基于改进SVM算法的调节阀空化状态识别

doi:10.3901/JME.2023.24.312

机械工程学报 ›› 2023, Vol. 59 ›› Issue (24): 312-322.doi: 10.3901/JME.2023.24.312

扫码分享

基于改进SVM算法的调节阀空化状态识别

李贝贝¹, 孙深圳¹, 刘秀梅¹, 刘启航¹, 刘申¹, 赵巧², 贺杰³

1. 中国矿业大学机电工程学院徐州 221116;
2. 中国矿业大学大学生创新训练中心徐州 221116;
3. 徐州工程学院电气与控制工程学院徐州 221018

收稿日期:2023-01-15 修回日期:2023-07-05 出版日期:2023-12-20 发布日期:2024-03-05
通讯作者: 刘秀梅(通信作者),女,1982年出生,博士,教授,博士研究生导师。主要研究方向为流体传动及控制、机电液一体化。E-mail:liuxm@cumt.edu.cn
作者简介:李贝贝,男,1984年出生,博士,副教授,硕士研究生导师。主要研究方向为流体传动及控制、机电液一体化。E-mail:li_cumt@cumt.edu.cn
基金资助:
国家自然科学基金(51875559)和江苏省优势学科建设(PAPD)资助项目

Cavitation State Recognition of Regulating Valve Based on Improved SVM Algorithm

LI Beibei¹, SUN Shenzhen¹, LIU Xiumei¹, LIU Qihang¹, LIU Shen¹, ZHAO Qiao², HE Jie³

1. School of Mechatronic Engineering, China University of Mining and Technology, Xuzhou 221116;
2. College Student Innovation Training Center, China University of Mining and Technology, Xuzhou 221116;
3. School of Electrical and Control Engineering, Xuzhou University of Technology, Xuzhou 221018

Received:2023-01-15 Revised:2023-07-05 Online:2023-12-20 Published:2024-03-05

摘要/Abstract

摘要： 空化现象往往致使调节阀产生振动、噪声以及工作效率下降等问题，严重影响煤液化系统的安全运行和元件寿命。准确识别调节阀内的空化状态可以为监测调节阀内空化状态、调节阀预测性维修提供数据支撑。针对调节阀空化状态难以有效识别的问题，构建一种基于遗传算法和核主成分分析(Kernel principle component analysis，KPCA)的支持向量机(Support vector machines，SVM)模型来对调节阀空化状态进行识别。利用时域、频域以及小波包变换提取振动信号的特征，通过KPCA提取特征向量的主成分，然后使用遗传算法优化后的SVM进行调节阀空化状态识别。试验结果表明，KPCA能够有效提取振动信号特征向量的非线性主成分，构建的SVM可有效识别调节阀空化状态。相比基于神经网络的空化状态识别而言，改进SVM具有更好的识别效果，识别准确率达98.7%。

关键词: 调节阀, 空化状态识别, SVM, 遗传算法

Abstract: The cavitation often causes vibration, noise and reduction of working efficiency of the regulating valve, which will seriously affect the safe operation and component life of the coal liquefaction system. Accurately identifying the cavitation state in the regulating valve can provide data support for monitoring the cavitation state in the regulating valve and predictive maintenance of the regulating valve. Due to it is difficult to effectively identify the cavitation state of the regulating valve, a support vector machines(Support vector machines, SVM) model based on genetic algorithm and kernel principle component analysis(Kernel principle component analysis, KPCA) is proposed to identify the cavitation state of the regulating valve in this paper. The characteristics of the vibration signal are extracted by time domain, frequency domain and wavelet packet transform, the principal components of the feature vector are extracted, then the SVM optimized by genetic algorithm is used for cavitation state Recognition of regulating valve. The experimental results show that KPCA can effectively extract the nonlinear principal components of the eigenvectors of vibration signals, and the constructed SVM can effectively identify the cavitation state of the regulating valve. Compared with the neural network, the improved SVM has better recognition effect, whose identification accuracy rate could reach to 98.7%.

Key words: regulating valve, cavitation status recognition, SVM, genetic algorithm

中图分类号:

TH137

李贝贝, 孙深圳, 刘秀梅, 刘启航, 刘申, 赵巧, 贺杰. 基于改进SVM算法的调节阀空化状态识别[J]. 机械工程学报, 2023, 59(24): 312-322.

LI Beibei, SUN Shenzhen, LIU Xiumei, LIU Qihang, LIU Shen, ZHAO Qiao, HE Jie. Cavitation State Recognition of Regulating Valve Based on Improved SVM Algorithm[J]. Journal of Mechanical Engineering, 2023, 59(24): 312-322.

参考文献

[1] 张微,许学超,余道刚. 静流元件调节阀气动及振动噪声性能试验研究[J]. 热能动力工程,2019,34(1):82-86. ZHANG Wei,XU Xuechao,YU Daogang. Experimental study on aerodynamic and vibration noise performance of quiet flow element valve[J]. Journal of Engineering for Thermal Energy and Power,2019,34(1):82-86.
[2] SHA Y,FABER J,GOU S P,et al. A multi-task learning for cavitation detection and cavitation intensity recognition of valve acoustic signals[J]. Engineering Applications of Artificial Intelligence,2022,113:104904.
[3] HABIBNEJAD D,AKBARZADEH P,SALAVATIPOUR A,et al. Cavitation reduction in the globe valve using oblique perforated cages:A numerical investigation[J]. Flow Measurement and Instrumentation,2022,83:102110.
[4] 闵为,王东,郑直,等. 低压下锥阀振荡空化的可视化试验研究[J]. 机械工程学报,2018,54(20):139-144. MIN Wei,WANG Dong,ZHENG Zhi,et al. Visualization experiment of vibration and cavitation in poppet valve under low pressure condition[J]. Journal of Mechanical Engineering,2018,54(20):139-144.
[5] 明廷锋,曹玉良,贺国,等. 流体机械空化检测研究进展[J]. 武汉理工大学学报(交通科学与工程版),2016,40(2):219-226. MING Tingfeng,CAO Yuliang,HE Guo,et al. Research progress of fluid machinery cavitation detection[J]. Journal of Wuhan University of Technology(Transportation Science & Engineering),2016,40(2):219-226.
[6] CUDINA M. Detection of cavitation phenomenon in a centrifugal pump using audible sound[J]. Mechanical Systems and Signal Processing,2003,17(6):1335-1347.
[7] MCKEE K K,FORBES G L,MAZHAR I,et al. A vibration cavitation sensitivity parameter based on spectral and statistical methods[J]. Expert Systems with Applications,2015,42(1):67-78.
[8] 段向阳,王永生,苏永生. 振动分析在离心泵空化监测中的应用[J]. 振动与冲击,2011,30(4):161-165. DUAN Xiangyang,WANG Yongsheng,SU Yongsheng. Vibration analysis applied in cavitation monitoring of a centrifugal pump[J]. Journal of Vibration and Shock,2011,30(4):161-165.
[9] 苏永生,王永生,段向阳. 基于多传感器数据融合的喷水推进泵空化分类识别[J]. 振动与冲击,2012,31(18):93-95,121. SU Yongsheng,WANG Yongsheng,DUAN Xiangyang. Classification of cavitation in a waterjet pump based on multi-sensor data fusion[J]. Journal of Vibration and Shock,2012,31(18):93-95,121.
[10] AZIZI R,ATTARAN B,HAJNAYEB A,et al. Improving accuracy of cavitation severity detection in centrifugal pumps using a hybrid feature selection technique[J]. Measurement,2017,108:9-17.
[11] PANDA A K,RAPUR J S,TIWARI R. Prediction of flow blockages and impending cavitation in centrifugal pumps using support vector machine(SVM) algorithms based on vibration measurements[J]. Measurement,2018,130:44-56.
[12] YAN Z L,LIU J,CHEN B,et al. Fluid cavitation detection method with phase demodulation of ultrasonic signal[J]. Applied Acoustics,2015,87:198-204.
[13] 曹玉良,明廷锋,贺国,等. 基于深度学习的离心泵空化状态识别[J]. 西安交通大学学报,2017,51(11):165-172. CAO Yuliang,MING Tingfeng,HE Guo,et al. Cavitation state recognition of centrifugal pump based on deep learning[J]. Journal of Xi'an Jiaotong University,2017,51(11):165-172.
[14] LI B,LI R,YAN J,et al. Vibration characteristics of the two-stage throttle valve induced by cavitation[J]. The Journal of Engineering,2019,2019(13):102-106.
[15] MA H,ZHANG T,AN Q,et al. Visualization experiment and numerical analysis of cavitation flow characteristics in diesel fuel injector control valve with different structure design[J]. Journal of Thermal Science,2021,30(1):76-78.
[16] SHI J,REN Y,TANG H,et al. Hydraulic directional valve fault diagnosis using a weighted adaptive fusion of multi-dimensional features of a multi-sensor[J]. Journal of Zhejiang University-Science A(Applied Physics & Engineering),2022,23(4):257-272.
[17] 谭树彬,刘建昌,钟云峰,等. 基于神经网络的电液伺服阀故障诊断[J]. 仪器仪表学报,2006(S1):401-403,418. TAN Shubin,LIU Jianchang,ZHONG Yunfeng,et al. Fault di agnosis of electro-hydraulic servo val ve based on neural net work[J]. Chinese Journal of Scientific Instrument,2006(S1):401-403,418.
[18] 李瑞,范玉刚. 基于CEEMDAN多尺度排列熵和SO-RELM的高压隔膜泵单向阀故障诊断[J]. 振动与冲击,2023,42(5):127-135. LI Rui,FAN Yugang. Fault diagnosis of one-way valve of high-pressure diaphragm pump based on CEEMDAN multi-scale permutation entropy and SO-RELM[J]. Journal of Vibration and Shock,2023,42(5):127-135.
[19] 陆亮. 液压节流阀中的空化流动与噪声[D]. 杭州:浙江大学,2012. LU Liang. Cavitaion flow and noise in hydraulic throttling valves[D]. Hangzhou:Zhejiang University,2012.
[20] 孙原理,宋志浩. 基于多物理场信号相关分析与支持向量机的离心泵故障诊断方法[J]. 振动与冲击,2022,41(6):206-212. SUN Yuanli,SONG Zhihao. Centrifugal pump fault diagnosis based on the multiple-physicail fieldsignal correalation analysis and suppot vector machine[J]. Journal of Vibration and Shock,2022,41(6):206-212.
[21] 胡晓强,仲训昱,张霄力,等. 基于支持向量机辅助的四轴陀螺两级故障诊断方法[J]. 上海交通大学学报,2020,54(11):1151-1156. HU Xiaoqiang,ZHONG Xunyu,ZHANG Xiaoli,et al. A two-level fault diagnosis method for gyro-quadruplet assisted by support vector machine[J]. Journal of Shanghai Jiao Tong University,2020,54(11):1151-1156.
[22] 李向杰,张向文. 基于人工蜂群支持向量机的电动汽车制动意图识别方法[J]. 中国机械工程,2021,32(17):2125-2135. LI Xiangjie,ZHANG Xiangwen. Recognition method of branking intention of electric vehicles based on ABC-SVM algorithm[J]. China Mechanical Engineering,2021,32(17):2125-2135.

基于改进SVM算法的调节阀空化状态识别

Cavitation State Recognition of Regulating Valve Based on Improved SVM Algorithm

PDF

可视化

摘要/Abstract

引用本文

使用本文

参考文献

相关文章 15

编辑推荐

Metrics

本文评价

[1]	石琴, 蒋正信, 刘翼闻, 魏宇江, 胡晓松, 贺林. 基于分数阶模型的锂离子电池荷电状态估计[J]. 机械工程学报, 2024, 60(8): 224-232,244.
[2]	王跃飞, 王超, 许于涛, 孙睿, 肖锴, 王凯林. 边-云协同下智能制造单元作业的数字孪生任务调度方法[J]. 机械工程学报, 2024, 60(6): 137-152.
[3]	秦涛, 李运华, 范茹军. 强力摇臂矿用液压挖掘机自主挖掘轨迹优化[J]. 机械工程学报, 2024, 60(18): 374-384.
[4]	刘承尚, 陈锐, 李芳, 王萌, 徐戊矫. 基于改进遗传算法的超高强钢硬化模型参数确定及折弯回弹预测[J]. 机械工程学报, 2024, 60(12): 240-249.
[5]	卢琪, 苏凯新, 张继旺, 闫涛, 杨冰, 张浩楠. 基于BP神经网络和遗传算法的简单链型悬挂接触网结构优化[J]. 机械工程学报, 2024, 60(12): 313-320.
[6]	田志强, 姜兴宇, 杨国哲, 刘伟军, 索英祁, 陈克强, 邢飞. 一种面向航天复杂构件的柔性作业车间能耗优化调度问题研究[J]. 机械工程学报, 2023, 59(8): 273-287.
[7]	刘浩, 陈正颖, 沈楷, 熊旭辉. 基于AC转角变化最小的五轴FDM熔覆路径的法矢量优化技术[J]. 机械工程学报, 2023, 59(6): 272-284.
[8]	苏进展, 李旭东, 尹逊民, 贾海涛, 郭芳. 人字齿轮直线型对角修形设计及成形磨原理[J]. 机械工程学报, 2023, 59(5): 121-129.
[9]	吴宏宇, 牛文栋, 张玉玲, 王树新, 阎绍泽. 水下滑翔机多点探测能耗最低路径的规划方法[J]. 机械工程学报, 2023, 59(5): 156-166.
[10]	李昆鹏, 刘腾博, 李文莉. 改进自适应遗传算法求解“货到人”拣选系统订单分批问题[J]. 机械工程学报, 2023, 59(4): 308-317.
[11]	吴姿宏, 刘秀梅, 李贝贝, 赵巧, 张弛, 谢永伟, 彭佳佳. 基于图像灰度统计的调节阀空化分布特性研究[J]. 机械工程学报, 2023, 59(2): 307-316.
[12]	娄维达, 秦国华, 王华敏, 吴竹溪. 基于复化Cotes积分和神经网络的稳定铣削工艺参数多目标优化方法[J]. 机械工程学报, 2023, 59(17): 279-290.
[13]	郭鑫, 王海涵, 王杰, 张凯, 赵武. 韧性产品交互式概念设计与知识推理方法研究[J]. 机械工程学报, 2023, 59(11): 74-83.
[14]	孙树栋, 周新民, 常昇博. 含私有信息的多代理作业车间协商调度算法[J]. 机械工程学报, 2022, 58(9): 210-217.
[15]	陈晋市, 张淼淼, 毕秋实, 李永奇, 霍东阳, 齐洪阳. 面向自主作业的挖掘机多目标最优挖掘运动规划[J]. 机械工程学报, 2022, 58(7): 237-245.