Noise Analysis of Air Disc Brake Systems Using Wavelet Synchro Squeezed Transform

Öz

Click here for manuscript sample template

Recently, signal processing methods are shown to be successful while diagnosing faults in mechanical systems, using noise or vibration data.  In this study, two different faulty air disc brakes; noisy and less noisy ones are investigated using Wavelet Synchrosqueezed Transform on audio recordings. The difference between two types are shown in scalogram and also verified by a quantitative measure of entropy. The audio recording has been carried out by using two identical microphones sited on the brakes via data acquisition unit at a sampling rate of 20 kHz, 16-bit resolution and these data are analyzed in MATLAB software. The average of the entropy values of faulty and non-faulty brakes were found to be 0.98 and 0.65, respectively. Therefore, it has been concluded that, the entropy could be used as a distinguishing tool to discriminate the faults.

Anahtar Kelimeler

• Air Disc Brake,Entropy,Noise analysis,Sound Processing,Wavelet Synchro Squeezed Transform

Kaynakça

1. Beloiu, DM, Ibrahim, RA. 2006. Analytical and experimental investigations of disc brake noise using the frequency‐time domain, Structural Control and Health Monitoring. The Official Journal of the International Association for Structural Control and Monitoring and of the European Association for the Control of Structures; 13(1): 277-300.
2. Rhee, SK, Tsang, PHS, Wang, YS. 1989. Friction-induced noise and vibration of disc brakes. Wear; 133(1): 39-45.
3. Chen, X, Feng, Z, Zhao, C. 2016. In Prognostics and System Health Management Conference, China, 2016, pp 1-6.
4. He, D, Cao, H, Wang, S, Chen, X. 2019. Time-reassigned synchrosqueezing transform: The algorithm and its applications in mechanical signal processing. Mechanical Systems and Signal Processing; 117: 255-279.
5. Chen, X, Feng, Z. 2016. Application of reassigned wavelet scalogram in wind turbine planetary gearbox fault diagnosis under nonstationary conditions. Shock and Vibration.
6. Li, H, Xu, F, Liu, H, Zhang, X. 2015. Incipient fault information determination for rolling element bearing based on synchronous averaging reassigned wavelet scalogram. Measurement; 65: 1-10.
7. Peng, Z, Meng, K, G, Chu, FL. 2011. Improved wavelet reassigned scalograms and application for modal parameter estimation. Shock and Vibration; 18(1-2), 299-316.
8. Peng, Z, Chu, F, He, Y. 2002. Vibration signal analysis and feature extraction based on reassigned wavelet scalogram. Journal of Sound and Vibration; 253(5): 1087-1100.

9. Liu, W, Cao, S, Wang, Z, Jiang, K, Zhang, Q, Chen, Y. 2018. A Novel Approach for Seismic Time-Frequency Analysis Based on High-Order Synchrosqueezing Transform. IEEE Geoscience and Remote Sensing Letters; 99: 1-5.
10. Herrera, R. H, Tary, J.B, van der Baan, M, Eaton, DW. 2015. Body wave separation in the time-frequency domain. IEEE Geoscience and Remote Sensing Letters; 12(2): 364-368.
11. Li, D, Liu, H, Gui, X, Zhang, X. 2016. An efficient ISAR imaging method for maneuvering target based on synchrosqueezing transform. IEEE Antennas and Wireless Propagation Letters; 15: 1317-1320.
12. Clausel, M, Oberlin, T, Perrier, V. 2015. The monogenic synchrosqueezed wavelet transform: a tool for the decomposition/demodulation of AM–FM images. Applied and Computational Harmonic Analysis; 39(3): 450-486.
13. Ertekin, Z, Özkurt, N, & Yılmaz, C. 2017. Disk Fren Sistemlerinde Dalgacık Tepeleri Yöntemi ile Ses Analizi. Çukurova Üniversitesi Mühendislik-Mimarlık Fakültesi Dergisi; 32(4): 193-200.
14. Hwang, W, Han, K, Huh, K. 2012. Fault detection and diagnosis of the electromechanical brake based on observer and parity space. International Journal of Automotive Technology; 13(5): 845-851.
15. Kampanalı Fren Sistemi Parçaları ve Çalışması https://otomobilteknoloji.blogspot.com.tr/2016/ 08/kampanali-fren-sistemi-parcalaricalismasi.html/ (accessed at 31.08.2016)
16. Meritor Inc. Air Disc Brake Service Manual ELSA 195: 225- 250 Issued 07/2014, 20.
17. The illustrated wavelet transform handbook: introductory theory and applications in science, engineering, medicine and finance. CRC press. Addison, P. S. 2017
18. Daubechies, I, Lu, J, Wu, HT. 2011. Synchrosqueezed wavelet transforms. An empirical mode decomposition-like tool. Applied and computational harmonic analysis; 30(2): 243-261.
19. https://www.mathworks.com/help/wavelet/gs/wavelet-synchrosqueezing.html
20. Gonzalez, R. C, Woods, RE, Eddins, S. L. (2004). Digital image processing using MATLAB (Vol. 624). Upper Saddle River: Pearson-Prentice-Hall.
21. Ertekin, Z, Özkurt, N, Yilmaz, C. 2018. In 2018 26th Signal Processing and Communications Applications Conference, İzmir, Turkey, 2018, pp 1-4.