Empirical mode decomposition and Hilbert–Huang transform-based eccentricity fault detection and classification with demagnetization in 120 kW interior permanent magnet synchronous motors
Author:
Funder
Ministry of Trade, Industry and Energy
Publisher
Elsevier BV
Subject
Artificial Intelligence,Computer Science Applications,General Engineering
Reference35 articles.
1. Off-line detection of static eccentricity of PMSM robust to machine operating temperature and rotor position misalignment using incremental inductance approach;Aggarwal;IEEE Transactions on Transportation Electrification,2020
2. Autonomous fault detection and diagnosis for permanent magnet synchronous motors using combined variational mode decomposition, the Hilbert-Huang transform, and a convolutional neural network;Al-Greer;Computers and Electrical Engineering,2023
3. Elastic net penalized quantile regression model and empirical mode decomposition for improving the accuracy of the model selection;Ambark;IEEE Access,2023
4. Static-, dynamic-, and mixed-eccentricity fault diagnoses in permanent-magnet synchronous motors;Ebrahimi;IEEE Transactions on Industrial Electronics,2009
5. Advanced eccentricity fault recognition in permanent magnet synchronous motors using stator current signature analysis;Ebrahimi;IEEE Transactions on Industrial Electronics,2013
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