Autoencoder

Industrial machinery is developing in the direction of large-scale, automation, and high precision, which brings novel troubles to mechanical equipment management and maintenance. Intelligent diagnosis of mechanical running state based on vibration signals is becoming increasingly important, and it is still a great challenge at pattern recognition. As one of the indispensable components in mechanical equipment, planetary gearboxes are widely used in wind power, aerospace, and heavy industry. However, the problem of automatically maximizing the accuracy of planetary gearbox under different working conditions has not been solved. Therefore, an intelligent diagnosis method for planetary wheel bearing based on constrained independent component analysis (CICA) and stacked sparse autoencoder (SSAE) is presented in this research. Firstly, the fault signal with obvious time-domain characteristics is extracted by constrained independent component analysis (CICA), and the fault signals and noise is separated. Then, calculating the correlation kurtosis value of the time domain signals at different iteration periods as the eigenvalue to obtain the training samples and the test samples. The parameters of the network layer, the number of hidden nodes and learning rate are determined to build the model of SSAE. In the end, the training samples are input into the model for training and the whole network is fine-tuned. The advantages and disadvantages of the model are verified by the test samples. The intelligent classification and diagnosis of the mechanical running state are completed. Experiments analysis with real datasets of planetary wheel bearing show that the proposed method can achieve higher accuracy and robustness for fault classification compared with other data-driven methods. The application of this method in other major machinery industry also has bright prospects.