An Entropy- and Attention-Based Feature Extraction and Selection Network for Multi-Target Coupling Scenarios
Author:
Affiliation:
1. School of Mechanical Engineering, Shanghai Jiao Tong University,department of Industrial Engineering and Management,Shanghai,China
Funder
National Science Foundation of China
National Science Foundation of Shanghai
Publisher
IEEE
Link
http://xplorestaging.ieee.org/ielx7/10260321/10260291/10260339.pdf?arnumber=10260339
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1. Prognostics of multiple failure modes in rotating machinery using a pattern-based classifier and cumulative incidence functions
2. Time Series FeatuRe Extraction on basis of Scalable Hypothesis tests (tsfresh – A Python package)
3. A data-level fusion approach for degradation modeling and prognostic analysis under multiple failure modes
4. Multiple failure behaviors identification and remaining useful life prediction of ball bearings
5. Fusing physics-based and deep learning models for prognostics
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