Dynamic bayesian network-based fault diagnosis for ASHRAE guideline 36: high performance sequence of operation for HVAC systems
Dynamic bayesian network-based fault diagnosis for ASHRAE guideline 36: high performance sequence of operation for HVAC systems
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ASHRAE 指南 36 基于动态贝叶斯网络的故障诊断：HVAC 系统的高性能操作顺序
DOI：
10.1145/3486611.3491124
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发表时间：
2021
期刊：
影响因子：
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通讯作者：
Candan, K. Selcuk
中科院分区：
文献类型：
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作者：
Pradhan, Ojas;Wen, Jin;Chen, Yimin;Lu, Xing;Chu, Mengyuan;Fu, Yangyang;O'Neill, Zheng;Wu, Teresa;Candan, K. Selcuk
本文提出一种动态贝叶斯网络（DBN）来诊断基于美国采暖、制冷与空调工程师学会（ASHRAE）指南36：HVAC高性能运行顺序（以下简称指南36）控制的建筑采暖、通风与空调（HVAC）系统的故障。准则36就监督一级的控制提出了建议。采用这些策略的HVAC系统比典型的HVAC系统具有更全面的设定点重置时间表和更先进的控制逻辑。因此，了解故障如何影响根据指南36控制的HVAC系统的性能以及我们是否可以制定策略来诊断和隔离故障，即使是具有这种综合控制序列的系统，也是很有意义的。与贝叶斯网络（BN）类似，DBN方法利用时间条件概率将故障节点在时间步之间的时间依赖性结合起来。这允许故障信念随着时间的推移而积累，从而提高诊断准确性。在这项研究中，所提出的方法的准确性和可扩展性进行评估，使用的数据从基于Modelica的模拟测试平台。总的来说，开发的DBN显示出良好的潜力，在诊断和隔离的HVAC系统，控制的基础上的指南36控制序列的根本故障原因。
A dynamic Bayesian Network (DBN) is proposed in this study to diagnose faults for building heating, ventilating, and air-conditioning (HVAC) systems that are controlled based on American Society of Heating, Refrigerating and Air-Conditioning Engineers (ASHRAE)'s Guideline 36: High Performance Sequence of Operation for HVAC (hereinafter Guideline 36). Guideline 36 provides recommendations on supervisory-level control. HVAC systems that adopt these strategies have more comprehensive setpoint reset schedules and more advanced control logics than typical HVAC systems. It is hence of interest to understand how faults might affect the performance of HVAC systems that are controlled based on Guideline 36 and whether we can develop strategies to diagnose and isolate faults even for systems with such comprehensive control sequences. Contrarily to a Bayesian Network (BN), DBN method incorporates the temporal dependencies of fault nodes between time steps using temporal conditional probabilities. This allows fault beliefs to accumulate over time and thus improves diagnosis accuracy. In this study, the accuracy and scalability of the proposed method is evaluated using the data from a Modelica-based simulated testbed. Overall, the developed DBN shows good potential in diagnosing and isolating the root fault causes for HVAC systems that are controlled based on the Guideline 36 control sequence.
DOI：
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发表时间：
2018-07
期刊：
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影响因子：
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作者：
Y. Chen;Jin Wen;T. Chen
通讯作者：
Y. Chen;Jin Wen;T. Chen