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Accurate prediction of Remaining Useful Life (RUL) of machines and machine components is very important for reliability evaluation. This paper proposes an Artificial Neural Network (ANN) as a method for accurate prediction of RUL of bearings based on vibration measurements during an accelerated life test. The input features to the neuro-predictor are: the vibration signal in the time domain, the dominant harmonics of the bearing vibration signal expressed in a set of selected coefficients of the discrete cosine transforms (DCT), and the main harmonics of the vibration signal as expressed by Fast Fourier Transform (FFT).The Euclidean distance which is a measure of time to failure based on RMS value is used as the figure of merit for the validation of the ANN. Henceforth; the RUL of the bearing can be predicted as the output of the neuropredictor. The results prove that the suggested methodology can successfully be applied for prediction of bearing RUL. Keywords— Remaining Useful Life (RUL), Reliability evaluation, Artificial Neural Network (ANN), Discrete Cosine Transform (DCT), Machine Tool Dynamics.

Prediction of Bearing Remaining Useful Life Based on Euclidean Distance Using an Artificial Neural Network Approach