Real Time Defect Detection of Wheel Bearing by Means of a Wirelessly Connected Microphone

In this work, an electronic system aiming to automatically monitor the state of health of wheel bearings, is proposed. The focus is on designing a low cost, smallsize and wirelessly interfaced module. Acoustic emissions are exploited to detect defects by means of a low cost micro electro-mechanical system (MEMS) microphone. Amicrocontroller was used to evaluate the frequency spectrum and to interface the system through a wireless data link. The designed module successfully achieved the proposed goals. Finally, a novel measurement process is presented to evaluate the system performance under realistic conditions.

[1]  C. Sidney Burrus,et al.  The quick Fourier transform: an FFT based on symmetries , 1998, IEEE Trans. Signal Process..

[2]  Datong Liu,et al.  DSP based module for processing vibration signals of rotation machinery , 2017, 2017 IEEE International Instrumentation and Measurement Technology Conference (I2MTC).

[3]  Yu-Ting Lin,et al.  Monitoring of micro-stamping procedures utilizing MEMS microphones under acoustic vibration transduction , 2017, 2017 IEEE 12th International Conference on Nano/Micro Engineered and Molecular Systems (NEMS).

[4]  Myeongsu Kang,et al.  A Massively Parallel Approach to Real-Time Bearing Fault Detection Using Sub-Band Analysis on an FPGA-Based Multicore System , 2016, IEEE Transactions on Industrial Electronics.

[5]  Maciej Orman,et al.  A signal processing approach to bearing fault detection with the use of a mobile phone , 2015, 2015 IEEE 10th International Symposium on Diagnostics for Electrical Machines, Power Electronics and Drives (SDEMPED).

[6]  Robert B. Randall,et al.  Rolling element bearing diagnostics—A tutorial , 2011 .

[7]  Wei Zhang,et al.  Dual-Impulse Response Model for the Acoustic Emission Produced by a Spall and the Size Evaluation in Rolling Element Bearings , 2015, IEEE Transactions on Industrial Electronics.

[8]  F. Harris On the use of windows for harmonic analysis with the discrete Fourier transform , 1978, Proceedings of the IEEE.

[9]  Yu Zhang,et al.  Roller element bearing acoustic fault detection using smartphone and consumer microphones comparing with vibration techniques , 2016, 2016 17th International Conference on Mechatronics - Mechatronika (ME).

[10]  Douglas L. Jones,et al.  Real-valued fast Fourier transform algorithms , 1987, IEEE Trans. Acoust. Speech Signal Process..