Presentation Details
A multimodal fusion mechanism neural network for ball bearing life prediction
ID：91
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Updated Time：2025-11-10 15:14:42 Hits：89
Poster Presentation
Start Time：Pending（Asia/Shanghai）
Duration：Pending
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Abstract
该文提出了一种多模态融合神经网络机制（MFMNN），用于滚珠轴承剩余使用寿命（RUL）的预测。通过使用 PHM2012 数据集比较单模态（振动）和多模态（振动和温度）配置的 CNN-LSTM、CNN-BiGRU-Atten 和 MFMNN 模型，我们证明多模态融合将 RMSE 的预测精度提高了 35.7%。MFMNN 的跨模态注意力机制动态加权模态的重要性，以实现自适应特征融合。实验结果证实了 MFMNN 的优越性，MSE=0.0038，为轴承健康管理提供了一种新的方法。
Keywords
ball bearing life prediction; multimodal fusion mechanism; Neural networks
Speaker
Zhanji Fang
学生 广州大学Submission Author
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Important Date
-
Conference Date
Nov 21
2025
toNov 23
2025
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Oct 20 2025
Draft paper submission deadline
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Dec 08 2025
Registration deadline
Sponsored By
IEEE Instrumentation and Measurement Society
South China University of Technology
South China University of Technology
Organized By
South China University of Technology
Contact Information
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