校准自适应教师用于域自适应智能故障诊断
1Intelligent Maintenance and Operations Systems, École Polytechnique Fédérale de Lausanne, 1015 Lausanne, Switzerland.
Sensors (Basel, Switzerland)
|December 17, 2024
概括
本研究介绍了用于智能故障诊断 (IFD) 的校准自适应教师 (CAT). 在新的运行条件下,CAT通过校准预测来提高模型准确性,实现最先进的结果.
科学领域:
- 工程 工程师 工程师 工程师
- 计算机科学 计算机科学
- 机器学习 机器学习
背景情况:
- 针对智能故障诊断 (IFD) 的深度学习在原始信号方面表现出色,但在分配转移方面存在困难.
- 当有标记的源数据和没有标记的目标数据可用时,无监督域调整 (UDA) 处理IFD.
- 使用伪标签的现有UDA方法受到不良不确定性校准和过度自信的预测的限制.
研究的目的:
- 提出一种新的方法,即校准适应教师 (CAT),用于域适应IFD.
- 在自我培训期间,解决目标领域的不确定性估计的校准不佳的挑战.
- 提高伪标签的质量,并减少IFD的UDA错误积累.
主要方法:
- 开发了IFD中UDA的校准自适应教师 (CAT) 框架.
- 在目标样本上校准教师网络预测的综合后期校准技术.
- 在Paderborn大学 (PU) 的基准上使用温度缩放,四种测试的校准方法之一,评估了CAT.
- 利用时间和频域输入在不同条件下用于滚动轴承故障诊断.
主要成果:
- 在大多数转移任务中,CAT方法,特别是CAT+TempScaling,实现了最先进的性能.
- 与域对抗性神经网络 (DANN) 相比,平均准确度增加了7.5%.
- 在12个传输任务中,与DANN相比,校准误差降低了4倍.
结论:
- 校准教师网络预测对于有效的自主培训领域适应IFD至关重要.
- CAT,特别是温度缩放,在具有挑战性的转移学习场景中显著提高了准确性并减少了校准误差.
- 拟议的方法提供了一个可靠的解决方案,用于在现实条件下部署IFD模型,并具有不同的操作分布.
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