| 基于贝叶斯网络的空调冷水阀故障检测与诊断 |
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| 引用本文： | 张慧,杨学宾,方兴,余莎莎,晏新奇.基于贝叶斯网络的空调冷水阀故障检测与诊断[J].东华大学学报(自然科学版),2020(1):141-147. |
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| 作者姓名： | 张慧 杨学宾 方兴 余莎莎 晏新奇 |
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| 作者单位： | 东华大学环境科学与工程学院;河南省高等学校供热空调重点学科开放实验室;中国舰船研究设计中心 |
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| 基金项目： | 国家重点研发计划资助项目(2018YFC0705305);河南省高等学校供热空调重点学科开放实验室研究基金资助项目(2017HAC102)。 |
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| 摘 要： | 针对空调系统运行中所产生的很多不确定性故障问题,建立了贝叶斯网络模型,开发了多故障状态的贝叶斯概率计算模型,利用Leaky Noisy-Max模型进行贝叶斯网络参数学习,提出了基于故障特征准则和阈值判别的故障检测与诊断技术。采用现场实测数据,以冷水阀故障为例,验证了该技术的可行性。结果表明,该技术能够诊断出冷水阀故障,诊断结果能够快速定位故障产生源,从而提高空调运行系统的稳定性。
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| 关 键 词： | 贝叶斯网络 冷水阀 故障检测与诊断 空调系统 现场实测数据 Leaky Noisy-Max |
A Fault Detection and Diagnosis Technology for Chilled Water Valve Based on Bayesian Network |
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| ZHANG Hui,YANG Xuebin,FANG Xing,YU Shasha,YAN Xinqi.A Fault Detection and Diagnosis Technology for Chilled Water Valve Based on Bayesian Network[J].Journal of Donghua University,2020(1):141-147. |
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| Authors: | ZHANG Hui YANG Xuebin FANG Xing YU Shasha YAN Xinqi |
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| Institution: | (College of Environmental Science and Engineering,Donghua University,Shanghai 201620,China;Key Laboratory of Heating and Air Conditioning,Education Department of Henan Province,Zhengzhou 450007,China;China Ship Development and Design Center,Shanghai 201108,China) |
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| Abstract: | In terms of many uncertain faults in the operation of the air conditioning system,the Bayesian network model was established and the Bayesian probability calculation model of multi-fault state was developed.The Leaky Noisy-Max model was used to learn Bayesian networks parameters.The fault detection and diagnosis technology based on fault feature criterion and threshold discrimination has been proposed.Using the field measured data,the proposed fault detection and diagnosis technology was applied to the chilled water valve.The results show that the technology can diagnose the chilled water valve,and the diagnosis results can quickly locate the fault source,thus improving the stability of the air conditioning system. |
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| Keywords: | Bayesian network chilled water valve fault detection and diagnosis air conditioning system field measured data Leaky Noisy-Max |
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