Evaluation of braking creeping flutter based on dual-stream CNN under fusion features

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Journal of Vibration and Shock ›› 2025, Vol. 44 ›› Issue (1) : 134-142.

FAULT DIAGNOSIS ANALYSIS

Evaluation of braking creeping flutter based on dual-stream CNN under fusion features

LI Yang, JIN Chang*, LI Tianshu, GU Dingyuan

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Abstract

To solve the problems of low efficiency, long time consuming and complex test flow of subjective evaluation method for vehicle creep groan, the temporal features and time-frequency domain feature extraction method of creep groan signal are studied. A creep groan evaluation method based on dual-stream convolutional neural network (DSCNN) with fused features is proposed by combining the spatial processing ability of 2D-CNN with the temporal processing ability of 1D-CNN. One input is time series features extracted by variational modal decomposition, the other input is image features extracted by fast Fourier transform, the one-dimensional time series feature and the high-dimensional image feature are fused, and a training model is used for scoring. By fusing the information of different modes, the method can capture the local waveform features and spatial texture features of creep groan. The results show that the eight-classification accuracy of the scoring model is 87.13%, which verifies the effectiveness of the feature fusion method in creep groan evaluation.

Key words

dual-stream convolution / fusion feature / variational modal decomposition / creep groan

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LI Yang, JIN Chang, LI Tianshu, GU Dingyuan. Evaluation of braking creeping flutter based on dual-stream CNN under fusion features[J]. Journal of Vibration and Shock, 2025, 44(1): 134-142

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