Temperature distribution estimation via data-driven model and adaptive Kalman filter in modular data centers

作     者：Jiang, Kai Shi, Shizhu Moazanigoodarzi, Hosein Hu, Chuan Pal, Souvik Yan, Fengjun 

作者机构：McMaster Univ Dept Mech Engn Hamilton ON L8S 4L7 Canada McMaster Univ Comp Infrastruct Res Ctr Hamilton ON Canada Univ Texas Austin Dept Mech Engn Austin TX 78712 USA 

出 版 物：《PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART I-JOURNAL OF SYSTEMS AND CONTROL ENGINEERING》 (机械工程师学会会报；I辑：系统与控制工程杂志)

年 卷 期：2020年第234卷第7期

页      面：809-819页

核心收录：

学科分类：08[工学] 0802[工学-机械工程] 0811[工学-控制科学与工程] 

基　　金：National Sciences and Engineering Research Council (NSERC) of Canada Cinnos Mission Critical Incorporated 

主　　题：Data center temperature distribution data-driven model adaptive algorithm Kalman filter 

摘      要：With the rapid development of information and communications technology, increasing number of data centers is required to support the cloud computing, and critical web-based services that run our daily lives. The conventional cloud data centers usually adopt computer room air conditioner or inRow units as the cooling sytem, while the rack mountable cooling unit is a more promising equipment due to the economy, exact controllability, flexibility, and scalability. To ensure the efficiency of control system in rack mountable cooling unit and the security of servers in the data centers, the information of temperature distribution is very essential. Basically, the temperature distribution could be obtained through physical sensors easily. However, considering the cost of whole system and the burden of fault diagnosis in sensor networks, the number of temperature sensors should be kept down to a bare minimum. Therefore, it is necessary to develop an effective and real-time observer to estimate the temperature distribution in the system. Besides, due to the complex air flow and heat transfer in the container, it is quite difficult to construct a physics model. To this end, a novel observer embracing data-driven model and adaptive Kalman filter is proposed in this work. Auto regression exogenous model is adopted as the framework of data-driven model, and the model is identified through a algorithm of partial least square. Moreover, to represent the nonlinear behaviors in the system, fuzzy c-means is applied for data classification and getting multiple local linear models. Finally, adaptive Kalman filter is utilized to estimate the temperature distribution on the basis of proposed data-driven model. The estimation results based on experimental data indicate the performance of proposed approach is remarkable.