家电科技Issue(z1)：170-174,5.DOI:10.19784/j.cnki.issn1672-0172.2024.99.035
卷积神经网络的空调系统故障诊断可解释研究
Interpretation study on convolutional neural networks-based fault diagnosis of air conditioning system
摘要
Abstract
Deep learning,particularly convolutional neural networks(CNN),has garnered significant attention in the field of building energy systems.In the context of fault diagnosis for air handling units(AHU),the effectiveness and applicability of CNN's diagnostic performance require further validation.Additionally,the lack of interpretability in CNN fault diagnosis models hinders their broader application in practical engineering.To address these issues,utilized the publicly available ASHRAE RP-1312 AHU fault data to develop a fault diagnosis model based on CNN,and employed the layer-wise relevance propagation(LRP)method to interpret the CNN model.The results demonstrated that the CNN-based diagnostic model exhibits good applicability,with an average diagnostic accuracy of 99.94％.The LRP method provides strong interpretability for the CNN model,and identifies the diagnosis mechanism of the model in the decision-making process.Finally,an in-depth analysis was conducted on the impact of model parameters such as the number of convolutional layers,learning rate and β parameter on the interpretation results.关键词
空气处理机组/卷积神经网络/逐层相关传播/故障诊断/可解释Key words
Air handling unit/Convolutional neural network/Fault diagnosis/Layer-wise relevance propagation/Interpretation分类
信息技术与安全科学引用本文复制引用
熊成龙,李冠男,劳春峰,李伟,王东岳,代传民,李锟..卷积神经网络的空调系统故障诊断可解释研究[J].家电科技,2024,(z1):170-174,5.基金项目
国家自然科学基金项目(51906181) （51906181）
武汉科技大学"十四五"湖北省优势特色学科(群)项目(2023D0504,2023D0501) （群）
武汉科技大学研究生创新创业基金(JCX2023026). （JCX2023026）
