Estimation of Bearing Remaining Useful Life Based on Multiscale Convolutional Neural Network

• Published in: IEEE transactions on industrial electronics (1982) Vol. 66; no. 4; pp. 3208 - 3216
• Main Authors: Zhu, Jun, Chen, Nan, Peng, Weiwen
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.04.2019; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Artificial neural networks; Bearing; Convolution; Convolutional neural networks; Degradation; Estimation; Feature extraction; Interpolation; Machine learning; Mathematical models; multiscale convolutional neural network (MSCNN); Neural networks; Performance enhancement; remaining useful life estimation; time frequency representation (TFR); Time-frequency analysis; Useful life; Wavelet transforms

Bearing remaining useful life (RUL) prediction plays a crucial role in guaranteeing safe operation of machinery and reducing maintenance loss. In this paper, we present a new deep feature learning method for RUL estimation approach through time frequency representation (TFR) and multiscale convoluti...