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.

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Bearing fault detection under time-varying speed based on empirical wavelet transform, cultural clan-based optimization algorithm, and random forest classifier

Journal of Vibration and Control ◽

10.1177/10775463211047034 ◽

2021 ◽

pp. 107754632110470

Author(s):

Moussaoui Imane ◽

Chemseddine Rahmoune ◽

Mohamed Zair ◽

Djamel Benazzouz

Keyword(s):

Wavelet Transform ◽

Random Forest ◽

Fault Detection ◽

Optimization Algorithm ◽

High Efficiency ◽

Time Varying ◽

Bearing Fault ◽

Bearing Fault Detection ◽

Empirical Wavelet Transform ◽

Bearing Condition Monitoring

Bearings are massively utilized in industries of nowadays due to their huge importance. Nevertheless, their defects can heavily affect the machines performance. Therefore, many researchers are working on bearing fault detection and classification; however, most of the works are carried out under constant speed conditions, while bearings usually operate under varying speed conditions making the task more challenging. In this paper, we propose a new method for bearing condition monitoring under time-varying speed that is able to detect the fault efficiently from the vibration signatures. First, the vibration signal is processed with the Empirical Wavelet Transform to extract the AM-FM modes. Next, time domain features are calculated from each mode. Then, the features’ set is reduced using the Cultural Clan-based optimization algorithm by removing the redundant and unimportant parameters that may mislead the classification. Finally, an ensemble learning algorithm “Random Forest” is used to train a model able to classify the fault based on the selected features. The proposed method was tested on a time-varying real dataset consisting of three different bearing health states: healthy, outer race defect, and inner race defect. The obtained results indicate the ability of our proposed method to handle the speed variability issue in bearing fault detection with high efficiency.

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Fast and robust neural network based wheel bearing fault detection with optimal wavelet features

Proceedings of the 2002 International Joint Conference on Neural Networks. IJCNN'02 (Cat. No.02CH37290) ◽

10.1109/ijcnn.2002.1007461 ◽

2003 ◽

Author(s):

Peng Xu ◽

A.K. Chan

Keyword(s):

Neural Network ◽

Fault Detection ◽

Bearing Fault ◽

Bearing Fault Detection ◽

Wavelet Features ◽

Wheel Bearing

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Wheel Bearing Fault Isolation and Prognosis Using Acoustic Based Approach

Annual Conference of the PHM Society ◽

10.36001/phmconf.2019.v11i1.798 ◽

2019 ◽

Vol 11 (1) ◽

Author(s):

Jianshe Feng ◽

Xinyu Du ◽

Mutasim Salman

Keyword(s):

Fault Detection ◽

Vibration Signal ◽

Fault Isolation ◽

Fault Detection And Isolation ◽

Installation Space ◽

Bearing Failure ◽

Bearing Fault ◽

Bearing Fault Detection ◽

Failure Prognosis ◽

Wheel Bearing

Wheel bearing fault detection, isolation and failure prognosis are critical to improve perceived quality and customer experience for retail vehicles, and to reduce the repair cost and down time for fleet vehicles. Currently, most of the research in bearing failure and degradation diagnosis focus on vibration signal analytics. However, these techniques are rarely applied in automotive industry due to the high sensor cost, installation space limitation, and limited communication bandwidth. In this work, an acoustic based approach for wheel bearing fault detection and isolation is developed to overcome these limitations. Since the bearing noise is a precursor of bearing failure, the proposed method is a prognosis solution. The whole solution is verified using the data collected from a production vehicle. The results show that the proposed method can predict the wheel bearing failure with promising accuracy and robustness.

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