In many technical applications, accurate crack localization and identification in the rotor shaft of rotary machinery is essential. premature machine component failures can occur due to excessive vibration in the shaft. In many technical applications, rotor shaft crack localization and detection are crucial. Therefore, it is necessary to promptly identify any damage to prevent catastrophic failure. It is observed that an experimental modal analysis is employed to investigate the vibration characteristics of the shaft, such as natural frequency, damping, and mode shapes. Additionally, artificial neural networks (ANN) trained with machine learning (ML) can be utilized to detect the depth and location of cracks. Several research papers have focused on rotary machine shafts with open transverse cracks, and they have explored using excessive speed and frequency to identify the position and depth of the cracks. This paper reviews crack detection in rotating shaft by its techniques.

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