自适应粒子群优化支持向量回归的工程系统可靠性预测

doi:10.3901/JME.2023.14.328

机械工程学报 ›› 2023, Vol. 59 ›› Issue (14): 328-338.doi: 10.3901/JME.2023.14.328

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自适应粒子群优化支持向量回归的工程系统可靠性预测

周策¹, 白斌², 叶楠¹

1. 河北工业大学机械工程学院天津 300401;
2. 三一重工股份有限公司长沙 400100

收稿日期:2022-01-10 修回日期:2022-12-05 出版日期:2023-07-20 发布日期:2023-08-16
通讯作者: 叶楠(通信作者),男,1986年出生,博士,讲师。主要研究方向为冲击力学,工业机器人运动轨迹规划。E-mail:giftye@qq.com
作者简介:周策,男,1996年出生。主要研究方向为工业机器人可靠性设计。E-mail:1661751624@qq.com;白斌,男,1981年出生,博士,副教授/高级工程师。主要研究方向为机械结构设计及优化、机械振动、工业机器人可靠性工程及稳健性设计。E-mail:baibin@126.com
基金资助:
国家重点研发计划(2017YFB1301300)、国家自然科学基金(11772011)、河北省自然科学基金(E2020202217)、河北省教育厅基金重点(ZD2020180)、国防科学与技术重点实验室粒子输运与富集技术开放基金(STPTS202111)和湖南省教育厅基金(21C1235)资助项目。

Reliability Prediction of Engineering System Based on Adaptive Particle Swarm Optimization Support Vector Regression

ZHOU Ce¹, BAI Bin², YE Nan¹

1. School of Mechanical Engineering, Hebei University of Technology, Tianjin 300401;
2. SANY Heavy Industry Co., Ltd., Changsha 400100

Received:2022-01-10 Revised:2022-12-05 Online:2023-07-20 Published:2023-08-16

摘要/Abstract

摘要： 针对可靠性预测精度低的问题提出了一种支持向量机回归预测模型。在可靠性预测过程中，开发了一种结合正弦映射和自适应策略来更新惯性权重的自适应粒子群优化算法，通过增强算法的局部挖掘能力与全局搜索能力，在一定程度上提高了粒子群算法的精度和收敛效率。基于8种测试基准函数将提出的算法与其他粒子群算法进行比较验证，结果表明，提出的自适应粒子群算法相比于其他算法具有更好的搜索能力。在此基础上，提出了一种新的自适应粒子群优化-支持向量机回归混合可靠性预测模型，对支持向量机回归的参数进行调整并预测涡轮增压器和工业机器人系统的可靠性，结果表明该混合模型在可靠性预测方面可达到实际工程精度要求。

关键词: 可靠性预测, 支持向量机, 粒子群算法, 涡轮增压器, 工业机器人

Abstract: Aiming at the problem of low reliability prediction accuracy, a support vector regression prediction model is proposed. In the process of reliability prediction, an adaptive particle swarm optimization algorithm that combines sine mapping and adaptive strategies to update inertia weights is developed. By enhancing the local mining capabilities and global search capabilities of the algorithm, it is improved to a certain extent. The accuracy and convergence efficiency of the particle swarm algorithm are verified. Based on 8 benchmark functions, the proposed algorithm is compared and verified with other particle swarm algorithms. The results show that the adaptive particle swarm optimization algorithm has better search capabilities than other algorithms. On this basis, a new adaptive particle swarm optimization-support vector machine regression hybrid reliability prediction model is proposed to adjust the parameters of support vector regression and predict the reliability of turbochargers and industrial robot systems. The results show that the hybrid model can meet the actual engineering accuracy requirements in terms of reliability prediction.

Key words: reliability prediction, support vector machine, particle swarm algorithm, turbocharger, industrial robot

中图分类号:

TH132

周策, 白斌, 叶楠. 自适应粒子群优化支持向量回归的工程系统可靠性预测[J]. 机械工程学报, 2023, 59(14): 328-338.

ZHOU Ce, BAI Bin, YE Nan. Reliability Prediction of Engineering System Based on Adaptive Particle Swarm Optimization Support Vector Regression[J]. Journal of Mechanical Engineering, 2023, 59(14): 328-338.

参考文献

[1] 郭建英,孙永全,于春雨,等. 复杂机电系统可靠性预测的若干理论与方法[J]. 机械工程学报,2014,50(14):1-13. GUO Jianying,SUN Yongquan,YU Chunyu,et al. Some theory and method for complex electromechanical system reliability prediction[J]. Journal of Mechanical Engineering,2014,50(14):1-13.
[2] 冯鹏飞,朱永生,王培功,等. 基于相关向量机模型的设备运行可靠性预测[J]. 振动与冲击,2017,36(12):146-149. FENG Pengfei,ZHU Yongsheng,WANG Peigong,et al. Operational reliability prediction of equipment based on relevance vector machine[J]. Journal of Vibration and Shock,2017,36(12):146-149.
[3] VILLEGAS M A,PEDREGAL D J,TRAPERO J R. A support vector machine for model selection in demand forecasting applications[J]. Computers & Industrial Engineering,2018,121:1-7.
[4] 舒星,刘永刚,申江卫,等. 基于改进最小二乘支持向量机与Box-Cox变换的锂离子电池容量预测[J]. 机械工程学报,2021,57(14):118-128. SHU Xing,LIU Yonggang,SHEN Jiangwei,et al. Capacity prediction for lithium-ion batteries based on improved least squares support vector machine and box-cox transformation[J]. Journal of Mechanical Engineering,2021,57(14):118-128.
[5] ZHAO Qi,QIN Xiaoli,ZHAO Hongbo,et al. A novel prediction method based on the support vector regression for the remaining useful life of lithium-ion batteries[J]. Microelectronics Reliability,2018,85:99-108.
[6] 周澄,邓菲,刘尧,等. 基于神经网络和支持向量机的导波弯管腐蚀损伤程度辨识研究[J]. 机械工程学报,2021,57(12):136-144. ZHOU Cheng,DENG Fei,LIU Yao,et al. Identification of corrosion damage degree of guided wave bend pipe based on neural network and support vector machine[J]. Journal of Mechanical Engineering,2021,557(12):136-144.
[7] 李素,袁志高,王聪,等. 群智能算法优化支持向量机参数综述[J]. 智能系统学报,2018,13(1):70-84. LI Su,YUAN Zhigao,WANG Cong,et al. Optimization of support vector machine parameters based on group intelligence algorithm[J]. CAAI Transactions on Intelligent Systems,2018,13(1):70-84.
[8] PAI Pingfeng. System reliability forecasting by support vector machines with genetic algorithms[J]. Mathematical and Computer Modelling,2006,43(3-4):262-274.
[9] AZADEH A,SEIF J,SHEIKHALISHAHI M,et al. An integrated support vector regression-imperialist competitive algorithm for reliability estimation of a shearing machine[J]. International Journal of Computer Integrated Manufacturing,2016,29(1):16-24.
[10] ZHAO Wei,TAO Tao,DING Zhuoshu,et al. A dynamic particle filter-support vector regression method for reliability prediction[J]. Reliability Engineering & System Safety,2013,119:109-116.
[11] 丁嘉鑫,王振亚,姚立纲,等. 广义复合多尺度加权排列熵与参数优化支持向量机的滚动轴承故障诊断[J]. 中国机械工程,2021,32(2):147-155. DING Jiaxin,WANG Zhenya,YAO Ligang,et al. Rolling bearing fault diagnosis based on GCMWPE and parameter optimization SVM[J]. China Mechanical Engineering,2021,32(2):147-155.
[12] YAZDANI M,BABAGOLZADEH M,KAZEMITASH N,et al. Reliability estimation using an integrated support vector regression-variable neighborhood search model[J]. Journal of Industrial Information Integration,2019,15:103-110.
[13] LINS I D,MOURA M D C,ZIO E,et al. A particle swarm-optimized support vector machine for reliability prediction[J]. Quality and Reliability Engineering International,2012,28(2):141-158.
[14] WANG Dongshu,TAN Dawei,LIU Lei. Particle swarm optimization algorithm:An overview[J]. Soft Computing,2018,22(2):387-408.
[15] TAO Xiang,LIAO Xiaofeng,WONG K W. An improved particle swarm optimization algorithm combined with piecewise linear chaotic map[J]. Applied Mathematics and Computation,2007,190(2):1637-1645.
[16] 宫华,李作华,刘洪涛,等. 基于改进PSO-BP神经网络的贮存可靠性预测[J]. 运筹与管理,2020,29(8):105-111. GONG Hua,LI Zuohua,LIU Hongtao,et al. Storage reliability prediction based on improved PSO-BP neural network[J]. Operations Research and Management,2020,29(8):105-111.
[17] 王尔申,庞涛,曲萍萍,等. 基于混沌的改进粒子群优化粒子滤波算法[J]. 北京航空航天大学学报,2016,42(5):885-890. WANG Ershen,PANG Tao,QU Pingping,et al. Improved particle filter algorithm based on chaos particle swarm optimization[J]. Journal of Beijing University of Aeronautics and Astronautics,2016,42(5):885-890.
[18] CHEN Ke,ZHOU Fengyu,LIU Aling. Chaotic dynamic weight particle swarm optimization for numerical function optimization[J]. Knowledge-Based Systems,2018,139:23-40.
[19] XU K,XIE M,TANG L C,et al. Application of neural networks in forecasting engine systems reliability[J]. Applied Soft Computing,2003,2(4):255-268.
[20] BAI Bin,ZHANG Junyi,WU Xuan,et al. Reliability prediction-based improved dynamic weight particle swarm optimization and back propagation neural network in engineering systems[J]. Expert Systems with Applications,2021,177:114952.

自适应粒子群优化支持向量回归的工程系统可靠性预测

Reliability Prediction of Engineering System Based on Adaptive Particle Swarm Optimization Support Vector Regression

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