基于融合的贝叶斯网络的冷水机组故障诊断

doi:10.11949/j.issn.0438-1157.20180003

化工学报 ›› 2018, Vol. 69 ›› Issue (7): 3167-3173.DOI: 10.11949/j.issn.0438-1157.20180003

基于融合的贝叶斯网络的冷水机组故障诊断

王占伟¹, 王林¹, 梁坤峰¹, 袁俊飞¹, 王智伟²

1 河南科技大学制冷热泵空调技术研究所, 河南洛阳 471023;
2 西安建筑科技大学环境学院, 陕西西安 710055

收稿日期:2018-01-02 修回日期:2018-03-12 出版日期:2018-07-05 发布日期:2018-07-05
通讯作者: 王林
基金资助:
国家自然科学基金项目（51641604，U1504524）。

Chiller fault diagnosis based on fusional Bayesian network

WANG Zhanwei¹, WANG Lin¹, LIANG Kunfeng¹, YUAN Junfei¹, WANG Zhiwei²

1 Institute of Refrigeration, Heat Pump, and Air Conditioning, Henan University of Science and Technology, Luoyang 471023, Henan, China;
2 School of Environment, Xi'an University of Architecture and Technology, Xi'an 710055, Shaanxi, China

Received:2018-01-02 Revised:2018-03-12 Online:2018-07-05 Published:2018-07-05
Supported by:
supported by the National Natural Science Foundation of China (51641604, U1504524).

摘要/Abstract

摘要：

基于贝叶斯网络（BN）的开放式结构，将现场观测信息融入到BN中，用以改善故障诊断性能。引入距离拒绝机制，以确定在故障为假时传感器测量参数的概率分布。提出了一种基于融合的BN的冷水机组故障诊断方法，该方法能够检测新故障和动态更新故障库。使用ASHRAE RP-1043的故障实验数据对提出的方法进行验证，结果显示：提出方法对新故障NF1的检测正确率为99.8%，现场观测信息的融入将新故障NF2的检测正确率提高了32.6%，并将已知故障rl（制冷剂泄露）和ro（制冷剂充注过量）的诊断正确率分别提高了4.8%和11.2%。

关键词: 冷水机组, 故障诊断, 算法, 控制, 集成

Abstract:

Based on a open network topology of Bayesian network (BN), on-site observed information is fused into BN to improve the fault diagnostic performances. A mechanism of distance rejection is introduced to determine the probability distribution of sensor measurement parameters. A chiller fault diagnosis method based on fusional BN is proposed. This method is able to detect new types of chiller fault and update its fault library dynamically. Use the experimental data from ASHRAE RP-1043 to evaluate the performances of the proposed method. The results show that the accuracy of the new type of fault (NF1) is 99.8%, and fusing on-site observed information increases the detection accuracies of the new types of fault (NF2) by 32.6% and the diagnostic accuracies of known fault rl and ro by 4.8% and 11.2% respectively.

Key words: chiller, fault diagnosis, algorithm, control, integration

中图分类号:

TB65

王占伟, 王林, 梁坤峰, 袁俊飞, 王智伟. 基于融合的贝叶斯网络的冷水机组故障诊断[J]. 化工学报, 2018, 69(7): 3167-3173.

WANG Zhanwei, WANG Lin, LIANG Kunfeng, YUAN Junfei, WANG Zhiwei. Chiller fault diagnosis based on fusional Bayesian network[J]. CIESC Journal, 2018, 69(7): 3167-3173.

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基于融合的贝叶斯网络的冷水机组故障诊断

Chiller fault diagnosis based on fusional Bayesian network

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