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The early detection of faults in rotating systems considers an integral approach that has received considerable attention from the industrial sector, as it contributes to preventing catastrophic failures in machines. In this research, the natural frequencies of a shaft, when it is healthy and when cracks with different depths are introduced, have been calculated. The deviation of the computed natural frequencies from the healthy ones is counted as a sign of the presence of an abnormality in the system. For this intention, the finite element analysis (FEA) method based on ANSYS software has been utilized to obtain the first five natural frequencies of the shaft when there is a crack of different severity at different positions. The results of the FEA are used for designing an artificial neural network (ANN) model that can be easily used to predict the first five natural frequencies of the shaft based on just the crack’s position and depth. Finally, the predicted natural frequencies by the deigned ANN have been compared to their peers that were computed using the FEA method. The absolute error percentage has then been calculated and used to get an indication of how close the result of both techniques is. The recorded highest error percentage was 0.67%, which is quite small and referring to that the designed ANN can accurately predict the natural frequencies of rotating systems.

The early detection of faults in rotating systems considers an integral approach that has received considerable attention from the industrial sector, as it contributes to preventing catastrophic failures in machines. In this research, the natural frequencies of a shaft, when it is healthy and when cracks with different depths are introduced, have been calculated. The deviation of the computed natural frequencies from the healthy ones is counted as a sign of the presence of an abnormality in the system. For this intention, the finite element analysis (FEA) method based on ANSYS software has been utilized to obtain the first five natural frequencies of the shaft when there is a crack of different severity at different positions. The results of the FEA are used for designing an artificial neural network (ANN) model that can be easily used to predict the first five natural frequencies of the shaft based on just the crack’s position and depth. Finally, the predicted natural frequencies by the deigned ANN have been compared to their peers that were computed using the FEA method. The absolute error percentage has then been calculated and used to get an indication of how close the result of both techniques is. The recorded highest error percentage was 0.67%, which is quite small and referring to that the designed ANN can accurately predict the natural frequencies of rotating systems.

The early detection of faults in rotating systems considers an integral approach that has received considerable attention from the industrial sector, as it contributes to preventing catastrophic failures in machines. In this research, the natural frequencies of a shaft, when it is healthy and when cracks with different depths are introduced, have been calculated. The deviation of the computed natural frequencies from the healthy ones is counted as a sign of the presence of an abnormality in the system. For this intention, the finite element analysis (FEA) method based on ANSYS software has been utilized to obtain the first five natural frequencies of the shaft when there is a crack of different severity at different positions. The results of the FEA are used for designing an artificial neural network (ANN) model that can be easily used to predict the first five natural frequencies of the shaft based on just the crack’s position and depth. Finally, the predicted natural frequencies by the deigned ANN have been compared to their peers that were computed using the FEA method. The absolute error percentage has then been calculated and used to get an indication of how close the result of both techniques is. The recorded highest error percentage was 0.67%, which is quite small and referring to that the designed ANN can accurately predict the natural frequencies of rotating systems.