Mechanical defects

in gas-insulated switchgear (GIS) equipment

have weak response characteristics

leading to significant difficulties in the classification of defects. Therefore

this paper proposes a novel mechanical defect feature extraction and classification method that combines independent intrinsic mode function (IIMF) analysis and an improved multi-kernel mapping fast multi-classification relevance vector machine (MKF -mRVM). Enlightened by the differences in the GIS operating vibration mode

the IIMF series were first obtained based on regenerated phase-shifted sinusoid-assisted empirical mode decomposition (RPSEMD) and modal judgments. Then singular value decomposition and time-frequency conversions were performed to construct combined feature matrices. Finally

multikernel mapping and domain sampling were introduced to improve the calculation speed and recognition accuracy of the mRVM

which was more suitable for on-line monitoring. Results show that the proposed RPSEMD-MKF-mRVM model achieves a faster training speed (14.23 s) and higher accuracy (98.21%) than other algorithms

and it can adapt to variable loads.