基于改进的混合扩展卷积网络用于不平衡样本的旋转机械的智能故障诊断
Qianqian Zhang1, Caiyun Hao1, Ying Wang2
1School of Automation and Software Engineering, Shanxi University, Taiyuan, P.R. China.
Scientific reports
|April 24, 2025
概括
本研究引入了一种改进的混合扩展卷积网络 (HDCN),用于设备故障诊断. HDCN有效地处理有限的,杂的故障数据,提高了工业环境中的分类准确性.
科学领域:
- 工程 工程师 工程师 工程师
- 机器学习 机器学习
- 信号处理 信号处理
背景情况:
- 工业设备故障诊断面临着不足和杂的故障样本的挑战.
- 与健康样本相比,缺陷样本较少的不平衡数据集很常见.
- 收集的数据中的高噪音水平进一步使准确的故障识别更加复杂.
研究的目的:
- 引入一种新的改进混合扩展卷积网络 (HDCN),用于改进设备故障分类.
- 解决工业应用中小型和杂的故障数据集的局限性.
- 为了提高故障诊断系统的准确性.
主要方法:
- 将时间域振动信号转换为时间频域图像,使用短时间里埃转换 (STFT).
- 采用多尺度混合扩展卷积网络用于特征提取.
- 使用适应性重量长短期存储器 (LSTM) 单元进行功能融合.
- 纳入缩放指数线性单位 (SELU) 激活和焦点损失功能.
主要成果:
- HDCN有效地从时间频率图像中提取多尺度的特征.
- 适应加权聚变放大了重要的特征,并减少了噪声的影响.
- 激活SELU可以减轻少数类样本的抑制.
- 焦点损失可以提高不平衡和杂数据集的性能.
结论:
- 拟议的HDCN方法在设备故障诊断方面表现出显著的有效性.
- 该方法成功地应对了有限和杂的故障数据所带来的挑战.
- 在实际的工业场景中,HDCN为提高分类准确性提供了一个强大的解决方案.
相关概念视频
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