Research on Fault-Diagnosis Technology of Rare-Earth Permanent Magnet Motor Based on Digital Twin
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Key Points
The proposed framework achieves over 98% accuracy in diagnosing bearing faults by analyzing vibration signals.
Faults introduce identifiable asymmetries in vibration signals, which are effectively extracted using a variational mode decomposition algorithm.
A digital twin model simulates both healthy and faulty states in motors to address data scarcity issues.
The method was validated on real-world datasets, achieving an accuracy exceeding 95%, indicating its industrial application potential.
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Implication
This research demonstrates an intelligent fault-diagnosis method using digital twin principles, suggesting enhanced accuracy through advanced algorithms.