| 摘要: |
| 鉴于现有基于数据驱动的故障诊断方法多以黑箱模型为主，诊断过程和结果难以解释的问题，本文提出一种基于关联规则分类的冷水机组故障诊断和故障作用机理解释的方法，在保证较好故障诊断精度的前提下，利用故障诊断模型中的规则库对诊断过程进行逆向分析，解析故障作用机理和模型的诊断过程，提升了基于数据驱动的故障诊断方法的可靠性。通过ASHRAE研究项目1043的实验数据对该方法进行验证。结果表明，基于关联规则分类的冷水机组故障诊断方法可以有效地识别7种典型冷水机组故障，平均故障诊断准确率高达90.84％。此外，提取的规则能够较好地吻合制冷原理及热力学相关知识，可用于故障作用机理分析与故障诊断的进一步研究。 |
| 关键词: 故障诊断 冷水机组 关联规则 知识发现 |
| DOI： |
| 投稿时间：2019-10-20 修订日期：2020-01-14
|
| 基金项目:国家重点研发计划（2018YFB0106102），重庆市自然科学基金（cstc2019jcyj-msxmX0537）资助。 |
|
| Fault Diagnosis of Chillers Based on Association Classification Rules |
| Shi Daliang,Liu Jiangyan,Li Kuining,Liu Bin |
| (Key Laboratory of Low-Grade Energy Utilization Technologies & Systems, School of Energy and Power Engineering, Chongqing University) |
| Abstract: |
| Most of the existing data-driven fault diagnosis methods are based on the black box model. Although their accuracy is high, it is difficult to explain the diagnosis process and result. In view of the aforementioned problems, this paper proposes a method for fault diagnosis of chillers and the interpretation of diagnosis mechanism based on associative classification. This method, on the premise of high accuracy, performs reverse analysis of diagnostic process based on the rule library in the diagnostic model and explains the mechanism of the faults and process of diagnosis, and thereby increases reliability of the method for diagnosis based on a data-driven mechanism. Experimental data from ASHRAE research project 1043 (ASHRAE rp-1043) was used for model validation of the system. The results indicated that this method, based on associative classification, can effectively identify seven typical chiller faults, and the average accuracy is as high as 90.84%. The important rules extracted in this study coincide well with refrigeration principles and the knowledge of thermodynamic cycles. Thus, the rules can be used for conducting further studies on fault mechanism analysis and fault diagnosis. |
| Key words: fault diagnosis chiller associative classification knowledge discovery |
