Method for optimizing reliability of surface mine system based on cost benefit importance

Abstract

The invention discloses a surface mine system reliability optimization method based on cost benefit importance, which comprises the following steps: setting preset parameters according to production task yield and working duration requirements of a surface mine; evaluating the reliability of a single device based on a genetic projection pursuit method, and calculating the overall reliability of the surface mine system based on a GO method; judging the arrival condition of the safe and uninterrupted running time of the system, and if so, judging the reliability range of the system and taking corresponding operation; when the reliability of the surface mine system is reduced to a lower limit threshold value, calculating the cost-benefit importance degree of each member device, and carrying out replacement according to a spare part corresponding to the maximum cost-benefit importance degree; and after the task is completed, counting the number of spare parts replaced by the member equipment each time to form an optimal spare part configuration scheme. According to the method, spare part configuration optimization in the whole guarantee time of the surface mine can be quickly realized.

Description

Technical Field

[0001] The invention relates to the technical field of transportation equipment failure in mines, and in particular to a reliability optimization method for an open-pit mine system based on cost-effectiveness importance. Background Art

[0002] Most of the production interruption accidents in open-pit mines are caused by the failure of the mine's transportation equipment, which then leads to production interruption. As the intelligent equipment and data information of mines are increasingly interconnected, the complexity of the network structure of open-pit mines increases the probability of large-scale production interruption accidents in open-pit mines. Therefore, in order to avoid large-scale production interruption accidents in open-pit mines, effectively identifying the key production equipment of open-pit mine networks has important theoretical and applied research value.

[0003] Research shows that most large-scale shutdown accidents in open-pit mines are caused by equipment cascading failures, which are manifested in that small local disturbances such as mine cars, excavators, and unloading stations cause a series of open-pit equipment to stop one after another through network connections, causing huge economic losses. Therefore, it is of great significance to quickly and accurately identify vulnerable faulty equipment in the open-pit mine network cascade effect and locate key equipment that causes the expansion of the cascading failure range to ensure the safe production operation of the open-pit mine system and prevent the occurrence of system shutdown accidents. Summary of the invention

[0004] The purpose of the present invention is to overcome the shortcomings of the prior art. To achieve the above purpose, an open-pit mine system reliability optimization method based on cost-effectiveness importance is adopted to solve the problems raised in the above background technology.

[0005] A method for optimizing system reliability of an open-pit mine based on cost-benefit importance comprises the following steps:

[0006] Step S1, setting preset parameters according to the production task output and working time requirements of the open-pit mine, wherein the preset parameters include the lower limit of the overall reliability of the open-pit mine system and the guarantee time for continuous safe and uninterrupted operation;

[0007] Step S2, evaluating the reliability of individual equipment based on the genetic projection pursuit method, and then calculating the overall reliability of the open-pit mine system based on the GO method;

[0008] Step S3: determine whether the system safe and uninterrupted operation time has arrived. If it has arrived, determine the system reliability range and take corresponding actions;

[0009] Step S4: when the reliability of the open-pit mine system drops to the lower threshold, the cost-effectiveness importance of each member equipment is calculated, and the spare parts corresponding to the maximum cost-effectiveness importance are replaced;

[0010] Step S5: After the task is completed, the number of spare parts replaced by each member device is counted to form an optimal spare parts configuration plan.

[0011] As a further solution of the present invention: the specific steps in step S1 include:

[0012] According to the output and working time requirements of the production task, set the lower limit of the overall reliability of the open-pit mine system and the guarantee time for continuous safe and uninterrupted operation.

[0013] As a further solution of the present invention: the specific steps in step S2 include:

[0014] Step S21, establishing projection data:

[0015] To use the projection pursuit method to conduct a comprehensive evaluation of the reliability of a single device, we must first establish the projection objective function of the reliability characteristics of each single indicator of the single device;

[0016] According to the obtained single equipment reliability grading standard, the projection index is established by randomly selecting values ​​within each grade range to form a sample set.<x(i,j),y(i)> , where x(i,j) is the reliability index normalized by the following formula:

[0017] x(i,j)=x * (i,j) / x max (j),i=1,2,…,n,j=1,2,…,m;

[0018] In the formula, n and m are the number of samples and the number of indicators respectively; x max (j) is the maximum value of the jth indicator; y(i) is the corresponding evaluation level;

[0019] Step S22, calculating the projection value:

[0020] Transform high-dimensional data information into low-dimensional space through the projection direction. Let a be an m-dimensional unit vector with components a(1), a(2),…, a(m). Then the projection eigenvalue of the i-th sample is:

[0021]

[0022] Define z(j)=a(j)x(i,j) as the projection component value of the index of sample i and index j;

[0023] The n-dimensional data x(i,j) is synthesized into a one-dimensional projection value z(i) with a=(a(1), a(2), …, a(m)) as the projection direction through the above projection eigenvalue formula z(i), and the mathematical relationship between the reliability evaluation index x(i,j) and the evaluation level y(i) is established;

[0024] Step S23, construct the projection objective function:

[0025] For the projection index value z(i) obtained by comprehensive analysis, its scattering characteristics are as follows: the local projection points are densely condensed into several point clusters, with the local density R of the projection value z(i) zy to describe; while the projection groups are scattered as a whole, with the standard deviation S of the projection value z(i) z To describe, finally solve R zy With S z The maximum value, constructor:

[0026] f(a)=S z |R zy |;

[0027] In the formula, S z is the standard deviation of z(i), R zy is the correlation coefficient between z(i) and y(i), E z 、E y are the means of the sequences {z(i)} and {y(i)} respectively;

[0028] Step S24, optimizing the projection index function:

[0029] The projection objective function f(a) changes with the projection direction a. The optimal projection direction a is estimated by solving the maximum value of the projection objective function. * , the formula is:

[0030] maxf(a)=S z |R zy

[0031]

[0032] In the formula, a=(a(1), a(2),…, a(m)) is used as the nonlinear optimization problem for the optimization variable, and the optimal projection direction is found using the genetic algorithm;

[0033] Step S25, comprehensively evaluating the genetic projection pursuit model;

[0034] The best projection direction a * Substitute into the projection eigenvalue formula z(i) to get the projection value z of the i-th sample * (i). According to z * The scatter plot of (i) and y(i) is used to establish a mathematical model for comprehensive reliability evaluation and obtain the comprehensive reliability evaluation result of a single device.

[0035] As a further solution of the present invention: the specific steps in step S3 include:

[0036] Determine whether the system's safe and uninterrupted operation time has been reached;

[0037] If it has not been reached, determine whether the system reliability has dropped to a preset lower limit;

[0038] If it is above the preset lower limit, the system continues to operate if no fault occurs;

[0039] If it is lower than the preset lower limit, go to step S4.

[0040] As a further solution of the present invention: the specific steps in step S4 include:

[0041] After replacing the spare parts of the i-th member device, the incremental system reliability per unit cost is expressed as:

[0042]

[0043] Where η i (t) is the cost-effectiveness of the i-th device, which is defined as the incremental improvement in system reliability per unit cost; R(t) is the system reliability function before the i-th device is replaced; R ω (t) is the system reliability function after the i-th member device is replaced; c i is the spare parts replacement cost of the i-th member equipment;

[0044] The system reliability function is expressed as R ω (t).

[0045] As a further solution of the present invention: the specific steps in step S5 include:

[0046] When the reliability of the open-pit mine system is lower than the preset threshold, the cost-effectiveness of each member device to be replaced is arranged in descending order, and the member device with the greatest cost-effectiveness is replaced with spare parts. Spare parts replacement is equivalent to extending the life of the original member device, making its reliability close to 1.

[0047] After the replacement is completed, the new system continues to run and decides whether to enter the next replacement based on the constraints;

[0048] Finally, the number of spare parts required to be replaced for each member equipment during the entire guarantee period is summarized to form the optimal spare parts configuration plan.

[0049] Compared with the prior art, the present invention has the following technical effects:

[0050] By adopting the above technical solution, through the open-pit mine system composed of various types of mining vehicles, excavators and various types of equipment, the spare parts configuration optimization under the system reliability constraint is realized by using the cost-effectiveness importance method. According to the maximum value of the cost-effectiveness importance, the equipment that needs to be replaced most in the system is selected for spare parts. The method of selecting equipment based on the cost-effectiveness importance not only helps to identify the bottleneck of the system and prevent the system from unexpected failure, but also can conveniently and quickly realize the spare parts configuration optimization of the open-pit mine throughout the guarantee time. BRIEF DESCRIPTION OF THE DRAWINGS

[0051] The specific embodiments of the present invention are described in detail below in conjunction with the accompanying drawings:

[0052] Figure 1 A schematic diagram of the steps of the reliability optimization method of the embodiment disclosed in this application;

[0053] Figure 2 This is a flowchart of the reliability optimization method of the embodiment disclosed in this application. DETAILED DESCRIPTION

[0054] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0055] Please refer to Figure 1 and Figure 2 In an embodiment of the present invention, a method for optimizing the reliability of an open-pit mine system based on cost-effectiveness importance comprises the following steps:

[0056] Step S1: according to the production task output and working time requirements of the open-pit mine, preset parameters are set, wherein the preset parameters include the lower limit of the overall reliability of the open-pit mine system and the guarantee time for continuous safe and uninterrupted operation. The specific steps include:

[0057] According to the output and working time requirements of the production task, set the lower limit of the overall reliability of the open-pit mine system and the guarantee time for continuous safe and uninterrupted operation.

[0058] Step S2: Evaluate the reliability of a single device based on the genetic projection pursuit method, and then calculate the overall reliability of the open-pit mine system based on the GO method. The specific steps include:

[0059] In this embodiment, the research focus of the comprehensive evaluation of the reliability of a single device is how to scientifically and objectively integrate multiple index problems into a single index problem, that is, how to transform the high-dimensional space points composed of the individual indicators of the reliability of a single device into a mapping problem of low-dimensional space. Projection pursuit method is a data analysis method that projects high-dimensional data into a low-dimensional subspace to objectively extract the characteristics of the indicator to be evaluated. The traditional projection pursuit method has a very large amount of calculation when finding the optimal projection direction of high-dimensional data. The use of genetic algorithms can well solve this complex optimization problem.

[0060] Step S21, establishing projection data:

[0061] To use the projection pursuit method to conduct a comprehensive evaluation of the reliability of a single device, we must first establish the projection objective function of the reliability characteristics of each single indicator of the single device;

[0062] According to the obtained single equipment reliability grading standard, the projection index is established by randomly selecting values ​​within each grade range to form a sample set.<x(i,j),y(i)> , where x(i,j) is the reliability index normalized by the following formula:

[0063] x(i,j)=x * (i,j) / x max (j),i=1,2,…,n,j=1,2,…,m;

[0064] In the formula, n and m are the number of samples and the number of indicators respectively; x max (j) is the maximum value of the jth indicator; y(i) is the corresponding evaluation level;

[0065] Step S22, calculating the projection value:

[0066] Transform high-dimensional data information into low-dimensional space through the projection direction. Let a be an m-dimensional unit vector with components a(1), a(2),…, a(m). Then the projection eigenvalue of the i-th sample is:

[0067]

[0068] Define z(j)=a(j)x(i,j) as the projection component value of the index of sample i and index j;

[0069] The n-dimensional data x(i,j) is synthesized into a one-dimensional projection value z(i) with a=(a(1), a(2), …, a(m)) as the projection direction through the above projection eigenvalue formula z(i), and the mathematical relationship between the reliability evaluation index x(i,j) and the evaluation level y(i) is established;

[0070] Step S23, construct the projection objective function:

[0071] For the projection index value z(i) obtained by comprehensive analysis, its scattering characteristics are as follows: the local projection points are as dense as possible, preferably condensed into several point clusters, with the local density R of the projection value z(i) zy to describe; while the projection groups are scattered as a whole, with the standard deviation S of the projection value z(i) z To describe, finally solve R zy With S z The maximum value, constructor:

[0072] f(a)=S z |R zy |;

[0073] In the formula, S z is the standard deviation of z(i), R zy is the correlation coefficient between z(i) and y(i), E z 、E y are the means of the sequences {z(i)} and {y(i)} respectively;

[0074] Step S24, optimizing the projection index function:

[0075] The projection objective function f(a) changes with the projection direction a. The optimal projection direction a is estimated by solving the maximum value of the projection objective function. * , the formula is:

[0076] maxf(a)=S z |R zy

[0077]

[0078] In the formula, a=(a(1), a(2),…, a(m)) is used as the nonlinear optimization problem for the optimization variable, and the optimal projection direction is found using the genetic algorithm;

[0079] Step S25, comprehensively evaluating the genetic projection pursuit model;

[0080] The best projection direction a * Substitute into the projection eigenvalue formula z(i) to get the projection value z of the i-th sample * (i). According to z * The scatter plot of (i) and y(i) is used to establish a mathematical model for comprehensive reliability evaluation and obtain the comprehensive reliability evaluation result of a single device.

[0081] Step S3: Determine whether the system safe and uninterrupted operation time has arrived. If it has arrived, determine the system reliability range and take corresponding actions. The specific steps include:

[0082] Determine whether the system's safe and uninterrupted operation time has been reached;

[0083] If it has not been reached, determine whether the system reliability has dropped to a preset lower limit;

[0084] If it is above the preset lower limit, the system continues to operate if no fault occurs;

[0085] If it is lower than the preset lower limit, go to step S4.

[0086] As a further solution of the present invention: in step S4

[0087] Step S4: When the open-pit mine system reliability drops to the lower threshold, the cost-effectiveness importance of each member equipment is calculated, and the spare parts corresponding to the maximum cost-effectiveness importance are replaced. The specific steps include:

[0088] In this embodiment, the problem of minimum cost of required spare parts is transformed into the problem of maximizing the system reliability by selecting spare parts for member devices.

[0089] After replacing the spare parts of the i-th member device, the incremental system reliability per unit cost is expressed as:

[0090]

[0091] Where η i (t) is the cost-effectiveness of the i-th device, which is defined as the incremental improvement in system reliability per unit cost; R(t) is the system reliability function before the i-th device is replaced; R ω (t) is the system reliability function after the i-th member device is replaced; c i is the spare parts replacement cost of the i-th member equipment;

[0092] The system reliability function is expressed as R ω (t).

[0093] Step S5: After the task is completed, the number of spare parts replaced by each member device is counted to form an optimal spare parts configuration plan. The specific steps include:

[0094] When the reliability of the open-pit mine system is lower than the preset threshold, the cost-effectiveness of each member device to be replaced is arranged in descending order, and the member device with the greatest cost-effectiveness is replaced with spare parts. Spare parts replacement is equivalent to extending the life of the original member device, making its reliability close to 1.

[0095] After the replacement is completed, the new system continues to run and decides whether to enter the next replacement based on the constraints;

[0096] Finally, the number of spare parts required to be replaced for each member equipment during the entire guarantee period is summarized to form the optimal spare parts configuration plan.

[0097] Although the embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention. The scope of the present invention is defined by the appended claims and their equivalents, and all should be included in the scope of protection of the present invention.

Claims

1. A method for optimizing the reliability of an open-pit mine system based on cost-effectiveness importance, characterized in that: The following steps are involved: Step S1, setting preset parameters according to the production task output and working time requirements of the open-pit mine, wherein the preset parameters include the lower limit of the overall reliability of the open-pit mine system and the guarantee time for continuous safe and uninterrupted operation; Step S2, evaluating the reliability of individual equipment based on the genetic projection pursuit method, and then calculating the overall reliability of the open-pit mine system based on the GO method; Step S3: determine whether the system safe and uninterrupted operation time has arrived. If it has arrived, determine the system reliability range and take corresponding actions; Step S4: when the reliability of the open-pit mine system drops to the lower threshold, the cost-effectiveness importance of each member equipment is calculated, and the spare parts corresponding to the maximum cost-effectiveness importance are replaced; Step S5: After the task is completed, the number of spare parts replaced by each member device is counted to form an optimal spare parts configuration plan.

2. The open-pit mine system reliability optimization method based on cost-effectiveness importance according to claim 1, characterized in that: The specific steps in step S1 include: According to the output and working time requirements of the production task, set the lower limit of the overall reliability of the open-pit mine system and the guarantee time for continuous safe and uninterrupted operation.

3. The open-pit mine system reliability optimization method based on cost-effectiveness importance according to claim 1 is characterized in that: The specific steps in step S2 include: Step S21, establishing projection data: To use the projection pursuit method to conduct a comprehensive evaluation of the reliability of a single device, we must first establish the projection objective function of the reliability characteristics of each single indicator of the single device; According to the obtained single equipment reliability grading standard, the projection index is established by randomly selecting values ​​within each grade range to form a sample set.<x(i,j),y(i)> , where x(i,j) is the reliability index normalized by the following formula: x(i,j)=x * (i,j) / x max (j),i=1,2,…,n,j=1,2,…,m; In the formula, n and m are the number of samples and the number of indicators respectively; x max (j) is the maximum value of the jth indicator; y(i) is the corresponding evaluation level; Step S22, calculating the projection value: Transform high-dimensional data information into low-dimensional space through the projection direction. Let a be an m-dimensional unit vector with components a(1), a(2),…, a(m). Then the projection eigenvalue of the i-th sample is: Define z(j)=a(j)x(i,j) as the projection component value of the index of sample i and index j; The n-dimensional data x(i,j) is synthesized into a one-dimensional projection value z(i) with a=(a(1), a(2), …, a(m)) as the projection direction through the above projection eigenvalue formula z(i), and the mathematical relationship between the reliability evaluation index x(i,j) and the evaluation level y(i) is established; Step S23, construct the projection objective function: For the projection index value z(i) obtained by comprehensive analysis, its scattering characteristics are as follows: the local projection points are densely condensed into several point clusters, with the local density R of the projection value z(i) zy to describe; while the projection groups are scattered as a whole, with the standard deviation S of the projection value z(i) z To describe, finally solve R zy With S z The maximum value, constructor: f(a)=S z |R zy |; In the formula, S z is the standard deviation of z(i), R zy is the correlation coefficient between z(i) and y(i), E z 、E y are the means of the sequences {z(i)} and {y(i)} respectively; Step S24, optimizing the projection index function: The projection objective function f(a) changes with the projection direction a. The optimal projection direction a is estimated by solving the maximum value of the projection objective function. * , the formula is: maxf(a)=S z |R zy In the formula, a=(a(1), a(2),…, a(m)) is used as the nonlinear optimization problem for the optimization variable, and the optimal projection direction is found using the genetic algorithm; Step S25, comprehensively evaluating the genetic projection pursuit model; The best projection direction a * Substitute into the projection eigenvalue formula z(i) to get the projection value z of the i-th sample * (i). According to z * The scatter plot of (i) and y(i) is used to establish a mathematical model for comprehensive reliability evaluation and obtain the comprehensive reliability evaluation result of a single device.

4. The open-pit mine system reliability optimization method based on cost-effectiveness importance according to claim 1 is characterized in that: The specific steps in step S3 include: Determine whether the system's safe and uninterrupted operation time has been reached; If it has not been reached, determine whether the system reliability has dropped to a preset lower limit; If it is above the preset lower limit, the system continues to operate if no fault occurs; If it is lower than the preset lower limit, go to step S4.

5. The open-pit mine system reliability optimization method based on cost-effectiveness importance according to claim 1 is characterized in that: The specific steps in step S4 include: After replacing the spare parts of the i-th member device, the incremental system reliability per unit cost is expressed as: Where η i (t) is the cost-effectiveness of the i-th device, which is defined as the incremental improvement in system reliability per unit cost; R(t) is the system reliability function before the i-th device is replaced; R ω (t) is the system reliability function after the i-th member device is replaced; c i is the spare parts replacement cost of the i-th member equipment; The system reliability function is expressed as R ω (t).

6. The open-pit mine system reliability optimization method based on cost-effectiveness importance according to claim 1, characterized in that: The specific steps in step S5 include: When the reliability of the open-pit mine system is lower than the preset threshold, the cost-effectiveness of each member device to be replaced is arranged in descending order, and the member device with the greatest cost-effectiveness is replaced with spare parts. Spare parts replacement is equivalent to extending the life of the original member device, making its reliability close to 1. After the replacement is completed, the new system continues to run and decides whether to enter the next replacement based on the constraints; Finally, the number of spare parts required to be replaced for each member equipment during the entire guarantee period is summarized to form the optimal spare parts configuration plan.

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