Predicting remaining useful life of rolling bearings based on deep feature representation and long short-term memory neural network

• Published in: Advances in mechanical engineering Vol. 10; no. 12
• Main Authors: Mao, Wentao, He, Jianliang, Tang, Jiamei, Li, Yuan
• Format: Journal Article
• Language: English
• Published: London, England SAGE Publications 01.12.2018; Sage Publications Ltd; SAGE Publishing
• Subjects: Artificial neural networks; Correlation coefficients; Degradation; Feature extraction; Life prediction; Machine learning; Neural networks; Numerical prediction; Numerical stability; Representations; Roller bearings; Short term; Useful life; deep feature representation; deep learning; Remaining useful life; health state assessment; long short-term memory

For bearing remaining useful life prediction problem, the traditional machine-learning-based methods are generally short of feature representation ability and incapable of adaptive feature extraction. Although deep-learning-based remaining useful life prediction methods proposed in recent years can...