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Temperature distribution estimation via data-driven model and adaptive Kalman filter in modular data centers
Proceedings of the Institution of Mechanical Engineers, Part I: Journal of Systems and Control Engineering ( IF 1.6 ) Pub Date : 2020-04-07 , DOI: 10.1177/0959651820903201
Kai Jiang 1, 2 , Shizhu Shi 1, 2 , Hosein Moazanigoodarzi 1, 2 , Chuan Hu 3 , Souvik Pal 2 , Fengjun Yan 1, 2
Affiliation  

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.

中文翻译:

模块化数据中心中通过数据驱动模型和自适应卡尔曼滤波器估计温度分布

随着信息和通信技术的快速发展,需要越来越多的数据中心来支持云计算和我们日常生活中基于网络的关键服务。传统的云数据中心通常采用机房空调或inRow单元作为冷却系统,而机架式冷却单元由于经济性、精确的可控性、灵活性和可扩展性,是更有前途的设备。为了保证机架式冷却单元控制系统的效率和数据中心服务器的安全,温度分布信息是非常重要的。基本上,温度分布可以很容易地通过物理传感器获得。然而,考虑到整个系统的成本和传感器网络故障诊断的负担,温度传感器的数量应保持在最低限度。因此,有必要开发一种有效且实时的观测器来估计系统中的温度分布。此外,由于容器内空气流动和传热复杂,物理模型的构建难度较大。为此,在这项工作中提出了一种包含数据驱动模型和自适应卡尔曼滤波器的新型观察器。采用自回归外生模型作为数据驱动模型的框架,通过偏最小二乘算法对模型进行识别。此外,为了表示系统中的非线性行为,模糊 c-means 被应用于数据分类和获得多个局部线性模型。最后,在提出的数据驱动模型的基础上,利用自适应卡尔曼滤波器来估计温度分布。基于实验数据的估计结果表明所提出的方法的性能是显着的。
更新日期:2020-04-07
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