Issue 49

S. Djaballah et alii, Frattura ed Integrità Strutturale, 49 (2019) 291-301; DOI: 10.3221/IGF-ESIS.49.29 296 Number Of input Neural network Structure Number Of outputs Performance Rate % 10 4 97.21 10 10 92.67 Table 2 : Classification rate with time and frequency indicators. From the results shown in Tab. 2, the neuronal classifiers {10 10 4 } and {10 10 10 } which is shown in Fig. 5 and Fig. 6 respectively, have classification rates of 97.21% and 92.67%, respectively. This confirms the contribution of frequency descriptors to the improvement of the classification rate and the diagnosis of defects. D IAGNOSIS OF DEFECTS BY THE WAVELET TRANSFORM Optimal choice of wavelet and decomposition level n practice, unfortunately, there is no wavelet that is better than the others for all cases; it all depends on the intended application. For vibratory analysis based on the wavelet transform, the selection of the mother wavelet depends on its properties or the similarity between the signal and the mother wavelet. Based on the DWPT, this step consists of determining the best mother wavelet (type and order) and the optimal decomposition level best suited to our application. The wavelets chosen for this study are:  The wavelets of Daubechies: db1, db2, db3, db20, db30, db40 and db44  Coiflets: coif1, coif2, ..., coif5  Symlets: sym2, ⋯ , sym10 and sym15 For the search of the decomposition level by the wavelet packet transform, we have considered the levels j ={3, 4, 5, 6, 7}. These characteristics can be expressed in a vector such as: 1 2 2 1 2 2 j j v j j j j j j F E E E K K K        (5) Fig. 7 shows the classification is based on artificial neural networks. Figure 7 : Classification scheme by RNA-based DPWT with a level of decomposition j = 3, 4, 5, 6, 7 I