Prediction of Bearing Remaining Useful Life With Deep Convolution Neural Network
Journal article, Peer reviewed
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Original versionIEEE Access. 2018, 6 13041-13049. 10.1109/ACCESS.2018.2804930
Cyber-physical-social system (CPSS) has drawn tremendous attention in industrial applications such as industrial Internet of Things (IIoT). As the fundamental component of IIoT, bearings play an increasingly important role in CPSS for IIoT. Better understanding of bearing working conditions and degradation patterns so as to more accurately predict the remaining useful life (RUL), becomes an urgent demand for industrial prognostics in IIoT. The data-driven approach has indicated good potential, but the prediction accuracy is still not satisfactory. This paper proposes a new method for the prediction of bearing RUL based on deep convolution neural network (CNN). A new feature extraction method is presented to obtain the eigenvector, named the spectrum-principal-energy-vector. The eigenvector is suitable for deep CNN. In the prediction phase, we propose a smoothing method to deal with the discontinuity problem found in the prediction results. To the best of our knowledge, we are the first to propose such a smoothing method for bearing RUL prediction. Experiments show that our method can significantly improve the prediction accuracy of bearing RUL.