Sound Analysis of Disc Brake Systems using Wavelet-Ridges Method
Öz
In this work, the sounds from faulty disc brake system have been
analyzed by wavelet ridge approach. The sounds have been acquired by two
identical Norsonic Type 1228 microphones on vehicle in the lab environment. The
data transferred into computer via data acquisition board was analyzed in
Matlab by wavelet ridge approach to discriminate normal brake sound from the
faulty one; the entropy of wavelet ridge
matrix have been calculated. All in all, it has been observed that the calculated
entropies collected from the faulty brake is greater than the normal one, and
it can be used as a discriminative feature in the fault analysis.
Anahtar Kelimeler
Kaynakça
- 1. Hwang, W., Han, K., Huh, K., Jung, J., Kim, M., 2011. Fault Detection and Diagnosis of the Electromechanical Brake Based on Observer and Parity Space. 14th International IEEE Conference on Intelligent Transportation Systems Washington, DC, USA. October 5-7, 2011.
- 2. Jegadeeshwaran, R., Sugumaran, V., 2015. Brake fault diagnosis using Clonal Selection Classification Algorithm (CSCA) – A Statistical Learning Approach, Engineering Science and Technology, an International Journal, Elsevier, Vol. 18, 14-23.
- 3. Jegadeeshwaran, R., Sugumaran, V., 2013. Comparative Study of Decision Tree Classifier and Best First Tree Classifier for Fault Diagnosis of Automobile Hydraulic Brake System using Statistical Features. Measurement, Elsevier, 3247-3260.
- 4. Liu, J., Li, Y., F., Zio, E., 2017. A SVM Framework for Fault Detection of the Braking System in a High-Speed Train. Mechanical Systems and Signal Processing, Elsevier, Vol. 87, 401-409.
- 5. Fraser, S., Nikora, V., Williamson, B., J., Scott, B., E., 2017. Automatic Active Acoustic Target Detection in Turbulent Aquatic Environments. Methods, Wiley, 15(2), 184-199.
- 6. Beloiu, D., M., Ibrahim, R., A., 2006. Analytical and Experimental Investigations of Disc Brake Noise Using the Time Frequency Domain. Structural Control and Health Monitoring, Wiley, Vol. 13, 277-300.
- 7. Xijun, ZHU., Jinyun, G., Chongyou, W., 2008. Control and Decision Conference. Prediction and Diagnosis of Mine Hoist Fault Based on Wavelet Neural Network. CCDC 2008. Chinese, IEEE, 598-601.
- 8. Wald, R., Khoshgoftaar, T., Sloan, J., C., 2011. Information Reuse and Integration (IRI), International Conference, IEEE, 366-371, Las Vegas, NV, USA
Ayrıntılar
Birincil Dil
Türkçe
Konular
-
Bölüm
Araştırma Makalesi
Yazarlar
Zeynep Ertekin
*
Yaşar Üniversitesi, Filim Tasarım Bölümü, İzmir, Türkiye
Nalan Özkurt
Bu kişi benim
Yaşar Üniversitesi, Filim Tasarım Bölümü, İzmir, Türkiye
Cem Yılmaz
Bu kişi benim
Ege Fren Sanayi ve Ticaret A.Ş., İzmir
Yayımlanma Tarihi
15 Aralık 2017
Gönderilme Tarihi
26 Haziran 2017
Kabul Tarihi
19 Aralık 2017
Yayımlandığı Sayı
Yıl 2017 Cilt: 32 Sayı: 4
Cited By
Noise Analysis of Air Disc Brake Systems Using Wavelet Synchro Squeezed Transform
Celal Bayar Üniversitesi Fen Bilimleri Dergisi
https://doi.org/10.18466/cbayarfbe.522686