基于PSO-LSSVM的离心泵状态预测方法  

Study of centrifugal pump fault prediction method based on PSO optimization LS-SVM

作　　者：左学谦 熊芝[2,3] 聂磊 丁善婷[2,3] Zuo Xueqian;XiongZhi;Nie Lei;Ding Shanting(College of Electromechanical and Automation,Wuchang Shouyi University,Wuhan 430064,China;School of Mechanical Engineering,Hubei University of Technology,Wuhan 430068,China;Hubei Key Laboratory of Modern Manufacturing Quality Engineering,Wuhan 430068,China)

机构地区：[1]武昌首义学院机电与自动化学院,武汉430064 [2]湖北工业大学机械工程学院,武汉430068 [3]湖北省现代制造质量工程重点实验室,武汉430068

出　　处：《电子测量技术》2022年第19期89-94,共6页Electronic Measurement Technology

基　　金：国家重点研发计划资助项目(2019YFB2006100);襄阳湖北工业大学产业研究院2022年度项目(XYYJ2022B01);国家自然科学基金面上项目(51975191)资助。

摘　　要：油田系统中离心泵因长期在恶劣环境下运行,受现场工况、介质腐蚀等因素影响,故障信号多表征出明显的非线性和时变非平稳性,数据量大,运行状态难以实时准确预测,本文提出了一种基于粒子群算法(PSO)优化最小二乘支持向量机(LS-SVM)的离心泵状态预测方法。首先利用粒子群算法的全局搜索特性,对最小二乘支持向量机的核参数g和惩罚因子C进行快速自动寻优,其次确定了平均绝对误差、平均相对误差和均方根误差为预测精度评估指标,最后通过实时采集的数据对本文的预测方法进行验证。结果表明:与LS-SVM预测模型相比,PSO优化LS-SVM模型降低了计算的复杂性,具有泛化能力强,预测精度高的优点,平均绝对误差、平均相对误差和均方根误差较LS-SVM模型分别减少了52%、56%和44%。该方法可为预测性维修提供理论依据,在工程实践方面具有良好的应用前景。Due to the long-term operation of centrifugal pump in harsh environment, affected by field working conditions, medium corrosion and other factors, many fault signals represent obvious nonlinearity and time-varying nonstationarity, large amount of data, and it is difficult to predict the operation state in real time and accurately. In this paper, a centrifugal pump state prediction method based on PSO(Particle Swarm Optimization)optimized LS-SVM(Least Squares Support Vector Machines)was proposed. Firstly, the kernel parameter g and penalty factor C of least squares support vector machine are quickly and automatically optimized by using the global search characteristics of particle swarm optimization algorithm. Secondly, the average absolute error, average relative error and root mean square error are determined as the prediction accuracy evaluation indexes. Finally, the prediction method in this paper was verified by the real-time collected data. The results show that compared with LS-SVM prediction model, PSO optimized LS-SVM model reduces the computational complexity, has the advantages of strong generalization ability and high prediction accuracy, and the average absolute error, average relative error and root mean square error are reduced by 52%, 56% and 44% respectively. This method can provide a theoretical basis for predictive maintenance and has a good application prospect in engineering practice.

关 键 词：离心泵 粒子群算法 最小二乘支持向量机 状态预测 

分 类 号：TH39[机械工程—机械制造及自动化]