Dense-Structured Network Based Bearing Remaining Useful Life Prediction System

Abstract

This work is focused on developing an effective method for bearing remaining useful life predictions. The method is useful in accurately predicting the remaining useful life of bearings so that machine damage, production outage, and human accidents caused by unexpected bearing failure can be prevented. This study uses the bearing dataset provided by FEMTO-ST Institute, Besançon, France. This study starts with the exploration of neural networks, based on which the biaxial vibration signals are modeled and analyzed. This paper introduces pre-processing of bearing vibration signals, neural network model training and adjustment of training data. The model is trained by optimizing model parameters and verifying its performance through cross-validation. The proposed model’s superiority is also confirmed through a comparison with other traditional models. In this study, the neural network model is trained with various types of bearing data and can successfully predict the remaining useful life. The algorithm proposed in this study achieves a prediction accuracy of coefficient of determination as high as 0.99.