Chiller fault diagnosis based on fusional Bayesian network

WANG Zhanwei; WANG Lin; LIANG Kunfeng; YUAN Junfei; WANG Zhiwei

doi:10.11949/j.issn.0438-1157.20180003

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Chiller fault diagnosis based on fusional Bayesian network

更新时间：2025-12-10

- Chiller fault diagnosis based on fusional Bayesian network
- CIESC Journal Issue 7, Pages: 3167-3173(2018)
- 作者机构：
  
  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
- 作者简介：
- 基金信息：
- DOI：10.11949/j.issn.0438-1157.20180003
  CLC： B65
- Online First：05 July 2018，
  
  Published：05 July 2018
- 稿件说明：
移动端阅览
WANG Zhanwei, WANG Lin, LIANG Kunfeng, et al. Chiller fault diagnosis based on fusional Bayesian network[J]. CIESC Journal, 2018, (7): 3167-3173.
DOI：

WANG Zhanwei, WANG Lin, LIANG Kunfeng, et al. Chiller fault diagnosis based on fusional Bayesian network[J]. CIESC Journal, 2018, (7): 3167-3173. DOI： 10.11949/j.issn.0438-1157.20180003.

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%。

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.

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