| 基于DR-BN的冷水机组故障检测 |
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| 引用本文： | 王占伟,王林,袁俊飞,谈莹莹,周西文. 基于DR-BN的冷水机组故障检测[J]. 制冷学报, 2020, 41(2): 87-92 |
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| 作者姓名： | 王占伟 王林 袁俊飞 谈莹莹 周西文 |
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| 作者单位： | 河南科技大学制冷热泵空调技术研究所,河南科技大学制冷热泵空调技术研究所,河南科技大学制冷热泵空调技术研究所,河南科技大学制冷热泵空调技术研究所,河南科技大学制冷热泵空调技术研究所 |
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| 基金项目： | 国家自然科学基金(51806060, 51876055),河南省重点研发与推广专项（科技攻关）（192102310204）和空调设备及系统运行节能国家重点实验室开放基金（ACSKL2018KT1205）资助项目。 |
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| 摘 要： | 在冷水机组现场的故障数据通常难以获得,这是导致基于多分类算法的故障检测方法未被广泛现场应用的主要障碍之一。本文将距离拒绝(DR)机制融入贝叶斯网络(BN)中,将冷水机组故障检测转化为一类划分问题,提出一种基于DR-BN的冷水机组故障检测方法,该方法仅使用正常数据训练模型,从而有效克服上述障碍。本文通过使用ASHRAE RP-1043的故障实验数据对提出方法的性能进行验证,并与传统方法的性能进行了对比,可知基于DR-BN的模型具有更高的故障检测性能,尤其对低劣化等级下的故障,故障检测正确率最高时可高出94%。
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| 关 键 词： | 冷水机组 故障检测 贝叶斯网络 距离拒绝 |
| 收稿时间： | 2019-01-02 |
| 修稿时间： | 2019-05-07 |
Fault Detection for Chiller Based on DR-BN |
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| Wang Zhanwei,Wang Lin,Yuan Junfei,Tan Yingying and Zhou Xiwen. Fault Detection for Chiller Based on DR-BN[J]. Journal of Refrigeration, 2020, 41(2): 87-92 |
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| Authors: | Wang Zhanwei Wang Lin Yuan Junfei Tan Yingying Zhou Xiwen |
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| Affiliation: | Institute of Refrigeration, Heat Pump, and Air Conditioning, Henan University of Science and Technology,Institute of Refrigeration, Heat Pump, and Air Conditioning, Henan University of Science and Technology,Institute of Refrigeration, Heat Pump, and Air Conditioning, Henan University of Science and Technology,Institute of Refrigeration, Heat Pump, and Air Conditioning, Henan University of Science and Technology and Institute of Refrigeration, Heat Pump, and Air Conditioning, Henan University of Science and Technology |
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| Abstract: | The chiller fault data are often difficult to obtain in the field, which is one of the key obstacles hindering the field applications of chiller fault detection. Considering this reality, the fault detection task is transformed into a typical one-class classification problem by merging a distance rejection (DR) technique into a Bayesian network (BN); therefore, a method based on DR-BN is proposed in this study. The proposed method effectively overcomes the above-mentioned limitation by using the normal data alone to train the model. The performance of the proposed method is evaluated by using the experimental data from the ASHRAE RP-1043, and compared with the other traditional methods. The proposed method shows a better performance than the other traditional methods. Especially for the low serious level, the maximum accuracy of the proposed method is increased by 94%. |
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| Keywords: | chiller fault detection Bayesian network distance rejection |
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