Logarithmic Cumulative Transformation: A Simple Yet Effective Approach for Bearing Remaining Useful Life Prediction
Author:
Affiliation:
1. Department of Industrial and Systems Engineering, The Hong Kong Polytechnic University, Kowloon, Hong Kong SAR
2. School of Instrumentation Science and Engineering, Harbin Institute of Technology, Harbin, China
Funder
Research Grants Council of the Hong Kong Special Administrative Region
Research Committee of The Hong Kong Polytechnic University
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Link
http://xplorestaging.ieee.org/ielx7/9424/10666802/10537050.pdf?arnumber=10537050
Reference41 articles.
1. Deep Transfer Learning Based on Sparse Autoencoder for Remaining Useful Life Prediction of Tool in Manufacturing
2. Dual-Threshold Attention-Guided GAN and Limited Infrared Thermal Images for Rotating Machinery Fault Diagnosis Under Speed Fluctuation
3. Incremental Learning for Remaining Useful Life Prediction via Temporal Cascade Broad Learning System With Newly Acquired Data
4. Data-Driven Prognostic Scheme for Bearings Based on a Novel Health Indicator and Gated Recurrent Unit Network
5. Applications of machine learning to machine fault diagnosis: A review and roadmap
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