| 彭波,张波,谭健,谭治斌,梁天佑,尹爱军.基于工艺参数灰色关联度分析的天然气脱水装置异常检测[J].装备环境工程,2019,16(5):18-23. PENG Bo,ZHANG Bo,TAN Jian,TAN Zhi-bin,LIANG Tian-you,YIN Ai-jun.Anomaly Detection of Natural Gas Dehydration Unit Based on Grey Correlation Analysis of Process Parameters[J].Equipment Environmental Engineering,2019,16(5):18-23. |
| 基于工艺参数灰色关联度分析的天然气脱水装置异常检测 |
| Anomaly Detection of Natural Gas Dehydration Unit Based on Grey Correlation Analysis of Process Parameters |
| 投稿时间：2019-03-05 修订日期：2019-05-25 |
| DOI：10.7643/ issn.1672-9242.2019.05.005 |
| 中文关键词: 异常检测 灰色关联度 参数聚类 脱水装置 |
| 英文关键词:anomaly detection grey correlation degree parameter clustering dehydration unit |
| 基金项目:重庆市重点产业共性关键技术创新专项重大研发项目（cstc2017zdcy-zdzxX0005）；中国石油重庆气矿科研项目（k18-11） |
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| Author | Institution |
| PENG Bo | 1. Chongqing Gas District of Southwest Oil and Gasfield Company-, Chongqing 400021, China |
| ZHANG Bo | 1. Chongqing Gas District of Southwest Oil and Gasfield Company-, Chongqing 400021, China |
| TAN Jian | 1. Chongqing Gas District of Southwest Oil and Gasfield Company-, Chongqing 400021, China |
| TAN Zhi-bin | 2. State Key Laboratory of Mechanical Transmissions, Chongqing University, Chongqing 400044, China |
| LIANG Tian-you | 2. State Key Laboratory of Mechanical Transmissions, Chongqing University, Chongqing 400044, China |
| YIN Ai-jun | 2. State Key Laboratory of Mechanical Transmissions, Chongqing University, Chongqing 400044, China |
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| 中文摘要: |
| 目的 针对石化设备常用的维修方式导致设备检维修成本高的问题，同时保障设备的可靠运行，建立基于灰色关联度的设备异常检测模型，快速识别异常设备。方法 利用灰色关联度分析法计算增压站数据清洗后各生产监测参数间的关联度，并以计算得到的关联度建立参数间的关联度矩阵，实现参数间的聚类。利用基于参数之间的灰色关联度变化的方法，识别参数聚类结果中同类监测参数对应设备的异常状态。结果 在大部分时间段，同类监测参数的关联性较高，预测关联性出现异常时为设备异常状态。结论 相对于监测参数阈值判断等方法，基于灰色关联度分析法的预测模型具有较高的预测精度，实现了异常设备的快速有效识别，保障了设备的可靠运行，降低了设备检维修成本。 |
| 英文摘要: |
| Objective To establish an abnormal detection model of equipment based on grey relational degree to quickly identify abnormal equipment to solve the high cost of equipment maintenance caused by the common maintenance methods of petrochemical equipment, and ensure the reliable operation of equipment. Methods The grey relational degree analysis method was used to calculate the relational degree among the production monitoring parameters of the supercharger station after data cleaning. And a relational degree matrix among the parameters was established by the relational degree calculated to realize clustering of parameters. The abnormal state of the equipment corresponding to the same monitoring parameters in the clustering results was identified by the method of grey relational degree change among the parameters. Results Similar monitoring parameters were highly correlated in most time periods. And the abnormal state of equipment was predicted when the correlation was abnormal. Conclusion Compared with the methods of threshold judgment of monitoring parameters, the prediction model based on grey correlation analysis method has higher prediction accuracy, realizes fast and effective identification of abnormal equipment. It ensures the reliable operation of equipment, and reduces the cost of equipment maintenance. |
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