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

2003 ◽  
Author(s):  
Peng Xu ◽  
A.K. Chan
Keyword(s):  
Neural Network ◽  
Fault Detection ◽  
Bearing Fault ◽  
Bearing Fault Detection ◽  
Wavelet Features ◽  
Wheel Bearing

scholarly journals Wheel Bearing Fault Isolation and Prognosis Using Acoustic Based Approach

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.

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

2021 ◽  
Vol 13 (1) ◽  
Author(s):  
Graeme Garner
Keyword(s):  
Fault Detection ◽  
Fault Injection ◽  
Detection System ◽  
Low Cost ◽  
Ground Truth ◽  
Business Case ◽  
Bearing Fault ◽  
Bearing Fault Detection ◽  
Bearing Condition Monitoring ◽  
Wheel Bearing

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.

Application of cepstrum and neural network to bearing fault detection

2009 ◽  
Vol 23 (10) ◽  
pp. 2730-2737 ◽  
Author(s):  
Yean-Ren Hwang ◽  
Kuo-Kuang Jen ◽  
Yu-Ta Shen
Keyword(s):  
Neural Network ◽  
Fault Detection ◽  
Bearing Fault ◽  
Bearing Fault Detection

scholarly journals Linear Bearing Fault Detection in Operational Condition Using Artificial Neural Network

2019 ◽  
Vol 24 ◽  
pp. 01004
Author(s):  
Siwanu Lawbootsa ◽  
Prathan Chommaungpuck ◽  
Jiraphon Srisertpol
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Fault Detection ◽  
Ball Bearing ◽  
Fault Detection And Isolation ◽  
Bearing Faults ◽  
Bearing Fault ◽  
Bearing Fault Detection ◽  
Artificial Neural ◽  
Bearing Damage

Nowadays, Factors of a competition of Hard Disk Drive (HDD) industry have reduced the cost of manufacturing process via increasing the rate of productivity and reliability of the automation machine. This paper aims to increase the efficacy of Condition-Based Maintenance (CBM) of linear bearing in Auto Core Adhesion Mounting machine (ACAM). The linear bearing faults considered in three causes such as healthy bearing, one ball bearing damage and one ball bearing damage with starved lubricant. The Fast Fourier Transform spectrum (FFT spectrum) can be detected for linear bearing faults and Artificial Neural Network (ANN) method used to analyze the cause of linear bearing faults in operational condition. The experimental results show the potential application of ANN and FFT spectrum technique as Fault Detection and Isolation (FDI) tool for linear bearing fault detection performance. The accuracy and decision making of ANN is enough to develop the diagnostic method for automation machine in operational condition.

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