Uncertainty Evaluation Method of Complex Nonlinear System Test Based on Support Vector Machine Model

doi:10.3901/JME.2018.08.177

Abstract

Abstract: Measurement uncertainty analysis of complex nonlinear system test is of great significance to ensure the quality and reliability of products. As a typical complex nonlinear system test, the whiplash test which evaluates the vehicle seat head restraint on the occupant neck protection effect is considered, and an uncertainty evaluation method based on support vector machine model is suggested. The probability density functions of main influence factors of whiplash test is studied, and design of experiments is implemented by using latin hypercube sampling method. The experiment results are utilized to construct the mathematical model of whiplash test based on the least squares support vector machine. The design result is compared with that of Back Propagation artificial neural networks mathematical model, and is shown that the prediction accuracy resulted from the least squares support vector machine is higher, which meets the follow-up assessment requirement. The evaluation of uncertainty of whiplash test is realized by using the Monte Carlo method. By the comparison of uncertainty evaluation based on the method specified by national standard《Guide to the Expression of Uncertainty in Measurement》, the suggested method is more accurate and reliable for complex nonlinear system test. The suggested method can be widely applied to the uncertainty analysis for various complex tests of products.

Key words: complex nonlinear system test, measurement uncertainty, Monte Carlo method, support vector machine

CLC Number:

TG707
TB9

ZHU Daye, DING Xiaohong, WANG Shenlong, WANG Haihua, YU Huijie. Uncertainty Evaluation Method of Complex Nonlinear System Test Based on Support Vector Machine Model[J]. Journal of Mechanical Engineering, 2018, 54(8): 177-184.

References

[1] 国家质量监督检验检疫总局,JJF 1059.1-2012.测量不确定度评定与表示[S]. 北京:中国质检出版社, 2012. General Administration of Quality Supervision, Inspection and Quarantine, JJF 1059.1-2012. Evaluation and Expression of Uncertainty in Measurement[S]. Beijing:China Zhijian Publishing House, 2012.
[2] 国家质量监督检验检疫总局, JJF 1059.2-2012.用蒙特卡罗法评定测量不确定度[S]. 北京:中国质检出版社, 2012. General Administration of Quality Supervision, Inspection and Quarantine, JJF 1059.2-2012. Monte Carlo Method for Evaluation of Measurement Uncertainty[S]. Beijing:China Zhijian Publishing House, 2012.
[3] WEN X, YOUXIONG X U, HONGSHENG L I, et al. Monte Carlo method for the uncertainty evaluation of spatial straightness error based on new generation geometrical product specification[J]. Chinese Journal of Mechanical Engineering, 2012, 25(5):875-881.
[4] 陈怀艳, 曹芸, 韩洁. 基于蒙特卡罗法的测量不确定度评定[J]. 电子测量与仪器学报, 2011, 25(4):301-308. CHEN Huaiyan, CAO Yun, HAN Jie, Evaluation of uncertainty in measurement based on a Monte Carlo method[J]. Journal of Electronic Measurement and Instrument, 2011, 25(4):301-308.
[5] 黄美发, 景晖, 匡兵, 等. 基于拟蒙特卡罗方法的测量不确定度评定[J]. 仪器仪表学报, 2009, 30(1):120-125. HUANG Meifa, JING Hui, KUANG Bing, et al. Measurement uncertainty evaluation based on quasi Monte-Carlo method[J]. Chinese Journal of Scientific Instrument, 2009, 30(1):120-125.
[6] 凌明祥, 李会敏, 黎启胜, 等. 含相关性的测量不确定度拟蒙特卡罗评定方法[J]. 仪器仪表学报, 2014, 35(6):1385-1393. LING Mingxiang, LI Huimin, LI Qisheng, et al. Quasi Monte Carlo method for the measurement uncertainty evaluation considering correlation[J]. Chinese Journal of Scientific Instrument, 2014,35(6):1385-1393.
[7] 刘尚明, 张丽, 魏成亮, 等. 基于多输出LSSVM的压气机特性的拟合和预测研究[J]. 热力透平, 2013, 42(2):77-83. LIU Shangming, ZHANG Li, WEI Chengliang, et al. Fitting and prediction study of compressor characteristics using least squares support vector machine technology[J]. Thermal Turbine, 2013, 42(2):77-83.
[8] 姜辉, 杨建国, 姚晓栋,等. 数控机床主轴热漂移误差基于贝叶斯推断的最小二乘支持向量机建模[J]. 机械工程学报, 2013, 49(15):115-121. JIANG Hui, YANG Jianguo, YAO Xiaodong, et al. Modeling of CNC machine tool spindle thermal distortion with LS-SVM based on Bayesian inference[J]. Journal of Mechanical Engineering. 2013, 49(15):115-121.
[9] SUYKENS J A K, LUKAS L, VANDEWALLE J. Sparse least squares support vector machine classifiers[J]. Neural Processing Letters, 1999, 1(3):293-300.
[10] 赵耀红, 钟萍, 王来生. 一种多输出支持向量机的增量学习算法[J]. 计算机应用与软件, 2010, 27(6):14-16. ZHAO Yaohong, ZHONG Ping, WANG Laisheng. An incremental learning algorithm for multi-output support vector machine[J]. Computer Applications and Software, 2010, 27(6):14-16.
[11] 中国汽车技术研究中心. C-NCAP管理规则[S]. 天津:中国汽车技术研究中心, 2015. China Automotive Technology Research Center. C-NCAP management rules[S]. Tianjin:China Automotive Technology Research Center, 2015.
[12] VAPNIK V N. The nature of statistical learning theory[M]. New York:Springer, 1999.
[13] BROWNE M W. Cross-validation methods[J]. Journal of Mathematical Psychology, 2000, 44(1):108-132.
[14] MIN J H, LEE Y C. Bankruptcy prediction using support vector machine with optimal choice of kernel function parameters[J]. Expert Systems with Applications, 2005, 28(4):603-614.
[15] XAVIERDESOUZA S, SUYKENS J A, VANDEWALLE J, et al. Coupled simulated annealing.[J]. IEEE Transactions on Systems Man & Cybernetics Part B Cybernetics A Publication of the IEEE Systems Man & Cybernetics Society, 2010, 40(2):320-335.
[16] 李丽娜, 甘晓晔, 徐攀峰, 等. 改进粒子群优化Takagi-Sugeno模糊径向基函数神经网络的非线性系统建模[J]. 计算机应用, 2014, 34(5):1341-1344. LI lina, GAN Xiaoye, XU Panfeng, et al. Nonlinear system modeling based on Takagi-Sugeno fuzzy radial basis functionneural network optimized by improved particle swarm optimization[J]. Journal of Computer Applications, 2014, 34(5):1341-1344.