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
摘要：
taptap点点手机端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。
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
Abstract：
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.
Keywords:fault diagnosis; air handling system; dynamic time-varying characteristic; batch-dynamic characteristic; slow feature; long short-term memory (LSTM) network