一种新的,短暂的超级学习策略,用于废水处理过程的故障诊断
Qili Chen1, Siqing Zhang1, Xiang Xu1
1College of Automation, Beijing Information Science and Technology University, Beijing, 100096, China.
ISA transactions
|February 25, 2026
概括
本研究引入了新的废水处理工艺 (WWTP) 的少数射击故障诊断框架. 该方法通过将元学习与高级特征加权集成,有效地使用有限的数据识别故障,提高诊断准确度.
科学领域:
- 环境工程 环境工程
- 人工智能的人工智能
- 过程控制 过程控制
背景情况:
- 废水处理过程 (WWTP) 面临的诊断挑战是由于缺少故障数据和复杂的,低密度的时间序列传感器信息.
- 现有的深度学习模型需要大量的标记数据,而传统的元学习则在传感器测量中与异质的时间模式作斗争.
研究的目的:
- 为WWTPs开发一种新的少数射击故障诊断框架,克服当前方法的局限性.
- 用最小的故障样本提高工业过程中故障检测的准确性和效率.
主要方法:
- 将原型网络与增强的TimesBlock模块集成在一起,以进行短暂的学习.
- 开发一个Meta-TimesBlock (MTB) 用于使用XGBoost的多维时间序列特征的自适应权重.
- 引入一个参数效率高的MetaLearner模块和一个辅助损失函数,以改善原型区分.
主要成果:
- 拟议的框架在挑战BSM1基准的8路5射击故障诊断任务时实现了高平均准确率 (94.72%,98.24%,82.75%).
- 与最先进的方法相比,表现出3.39%-8.43%的性能改善.
- 在不同的天气场景中验证了框架的有效性.
结论:
- 新的框架有效地解决了WWTP中少数射击故障诊断的挑战.
- 将特定领域的功能工程与元学习相结合,显著提高了故障检测能力.
- 该方法为在数据有限的工业环境中可靠的故障诊断提供了有希望的解决方案.
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