Amirkabir University of Technology Amirkabir Journal of Mechanical Engineering 2008-6032 54 6 2022 08 23 Pattern Recognition of Unbalanced Rigid Rotor Bearing Forces Pattern Recognition of Unbalanced Rigid Rotor Bearing Forces 1249 1270 4852 10.22060/mej.2022.20592.7267 FA Mohammad Reza Homaeinezhad K. N. Toosi University Of Technology, No. 17, Pardis Street, Mollasadra Ave., Vanak Square, Tehran, Iran. P.O Box: 19395-1999, Postal Code: 19991-43344.Tel: (+98 21) 84063284, Mobile: (+98) 9121899445, Fax: (+98 21) 88677274. 0000-0003-4329-649X Mohammad Hosein Saeidi Mostaghim K. N. Toosi University Of Technology, No. 17, Pardis Street, Mollasadra Ave., Vanak Square, Tehran, Iran. P.O Box: 19395-1999, Postal Code: 19991-43344.Tel: (+98 21) 84063284, Mobile: (+98) 9121899445, Fax: (+98 21) 88677274. Farnood Arab K. N. Toosi University Of Technology, No. 17, Pardis Street, Mollasadra Ave., Vanak Square, Tehran, Iran. P.O Box: 19395-1999, Postal Code: 19991-43344.Tel: (+98 21) 84063284, Mobile: (+98) 9121899445, Fax: (+98 21) 88677274. Journal Article 2021 09 23 In industrial rotatory machines, different forces in rotor bearings are generated due to various impaired mechanical sources, namely bearing misalignment and nonhomogeneous mass distribution (unbalance). By precisely analyzing and diagnosing the produced patterns of bearing forces, one can determine the unbalance parameters such as quantities of masses, their distance from the rotational axis, and characteristics of corresponding parallel planes. Consequently, it will be possible to formulate pragmatic protocols according to which the maintenance engineers of rotatory systems will pinpoint properties of problematic imbalance masses and then straightforwardly balance them. In the procedure of conducting this research, several exemplary imbalance masses are deployed on a rotatory mechanical shaft and the equations of motion and forces in perfectly aligned rigid bearings are extracted. Then, by applying a neural network-oriented system the patterns of bearing forces are recognized and the characteristics of the nominal masses including magnitudes, distances from the rotational axis, angles as well as the unbalance type are determined. The accuracy of predicting 8 variables of balancing masses was 41% and after eliminating the redundant overlaps from principal components, the accuracy of predicted 5 variables of balancing masses significantly increased to 95%. Also, by implementing another comprehensive neural network system, it was shown that by exerting two separate balancing masses, the applicability of this method in balancing any faulty systems with dynamic unbalance is possible. In industrial rotatory machines, different forces in rotor bearings are generated due to various impaired mechanical sources, namely bearing misalignment and nonhomogeneous mass distribution (unbalance). By precisely analyzing and diagnosing the produced patterns of bearing forces, one can determine the unbalance parameters such as quantities of masses, their distance from the rotational axis, and characteristics of corresponding parallel planes. Consequently, it will be possible to formulate pragmatic protocols according to which the maintenance engineers of rotatory systems will pinpoint properties of problematic imbalance masses and then straightforwardly balance them. In the procedure of conducting this research, several exemplary imbalance masses are deployed on a rotatory mechanical shaft and the equations of motion and forces in perfectly aligned rigid bearings are extracted. Then, by applying a neural network-oriented system the patterns of bearing forces are recognized and the characteristics of the nominal masses including magnitudes, distances from the rotational axis, angles as well as the unbalance type are determined. The accuracy of predicting 8 variables of balancing masses was 41% and after eliminating the redundant overlaps from principal components, the accuracy of predicted 5 variables of balancing masses significantly increased to 95%. Also, by implementing another comprehensive neural network system, it was shown that by exerting two separate balancing masses, the applicability of this method in balancing any faulty systems with dynamic unbalance is possible. https://mej.aut.ac.ir/article_4852_5cf68969fb67aa6082363a6d4e6468e2.pdf