In the modern era, maintenance of rotating machines has the major task for accurate diagnosis of fault is much essential. The demand in system focuses on safety, high reliability, reduced downtime and maintenance. The higher level of vibration magnitude identifies the failure to diagnose and maintain proper condition of machines. The techniques incorporated to analyse the signals leads to reliability increase in rotating machines. The vibration signal features like skewness, kurtosis and RMS values represents the degradation information of the machine failure. This proposed data collection, fault detection, diagnosis and prognosis of rotating machines which can increase reliability and accuracy of machine failure. In this paper, we consider the vibration signal levels to identify the failure due to scarcity of data. Mainly data is collected from the rotating machines using vibrometer and obtained spectrums is analyzed by using signal processing techniques which defines clearly the severity level of vibration in a component.
