Issue 49

S. Djaballah et alii, Frattura ed Integrità Strutturale, 49 (2019) 291-301; DOI: 10.3221/IGF-ESIS.49.29 293 indicators, energy, and kurtosis can be combined, instead of using one, to better characterize the vibratory signal and improve fault detection [12]. B EARING D ATA C ENTER ibration signals (accelerations) are obtained by exploiting the data made available on the Case Western Reserve University (CWRU, Bearing Data Center) site [ 13 ]. The signals are measured at a sampling frequency Fe = 12 kHz for 10 sec on a three-phase 1.5 kW (2 hp: horsepower) electric machine coupled to a load that determines its speed. The data were acquired from the rolling bearings under different loads (0, 1, 2 and 3 loads) and under different rolling conditions: normal condition, ball defect (BF), inner race defect (IRF), and defect in the outer race (ORF). As illustrated in Fig 2. Figure 2 : Bearing Test Stand The bearings used in this work are SKF 6205 type ball bearings. Internal ring, outer ring, and ball defects are introduced into the bearings by Electrical Discharge Machining (EDM). The defect diameters are 0.1778 mm (0.007 inches ), 0.3556 mm (0.014 in ), 0.5334 mm (0.021 in ) and 0.7112 mm (0.028 in ) corresponding to incipient, moderate, severe and very severe defects respectively. D IAGNOSIS OF DEFECTS BY METHODS OF ANALYSIS USING A CLASSIFIER ON NEURAL NETWORKS Detection of bearing defects by time analysis or temporal analysis, the input vector of the neural network is formed by the 7 temporal indicators already mentioned and are given by:   T Peak RMS Ku ImF CF TALAF THIKAT (1) Two cases are considered according to the number of outputs. As shown in Fig. 3, we have 4 output neural network and 10 output neural network is shown in Fig. 4. The performance of the fault diagnosis is evaluated by the recognition rate which is defined as % 100 c r t N t N   (2) : c N Number of correct decisions t N : Total number of tests V F