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<p>Gearboxes are crucial for reliable power transmission in rotating machinery. Single-modal diagnostic methods limit feature expression, while multimodal methods, while improving accuracy, are still limited by insufficient cross-modal feature mining and inadequate representation of multi-scale fault features. To address this issue, this paper proposes a multimodal time-frequency feature fusion network (MFFNet). This network efficiently extracts fault signatures by collaboratively analyzing short-time Fourier transform (STFT) time-frequency graphs and Markov transition field (MTF) state transition features. First, feature mode decomposition (FMD) based on finite impulse response (FIR) filters is used to adaptively extract fault-sensitive modes from the raw vibration signal. Then, a dual-channel time-frequency representation is constructed using the STFT and MTF to encode the time-frequency and state transition features of the time series. To enhance feature integration, a feature fusion module (FFM) combining a cross-modal self-attention mechanism and an adaptive weighted fusion strategy was designed, as well as a multi-scale fusion module (MFM) for cross-resolution feature alignment. This enhanced the model’s ability to model fault information in complex non-stationary vibration signals and ensured the integrity of fault feature extraction. Finally, an adaptive decision-level fusion strategy was employed to integrate multi-branch diagnosis results. Experimental results demonstrate that MFFNet outperforms the comparison models on the planetary gearbox vibration dataset, achieving a fault diagnosis accuracy of 99.50 %. This demonstrates the method’s excellent diagnostic performance and generalizability under diverse operating conditions.</p>

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MFFNet: An adaptive dual-channel multimodal fusion network based on time–frequency representations for fault diagnosis of variable-speed gearboxes

  • 期刊论文

  • 发表日期：

    2026年6月3日

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  • Cheng Hu,
  • CanFei Sun,
  • Youren Wang

摘要

Gearboxes are crucial for reliable power transmission in rotating machinery. Single-modal diagnostic methods limit feature expression, while multimodal methods, while improving accuracy, are still limited by insufficient cross-modal feature mining and inadequate representation of multi-scale fault features. To address this issue, this paper proposes a multimodal time-frequency feature fusion network (MFFNet). This network efficiently extracts fault signatures by collaboratively analyzing short-time Fourier transform (STFT) time-frequency graphs and Markov transition field (MTF) state transition features. First, feature mode decomposition (FMD) based on finite impulse response (FIR) filters is used to adaptively extract fault-sensitive modes from the raw vibration signal. Then, a dual-channel time-frequency representation is constructed using the STFT and MTF to encode the time-frequency and state transition features of the time series. To enhance feature integration, a feature fusion module (FFM) combining a cross-modal self-attention mechanism and an adaptive weighted fusion strategy was designed, as well as a multi-scale fusion module (MFM) for cross-resolution feature alignment. This enhanced the model’s ability to model fault information in complex non-stationary vibration signals and ensured the integrity of fault feature extraction. Finally, an adaptive decision-level fusion strategy was employed to integrate multi-branch diagnosis results. Experimental results demonstrate that MFFNet outperforms the comparison models on the planetary gearbox vibration dataset, achieving a fault diagnosis accuracy of 99.50 %. This demonstrates the method’s excellent diagnostic performance and generalizability under diverse operating conditions.