Method for optimizing wind power gear box assembly factory design

Abstract

The invention provides a method for optimizing wind power gear box assembly factory design. The method comprises the steps that S1, simulation basic data are prepared; s2, according to the simulation basic data, establishing a simulation model of the wind power gear box assembly factory based on a discrete event simulation engine; s3, running the simulation model in two scenes of personnel sufficiency and personnel according to current configuration, comparing a simulation calculation result with the simulation basic data, and verifying the reliability of the simulation model; if the simulation model is reliable, entering the step S4, otherwise, returning to the step S2; s4, designing a plurality of optimization design schemes, running the simulation model under the plurality of optimization design schemes, and outputting the average output per shift and crown block type resource collision avoidance conditions under the plurality of optimization design schemes; and S5, selecting an optimal optimization scheme according to an output result of the step S4. Compared with an optimization design method in the prior art, the method has the advantages of being time-saving, labor-saving, accurate in process, visual in result and the like.

Description

Technical Field

[0001] The present invention relates to the technical field of simulation design, and in particular to a method for optimizing the design of a wind power gearbox assembly plant. Background Art

[0002] Wind turbine gearbox assembly plants are typical assembly plants under discrete manufacturing. In the process of assembling wind turbine gearboxes, the assembly work faces many challenges due to the large size of the product, the complex assembly process, the long assembly cycle, the frequent use of specific resources such as semi-gantry cranes, overhead cranes and gantry cranes, the large number of parallel workstations, and the limited available manpower. In the optimization design stage of the wind turbine gearbox assembly plant, the optimization design of the assembly process, assembly workstations, assembly personnel, overhead cranes, semi-gantry cranes and gantries is crucial to the output of the assembly plant. Different optimization design schemes may result in different factory outputs and affect the delivery cycle of factory orders. At present, the optimization design of wind turbine gearbox assembly plants still mainly relies on the design method of manual experience.

[0003] However, problems such as personnel allocation and avoiding collisions between cranes are difficult to obtain the optimal solution through simple calculations during the production process. When trying to optimize the factory based on experience, it is difficult to accurately evaluate the effect of the overall improvement. Considering the existence of multiple factors such as material flow, crane resource management, and personnel scheduling within the wind turbine gearbox assembly factory, overly simplified mathematical models may ignore key details, such as collisions between cranes, which will lead to inaccurate descriptions of complex assembly environments, greatly reducing the effectiveness of any evaluation or improvement measures. In addition, if the design plan needs to be changed, it means that the factory must make corresponding adjustments, and each adjustment will incur a certain cost. If the production plant is adjusted based on experience alone, it will be found that the output still cannot meet the order demand after a period of actual operation, which will not only lead to delayed delivery of orders, but also bring additional cost losses and time waste. Therefore, it is particularly important to use scientific methods for optimization design, especially when most optimization designs still rely on manual experience and intuition.

[0004] In summary, the prior art has at least the following defects:

[0005] 1. The existing optimization design method is designed manually based on experience, which is time-consuming and labor-intensive, and the optimization effect is poor;

[0006] 2. The existing optimization design method ignores complex dynamic factors such as the details of overhead crane resource avoidance, and lacks overall effect evaluation. It is impossible to intuitively compare the effects of different design schemes and obtain the best optimization scheme. Summary of the invention

[0007] The purpose of the present invention is to overcome the defects of the prior art and provide a method for optimizing the design of a wind turbine gearbox assembly plant, which can solve the problems in the prior art that the optimization scheme design process is time-consuming and labor-intensive, the effects of different schemes cannot be intuitively compared, and it is difficult to obtain the best optimization scheme.

[0008] In order to achieve the above and other purposes, the present invention is implemented by including the following technical solutions: The present invention proposes a method for optimizing the design of a wind turbine gearbox assembly plant, including the steps of:

[0009] S1. Prepare basic simulation data;

[0010] S2. Establishing a simulation model of a wind power gearbox assembly plant based on a discrete event simulation engine according to the simulation basic data;

[0011] S3, running the simulation model in two scenarios: sufficient personnel and personnel according to the current configuration, comparing the simulation calculation results with the simulation basic data, and verifying the reliability of the simulation model; if the simulation model is reliable, proceed to step S4, otherwise return to step S2;

[0012] S4, designing a plurality of optimization design schemes, running the simulation model under the plurality of the optimization design schemes, and outputting the average shift output and the collision avoidance of overhead crane resources under the plurality of the optimization design schemes;

[0013] S5. According to the output result of step S4, the best optimization scheme is selected.

[0014] In one embodiment, the simulation basic data includes critical paths, critical working hours and total working time of overhead crane resources. The preparation process of the simulation basic data includes the steps of:

[0015] S11, obtaining process data of each assembly process section of the wind turbine gearbox assembly factory;

[0016] S12, constructing a wind turbine gearbox assembly process flow chart according to the process data, and summing the working time of different overhead crane resources in each assembly process section to obtain the total working time of the overhead crane resources;

[0017] S13. Starting from the starting node of the wind turbine gearbox assembly process flow chart, the earliest end time of each process is calculated step by step along the path of the wind turbine gearbox assembly process flow chart, and the process path corresponding to the largest earliest end time is confirmed as the critical path, and the sum of the process times of the critical path is the critical working time.

[0018] In one embodiment, in step S2, the establishment of the simulation model includes object modeling, data modeling and process modeling; the object modeling refers to the graphical modeling and layout placement of the wind turbine gearbox assembly plant; the data modeling refers to the standardized expression of various types of data, and defining the data to the object component through the simulation object component property panel; the process modeling refers to the design of the work-in-progress flow process while considering the constraints of personnel resources and the collision avoidance of overhead crane resources.

[0019] In one embodiment, the object modeling includes assembly station object modeling, logistics tool object modeling, work-in-progress object modeling, and assembly logic object modeling.

[0020] In one embodiment, the data includes object attribute data, process data, simulation input and output data, and simulation process data.

[0021] In one embodiment, in step S3, when the deviation between the completion time of each assembly process section in the simulation calculation results and the critical working time of each assembly process section in the simulation basic data is less than 5%, and the deviation between the total working time of the overhead crane resources in the simulation calculation results and the total working time of the overhead crane resources in the simulation basic data is less than 5%, the simulation model is considered to be reliable.

[0022] In one embodiment, in step S4, the optimization direction of the optimization design scheme includes adjusting the occupancy time of overhead crane resources, adjusting personnel allocation, adjusting the number of assembly stations, and adjusting the working time of some processes.

[0023] In one embodiment, in step S4, the collision avoidance status of the overhead crane resources includes the waiting time and the number of avoidance times of the overhead crane resources.

[0024] In one embodiment, in step S5, a weighted scoring method is used to select the best optimization solution.

[0025] In one embodiment, the weighted scoring method comprises the steps of

[0026] S51, based on the average shift output outputted in step S4, screening an optimized design scheme that meets the target shift output;

[0027] S52, respectively weighting and scoring the output index, cost index, and operability index of the optimization design scheme that meets the target shift output;

[0028] S53, calculating the weighted total score of the optimization design solutions that meet the target shift production, and determining the one with the highest score as the best optimization solution.

[0029] Compared with the prior art, the present invention has the following beneficial effects:

[0030] 1. The optimization design method provided by the present invention establishes a simulation model of a wind power gearbox assembly plant based on a discrete event simulation engine, which can comprehensively consider various complex dynamic factors and abnormal situations, and provide a more intuitive and accurate way to describe the actual operation status of the wind power gearbox assembly plant. It can quantitatively evaluate the effects of different design schemes by simulating possible scenarios in the future, thereby providing a scientific basis for the planning and construction of the plant; compared with the optimization design method of the prior art, it has the advantages of saving time and labor, accurate process, and intuitive results;

[0031] 2. The optimization design method provided by the present invention establishes a collision avoidance strategy for overhead crane resources in a high-fidelity simulation model, collects statistical avoidance data, and provides data reference for judging the use of overhead crane resources in actual production. Compared with simplified mathematical models, it can more intuitively reflect the complexity of factory production and overcome the difficulty of traditional methods in describing complex production systems. At the same time, it provides a new direction for the simulation optimization of wind power gearbox assembly production.

[0032] 3. The simulation model constructed by the present invention is conducive to realizing the rapid evaluation of production performance and usage data of overhead crane resources in the assembly plant after the resource allocation plan is adjusted;

[0033] 4. The optimization design method provided by the present invention is simple and easy to implement. It can accurately digitally express the production process of a wind turbine gearbox assembly plant. At the same time, it fully considers the personnel resource constraints, overhead crane collision avoidance and other situations in future operations, improves the robustness of the design system, and quantitatively evaluates the optimization design scheme to scientifically assist factory planning and construction. This method is also of reference significance for the optimization design of similar workshops / production lines. BRIEF DESCRIPTION OF THE DRAWINGS

[0034] Figure 1 Shown is a schematic flow chart of a method for optimizing the design of a wind turbine gearbox assembly plant according to the present invention.

[0035] Figure 2 Shown is a simulation model of a complete assembly line at a wind turbine gearbox assembly plant. DETAILED DESCRIPTION

[0036] The embodiment of the present invention provides a method for optimizing the design of a wind turbine gearbox assembly plant, thereby solving the problems existing in the prior art that the optimization scheme design process is time-consuming and labor-intensive, the effects of different schemes cannot be intuitively compared, and it is difficult to obtain the best optimization scheme.

[0037] Below, the technical solutions in the embodiments of the present invention will be clearly and completely described with reference to the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all of the embodiments of the present invention. It should be understood that the present invention is not limited to the example embodiments described herein. 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. It should also be noted that, for the convenience of description, only the parts related to the present invention are shown in the accompanying drawings, rather than all of them.

[0038] like Figure 1 As shown, the overall idea of ​​the technical solution provided by the present invention is as follows: A method for optimizing the design of a wind turbine gearbox assembly plant comprises the following steps:

[0039] S1. Prepare basic simulation data;

[0040] S2. Establishing a simulation model of a wind power gearbox assembly plant based on a discrete event simulation engine according to the simulation basic data;

[0041] S3, running the simulation model in two scenarios: sufficient personnel and personnel according to the current configuration, comparing the simulation calculation results with the simulation basic data, and verifying the reliability of the simulation model; if the simulation model is reliable, proceed to step S4, otherwise return to step S2;

[0042] S4, designing a plurality of optimization design schemes, running the simulation model under the plurality of the optimization design schemes, and outputting the average shift output and the collision avoidance of overhead crane resources under the plurality of the optimization design schemes;

[0043] S5. According to the output result of step S4, the best optimization scheme is selected.

[0044] The main idea is to establish a simulation model of a wind turbine gearbox assembly factory based on a discrete event simulation engine, and provide a more intuitive and accurate way to describe the actual operation of the wind turbine gearbox assembly factory by comprehensively considering various complex dynamic factors and abnormal situations, especially the collision avoidance of overhead crane resources. At the same time, by simulating possible scenarios in the future, the effects of different design schemes are quantitatively evaluated to provide a scientific basis for the planning and construction of the factory. Compared with the existing optimization design method, it has the advantages of saving time and labor, precise process, and intuitive results.

[0045] After introducing the basic principle of the present invention, various non-limiting implementation methods of the present invention will be specifically introduced below by taking a complete assembly line of a wind turbine gearbox assembly factory as an example.

[0046] The wind turbine gearbox assembly process of the wind turbine gearbox assembly factory includes 13 assembly process sections, including primary planetary gear assembly, secondary planetary gear assembly, primary planetary carrier assembly, secondary planetary carrier assembly, rear box cover assembly, rear box assembly, high-speed shaft assembly, high-speed intermediate shaft assembly, high-speed box assembly, low-speed box assembly, secondary general assembly, primary general assembly and accessory assembly. It can be divided into 8 assembly work areas, including planetary gear assembly, planetary carrier assembly, rear box assembly, high-speed box assembly, low-speed box assembly, secondary general assembly, primary general assembly and accessory assembly. There are 5 semi-gantry on a complete assembly line, with 4 overhead cranes and 1 gantry from component assembly to general assembly. Among them, the semi-gantry is lower than the overhead crane, covering the primary planetary gear assembly to the secondary general assembly area, and cannot work at the same station with the overhead crane; the overhead crane can cross the semi-gantry, and the overhead crane track covers the entire assembly line; the gantry track can only cover the primary general assembly and accessory assembly area, and cannot cross the overhead crane. Overhead cranes, semi-gantry cranes and gantry cranes are collectively referred to as overhead crane resources. The assembly line adopts a two-shift production system, with a production capacity target of 2 units per shift.

[0047] According to the assembly line situation of the wind turbine gearbox assembly factory, the discrete event simulation planning method is used for simulation optimization design. The specific steps are as follows:

[0048] S1. Prepare basic simulation data: obtain wind turbine gearbox assembly process flow and factory design data, such as regional layout information of each assembly station, regional personnel design information, overhead crane resource information (including overhead crane, semi-gantry and gantry), organize the assembly process flow chart corresponding to each assembly station area, and find the critical path of each of the 13 assembly process sections according to the assembly flow chart of the 13 assembly process sections without considering any resource restrictions. The critical path refers to the process path with the longest assembly working time from the beginning of the assembly process of a certain process section to the end of the assembly of the assembly process section. The working time of each assembly process section is counted according to the three categories of gantry, semi-gantry and overhead crane, and the critical working time of each assembly process section is calculated. The critical working time refers to the sum of the process operation time on the critical path.

[0049] Specifically, the critical working time is calculated as follows: starting from the starting node of the assembly process segment (time is 0), the earliest end time EF of each process is calculated step by step along the flow chart path = earliest start time (ES) + process operation time, where ES refers to the maximum value of the earliest end time EF of all parallel preceding processes of the process; finally, the critical working time data table of each assembly process segment is obtained as shown in Table 1.

[0050] Table 1 Key working time data for each assembly process section

[0051] Serial number Process section Critical working time (min) 1 First-stage planetary gear assembly 138 2 Secondary planetary gear assembly 90 3 First stage planet carrier assembly 885 4 Secondary planet carrier assembly 798 5 Rear box cover assembly 146 6 Rear box assembly 210 7 High speed shaft assembly 104 8 High speed intermediate shaft assembly 117 9 High-speed box assembly 451 10 Low speed gearbox assembly 217 11 Secondary assembly 291 12 First level assembly 509 13 Accessory assembly 199

[0052] S2. According to the factory layout, staffing and other information in the simulation basic data, based on a discrete event simulation engine, such as the domestic Factory Simulation platform, graphical modeling is performed on the factory layout, the cleaning machine, warehouse, buffer zone, assembly station, and overhead crane resource locations are laid out, the basic logic of the production and logistics process is set, the rules for the ingredients to enter the assembly line are set, and the simulation model of the wind power gearbox assembly factory considering the overhead crane resource collision avoidance simulation is completed;

[0053] Specifically, the basic logic of the production and logistics process includes the scheduling strategy of overhead crane resources, assembly process working hours, and other important time parameters, such as factory calendar, transit time, etc. Among them, the scheduling strategy of overhead crane resources is: first, according to the demand for overhead crane resources in the assembly process and the number of overhead crane resources, the semi-gantry and the main areas responsible for the overhead crane are preliminarily divided to reduce unnecessary collisions during the work process; second, collision judgment and waiting before moving, before the overhead crane resources need to move, consider the collision avoidance with the adjacent overhead crane resources on the left and right sides. If there will be a collision when moving to the target station, wait in place until the overhead crane resource in front is free and avoids to a position where there will be no collision, and then the overhead crane resource moves to the target station. During the simulation, avoidance data such as the waiting time and end time are recorded.

[0054] The rule for ingredients to enter the assembly line includes considering that the ingredients are delivered one shift in advance, that is, the ingredients are sufficient.

[0055] S3. Run the simulation model in two scenarios: sufficient personnel and personnel according to the current configuration. Simulate and calculate the completion time of each assembly process section and the total working time of overhead crane resources. Compare with the theoretical calculation results in the simulation basic data (i.e., the critical working hours of each assembly process section and the total working time of overhead crane resources). If the deviation between the simulation calculation results and the theoretical calculation results is less than 5%, the simulation model is reliable. Table 2 below shows the simulation calculation results and theoretical calculation results of the two assembly process sections of high-speed box assembly and secondary assembly in this embodiment, as well as the errors between the two. All errors are less than 5%, so the simulation model established in step S2 is reliable.

[0056] Table 2 Simulation and theoretical calculation results of the two assembly process sections of high-speed stage assembly and secondary assembly

[0057]

[0058] S4. Design multiple optimization schemes, run simulation models under multiple optimization schemes, and output the average shift output and overhead crane resource collision avoidance under multiple optimization schemes.

[0059] As shown in Table 3, optimization and adjustment were made in terms of adjusting the occupancy time of overhead crane resources, adjusting personnel allocation, adjusting the number of assembly stations, and adjusting the working hours of some processes. The simulation model was run to obtain the statistical results of the average shift output of the assembly line under various schemes, as well as the time and position data of overhead crane resources waiting for collision avoidance.

[0060] Table 3 Statistics of average shift output of assembly lines under various optimization design schemes

[0061]

[0062] In Table 3, the crane occupation time × 0.8 means reducing the crane occupation time to 80% of the original time, and other occupation times multiplied by coefficients have similar meanings. The overall proportional coefficient of the crane occupation time is in the range of 0.5 to 1, with an adjustment step of 0.1; the local proportional coefficient of the crane occupation time is in the range of 0.8 to 1, with a step of 0.05.

[0063] S5. According to the average shift output outputted in step S4, the optimization design schemes that meet the target shift output are screened, i.e., the two optimization design schemes with serial numbers 7 and 8. In combination with the actual situation, the production capacity requirement of 2 units / shift is mainly considered. In this embodiment, the weights of shift output, cost, and feasibility are set to 0.8, 0.1, and 0.1, respectively (the weights can be adjusted according to the needs of the factory). The output index (meeting the target is 100 points), cost index (the lower the cost, the higher the score), and operability index (the higher the operability, the higher the score) under serial numbers 7 and 8 are scored respectively; the weighted total score of each optimization design scheme is calculated, and the one with the highest score is determined as the best optimization scheme.

[0064] Table 4 Weighted scoring table of the optimal design scheme that meets the target shift production

[0065]

[0066] As shown in Table 4, the optimal design solution 8 with the highest score is selected. The remaining solutions can also provide a certain degree of reference for process improvement, but they are not the optimal choices under current needs.

[0067] Therefore, the present invention effectively overcomes various shortcomings in the prior art and has a high industrial utilization value. The above embodiments are only illustrative of the principles and effects of the present invention, and are not intended to limit the present invention. Anyone familiar with the technology can modify or change the above embodiments without violating the spirit and scope of the present invention. Therefore, all equivalent modifications or changes made by those with ordinary knowledge in the technical field without departing from the spirit and technical ideas disclosed by the present invention should still be covered by the claims of the present invention.

Claims

1. A method for optimizing the design of a wind turbine gearbox assembly plant, characterized in that: Included steps S1. Prepare basic simulation data; S2. Establishing a simulation model of a wind power gearbox assembly plant based on a discrete event simulation engine according to the simulation basic data; S3, running the simulation model in two scenarios: sufficient personnel and personnel according to the current configuration, comparing the simulation calculation results with the simulation basic data, and verifying the reliability of the simulation model; if the simulation model is reliable, proceed to step S4, otherwise return to step S2; S4, designing a plurality of optimization design schemes, running the simulation model under the plurality of the optimization design schemes, and outputting the average shift output and the collision avoidance of overhead crane resources under the plurality of the optimization design schemes; S5. According to the output result of step S4, the best optimization scheme is selected.

2. The method according to claim 1, characterized in that: The simulation basic data includes critical paths, critical working hours and total working time of overhead crane resources. The preparation process of the simulation basic data includes the steps S11, obtaining process data of each assembly process section of the wind turbine gearbox assembly factory; S12, constructing a wind turbine gearbox assembly process flow chart according to the process data, and summing the working time of different overhead crane resources in each assembly process section to obtain the total working time of the overhead crane resources; S13. Starting from the starting node of the wind turbine gearbox assembly process flow chart, the earliest end time of each process is calculated step by step along the path of the wind turbine gearbox assembly process flow chart, and the process path corresponding to the largest earliest end time is confirmed as the critical path, and the sum of the process times of the critical path is the critical working time.

3. The method according to claim 1, characterized in that In step S2, the establishment of the simulation model includes object modeling, data modeling and process modeling; the object modeling refers to the graphical modeling and layout placement of the wind turbine gearbox assembly plant; the data modeling refers to the standardized expression of various types of data, and the definition of data to object components through the simulation object component property panel; the process modeling refers to the design of the work-in-progress flow process while considering the constraints of personnel resources and the collision avoidance of overhead crane resources.

4. The method according to claim 3, characterized in that The object modeling includes assembly station object modeling, logistics tool object modeling, work-in-progress object modeling and assembly logic object modeling.

5. The method according to claim 3, characterized in that: The data includes object attribute data, process data, simulation input and output data, and simulation process data.

6. The method according to claim 1, characterized in that In step S3, when the deviation between the completion time of each assembly process section in the simulation calculation results and the critical working time of each assembly process section in the simulation basic data is less than 5%, and the deviation between the total working time of the overhead crane resources in the simulation calculation results and the total working time of the overhead crane resources in the simulation basic data is less than 5%, the simulation model is considered to be reliable.

7. The method according to claim 1, characterized in that In step S4, the optimization direction of the optimization design scheme includes adjusting the occupancy time of overhead crane resources, adjusting personnel allocation, adjusting the number of assembly stations, and adjusting the working time of some processes.

8. The method according to claim 1, characterized in that In step S4, the collision avoidance situation of the overhead crane resources includes the waiting time and the number of avoidance times of the overhead crane resources.

9. The method according to claim 1, characterized in that: In step S5, a weighted scoring method is used to select the best optimization solution.

10. The method according to claim 9, characterized in that The weighted scoring method comprises the steps of S51, based on the average shift output outputted in step S4, screening an optimized design scheme that meets the target shift output; S52, respectively weighting and scoring the output index, cost index, and operability index of the optimization design scheme that meets the target shift output; S53, calculating the weighted total score of the optimization design solutions that meet the target shift production, and determining the one with the highest score as the best optimization solution.

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