Study on preventive maintenance strategy for switch machine components based on GO-FLOW method
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摘要: 为解决目前转辙机因采用固定时间间隔的预防性维修导致的维修资源浪费及潜在人为失误造成的安全隐患,提出一种基于GO-FLOW方法的转辙机部件预防性维修策略;依据转辙机的组成结构和工作流程,建立转辙机GO-FLOW模型,在设备整体可靠性约束下,利用故障统计数据,计算在不同服役时间段内转辙机主要部件的可靠度,据此确定维修时间间隔变化的转辙机部件预防性维修方案,以实施针对性维修,更好地平衡维修经济性和安全性。Abstract: To deal with maintenance resources waste and potential human error hazards caused by fixed-interval preventive maintenance of switch machine components, a preventive maintenance strategy for switch machine components based on the GO-FLOW model is proposed. According to the composition, structure and working flow of the switch machine, a GO-FLOW model of the switch machine is constructed. Under the constraints of overall reliability of the switch machine, the degree of reliability of major components of the switch machine in different servicing periods can be quantitatively derived by use of failure statistics. Based on that, the preventive maintenance plan for switch machine components at variant maintenance intervals can be formulated so as to make more targeted maintenance, better balancing the economy and safety of maintenance.
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表 1 转辙机模型参数
编号 类型 单元名称 参数 S1 25 输入信号 $S = 1$ A2 21 电动机 ${K_{A2} } = {\rm{0} }{\rm{.999\,998\,151\,8} }$ A3 35 模拟失效单元 ${K_{A3} } = 5.544\,7 \times {10^{ {\rm{ - } }6} }$ B2 21 齿轮组 ${K_{B2} } = {\rm{0} }{\rm{.999\,994\,699\,9} }$ B3 35 模拟失效单元 ${K_{B3} } = 5.300\,1 \times {10^{ {\rm{ - } }6} }$ C2 21 摩擦联结器 ${K_{C2} } = {\rm{0} }{\rm{.999\,999\,565\,1} }$ C3 35 模拟失效单元 ${K_{C3} } = 0.652\,3 \times {10^{ {\rm{ - } }6} }$ D2 21 滚珠丝杠 ${K_{D2} } = {\rm{0} }{\rm{.999\,998\,532\,3} }$ D3 35 模拟失效单元 ${K_{D3} } = 1.467\,7 \times {10^{ {\rm{ - } }6} }$ E2 21 保持连接器 ${K_{E2} } = {\rm{0} }{\rm{.999\,999\,429\,2} }$ E3 35 模拟失效单元 ${K_{E3}} = 0.5708 \times {10^{{\rm{ - }}6}}$ F2 21 动作杆 ${K_{F2} } = {\rm{0} }{\rm{.999\,996\,901\,6} }$ F3 35 模拟失效单元 ${K_{F3} } = 1.549\,2 \times {10^{ {\rm{ - } }6} }$ G2 21 检测杆 ${K_{G2} } = {\rm{0} }{\rm{.999\,997\,227\,6} }$ G3 35 模拟失效单元 ${K_{G3} } = 1.386\,2 \times {10^{ {\rm{ - } }6} }$ H2 21 锁闭块和锁舌 ${K_{H2} } = {\rm{0} }{\rm{.999\,999\,633\,1} }$ H3 35 模拟失效单元 ${K_{H3} } = 0.733\,9 \times {10^{ {\rm{ - } }6} }$ I2 21 速动开关组 ${K_{I2} } = {\rm{0} }{\rm{.999\,999\,673\,8} }$ I3 35 模拟失效单元 ${K_{I3} } = 0.326\,2 \times {10^{ {\rm{ - } }6} }$ 注:${K_{i2}} $,${K_{i3}}$$(i \in \{ {\rm{A} },{\rm{B} }\cdots,{\rm{I} }\})$分别表示每小时部件正常工作的概率和部件的失效率。 表 2 转辙机部件可靠度的变化
设备服役
时间/h部件可靠度变化/% A B C D E F G H I t=6010 *3.28 *3.14 0.39 0.88 0.34 0.93 0.83 0.44 0.20 t=9760 *2.06 *1.97 0.64 1.42 0.56 1.50 1.34 0.71 0.32 t=12060 1.23 1.21 0.78 *1.75 0.67 *1.85 1.66 0.88 0.39 t=14160 *2.41 *2.31 0.92 0.31 0.81 0.33 1.94 1.03 0.46 t=16860 1.49 1.42 1.07 0.70 0.94 0.74 *2.26 1.20 0.54 t=18210 *2.22 *2.12 1.16 0.90 1.01 0.95 0.19 1.30 0.56 t=20710 *1.38 *1.32 *1.32 *1.26 *1.15 *1.33 0.53 *1.38 0.66 表 3 转辙机设备整体可靠度和部件维修间隔
工作时间/h 设备整体可靠度 维修部件 t=1 0.999984 无 t=6010 (6010) 0.899984 电动机、齿轮组 t=9760 (3750) 0.899476 电动机、齿轮组 t=12060 (2300) 0.899788 滚珠丝杆、电动杆 t=14160 (2100) 0.899400 电动机、齿轮组 t=16860 (2700) 0.899745 检测杆 t=18210 (1350) 0.899479 电动机、齿轮组 t=20710 (2500) 0.899563 全面维修 -
[1] 郭 进. 铁路信号基础 [M]. 北京: 中国铁道出版社, 2010. [2] 王瑞峰,陈旺斌. 基于灰色神经网络的S700K转辙机故障诊断方法研究 [J]. 铁道学报,2016,38(6):68-72. DOI: 10.3969/j.issn.1001-8360.2016.06.012 [3] Atamuradov V, Camci F, Baskan S, et al. Failure diagnostics for railway point machines using expert systems[C]//IEEE International Symposium on Diagnostics for Electric Machines, Power Electronics and Drives. IEEE, 2009: 1-5.
[4] Jonguk L, Heesu C, Daihee P, et al. Fault Detection and Diagnosis of Railway Point Machines by Sound Analysis: [J]. Sensors, 2016, 16(4): 549. DOI: 10.3390/s16040549
[5] 刘大为,郭 进,王小敏,等. 中国铁路信号系统智能监测技术 [J]. 西南交通大学学报,2014,49(5):904-912. DOI: 10.3969/j.issn.0258-2724.2014.05.025 [6] 伏玉明. 转辙机健康管理关键技术的研究[D]. 兰州: 兰州交通大学, 2017. [7] 王 宁,孙树栋,李淑敏,等. 基于DD-HSMM的设备运行状态识别与故障预测方法 [J]. 计算机集成制造系统,2012,18(8):1861-1868. [8] Ardakani H D, Lucas C, Siegel D, et al. PHM for railway system — A case study on the health assessment of the point machines[C]// Prognostics and Health Management. IEEE, 2012: 1-5.
[9] García F P, Pedregal D J, Roberts C. Time series methods applied to failure prediction and detection [J]. Reliability Engineering & System Safety, 2017, 95(6): 698-703.
[10] Shen Z, Yao W, Huang X. A quantification algorithm for a repairable system in the GO methodology [J]. Reliability Engineering & System Safety, 2003, 80(3): 293-298.
[11] Matsuoka T, Kobayashi M. GO-FLOW: A New Reliability Analysis Methodology [J]. Nuclear Science & Engineering, 1988, 98(1): 64-78.
[12] Matsuoka T. Improvement of the GO-FLOW Methodology [J]. Nuclear Engineering and Design, 1997, 175: 205-214. DOI: 10.1016/S0029-5493(97)00038-1
[13] 伏玉明,刘伯鸿,宋 爽. 基于模糊综合评判的转辙机健康评估研究 [J]. 铁道科学与工程学报,2017,14(5):1070-1076. DOI: 10.3969/j.issn.1672-7029.2017.05.026 -
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