Fault diagnosis of HVAC system with imbalanced data using multi-scale convolution composite neural network  

作　　者：Rouhui Wu Yizhu Ren Mengying Tan Lei Nie 

机构地区：[1]School of Mechanical Engineering,Hubei University of Technology,Wuhan,430068,China [2]Industrial Research Institute of Xiangyang Hubei University of Technology,Xiangyang,441100,China

出　　处：《Building Simulation》2024年第3期371-386,共16页建筑模拟（英文）

基　　金：The authors of this paper acknowledge the support from the National Natural Science Foundation of China(No.51975191);the Funds for Science and Technology Creative Talents of Hubei,China(No.2023DJC048);This work was supported by the Xiangyang Hubei University of Technology Industrial Research Institute Funding Program(No.XYYJ2022B01).

摘　　要：Accurate fault diagnosis of heating,ventilation,and air conditioning(HVAC)systems is of significant importance for maintaining normal operation,reducing energy consumption,and minimizing maintenance costs.However,in practical applications,it is challenging to obtain sufficient fault data for HVAC systems,leading to imbalanced data,where the number of fault samples is much smaller than that of normal samples.Moreover,most existing HVAC system fault diagnosis methods heavily rely on balanced training sets to achieve high fault diagnosis accuracy.Therefore,to address this issue,a composite neural network fault diagnosis model is proposed,which combines SMOTETomek,multi-scale one-dimensional convolutional neural networks(M1DCNN),and support vector machine(SVM).This method first utilizes SMOTETomek to augment the minority class samples in the imbalanced dataset,achieving a balanced number of faulty and normal data.Then,it employs the M1DCNN model to extract feature information from the augmented dataset.Finally,it replaces the original Softmax classifier with an SVM classifier for classification,thus enhancing the fault diagnosis accuracy.Using the SMOTETomek-M1DCNN-SVM method,we conducted fault diagnosis validation on both the ASHRAE RP-1043 dataset and experimental dataset with an imbalance ratio of 1:10.The results demonstrate the superiority of this approach,providing a novel and promising solution for intelligent building management,with accuracy and F1 scores of 98.45%and 100%for the RP-1043 dataset and experimental dataset,respectively.

关 键 词：fault diagnosis CHILLER imbalanced data SMOTETomek MULTI-SCALE neural networks 

分 类 号：TU83[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]