Fault Diagnosis of Planetary Gearbox Based on Frequency Slice Wavelet Transform and Attention-enhanced ConvNeXt Model

doi:10.12382/bgxb.2024.0329

Abstract

Abstract:

The excessive reliance on experts prior knowledge in traditional manual fault feature extraction leads to incomplete information extraction,low efficiency,high cost,and missed or misdiagnosed cases.A fault diagnosis method based on frequency slice wavelet transform (FSWT) and attention-enhanced ConvNeXt is proposed for planetary gearboxes.This method integrates the convolutional block attention module (CBAM) into the ConvNeXt model,enabling the network to focus more on key regional features and reduce the interference from irrelevant targets.The one-dimensional vibration signals are transformed into the two-dimensional time-frequency spectrum images by applying FSWT,which are then input into the improved network for automatic feature extraction.A mapping relationship between the feature space and the fault space is established to accurately distinguish different fault modes.The proposed method is validated experimentally using the data from a dynamic drive simulation experimental platform.The results show that compared to other network models,the improved ConvNeXt model can accurately identify specific types of gear faults and exhibit good robustness even under noise interference.The research findings provide valuable reference for intelligent fault diagnosis of planetary gearboxes.

Key words: planetary gearbox, frequency slice wavelet transform, attention mechanism, ConvNeXt model, fault diagnosis

CUI Suxiao, WU Zhe, CUI Yanping, ZHANG Qiang, ZHAO Yuejing. Fault Diagnosis of Planetary Gearbox Based on Frequency Slice Wavelet Transform and Attention-enhanced ConvNeXt Model[J]. Acta Armamentarii, 2025, 46(2): 240329-.

Figures/Tables 15

Fig.1 Schematic diagram of FSWT conversion process

Fig.2 CBAM structure diagram

Fig.3 Structure of CBAM-ConvNeXt model

Fig.4 Flow chart of intelligent fault diagnosis

Fig.5 Power transmission simulation test bench

Table 1 Specification of planetary gearbox

部件	齿轮齿数
部件	第1级	第2级
齿圈	100	100
行星轮	40×3	36×4
太阳轮	20	28

Fig.6 Time-domain waveforms and spectra of vibration signals under 5 working conditions

Fig.7 FSWT diagram of vibration signals under 5 working conditions

Fig.8 Comparison of diagnostic results of different two-dimensional time-frequency image conversion methods

Fig.9 Accuracy and loss rate curves of CBAM-ConvNeXt model

Fig.10 Feature dimensionality reduction visualization

Fig.11 Accuracy and loss rate curves of each comparison network model

Table 2 Comparison of evaluation indicators for different network model test sets %

网络模型	准确率	精确率	召回率	F₁
CBAM-ConvNeXt模型	99.98	99.98	99.98	99.98
ConvNeXt-T模型	99.89	99.88	99.90	99.89
Resnet34模型	99.10	99.10	99.10	99.10
Densnet121模型	95.21	95.34	95.20	95.27
Swin Transformer网络模型	99.31	99.32	99.30	99.31

Fig.12 Comparison of confusion matrices for different network models

Fig.13 Accuracy of each network model at 5dB signal-to-noise ratio

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