Issue 49

S. Djaballah et alii, Frattura ed Integrità Strutturale, 49 (2019) 291-301; DOI: 10.3221/IGF-ESIS.49.29 301 C ONCLUSIONS he work presented in this article is part of the preventive maintenance of rotating equipments and particularly the detection of defects bearings by vibration analysis. The temporal and frequency analysis is used to improve the classification rate and the diagnosis of bearing failure and the time and frequency analysis is used to determining its diameter and location. This objective was mostly achieved with a precision of about 92.67% when determining the diameter of the defect, and 97.21% when locating. Next, we have tried to determine the optimal wavelet best suited to the diagnosis and classification of bearing defects using the wavelet packet transform and Artificial Neural Networks (ANN). The main objective was to determine the wavelet that generates indicators in this case energy and kurtosis best reflecting the state of the bearings. We could show that the wavelet db6 with decomposition level 3 is the most appropriate diagnosis and classification of bearing fault. This wavelet db6 and decomposition level 3 improve our result at 99.33 % when determining the diameter of the defect, and 99.47 % when locating R EFERENCES [1] Gomez, M., Castejon, C and García-Prada, J. (2014) Incipient Fault Detection in Bearings Through the use of WPT Energy and Neural Networks, Advances in Condtion Monitoring of machinery in Non-Stationary operations, Springer, pp 63-72, DOI: 10.1007/978-3-642-39348-8_4 [2] Du Q., Yang, S. (2007) Application of the EMD method in the vibration analysis of ball bearings, MechSyst Sig Process 21, pp. 2634–2644, DOI: 10.1016/j.ymssp.2007.01.006 [3] Hu, Q., He, Z., Zhang, Z., Zi, Y. (2007) Fault diagnosis of rotating machinery based on improved wavelet package transform and SVMs ensemble, Mech Syst Sig Process 21, pp- 668–705. DOI: 10.1016/j.ymssp.2006.01.007 [4] Aherwar, A., Khalid, Md S., Hemaint, K N. (2012) Vibration analysis of machine fault signature, National [5] Conference on recent advances in mechanical engineering pp. 487-492 [6] Amarnath, M., Sugumaran, V., Kumar, H. (2013) Exploiting sound signals for fault diagnosis of bearings using decision tree, Measurement. 2013 Apr; 46(3), pp. 1250–1256, DOI: 10.1016/j.measurement.2012.11.011 [7] Rosani M. (1999). Signal analysis in transient applying the wavelet technique, [Master Thesis], Sao.Paulo University [8] Memon, A., Khokhar, S., Memon, Z. (2014). Discrete Wavelet Transform and Multiresolution Analysis Algorithm with Appropriate Feedforward Neural Network Classifier For Power System Transient Disturbances, Sci.Int.(Lahore), 26(5), pp. 2231-2238. [9] Liu, B., Ling, S. (1997) Machinery diagnostic based on wavelet packets, Vib Control 3, pp. 5–17. DOI: 10.1177/107754639700300102. [10] Kafiey Khan, A., Azizur, R. (2010) Wavelet Based Diagnosis and Protection of Electric Motors, Fault Detection, Wei Zhang (Ed.), InTech, DOI: 10.5772/9068. [11] Michel, Misiti., Yves, Misiti. (2003). Les ondelettes et leurs applications, Edition Hermes, Paris. [12] Altmann, J., Mathew, J. (2001). Multiple band pass autoregressive demodulation for rolling element bearing fault diagnosis, Mech Syst Signal Process, 15, pp. 963-977, DOI: 10.1006/mssp.2001.1410. [13] Pandya, D_H., Upadhyay, S_H., Harsha, S P. (2013) Fault diagnosis of rolling element bearing by using multinomial logistic regression and wavelet packet transform, DOI: 10.1007/s00500-013-1055-1. [14] Loparo, K. (2018). Bearings Vibration Data Set, Case Western Reserve University. Available online: http://csegroups.case.edu/bearingdatacenter/home (10 January 2018). [15] Castejon, C., Lara, O., García-Prada, J. (2010) Automated diagnosis of rolling bearings using MRA and neural networks , Mech Syst Sig Process 24, pp. 289–299, DOI: 10.1016/j.ymssp.2009.06.004. [16] Lara, O., Castejon, C., Garcia-Prada, J. (2006) Bearing fault diagnosis based on neural network classification and wavelet transform, WSEAS Trans. DOI: 10.5555/1974867. [17] Adewusi, S.A. (2001) Wavelet analysis of vibration signals of an overhang rotor with a propagating transverse crack, J Sound Vib 5, pp. 777–793, DOI: 10.1006/jsvi.2000.3611. T