A Weighted kNN Fault Detection Based on Multistep Index and Dynamic Neighborhood Scale Under Complex Working Conditions
Author:
Affiliation:
1. Liaoning Key Laboratory of Power Grid Energy Conservation and Control, Shenyang Institute of Engineering, Shenyang, China
2. School of Electrical Engineering, Shenyang University of Technology, Shenyang, China
Funder
Natural Science Foundation of Liaoning Province
Basic Research Project of the Educational Commission of Liaoning Province
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Subject
General Engineering,General Materials Science,General Computer Science,Electrical and Electronic Engineering
Link
http://xplorestaging.ieee.org/ielx7/6287639/10005208/10113605.pdf?arnumber=10113605
Reference32 articles.
1. A novel self-training semi-supervised deep learning approach for machinery fault diagnosis
2. Self-Adaptation Graph Attention Network via Meta-Learning for Machinery Fault Diagnosis With Few Labeled Data
3. Fault Detection Using the k-Nearest Neighbor Rule for Semiconductor Manufacturing Processes
4. Multimode Process Monitoring and Fault Detection: A Sparse Modeling and Dictionary Learning Method
5. A Data-Driven Design for Fault Detection of Wind Turbines Using Random Forests and XGboost
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