A doctoral thesis at University of Basrah discusses early and cross-domain detection of vibration in rolling element bearings using artificial intelligence techniques.
His doctoral thesis was discussed at the College of Engineering at University of Basrah. Early and cross-domain detection of vibration of bearings with rolling elements using artificial intelligence techniques.
Student Haider Suhail Najm’s thesis included intelligence techniques for fault diagnosis to address two basic challenges in analyzing vibrations in ball bearings.
First, these techniques must be able to detect and classify faults in the early stages of their development, as vibrations during this stage typically have small amplitudes, making them difficult to capture.
Second, the developed methods should be applicable to fault diagnosis in a variety of ball bearing types, rotational speeds, and loading conditions.
The researcher concluded that the results of the cross-domain analysis using the classifier (FD-ANN) with the highest accuracy reached an average accuracy of 98.33% for the NU 205 bearing, and an average accuracy of 97.3% for the defective bearing group, with an average success rate of 99.81% significantly achieved for the bearing. (Koyo 1205) under loading conditions equal to (1.325 kg).
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