基于增强长短期记忆网络的空气处理系统故障诊断  

Fault diagnosis of air handling system based on enhanced long short-term memory network

作　　者：陆由付 高鹤 冯雅卫 Lu Youfu;Gao He;Feng Yawei(Shandong High-speed Group Co.,Ltd.,Jinan;Shandong Zhengchen Technology Co.,Ltd.,Jinan)

机构地区：[1]山东高速集团有限公司,济南 [2]山东正晨科技股份有限公司,济南

出　　处：《暖通空调》2024年第5期58-66,共9页Heating Ventilating & Air Conditioning

摘　　要：暖通空调空气处理系统具有很强的动态时变特性和批次动态特性,为了能有效地诊断所检测到的故障模式,本文构建了一种基于增强长短期记忆(LSTM)网络、能高效识别待辨识故障数据稀疏慢特征的故障诊断模式。在ASHRAE研究项目RP-1312实验数据集上进行的案例研究表明,与相关的故障识别方法相比,该方法在识别空气处理系统故障方面有较大的改进。HVAC air handling systems have strong dynamic time-varying and batch-dynamic characteristics.In order to effectively diagnose the detected fault patterns,this paper constructs a fault diagnosis mode based on enhanced long short-term memory(LSTM)network,which can efficiently identify the sparse and slow features of the fault data.A case study based on the ASHRAE research project RP-1312 experimental dataset shows that the proposed method has a significant improvement in identifying air handling system faults compared with the related fault identification methods.

关 键 词：故障诊断 空气处理系统 动态时变特性 批次动态特性 慢特征 长短期记忆网络 

分 类 号：TU831[建筑科学—供热、供燃气、通风及空调工程] TP183[自动化与计算机技术—控制理论与控制工程]