多特征融合用于光纤振动识别,基于否认扩散概率模型
Keju Zhang1,2, Tingshuo Wang1,2, Jianwei Wu3
1College of Computer and Information Sciences, Fujian Agriculture and Forestry University, Fuzhou 350002, China.
Sensors (Basel, Switzerland)
|November 27, 2025
概括
一种新的DR-LSTM方法通过结合图像和时间特征来增强光纤振动识别. 这种方法提高了准确性,特别是对于不平衡的数据集,这对于结构性健康评估和安全监测至关重要.
科学领域:
- 工程 工程师 工程师 工程师
- 信号处理 信号处理
- 人工智能的人工智能
背景情况:
- 目前的光纤振动识别方法难以整合图像和时间特征.
- 这种限制阻碍了性能,特别是数据分布不均,影响了结构健康评估等应用程序.
研究的目的:
- 提出一种新的光纤振动识别方法,DR-LSTM,有效地整合图像和时间特征.
- 为了确保在平衡和不平衡的数据分布中实现高识别准确度.
主要方法:
- 剩余神经网络的整合,长期短期记忆网络,以及扩散否定概率模型.
- 提取Mel频谱图像特征和时间特征.
- 开发专门的神经网络模型,用于稀缺数据类别的数据增强.
主要成果:
- 在平衡数据集上,DR-LSTM模型显示了更好的分类准确性 (增加了0.67%和7.4%).
- 在不平衡的数据集上,DR-LSTM实现了显著的准确性增长 (最低18.79%和2.4%的增长).
结论:
- 对于提高光纤振动识别精度,DR-LSTM 方法是有效和可行的.
- 拟议的方法成功地解决了振动识别中数据分布不平衡所带来的挑战.
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