在HVAC系统中提升故障检测:统一格拉米安角场和2D深度卷积神经网络以提高性能
Wunna Tun1, Kwok-Wai Johnny Wong1, Sai-Ho Ling2
1Faculty of Design, Architecture and Building, University of Technology Sydney, Ultimo, NSW 2007, Australia.
Sensors (Basel, Switzerland)
|September 28, 2023
概括
这项研究引入了一个新的HVAC故障检测框架,使用格拉米安角场和2D CNNs. 该方法在实时操作期间识别HVAC故障时达到97%的准确性.
科学领域:
- 建筑科学与工程 建筑科学与工程
- 在HVAC中的人工智能
- 数据驱动的故障诊断数据驱动的故障诊断
背景情况:
- 暖通空调系统对于建筑效率和舒适性至关重要,但故障会降低性能.
- 现有的数据驱动故障检测方法在占用期间与复杂的HVAC动态作斗争.
- 在活跃运行期间实时故障检测对于捕获动态交互非常有价值.
研究的目的:
- 为实时运行场景开发和评估一个先进的HVAC故障检测框架.
- 利用模拟的HVAC数据和新的深度学习技术来改进故障识别.
- 提高HVAC故障检测系统的稳定性和可靠性.
主要方法:
- 使用HVACSIM+动态模拟与194个传感器信号开发了一个HVAC故障模型.
- 利用格拉米安角场 (GAF) 将时间序列传感器数据转换为2D图像.
- 使用2D卷积神经网络 (2DCNNs) 进行自动特征提取和故障分类.
主要成果:
- 该GAF-2DCNN框架在HVAC故障检测方面实现了97%的整体准确性.
- 单个故障检测显示精度,回忆和F1得分超过90%.
- 在准确性和可靠性方面超过了SVM,RF和1D-CNNs等传统方法.
结论:
- 使用HVACSIM+数据和GAF-2DCNN的综合方法为HVAC故障检测提供了一个强大的解决方案.
- 这种方法有效地捕获传感器数据中隐藏的时间关系,用于准确的故障诊断.
- 该框架为在运行过程中检测大量HVAC故障的可靠性提供了显著的提升.
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