Defects Detection of Rotating Machine Using ‎Vibration Analysis and Neural Network ‎

The base of diagnosing the possible defects of a machine is comparing the frequency ‎spectra of the vibrations at different points with the existing reference spectra. Due to the ‎needless stoping of machine for investigation of its various parts, use of this ‎troubleshooting method is affordable; Also, regarding to progress of possible ‎defectes, the machine can be rapaired in any required times. In this study , using ‎Neural Network (MLP and FNN), firstly common defects in rotating machines were created ‎separately, then the produced vibrational frequency were measured by ADASH 4400 ‎analyzer. Introducing four vibrational characteristics including angular misalignment, ‎clearance, failure and unbalance of bearing as input data of artificial neural network ,the ‎results were compared to the reference frequency signals. The results show that neural ‎networks MLP and FNN increase the defects detection ability by 73% and 78%, ‎respectively. So, FNN method is proposed for useful life prediction and detection of rotating ‎parts.‎