Issue 49

S. Djaballah et alii, Frattura ed Integrità Strutturale, 49 (2019) 291-301; DOI: 10.3221/IGF-ESIS.49.29 300 Number Of input Neural network Structure Number Of outputs Performance Rate % 14 4 99.47 14 10 99.33 Table 5: Classification Rate with indicators based on wavelet db6 Figure 10 : Structure of RNA classifier 4 outputs Figure 11 : Structure of RNA classifier 10 outputs The results shown in Tab. 5 show a classification rate of 99.47% for the detection of the fault location (four outputs), and a rate of 99.33% for the detection of the diameter (severity) of the fault (ten outputs). These results confirm the efficiency of the use of the wavelet packet transform (with the db6 wavelet) for the extraction of indicators sensitive to the variations of the state of the bearing to be monitored.