往复压缩机气量调节控制失稳自愈调控方法研究

doi:10.3901/JME.2020.22.131

机械工程学报 ›› 2020, Vol. 56 ›› Issue (22): 131-141.doi: 10.3901/JME.2020.22.131

• 可再生能源与工程热处理 • 上一篇下一篇

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往复压缩机气量调节控制失稳自愈调控方法研究

江志农^1,2, 周超¹, 张进杰^1,3, 刘雯华¹, 王瑶^1,3, 孙旭¹

1. 北京化工大学压缩机技术国家重点实验室压缩机健康智能监控中心北京 100029;
2. 北京化工大学高端机械装备健康监控与自愈化北京市重点实验室北京 100029;
3. 压缩机技术国家重点实验室(压缩机技术安徽省实验室) 合肥 230031

收稿日期:2019-11-04 修回日期:2020-07-30 出版日期:2020-11-20 发布日期:2020-12-31
通讯作者: 张进杰(通信作者),男,1987年出生,博士,副教授。主要研究方向为设备故障诊断机理与诊断方法智能化。E-mail:zjj87427@163.com
作者简介:江志农,男,1967年出生,教授,博士研究生导师。主要研究方向为关键设备故障监测诊断与性能分析等。E-mail:jiangzhinong@263.net;周超,男,1991年出生,博士。主要研究方向为往复机械气量调节与故障诊断。E-mail:15117950620@163.com
基金资助:
国家重点研发计划（2016YFF0203305）、中央高校基本科研业务费专项资金资助（JD1912）和压缩机技术国家重点实验室（压缩机技术安徽省实验室）开放基金（SKL-YSJ201808）资助项目。

Research on Self-healing Control Method of Reciprocating Compressor Capacity Control Instability

JIANG Zhinong^1,2, ZHOU Chao¹, ZHANG Jinjie^1,3, LIU Wenhua¹, WANG Yao^1,3, SUN Xu¹

1. Compressor Health and Intelligent Monitoring Center of National Key Laboratory of Compressor Technology, Beijing University of Chemical Technology, Beijing 100029;
2. Beijing Key Laboratory of Health Monitoring Control and Fault Self-recovery for High-end Machinery, Beijing University of Chemical Technology, Beijing 100029;
3. State Key Laboratory of Compressor Technology(Anhui Provincial Laboratory of Compressor Technology), Hefei 230031

Received:2019-11-04 Revised:2020-07-30 Online:2020-11-20 Published:2020-12-31

摘要/Abstract

摘要： 针对往复压缩机无级气量调节常见的调控失稳故障，构建包含压缩机、管道、缓冲罐、执行机构等部件的无级气量调节多系统耦合控制模型，仿真往复压缩机无级气量调节动态特性。以控制系统输出的脉宽调制控制信号作为输入，以压缩机各级排气压力作为输出，研究执行机构动态响应和控制系统调控参数对气量调节结果影响的规律；进一步针对执行机构性能参数变化导致气量调节控制失稳的问题，利用BP神经网络构建多参数负荷动态反馈模型实现调控失稳故障诊断和故障类型识别。基于故障异常类型识别结果提出一种调控参数自适应优化补偿的自愈调控方法。试验结果表明，提出的自愈调控方法可在失稳故障发生后主动施加调控参数补偿量，使得气量调节系统恢复到正常状态，实现故障在线自愈。

关键词: 往复压缩机, 调控失稳故障, 自愈调控, BP神经网络

Abstract: For the common regulation instability fault of reciprocating compressor stepless capacity regulation system, a multi-system coupling control model of stepless capacity regulation system which includes compressor, pipeline, buffer tank, actuator and so on, is built to simulate the dynamic characteristics of reciprocating compressor stepless capacity regulation system. By using the pulse width modulation control signal from the control system as input and the compressor exhaust pressure of every stage as output. The law of the dynamic response of the actuator and the influence of the control parameters in control system on the capacity regulation results are studied. Further, for the problem of capacity control instability caused by the change of actuators' performance parameters, a multi-parameter load dynamic feedback model is built by using BP neural network to realize the regulation instability fault diagnosis and fault type identification. Based on the results of abnormal fault type recognition, a self-healing control method based on adaptive optimal compensation of control parameters is proposed. The experimental results indicated that the proposed self-healing control method can automatically apply the compensation of control parameters after the instability fault, so that the capacity regulation system returned to the normal state, and the fault self-healing can be realized on-line.

Key words: reciprocating compressor, regulatory instability failure, self-healing control, BP neural network

中图分类号:

TH457
TB126

江志农, 周超, 张进杰, 刘雯华, 王瑶, 孙旭. 往复压缩机气量调节控制失稳自愈调控方法研究[J]. 机械工程学报, 2020, 56(22): 131-141.

JIANG Zhinong, ZHOU Chao, ZHANG Jinjie, LIU Wenhua, WANG Yao, SUN Xu. Research on Self-healing Control Method of Reciprocating Compressor Capacity Control Instability[J]. Journal of Mechanical Engineering, 2020, 56(22): 131-141.

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往复压缩机气量调节控制失稳自愈调控方法研究

Research on Self-healing Control Method of Reciprocating Compressor Capacity Control Instability

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