考虑最小成本的风机轴承维护周期优化
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摘要
针对风机主轴承可靠度要求高、维护费用高昂这一现状提出了一种基于单位时间维护成本最低的维护优化模型。首先通过对风机主轴承振动信号进行分析,提取能够较好地反映轴承退化过程的特征值,建立威布尔比例风险模型;其次对主轴承退化期内的失效率曲线进行分析,确定主轴承的失效更换阈值;然后通过改进传统的役龄回退因子对维护后的失效率进行修正,通过对模型运算结果进行分析,避免了定期维护出现欠维护的现象;最后确定单位时间内维护成本最优的维护周期。对维护优化模型进行仿真分析,计算结果表明,通过模型优化可以使单位时间维护费用降低14.4%。
Aiming at the high reliability and high maintenance cost of the main bearing in the wind turbine, a maintenance optimization model based on the lowest maintenance cost per unit time is proposed. Firstly, by analyzing the vibration signal of the main bearing of the wind turbine, the characteristic value which can reflect the degradation process of the bearing is extracted, and then establish the Weibull proportional hazard model. Secondly, the failure rate curve of the main bearing during the degradation period is analyzed to determine the failure replacement threshold of the main bearing; then improve the traditional failure rate by modifying the traditional age-return factor, and analyze the model operation results to avoid the phenomenon of under-maintenance during regular maintenance; Finally, determine the maintenance cycle with the best maintenance cost per unit time. Analysis of the maintenance optimization model and the calculation results show that the maintenance cost per unit time can be reduced by 14.4% through model optimization.
Aiming at the high reliability and high maintenance cost of the main bearing in the wind turbine, a maintenance optimization model based on the lowest maintenance cost per unit time is proposed. Firstly, by analyzing the vibration signal of the main bearing of the wind turbine, the characteristic value which can reflect the degradation process of the bearing is extracted, and then establish the Weibull proportional hazard model. Secondly, the failure rate curve of the main bearing during the degradation period is analyzed to determine the failure replacement threshold of the main bearing; then improve the traditional failure rate by modifying the traditional age-return factor, and analyze the model operation results to avoid the phenomenon of under-maintenance during regular maintenance; Finally, determine the maintenance cycle with the best maintenance cost per unit time. Analysis of the maintenance optimization model and the calculation results show that the maintenance cost per unit time can be reduced by 14.4% through model optimization.
引文
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[15] 陈法法,杨勇,马婧华,等.信息熵与优化LS-SVM的轴承性能退化模糊粒化预测[J].仪器仪表学报, 2016,37(4): 779-787.CHEN F F, YANG Y, MA J H, et al. Fuzzy granulation prediction for bearing performance degradation based on information entropy and optimized LS-SVM [J]. Chinese Journal of Scientific Instrument, 2016, 37(4): 779-787.
[16] 赵洪山,刘宏杨,宋鹏,等.风电机组大部件的备品备件区域库存优化控制策略[J].可再生能源,2018,36(3): 422-428.ZHAO H SH, LIU H Y, SONG P, et al. Regional inventory optimization control strategy of spare parts for big parts of wind turbines [J]. Renewable Energy Resources, 2018,36 (3): 422-428.
[17] 马慧.基于状态的滚动轴承寿命预测与维修计划优化研究[D]. 北京:北京交通大学,2017.MA H. Research of the residual useful life prediction and maintenance optimization based on service status for rolling bearings [D]. Beijing: Beijing Jiaotong University, 2017.
[18] 李东钰,田慕琴,宋建成,等.基于最优小波基选取的掘进机振动信号去噪方法[J].工矿自动化,2016,42(10): 35-39.LI D Y, TIAN M Q, SONG J CH, et al. Denoising method of vibration signal of roadheader based on the optimal wavelet basis selection [J]. Industry and Mine Automation, 2016, 42(10): 35-39.
[2] 吴浩.基于物联网和模糊聚类的风力发电设备故障诊断系统及方法[J].电子测量技术, 2016, 39(3): 162-165.WU H. Fault diagnosis system and method of wind power equipment based on the Internet of things and fuzzy clustering [J]. Electronic Measurement Technology, 2016, 39(3): 162-165.
[3] BYON E, DING Y. Season-dependent condition-based maintenance for a wind turbine using a partially observed markov decision process [J]. IEEE Transactions on Power Systems, 2010, 25(4): 1823-1834.
[4] TAI A H, CHAN L Y. Maintenance models for a continuously degrading system [J]. Computers and Industrial Engineering, 2010, 58(4): 578-583.
[5] 程志君,杨征,谭林.基于机会策略的复杂系统视情维修决策模型[J].机械工程学报,2012,48(6):168-174.CHENG ZH J, YANG ZH, TAN L. Condition-based maintenance model of deteriorating complex system based on opportunistic policy [J]. Journal of Mechanical Engineering, 2012, 48(6): 168-174.
[6] TIAN Z, JIN T, WU B, et al.Condition based maintenance optimization for wind power generation system under continuous monitoring [J]. Renewable Energy, 2011, 36(5): 1502-1509.
[7] 李彪,柴江涛,吴仕明,等.基于最小期望维修损失的风电机组部件定期维修策略研究[J].河北电力技术, 2018, 37(2): 29-32.LI B, CHAI J T, WU SH M,et al. Maintenance decision of wind turbines based on minimal expected cost [J]. Hebei Electric Power, 2018, 37(2): 29-32.
[8] 李大字,冯园园,刘展,等.风力发电机组可靠性建模与维修策略优化[J].电网技术,2011,35(9):122-127.LI D Z,FENG Y Y,LIU ZH, et al. Reliability modeling and maintenance strategy optimization for wind power generation sets[J]. Power System Technology, 2011, 35(9): 122-127.
[9] 刘华鹏.基于威布尔分布的风机齿轮箱原件最优更换时间[J].电网与清洁能源,2011,27(4):62-65.LIU H P. Optimal replacing intervals of wind turbine gearboxes elements based on Weibull distribution [J]. Power System and Clean Energy, 2011, 27(4): 62-65.
[10] 毛昭勇,宋保维,潘光,等.预防周期不同的最佳系统预防性维修优化模型[J].火力与指挥控制,2010, 35(3): 58-60.MAO SH Y, SONG B W, PAN G, et al. Optimal system preventive maintenance model of different preventive maintenance period [J]. Fire Control & Command Control, 2010, 35(3): 58-60.
[11] 陈运胜.发电机传动轴承的异常振动谱特征提取算法[J].国外电子测量技术,2016,35(5):20-23,38.CHEN Y SH. Abnormal vibration spectrum feature extraction algorithm for generator drive bearing [J].Foreign Electronic Measurement Technology, 2016, 35(5): 20-23,38.
[12] 王瑞军,董海鹰,杨立霞.基于Weibull分布的双馈风力发电机轴承寿命预测[J].兰州交通大学学报,2015, 34(6): 117-121.WANG R J, DONG H Y, YANG L X. Bearing life prediction of doubly-fed wind generator based on weibull distribution [J]. Journal of Lanzhou Jiaotong University, 2015, 34(6): 117-121.
[13] 石明江,罗仁泽,付元华.小波和能量特征提取的旋转机械故障诊断方法[J].电子测量与仪器学报,2015, 29(8):1114-1120.SHI M J, LUO R Z, FU Y H. Fault diagnosis of rotating machinery based on wavelet and energy feature extraction [J]. Journal of Electronic Measurement and Instrumentation, 2015, 29(8): 1114-1120.
[14] GAO Y D, VILLECCO F, LI M, et al. Multi-scale permutation entropy based on improved LMD and HMM for rolling bearing diagnosis [J]. Entropy, 2017, 19(4):176-186.
[15] 陈法法,杨勇,马婧华,等.信息熵与优化LS-SVM的轴承性能退化模糊粒化预测[J].仪器仪表学报, 2016,37(4): 779-787.CHEN F F, YANG Y, MA J H, et al. Fuzzy granulation prediction for bearing performance degradation based on information entropy and optimized LS-SVM [J]. Chinese Journal of Scientific Instrument, 2016, 37(4): 779-787.
[16] 赵洪山,刘宏杨,宋鹏,等.风电机组大部件的备品备件区域库存优化控制策略[J].可再生能源,2018,36(3): 422-428.ZHAO H SH, LIU H Y, SONG P, et al. Regional inventory optimization control strategy of spare parts for big parts of wind turbines [J]. Renewable Energy Resources, 2018,36 (3): 422-428.
[17] 马慧.基于状态的滚动轴承寿命预测与维修计划优化研究[D]. 北京:北京交通大学,2017.MA H. Research of the residual useful life prediction and maintenance optimization based on service status for rolling bearings [D]. Beijing: Beijing Jiaotong University, 2017.
[18] 李东钰,田慕琴,宋建成,等.基于最优小波基选取的掘进机振动信号去噪方法[J].工矿自动化,2016,42(10): 35-39.LI D Y, TIAN M Q, SONG J CH, et al. Denoising method of vibration signal of roadheader based on the optimal wavelet basis selection [J]. Industry and Mine Automation, 2016, 42(10): 35-39.