Decision Fusion Scheme Based on Mode Decomposition and Evidence Theory for Fault Diagnosis of Drilling Process
Author:
Affiliation:
1. School of Automation, China University of Geosciences, Wuhan, China
2. Graduate School of Environment and Energy Engineering, Waseda University, Tokyo, Japan
Funder
National Natural Science Foundation of China
Natural Science Foundation of Hubei Province, China
Higher Education Discipline Innovation Project
China Scholarship Council scholarship
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Subject
Electrical and Electronic Engineering,Computer Science Applications,Information Systems,Control and Systems Engineering
Link
http://xplorestaging.ieee.org/ielx7/9424/10411967/10148840.pdf?arnumber=10148840
Reference32 articles.
1. A New Hybrid Bat Algorithm and its Application to the ROP Optimization in Drilling Processes
2. Risk analysis of well blowout scenarios during managed pressure drilling operation
3. Low-Rank Characteristic and Temporal Correlation Analytics for Incipient Industrial Fault Detection With Missing Data
4. Slow Down to Go Better: A Survey on Slow Feature Analysis
5. A generalized probabilistic monitoring model with both random and sequential data
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1. Full condition monitoring of geological drilling process based on just-in-time learning-aided slow feature analysis;Journal of Process Control;2024-10
2. Bearing Fault Diagnosis Method Based on Multiple-Level Feature Tensor Fusion;IEEE Sensors Journal;2024-07-15
3. Evidential Ensemble Preference-Guided Learning Approach for Real-Time Multimode Fault Diagnosis;IEEE Transactions on Industrial Informatics;2023
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