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基于增强长短期记忆网络的空气处理系统故障诊断

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

fault diagnosisair handling systemdynamic time-varying characteristicbatch-dynamic characteristicslow featurelong short-term memory(LSTM)network

陆由付、高鹤、冯雅卫

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山东高速集团有限公司,济南

山东正晨科技股份有限公司,济南

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

2024

暖通空调
亚太建设科技信息研究院 中国建筑设计研究院 中国建筑学会暖通空调分会

暖通空调

CSTPCD
影响因子:0.711
ISSN:1002-8501
年,卷(期):2024.54(5)
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