Deep Learning Approach to Multiple Features Sequence Analysis in Predictive Maintenance

Abstract

The representation learning of life cycle dataset has its particularity in the correlation of different features and the dependency of adjacent sampling time. This paper addresses the difficulty of segmentation to high-dimensional nonlinear life cycle long CBM data, and propose a new deep learning approach based on unsupervised representation learning named Autoencoder for rolling bearing diagnosis. Two kinds of Autoencoder with encoder and decoder model are developed respectively using fully connected and convolutional hidden layers to automatically extract the dataset’s representative features. Compared to the fully connected one, the convolutional Autoencoder shows clearer in a lower dimensional feature space by preserving the local neighborhood structure, and more effective to discover subjectively the intrinsic structure of nonlinear high-dimensional data of deterioration process.