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个人信息Personal Information
讲师（高校）
教师英文名称：Ian
教师拼音名称：yanying
电子邮箱： 003169@nuist.edu.cn
所在单位：自动化学院
学历：博士研究生毕业
办公地点：学科楼3号楼N505
性别：男
联系方式：手机: 15205176357； 微信: 15205176357
职称：讲师（高校）
主要任职：电气工程及其自动化系副系主任；学院外事秘书；江苏省气象能源利用与控制工程技术研究中心秘书
毕业院校：康涅狄格大学
学科：控制理论与控制工程
A Decentralized Boltzmann-machine-based Fault Diagnosis Method for Sensors of Air Handling Units in HVACs
点击次数：
影响因子：7.144
DOI码：10.1016/j.jobe.2022.104130
所属单位：南京信息工程大学
发表刊物：Journal of Building Engineering
项目来源：National Natural Science Foundation of China No. 52077105
关键字：Fault diagnosis, voting, decentralized, sensors, Boltzmann machine
摘要：As a key module in a Heating, Ventilation, and Air Conditioning (HVAC) system, an Air Handling Unit (AHU) is controlled based on information collected by sensors to satisfy human thermal comfort and air quality requirements. Fault diagnosis is critical since it allows maintenance crews to know which faults have occurred to improve system availability. However, fault diagnosis in AHUs is challenging because of the following reasons. First, widely used fault indicators are correlated with changing environments, e.g., weather dynamics or occupants, thus may not be enough to distinguish faults. Second, existing decentralized fault diagnosis methods developed for sensors require solving many optimization problems, leading to high computational requirements. To overcome these challenges, this paper develops a decentralized Boltzmann-machine-based method. To address the first issue, residuals between actual values of several sensor readings and their estimates are considered as fault indicators since they are not related to changing environments. To address the second issue, a novel decentralized voting mechanism is developed based on the convergence characteristic of the Boltzmann machine to locate sensor faults while avoiding solving many optimization problems. However, the established Boltzmann machine usually has an asymmetric weight matrix, and thus it does not converge to the state estimates of sensors. To guarantee convergence, a new symmetrization method is developed to symmetrize the Boltzmann machine by adding an extra unit into the Boltzmann machine to reset the weight matrix while retaining the original voting. Experimental results demonstrate that our method can effectively diagnose sensor faults with high diagnostic accuracy.
全部作者：Jun Cai,Yun Tang
第一作者：Ying Yan
论文类型：期刊论文
通讯作者：Yaowen Yu
学科门类：工学
文献类型：J
卷号：50
ISSN号：2352-7102
是否译文：否
发表时间：2022-01-26
收录刊物：SCI
发布刊物链接：https://sciencedirect.53yu.com/science/article/pii/S2352710222001437