WO2018214348A1 - 千米深井提升机主轴多失效模式可靠性评估方法 - Google Patents
千米深井提升机主轴多失效模式可靠性评估方法 Download PDFInfo
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- 230000004044 response Effects 0.000 claims abstract description 24
- 241000039077 Copula Species 0.000 claims abstract description 16
- 238000013528 artificial neural network Methods 0.000 claims abstract description 8
- 238000013461 design Methods 0.000 claims abstract description 5
- 238000004458 analytical method Methods 0.000 claims description 16
- 238000011156 evaluation Methods 0.000 claims description 10
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- 230000003864 performance function Effects 0.000 abstract 1
- 239000003245 coal Substances 0.000 description 2
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- 238000004804 winding Methods 0.000 description 2
- 230000002596 correlated effect Effects 0.000 description 1
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- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
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- G—PHYSICS
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- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
- G06F17/10—Complex mathematical operations
- G06F17/18—Complex mathematical operations for evaluating statistical data, e.g. average values, frequency distributions, probability functions, regression analysis
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F30/00—Computer-aided design [CAD]
- G06F30/20—Design optimisation, verification or simulation
- G06F30/23—Design optimisation, verification or simulation using finite element methods [FEM] or finite difference methods [FDM]
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- G06N7/00—Computing arrangements based on specific mathematical models
- G06N7/02—Computing arrangements based on specific mathematical models using fuzzy logic
- G06N7/04—Physical realisation
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- the invention relates to a system reliability evaluation method for a mechanical product of a kilometer deep well hoist main shaft and considering the failure mode probability correlation, and belongs to the technical research field of mechanical structure reliability technology.
- the maximum static tension of the hoist and the number of winding layers of the spindle reel are greatly increased, resulting in a wire rope on the reel that is much larger than the winding pressure of the existing structure, and the tension and torque of the wire rope acting on the main shaft A significant increase.
- the static load of the hoist terminal will reach 240t or more, and the economic lifting speed will reach 20m/s or more.
- the huge dynamic load generated will seriously endanger the service life of the main shaft. Therefore, the kilometer deep well hoist puts higher requirements on the reliability of the main shaft.
- the object of the present invention is to provide a feasible probabilistic modeling and analysis method for solving system reliability assessment in a multi-failure mode combined failure state of a kilometer deep well hoist spindle.
- a multi-failure mode reliability evaluation method for a shaft deep shaft hoist spindle Firstly, a parametric 3D model of the main shaft is established according to the structural size of the main shaft. Secondly, a sampling matrix of random variables is established according to the probability attribute of the spindle random variable, and used. The finite element method is used to solve the strength and stiffness response of the main axis under the sampling matrix. Again, the neural network method is used to establish an explicit function between the response and the random variable matrix. According to the strength and stiffness design criteria, the intensity and stiffness failure modes are respectively established. The function function is then used to calculate the two failure probabilities using the saddle point approximation method. Finally, the joint failure probability model between the two failure modes is constructed by Clayton copula function, and the interval reliability method is used to solve the system reliability under joint failure.
- Step 1 Determine the mean value and variance of the dimensional parameters, material property parameters and working condition loads in the shaft of the deep shaft hoist of the kilometer, and determine the distribution type of each parameter;
- Step 2 According to the structural parameters of the main shaft of the hoist, establish a three-dimensional parametric model of the main shaft, and introduce the three-dimensional parametric model of the main shaft into the finite element software for static analysis;
- Step 3 According to the mean and variance of each basic parameter of the main axis determined in step 1, combined with the sampling method, establish a random sampling matrix of each basic parameter;
- Step 4 repeatedly generate a new three-dimensional model of the main axis according to the parameter values of each row of the random sampling matrix, and perform finite element analysis again to obtain a new stress and strain response sample;
- Step 5 Using a neural network method to fit the random sampling matrix and the stress-strain value, and obtain a functional relationship between the stress-strain response of the main axis and the change of the structural performance parameter;
- Step 6 According to the requirements of the strength and stiffness of the hoist main shaft, establish the reliability function function under the failure state of strength and stiffness respectively; calculate the third moment and the fourth moment according to the mean and variance of the basic parameters, and then according to the established The function function obtains the mean, variance, third moment and fourth moment of the function function, and uses the saddle point approximation method to calculate the failure probability of the strength and stiffness failure respectively;
- Step 7 The correlation coefficient between strength failure and stiffness failure is obtained by statistical method.
- the joint failure distribution of strength and stiffness failure is established by Clayton copula function, and then the interval reliability method is used to solve the system failure probability of failure correlation.
- Step 1 is specifically as follows:
- Step 2 is specifically as follows:
- the finite element model of the main shaft is established, and external loads such as bending moment, torque and maximum static load are applied.
- the structural parameters of the main shaft include the diameter and length of each shaft section of the main shaft, the diameter and length of each reel, etc.; the material performance parameters include elastic modulus, Poisson's ratio and density.
- Step 4 is specifically as follows:
- variable value is modified in the command stream file of the modeling process to generate a new spindle model
- the newly generated spindle model is analyzed using a command stream of finite element analysis to obtain a new stress-strain response value
- Step 7 is specifically as follows:
- Random sampling is performed according to the distribution type of the spindle random variable, and the calculated value between the strength and the stiffness failure is obtained by the reliability function function established in step 6;
- the strength failure probability and the stiffness failure probability and the joint failure probability obtained in step 6 are substituted to calculate the system failure probability of the hoist spindle.
- FIG. 1 is a flow chart showing the implementation of a multi-failure mode reliability evaluation method for a shaft of a deep well hoist in the present invention.
- Figure 2 is a two-dimensional structure diagram of the hoist main shaft.
- Figure 3 is a probability density plot of the Clayton copula function.
- Figure 4 is a scatter plot of the Clayton copula function.
- D1 is the diameter of the joint between the main shaft and the left bearing
- L1 is the length of the joint between the main shaft and the left bearing
- D2 is The diameter of the spindle mounting sleeve
- D3 is the diameter of the joint between the main shaft and the right bearing
- L2 is the length of the joint between the main shaft and the right bearing.
- the system reliability evaluation method proposed by the present invention considering multiple failure modes includes the following steps:
- Step 1 Through the on-site mapping and design drawings of the hoist main shaft, obtain the mean value and variance of the dimensional parameters, material properties and working condition loads, and determine the distribution type of each parameter;
- Step 2 According to the structural parameters of the hoist main shaft, a three-dimensional parametric model of the main shaft is established, and the three-dimensional parametric model of the main shaft is introduced into the finite element software for static analysis.
- Step 3 According to the mean and variance of each basic parameter of the main axis determined in step 1, combined with WSP (Wootton, Sergent, Phan-Tan-Luu) sampling method, a random sampling matrix of each basic parameter is established;
- WSP Wiotton, Sergent, Phan-Tan-Luu
- Step 4 repeatedly generate a new three-dimensional model of the main axis according to the parameter values of each row of the random sampling matrix, and perform finite element analysis again to obtain a new stress and strain response sample;
- Step 5 Using a neural network method to fit a random sampling matrix (input sample) and a stress-strain value (response sample) to obtain a functional relationship between the stress-strain response of the spindle and the change in structural performance parameters;
- Step 6 According to the requirements of the strength and stiffness of the hoist main shaft, establish the reliability function function under the failure state of strength and stiffness respectively; calculate the third moment and the fourth moment according to the mean and variance of the basic parameters, and then according to the established The function function obtains the mean, variance, third moment and fourth moment of the function function, and uses the saddle point approximation method to calculate the failure probability of the strength and stiffness failure respectively;
- Step 7 The correlation coefficient between strength failure and stiffness failure is obtained by statistical method.
- the joint failure distribution of strength and stiffness failure is established by Clayton copula function, and then the interval reliability method is used to solve the system failure probability of failure correlation.
- the present invention is directed to the structural stability of the strength and stiffness associated with the proposed spindle structure of the deep well hoist as shown in FIG.
- the spindle structure is subjected to bending moments and torque. Based on the structural dimensions and load conditions of the main shaft, a random sampling matrix of the main shaft can be established, and the response sample matrix of the stress and strain of the main shaft can be obtained by the finite element method.
- the explicit function relationship between the response and the input matrix is established by the neural network method, and then the explicit limit state equations of the two failure modes are established according to the strength criterion and the stiffness criterion of the hoist main shaft, namely the limit state equation of the strength failure and the limit of the stiffness failure. Equation of state.
- Table 1 gives The probability information of the spindle random variable in this embodiment is shown.
- D1 is the diameter of the joint between the main shaft and the left bearing
- L1 is the length of the joint between the main shaft and the left bearing
- D2 is the diameter at the main shaft mounting sleeve
- D3 is the diameter of the joint between the main shaft and the right bearing
- L2 is The length of the joint between the main shaft and the right bearing.
- the n sample values of random variables of random spindle structure are generated by random sampling method, and the explicit limit state equations of the two failure modes are substituted, and n response values are calculated respectively.
- the intensity response vector and stiffness are calculated by using the commands in MATLAB.
- the correlation coefficient between the response vectors and the pending parameters of the Clayton copula function Substitution of failure probabilities Pf 1 and Pf 2 into the formula
- P f1 is the maximum failure probability in the failure mode of the hoist spindle
- P fi is the failure probability of the ith failure mode
- P fij is the ith and jth
- the joint failure probability between failure modes, P fs represents the probability of system failure associated with the failure of the hoist spindle.
- the method proposes a system reliability solution method for the strength of the kilometer deep well hoist main shaft considering the strength and stiffness failure correlation.
- the parametric 3D model of the main axis is established according to the structural size of the main shaft.
- the sampling matrix of the random variable is established, and the finite element method is used to solve the strength and stiffness response of the main axis under the sampling matrix.
- the neural network method is used to establish an explicit function between the response and the random variable matrix.
- the explicit function functions in the intensity and stiffness failure modes are respectively established.
- the saddle point approximation method is used to calculate the two failure probabilities.
- the joint failure probability model between two failure modes is constructed by Clayton copula function, and the reliability of the system under joint failure is solved by the interval reliability method.
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Abstract
Description
Claims (6)
- 一种千米深井提升机主轴多失效模式可靠性评估方法,其特征在于:首先,根据主轴的结构尺寸建立主轴的参数化三维模型,其次,根据主轴随机变量的概率属性,建立随机变量的抽样矩阵,并使用有限元方法求解抽样矩阵下主轴的强度和刚度响应,再次,使用神经网络方法建立响应与随机变量矩阵之间的显式函数,根据强度和刚度设计准则,分别建立强度和刚度失效模式下的显式功能函数,然后,使用鞍点逼近方法计算两种失效概率,最后,通过Clayton copula函数构建两种失效模式间的联合失效概率模型,使用区间可靠性方法求解联合失效下的系统可靠性。
- 根据权利要求1所述千米深井提升机主轴多失效模式可靠性评估方法,其特征在于,其实现步骤具体如下:步骤1、确定千米深井提升机主轴中尺寸参数、材料属性参数及工况载荷的均值和方差,确定各参数的分布类型;步骤2、根据提升机主轴的结构参数,建立主轴的三维参数化模型,将主轴的三维参数化模型导入有限元软件,进行静力学分析;步骤3、根据步骤1所确定的主轴各个基本参数的均值和方差,结合抽样方法,建立各基本参数的随机抽样矩阵;步骤4、根据随机抽样矩阵每一行的参数值,重复生成新的主轴的三维模型,并重新进行有限元分析,获得新的应力与应变的响应样本;步骤5、使用神经网络方法将随机抽样矩阵和应力应变值进行拟合,得到主轴应力应变响应与结构性能参数变化的函数关系;步骤6、根据提升机主轴强度和刚度的要求,分别建立强度和刚度失效状态下的可靠性功能函数;根据基本参数的均值和方差,计算其三阶矩和四阶矩,进而根据已建立的功能函数,求得功能函数的均值、方差、三阶矩和四阶矩,使用鞍点逼近方法分别计算强度和刚度失效的失效概率;步骤7、通过统计方法获得强度失效与刚度失效间的关联系数,通过Clayton copula函数建立强度与刚度失效的联合失效分布,进而结合区间可靠性方法求解失效相关时的系统失效概率。
- 根据权利要求2所述的一种千米深井提升机主轴多失效模式可靠性评估方法,其特征在于,步骤1具体为:确定提升机主轴的结构尺寸和材料属性的均值和方差;确定提升机主轴的工况,由此确定各工况下主轴所承担的静载荷、动载荷、弯矩和扭矩等载荷的均值和方差;确定上述各参数的分布类型。
- 根据权利要求2所述的一种千米深井提升机主轴多失效模式可靠性评估方法,其特征在于,步骤2具体为:通过提升机主轴的参数化建模,生成建模的命令流文件,导出建立的主轴模型,保存在工作目录中,通过提升机主轴的有限元分析,生成分析过程的命令流文件,并导出包含分析结果的文本文件,保存在同一工作目录下;根据主轴的材料性能参数,建立主轴的有限元模型,并施加弯矩、扭矩及最大静载荷等外载荷,其中,主轴的结构参数包括主轴各轴段的直径、长度,各卷筒的直径和长度等;材料性能参数包括弹性模量、泊松比和密度。
- 根据权利要求2所述的一种千米深井提升机主轴多失效模式可靠性评估方法,其特征在于,步骤4具体为:在设定的工作目录下,根据所生成的随机抽样矩阵,在建模过程的命令流文件中进行变量值的修改,生成新的主轴模型;使用有限元分析的命令流分析新生成的主轴模型,获得新的应力应变响应值;重复上述步骤,直到随机变量矩阵中的每组随机变量值都获得了对应的应力应变响应值。
- 根据权利要求2所述的一种千米深井提升机主轴多失效模式可靠性评估方法,其特征在于,步骤7具体为:根据主轴随机变量的分布类型进行随机抽样,并通过步骤6所建立的可靠性功能函数获得强度与刚度失效之间的计算值;使用统计方法求得两失效模式间的秩相关系数,并计算Clayton copula的待定参数;使用Clayton copula计算强度与刚度的联合失效概率;使用二阶窄界限理论,代入步骤6所得到的强度失效概率和刚度失效概率以及联合失效概率,计算提升机主轴的系统失效概率。
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CA3014415A CA3014415C (en) | 2017-05-25 | 2017-09-18 | Reliability evaluation method for hoist main shaft of kilometer-deep mine considering multiple failure modes |
AU2017396541A AU2017396541B9 (en) | 2017-05-25 | 2017-09-18 | Reliability evaluation method for hoist main shaft of kilometer-deep mine considering multiple failure modes |
RU2018130014A RU2682821C1 (ru) | 2017-05-25 | 2017-09-18 | Метод оценки надежности подъемной системы шахтного ствола с подъемником в километровых шахтах |
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