A deep neural network based fault diagnosis method for centrifugal chillers

4th Asia Conference of International Building Performance Simulation Association

土木建筑工程

Li, G.N.^1 ; Hu, Y.P.^2 ; Mao, Q.J.^1 ; Zhou, C.H.^1 ; Jiao, L.Z.^1

School of Urban Construction, Wuhan University of Science and Technology, Wuhan

430065, China^1

Department of Building Environment and Energy Engineering, Wuhan Business University, Wuhan

430056, China^2

关键词: Building energy systems;    Centrifugal chillers;    Data analysis techniques;    Data classification problems;    Diagnosis strategies;    Fault diagnosis method;    Modeling flexibility;    Performance degradation;   

Others  :  https://iopscience.iop.org/article/10.1088/1755-1315/238/1/012047/pdf
DOI  :  10.1088/1755-1315/238/1/012047

学科分类：土木及结构工程学

来源: IOP

【 摘 要 】

Various types of faults occur in building energy systems throughout their life-cycles. Some faults grow gradually causing system energy penalty and performance degradation. Hence, it is crucial to implement an efficient fault diagnosis strategy and maintain optimal operations for systems. Recently, data-driven methods have got increasing interests due to the model flexibility and data availability. The fast development of data science has provided advanced data analytics to tackle data classification problems in a more convenient and efficient way. This paper attempts to investigate the potential of a promising data analysis technique, i.e., deep neural network, in classifying and diagnosing faults in a building energy system, i.e., centrifugal chiller plant. This study exploits the deep neural network based method in both supervised and unsupervised manners, and compares the fault diagnosis accuracy. Centrifugal chiller experimental data from the ASHRAE Research Project 1043(RP-1043) are used to validate the proposed method. Results show that the method can correctly diagnoses the fault data for seven typical chiller faults.