Reliability Parameter Estimation of Mining Equipment Based on GM-noise SVR Method

doi:10.3969/j.issn.1007-7375.2021.02.015

Abstract

Abstract: A parameter estimation method based on GM-noise SVR is proposed to effectively estimate the parameters of the three-parameter Weibull distribution reliability model of mining equipment under the condition of small samples. The method is based on grey estimation method (GM), and then a ε-band support vector regression (ε-SVR) training model based on training sample number and noise parameter optimization is established. The location parameter, scale parameter and shape parameter of the three-parameter Weibull distribution model are estimated sequentially, and the reliability of the equipment analyzed and predicted through fitting the three-parameter Weibull distribution function. The results of the example show that the GM-noise SVR method can be well used to estimate the reliability model parameters of mining equipment. The location parameter, scale parameters and shape parameter of the three-parameter Weibull distribution of a certain type of belt conveyor estimated to be 3.1525, 188.376, 1.0476, the mean time between failures is 188 hours, and the normalized root mean square error is 0.0519, indicating the feasibility and effectiveness of the method.

Key words: small sample, grey estimation, ε-band support vector regression, reliability analysis

CLC Number:

TD528

WANG He, WU Zhenbo, XU Tian, WANG Zhiqiang, LIU Chao. Reliability Parameter Estimation of Mining Equipment Based on GM-noise SVR Method[J]. Industrial Engineering Journal, 2021, 24(2): 119-124.

References

[1] 于嘉成, 王刚, 刘卫东, 等. 矿山设备全生命周期信息集成与工况判别算法研究[J]. 煤炭科学技术, 2019, 47(4): 38-43
YU Jiacheng, WANG Gang, LIU Weidong, et al. Life cycle information integration and working condition discriminational gorithm of mining equipment[J]. Coal Science and Technology, 2019, 47(4): 38-43
[2] 张健, 刘混举, 席庆祥, 等. 刮板输送机链传动系统故障的FMECA研究[J]. 煤炭技术, 2017, 36(4): 282-284
ZHANG Jian, LIU Hunju, XI Qingxiang, et al. Research on FMECA analytic method of scraper conveyor chain drive system[J]. Coal Technology, 2017, 36(4): 282-284
[3] 马霖. 乳化液泵站液压系统可靠性分析[D]. 西安: 西安科技大学, 2018.
MA Lin. The reliability analysis of hydraulic system of emulsion pumping station[D]. Xi'an: Xi'an University of Science and Technology, 2018.
[4] 董茜, 韩刚, 解鑫. 带式输送机驱动装置机械结构的可靠性分析[J]. 太原科技大学学报, 2016, 37(5): 385-388
DONG Qian, HAN Gang, XIE Xin. Reliability analysis of the mechanical structure of belt conveyor driver[J]. Journal of Taiyuan University of Science and Technology, 2016, 37(5): 385-388
[5] 王敏, 武兵, 王娟. 考虑相关性的提升机制动系统可靠性分析[J]. 工矿自动化, 2017, 43(4): 41-45
WANG Min, WU Bing, WANG Juan. Reliability analysis of braking system of hoister considering relevance[J]. Industrial and Mine Automation, 2017, 43(4): 41-45
[6] 丁飞, 金鑫, 王春华, 等. 小样本事件下液压支架可靠性评估[J]. 煤炭科学技术, 2016, 44(11): 116-120
DING Fei, JIN Xin, WANG Chunhua, et al. Evaluation on reliability of hydraulic powered under small sample event[J]. Coal Science and Technology, 2016, 44(11): 116-120
[7] 张永强, 马宪民, 杨洁. Weibull分布中BP参数优化的刮板输送机可靠性寿命预测分析[J]. 煤炭工程, 2017, 49(1): 106-109
ZHANG Yongqiang, MA Xianmin, YANG Jie. BP parameters optimization algorithm in Weibull distribution of reliability life prediction analysis for scraper conveyor[J]. Coal Engineering, 2017, 49(1): 106-109
[8] TSIONAS E G. Posterior analysis, prediction and reliability in three-parameter weibull distributions[J]. Communications in Statistics Theory & Methods, 2000, 29(7): 1435-1449
[9] 曹克强, 胡良谋, 熊申辉, 等. 基于三参数威布尔分布的定量泵可靠性分析[J]. 现代制造工程, 2016(12): 99-102
CAO Keqiang, HU Liangmou, XIONG Shenhui, et al. Reliability analysis of constant displacement pump based on three parameter weibull distribution[J]. Modern Manufacturing Engineering, 2016(12): 99-102
[10] 蔡浩. 风速威布尔分布参数估算方法的比较[J]. 统计与决策, 2016(16): 73-75
[11] 王嘉琦, 徐岩, 彭雅楠, 等. 基于灰色-三参数威布尔分布模型的继电保护装置可靠性参数估计[J]. 电网技术, 2019, 43(4): 1354-1360
WANG Jiaqi, XU Yan, PENG Yanan, et al. Estimation of reliability parameter protective relays based on grey-three-parameter weibull distribution model[J]. Power System Technology, 2019, 43(4): 1354-1360
[12] 严晓东, 马翔, 郑荣跃, 等. 三参数威布尔分布参数估计方法比较[J]. 宁波大学学报(理工版), 2005, 18(3): 301-305
YAN Xiaodong, MA Xiang, ZHENG Rongyue, etal. Comparison of the parameters estimation methods for three-parameter Weibull distribution[J]. Journal of Ningbo University (Natural Science & Engineering Edition), 2005, 18(3): 301-305
[13] 赵江平, 丁洁, 陈敬龙. 基于GM-SVR的小样本条件下化工设备可靠性预测[J]. 中国安全生产科学技术, 2019, 15(1): 145-150
ZHAO Jiangping, DING Jie, CHEN Jinglong. Reliability prediction of chemical equipment under small sample condition based on GM and SVR[J]. Journal of Safety Science and Technology, 2019, 15(1): 145-150
[14] 尹浩霖, 王达梦, 马志勇, 等. 支持向量回归参数估计在风电机组故障模式分析中的应用[J]. 机械科学与技术, 2018, 37(11): 1755-1761
YIN Haolin, WANG Dameng, MA Zhiyong, et al. Application of support vector regression parameter estimation to fault mode analysis in wind turbines[J]. Mechanical Science and Technology, 2018, 37(11): 1755-1761
[15] 曾波, 尹小勇, 孟伟. 实用灰色预测建模方法及其MAT-LAB程序实现[M]. 北京: 科学出版社, 2018.
[16] 周志华. 机器学习[M]. 北京: 清华大学出版社, 2016.
[17] CHERKASSK Y V, MA Y. Practical selection of SVM parameters and noise estimation for SVM regression[J]. Neural Networks, 2004, 17(1): 113-126