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

DALIA, M. AMMAR and ELSAYED, S. ELSAYED and MOHAMMAD, A. YOUNES (2017) Prediction of Bearing Remaining Useful Life Based on Euclidean Distance Using an Artificial Neural Network Approach. In: Fifth International Conference on Advances in Mechanical and Robotics Engineering - AMRE 2017, 27-28 May, 2017, Rome, Italy.

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Abstract

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 neuro-predictor. The results prove that the suggested methodology can successfully be applied for prediction of bearing RUL.

Item Type: Conference or Workshop Item (Paper)
Uncontrolled Keywords: Remaining Useful Life (RUL), Reliability evaluation, Artificial Neural Network (ANN), Discrete Cosine Transform (DCT),Machine Tool Dynamics.
Depositing User: Mr. John Steve
Date Deposited: 15 Mar 2019 11:06
Last Modified: 15 Mar 2019 11:06
URI: http://publications.theired.org/id/eprint/461

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