SUMMARY
This paper considers a comparison of six wavelets for bearing fault diagnosis. Five wavelets Symlet_05, Symlet_08, Daubechies_04, Daubechies_06, Daubechies_08 are typical ones which are used for fault diagnosis due to several researches. The purpose is to design a new discrete wavelet which has higher efficiency to reveal minor defects on the bearing rings. Defects derive from either manufacturing or operational problems. Detecting of tiny manufacturing defects, especially manufacturing grinding marks is quite difficult due to their special geometrical shapes, however they can cause serious problems in machines during operation. Therefore, it is an important task to diagnose these marks with the most adequate methods. The transient vibration signal model of the defect is established for signals generated by tapered roller bearing on the outer race. The wavelet creation used the sub-optimal algorithm devised by Chapa and Rao. The applicability of the matched wavelet is tested for identifying this kind of bearing failure. The new wavelet analysis and synthesis filter coefficients are determined which define the designed wavelet. To determine the efficiency of the designed wavelet and to establish comparison with the other wavelets, a test-rig was constructed with high-precision measuring sensors and devices. By using the Maximum Energy-to-Shannon Entropy criteria the efficiency of the wavelets is determined. The designed wavelet is found to be the most effective to detect the manufacturing fault compared to the others in this article. The final purpose is not only to detect the faults but to determine their sizes. By analyzing the entry points of the rollers into the defects, the de-stressing point and the exit points of the rollers from the defects the width of the grinding marks is calculated. It is proved that the new-designed wavelet obtains the most precise way for fault width measurement. Finally, the size of the failure is measured by a contact type Mahr Perthometer to compare the results to the calculated parameters and validate them. The width deviation is only 1.18 % in the case of the new-designed wavelet which is remarkable precision level for bearing fault analysis.