| 摘要: |
| 为提高冷水机组故障诊断的准确度，本文提出一种基于EWMA-BN的冷水机组故障诊断策略。EWMA-BN模型通过EWMA控制图进行故障检测，以其控制限为阈值将各性能指标的故障数据分为高、低、正常3种状态，通过概率统计获得条件概率表，将条件概率表和由专家知识获得的先验概率表输入BN进行故障诊断。利用实验数据从输入模型的证据节点数量、顺序及完整性等方面分析该模型的故障诊断特性。结果表明：EWMA-BN方法对冷水机组常见故障的诊断效果显著，后验概率值（故障诊断结果）均大于0.85，且输入模型的证据节点越多，故障诊断结果越准确，但证据节点输入模型的顺序对最终故障诊断结果无任何影响；对不确定、不完整信息的利用进一步提高了模型的故障诊断能力。采用ASHRAE Project提供的数据对EWMA-BN模型进行验证，故障诊断结果良好。 |
| 关键词: 贝叶斯网络 故障诊断 EWMA控制图 条件概率表 冷水机组 |
| DOI： |
| Received:March 27, 2018Revised:May 25, 2018 |
| 基金项目:国家自然科学基金项目（51576074）和空调设备及系统运行节能国家重点实验室开放基金项目(SKLACKF201606)资助。 |
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| Fault Diagnosis Strategy based on EWMA-BN for Chillers |
|
Shang Pengtao1, Guo Yabin1, Tan Zehan2, Chen Huanxin1, Ding Xinlei1
|
| (1.School of Energy and Power Engineering, Huazhong University of Science and Technology;2.State Key Laboratory of Energy Conservation and Operation of Air-conditioning Equipment and Systems) |
| Abstract: |
| To improve the accuracy of fault diagnosis for chillers, a fault diagnosis strategy based on an exponentially weighted moving average (EWMA) and Bayesian network (BN) is proposed in this study. The EWMA-BN method used an EWMA control chart to detect faults, and its control limits classified the fault data into the three states of higher, lower, and normal. The conditional probability table was obtained through probability statistics, and the prior probabilities were obtained from expert knowledge. The conditional probabilities were input to BN for fault diagnosis. With respect to number, input order, and completeness of evidence nodes, experimental data were used to analyze the characteristics of the method for fault diagnosis. The results showed that the EWMA-BN method had a significant effect on fault diagnosis for chillers, and the posterior probability values (fault diagnosis results) were all higher than 85%. The results also showed that the increase of evidence nodes could improve the accuracy of fault diagnosis results, but the order of the input nodes had no effect on the final results. The use of uncertain and incomplete information further improved the fault diagnosis capability of the method. The EWMA-BN method was validated using the data provided by the ASHRAE Project, which revealed that this strategy is robust and effective. |
| Key words: Bayesian network fault diagnosis EWMA control chart conditional probability table chiller |
