Fault diagnosis of hydraulic power system for coal mine tunnel drilling rig based on T-S fuzzy fault tree
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摘要:
针对煤矿坑道钻机液压动力系统复杂化带来系统中各元件间逻辑关系模糊、失效形式多态性及故障概率获取困难等问题，克服传统故障树在复杂机电液装备故障诊断分析中的局限性，实现及时准确获取设备故障信息，发现故障原因并采取措施，提出一种基于T-S模糊故障树煤矿坑道钻机液压动力系统的故障诊断方法。以ZDY25000LK型钻机液压动力系统为研究对象，对系统的层次结构进行分析，以T-S模糊故障树描述系统的性能变量，进行分析建树。根据质心距离相似度的度量法对专家评估的底事件模糊概率进行修正，求解顶事件在不同故障程度时发生概率的梯形模糊数。通过T-S概率重要度分析，确定各底事件的重要度排序，指出在不同故障程度时液压动力系统的薄弱环节。结果表明：部件级到系统级事件发生严重故障的概率呈递增趋势，系统中多个部件发生轻微故障时，系统发生严重故障的可能性较大。各事件的故障程度对上级事件发生的贡献度不同，根据重要度排序锁定故障位置并判断故障原因。当液压动力系统出现严重故障时，根据重要度分析结果，应优先排查安全阀工作情况、油泵内部磨损情况及内部串油情况。该方法实现在不同故障程度时系统中薄弱环节的快速定位，在鄂尔多斯淮河能源唐家会煤矿的工程试验中得到验证，为提高煤矿坑道钻机的可靠性提供了参考依据。
Abstract:In view of the problems of fuzzy logic relation between the elements of the system, the polymorphism of fault mode, and the difficulty in obtaining the fault probability as a result of the complication of the hydraulic power system for coal mine tunnel drilling rig, a fault diagnosis method of hydraulic power system for coal mine tunnel drilling rig based on Takagi-Sugeno (T-S) fuzzy tree was proposed to overcome the limitations of traditional fault tree in fault diagnosis and analysis of the complex electromechanical hydraulic equipment. The method could timely and accurately obtain the fault information of equipment, and to find out the reasons and to take appropriate measures. Herein, research was conducted based on the hydraulic power system of ZDY25000LK drilling rig, with the hierarchical structure of the system analyzed and the performance variation described by the T-S fuzzy fault tree for analysis and tree establishment. Moreover, the fuzzy probability of the bottom events evaluated by experts was corrected according to the measurement method for centroid distance similarity, and thereby the trapezoidal fuzzy number of the probability of top event occurring in different fault conditions was solved. Furthermore, the importance of the bottom events were ranked based on the importance analysis of T-S probability, with the weak link of hydraulic power system in different fault conditions pointed out. The results show that the probability of serious faults is increasing for the events from component level to system level, and serious faults are more possible to occur if minor faults occur in multiple components of the system. As the fault degree of each event has different contribution to the occurrence of superior events, the fault should be located according to the importance ranking, and further the causes of fault could be judged. When the hydraulic power system has a serious fault, the working condition of safety valve, the internal wear condition of the oil pump and the internal oil running condition should be investigated first according to the importance analysis results. Therefore, the weak link of the system could be located rapidly with this method in various degrees of fault, which was verified in the engineering test of Tangjiahui Coal Mine of Huaihe Energy in Ordos, thus providing a reference basis for improving the reliability of coal mine tunnel drilling rig.
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图 3 “液压动力系统压力异常”T-S模糊故障树
Fig. 3 T-S fuzzy fault tree of “insufficient hydraulic power system pressure”
表 1 T-S模糊门规则
Table 1 Rules of T-S fuzzy gate
规则 x1 x2 ··· xi y1 y2 ··· $y^{k_j} $ r $x_1^{k_1}$ $x_2^{k_2}$ ··· $x_i^{k_i}$ Pr(y1) Pr(y2) ··· Pr($y^{k_j} $) 
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表 2 T-S模糊故障树各事件明细
Table 2 T-S fuzzy fault tree event details
事件 异常描述 事件 异常描述 T 液压动力系统压力异常 x4 吸油过滤器堵塞 y1 液压系统内漏 x5 回油滤油器堵塞 y2 柱塞泵不排油 x6 空气滤清器阻塞 y3 安全阀溢流异常 x7 油黏度过高 y4 过滤器故障 x8 电机转向错误 y5 电机泵组故障 x9 油泵磨损过度 x1 内部串油 x10 安全阀开启压力低 x2 管道漏气 x11 安全阀阀芯卡死 x3 密封损坏 x12 密封圈损坏
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表 3 T-S模糊门4规则
Table 3 Rules for T-S fuzzy gate 4
规则 x10 x11 x12 y3 0 0.5 1 1 0 0 0 P1($y_3^0 $) P1($y_3^{0.5} $) P1($y_3^1 $) 2 0 0 0.5 P2($y_3^0 $) P2($y_3^{0.5} $) P2($y_3^1 $) 3 0 0 1 P3($y_3^0 $) P3($y_3^{0.5} $) P3($y_3^1 $) $ \vdots $ $ \vdots $ $ \vdots $ $ \vdots $ $ \vdots $ $ \vdots $ $ \vdots $ 27 1 1 1 P27($y_3^0 $) P27($y_3^{0.5} $) P27($y_3^1 $)
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表 4 T-S模糊门4规则
Table 4 Rules for T-S fuzzy gate 4
规则 x10 x11 x12 y3 0 0.5 1 1 0 0 0 1 0 0 2 0 0 0.5 0.3 0.5 0.2 3 0 0 1 0 0 1 4 0 0.5 0 0.1 0.6 0.3 5 0 0.5 0.5 0 0.5 0.5 6 0 0.5 1 0 0 1 7 0 1 0 0 0 1 8 0 1 0.5 0 0 1 $ \vdots $ $ \vdots $ $ \vdots $ $ \vdots $ $ \vdots $ $ \vdots $ $ \vdots $ 27 1 1 1 0 0 1
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表 5 T-S模糊门6规则
Table 5 Rules for T-S fuzzy gate 6
规则 x8 x9 y5 0 0.5 1 1 0 0 1 0 0 2 0 0.5 0.3 0.2 0.5 3 0 1 0 0 1 4 0.5 0 0.4 0.2 0.4 5 0.5 0.5 0.2 0.2 0.6 6 0.5 1 0 0 1 7 1 0 0 0 1 8 1 0.5 0 0 1 9 1 1 0 0 1
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表 6 T-S模糊门4底事件隶属度及执行度
Table 6 Degree of membership and execution of T-S fuzzy gate 4
规则r μF $\beta _r^{'} $ x10 x11 x12 1 1 1 2/3 2/3 2 1 1 1/3 1/3 3 1 1 0 0 4 1 0 2/3 0 5 1 0 1/3 0 6 1 0 0 0 7 1 0 2/3 0 8 1 0 1/3 0 $ \vdots $ $ \vdots $ $ \vdots $ $ \vdots $ $ \vdots $ 27 0 0 0 0
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表 7 各底事件模糊故障率
Table 7 Fuzzy failure rate of each bottom even
事件 模糊概率/10−5 x1 2.116 2.459 2.803 3.126 x2 0.985 1.033 1.375 1.421 x3 2.048 2.355 2.721 3.132 x4 3.354 3.554 3.845 4.127 x5 1.051 1.344 1.648 1.986 x6 1.482 1.733 1.958 2.146 x7 1.309 1.485 1.736 1.924 x8 0.446 0.616 0.811 0.894 x9 4.673 4.904 5.413 5.862 x10 0.667 0.713 0.841 0.898 x11 0.232 0.326 0.417 0.522 x12 1.931 2.279 2.588 2.806
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表 8 各中间事件模糊故障率
Table 8 Fuzzy failure rate of each intermediate event
事件 模糊概率/10−5 0.5 1 y1 (1.538, 1.714, 2.062, 2.304) (6.807, 7.744, 9.112, 10.153) y2 (2.803, 3.173, 3.634, 4.409) (16.056, 17.606, 19.802, 21.645) y3 (1.305, 1.493, 1.797, 1.986) (3.486, 4.086, 4.741, 5.213) y4 (2.354, 2.757, 3.193, 3.653) (6.664, 7.372, 8.172, 8.933) y5 (1.024, 1.104, 1.245, 1.351) (7.634, 8.218, 9.255, 10.045)
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表 9 各底事件T-S模糊概率重要度
Table 9 T-S fuzzy probability importance of each bottom event
事件 $I_{0.5}^{ {P_v} }({x_i})$ $I_1^{ {P_v} }({x_i})$ x1 0.030 0.710 x2 0.045 0.615 x3 0.060 0.670 x4 0.063 0.668 x5 0.113 0.443 x6 0.050 0.615 x7 0.150 0.590 x8 0.020 0.752 x9 0.120 0.702 x10 0.045 0.680 x11 0.090 0.710 x12 0.075 0.650
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