Novel Ramanujan Digital Twin for Motor Periodic Fault Monitoring and Detection
Wenyang Hu, Tianyang Wang, Fulei Chu
Abstract
The signal-processing and intelligent diagnostic and monitoring methods based on motor current signature analysis for induction motors (IM) usually depend on preset parameters. Moreover, many of them have difficulty in achieving ideal health monitoring effect with strong noise interference and switching working conditions. To overcome these limitations, a novel digital twin architecture called the Ramanujan digital twin (RDT) is composed. This architecture uses the Ramanujan periodic transform as its computational core to detect the potential fault signatures in each monitoring frame. The quantity of interest from IM will be selected and calibrated based on the Bayesian-updated driven calibration mechanism to construct the phenomenal simulation signals with high fidelity to the potential fault signatures. These signals will provide guidance information. The effectiveness and robustness of the RDT are validated through experimental cases.