CN114936472A - Multi-stage task reliability simulation evaluation method for space launch field - Google Patents
Multi-stage task reliability simulation evaluation method for space launch field Download PDFInfo
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Abstract
The invention relates to a simulation evaluation method for reliability of a multi-stage task of an aerospace launching field, and belongs to the technical field of reliability. The method comprises the steps of customizing and constructing a multi-stage task reliability model of the space launching field; inputting parameters required to be used in a multi-stage task reliability model of the space launch field; combining the condition that equipment participating in the task is repairable or unrepairable, performing simulation analysis on the whole multi-stage task by adopting a Monte Carlo simulation method, determining the time sequence relation of fault events and maintenance events of all equipment units through simulation, and scheduling and processing discrete events to advance a simulation clock so as to repeatedly complete multiple times of simulation; and (4) sorting the simulation evaluation result through the simulation data, and outputting and displaying. The invention can output the success probability and confidence interval of the key task node and key equipment influencing the task, and helps a decision maker to visually analyze the success rate of the task and the places needing improvement, thereby being beneficial to the full success of the whole space launch task.
Description
Technical Field
The invention belongs to the technical field of reliability, and particularly relates to a simulation evaluation method for reliability of a multi-stage task of an aerospace launch site, aiming at reasonably and effectively carrying out relevant reliability analysis on the multi-stage task of aerospace launch.
Background
The space launch site is a set of ground facilities and equipment specially built for the assembly, test, transportation and other launch preparations of a space launch vehicle, the launching, measurement and control instruction sending of a spacecraft, and the receiving and processing of telemetering information. The one-time space launching task executed in the space launching field has the characteristics of multiple stages, multiple tasks, multiple functions and the like, the reliability evaluation of the whole task by using the traditional reliability cartographic method cannot meet the actual engineering requirements, the task has extremely high reliability requirements, and a reasonable and effective reliability modeling and evaluation method for a multi-stage task system is required.
The existing multi-stage task reliability assessment method has some defects or assumptions, so that the existing multi-stage task reliability assessment method cannot be effectively applied to multi-stage task reliability assessment of an aerospace launching field. For example, the conventional multi-stage task has the characteristics of continuity and non-overlapping in time, however, the space launching task often has multiple tasks performed simultaneously, such as: the filling and launching link comprises a plurality of overlapped and parallel subtasks such as kerosene filling, gas supply and the like, and is not suitable for space launch tasks. In addition, the actual space launching system often has the characteristic of device repairability, so that most of analytical methods cannot reasonably and effectively model the space launching system, and the problem of reliability analysis of repairable multi-stage tasks by utilizing a simulation technology becomes a hot method. Therefore, a reasonable and effective multi-stage task reliability modeling and evaluating method aiming at space launch task customization is urgently needed, key equipment influencing task success probability can be provided, and important reference values are provided for reliability prediction, distribution and expert decision of the whole follow-up space launch site.
Disclosure of Invention
Technical problem to be solved
The invention aims to solve the technical problem of how to provide a multi-stage task reliability simulation evaluation method for an aerospace launch site, so as to solve the problem that the existing multi-stage task reliability evaluation method has defects or assumptions and cannot be effectively applied to multi-stage tasks of the aerospace launch site.
(II) technical scheme
In order to solve the technical problem, the invention provides a simulation evaluation method for reliability of a multi-stage task of an aerospace launch site, which comprises the following steps:
step S1, constructing a space launch field multi-stage task reliability model;
step S2, inputting parameters required to be used in the space launch field multi-stage task reliability model;
step S3, performing simulation analysis on the whole multi-stage task by using a Monte Carlo simulation method, determining the time sequence relation of all equipment fault events and maintenance events along with the lapse of a simulation clock, and advancing the simulation clock by scheduling and processing the discrete events so as to repeatedly complete multiple simulations;
and step S4, sorting the simulation evaluation result through the simulation data.
(III) advantageous effects
The invention provides a simulation evaluation method for reliability of a multi-stage task of an aerospace launching field, which has the beneficial effects that: the method provided by the invention provides a multi-stage task reliability analysis modeling and evaluation method for the space launching task, can perform simulation evaluation calculation on the task under repairable and non-repairable conditions, outputs the success probability of a key task node and key equipment influencing the task by means of statistical analysis, helps a decision maker to intuitively analyze the success rate of the task and places needing improvement, and thus is beneficial to the full success of the whole space launching task.
Drawings
FIG. 1 is a general flowchart of the method for evaluating reliability of a multi-stage task in an aerospace launch site according to the present invention;
FIG. 2 is a schematic flow chart of a Monte Carlo simulation algorithm used in the present invention;
FIG. 3 is a diagram of a model of an aerospace launch field multi-stage mission embodiment used in the present invention;
FIG. 4 is a reliability block diagram of an aerospace launch field multi-stage mission embodiment used in the present invention.
Detailed Description
In order to make the objects, contents and advantages of the present invention clearer, the following detailed description of the embodiments of the present invention will be made in conjunction with the accompanying drawings and examples.
The invention aims to overcome the defects of the prior art and customize the particularity of meeting space launching tasks, and provides a Monte Carlo simulation-based multi-stage task reliability evaluation method for a space launching field.
The invention discloses a simulation evaluation method for reliability of a multi-stage task of a space launch site, which comprises the following steps: step 1, customizing and constructing a multi-stage task reliability model of an aerospace launching field; step 2, inputting parameters required to be used in the space launch field multi-stage task reliability model; step 3, performing simulation analysis on the whole multi-stage task by using a Monte Carlo simulation method, effectively processing the condition that equipment participating in the task is repairable or unrepairable, determining the time sequence relation of fault events and maintenance events of all equipment units through simulation, scheduling and processing the discrete events to advance a simulation clock, and further repeatedly completing multiple times of simulation; and 4, sorting the simulation evaluation result through the simulation data, and outputting and displaying. The invention can output the success probability and confidence interval of the key task node and key equipment influencing the task, help a decision maker to visually analyze the success rate of the task and improve the places needing improvement, thereby being beneficial to the full success of the whole space launching task.
The specific technical scheme of the invention is as follows: a multi-stage task reliability assessment method for an aerospace launch site comprises the following steps:
according to the requirement of the space launching task, a multi-stage task is defined to successfully complete each task in each stage, and subtasks in each stage continuously work for a certain time. Furthermore, the method focuses on the dependency of a system equipment unit between phases, i.e. the unit operating/failure status of a later phase depends on the operating/failure status of the unit of a previous phase. A repairable space launch multi-stage task reliability model M is defined by a quadruple M ═ { MS, MSM, MP, MRBD }.
Stage (2): the MS represents a set of stages of multi-stage task partitioning, each stage has a corresponding critical task time node, and the task success can be described as: each stage completes the respective specified task in sequence.
And (3) subtasks: MSM represents a subtask set in a phase, each phase may contain multiple parallel subtasks, which can run in a specified time without mutual interference, where the success of one subtask can be described as: under serviceable conditions, the subtasks are run for a specified time and the total elapsed time does not exceed the deadline for this phase. When all subtasks at this stage are completed, the decision stage task is completed.
Subtask profile: the MP represents the set of devices used in the subtask profile, and in general, the subtask can only be continuously performed when the devices participating in the subtask are all in a normal operating state, so that all devices participating in the subtask are processed in a serial configuration. And when the duration of the subtask reaches the specified length, the current subtask is considered to be completed. When the subtask fails in the midway, if the current stage is not finished, the equipment participating in the subtask can be put into use again after being maintained, and the subtask is restarted until the specified time length is reached; and if the time reaches the phase end time and the subtask is not completed yet, judging that the subtask fails.
And (3) equipment model: MRBD represents the set of component parts U and the reliability block structure of the devices participating in the subtasks. When the devices participating in the subtasks cannot be split into smaller components, the MRBD represents a reliability diagram of a single component whose information in turn contains the failure distribution type and parameters and the maintenance distribution type and parameters. When the interior of the large-scale equipment participating in the subtasks can be continuously split into smaller components, the reliability block diagram is used for representing the structures among the components, including series connection, parallel connection, series-parallel connection, voting structures and backup structures.
Through the description of the multi-stage task, a universal space launching multi-stage task reliability model is established, the reliability model can comprise a plurality of stage tasks, each stage task is provided with a key task time node and a different number of subtasks, different subtasks are represented by a structure formed by connecting single or a plurality of devices in series, and the time required to continuously run can be different.
Step 2, inputting parameters required to be used in the space launch field multi-stage task reliability model;
establishing a customized space launch multi-stage task reliability model according to the step 1, inputting parameters used specifically, including total simulation times, repeated simulation times, total number of stages, number of sub-tasks in a stage, time of key task nodes, total task time, continuous operation time of sub-tasks, failure of all equipment and maintenance distribution parameters, and initializing the simulation times, the number of task failures, the number of stages and the number of sub-tasks.
Step 3, performing simulation analysis on the whole multi-stage task by using a Monte Carlo simulation method, determining the time sequence relation of all equipment fault events and maintenance events along with the transition of a simulation clock, and advancing the simulation clock by scheduling and processing the discrete events so as to repeatedly complete multiple simulations, wherein a specific operation flow chart is shown in FIG. 2, and the operation steps are as follows:
step 31, the number of initialization simulations and the number of task failures are all 0.
And 32, executing one-time simulation, wherein the simulation times are +1, and the number of the initialization current task stages is 1.
Step 33, reading the information of the current subtask: subtask duration run time, current phase time node, reliability block structure of this subtask, used equipment failure, repair distribution parameters, and run time. And the number of initialization subtasks is 1.
And step 34, performing failure time simulation on all the devices participating in the subtask to obtain failure time. When the subtask is simulated for the first time, the devices are considered as being perfect as new, namely the running time is 0; when the subtask is not simulated for the first time, the simulation information of the previous time is read, and the used time of the subtask and the running time of the equipment are updated. The failure type of the equipment can be selected from exponential distribution, normal distribution, uniform distribution, gamma distribution, lognormal distribution, Weibull distribution and set non-failure type, wherein the failure time is considered as a large enough constant in the simulation for the non-failure equipment; if the failure distribution type of the equipment is exponential distribution, directly simulating to generate failure time according to exponential distribution parameters of the equipment; if the failure distribution type of the equipment is not exponential distribution, the simulation value is continuously calculated according to the distribution parameters of the equipment until the simulation value is larger than the running time, and the running time is subtracted from the simulation value to be used as the failure time of the equipment.
Inputting the failure time of all the devices into a reliability block diagram structure to further obtain the failure time of the whole subtask, wherein for a serial structure, the minimum failure time of the constituent elements is taken as the failure time of the structure; for a parallel structure, taking the maximum failure time of the constituent elements as the failure time of the structure; for a k-out-of-n voting structure (a k-out-of-n system is a system which is provided with n devices, and when k devices are normal, the whole system can work normally), sequencing simulation failure time of all the n devices from large to small, and taking the kth failure time as the failure time of the structure; and for the backup structure, taking the sum of all cold backup failure times and the maximum failure time in all hot backups as the failure time of the structure.
And step 35, judging whether the simulation of the subtask is finished, wherein the judgment is based on the failure time of the current reliability block diagram, namely whether the fault arrival time of the subtask is greater than the stage task time, if so, the subtask represents that the subtask meets the requirement of successful continuous work and enters the next simulation before each simulated device fails, namely, step 36, and if not, the subtask fails and needs to generate corresponding maintenance activities. And regarding the equipment with the simulation failure time less than the fault arrival time of the subtask, considering that the equipment has a fault in the simulation, and updating the working time of the rest equipment for normal working.
The method comprises the steps that one-time maintenance time of the equipment can be simulated according to maintenance types and parameters of the fault equipment, the maintenance types of the equipment can be selected from exponential distribution, normal distribution, uniform distribution, gamma distribution, lognormal distribution, Weibull distribution, fixed maintenance duration and non-repairable types, the maintenance time can be considered to be a large enough constant in the simulation of the non-repairable equipment, and the other maintenance type equipment can finish the maintenance time simulation of the equipment by using corresponding random number generation operators. Taking the maximum value of the maintenance time of all fault equipment as the maintenance time of the next subtask, pushing the simulation clock and judging whether the current simulation clock exceeds the stage task time, if not, returning to the step 34, if so, the task simulation fails, recording the number of times of task failure plus 1, if the number of times of simulation is less than the total number of times of simulation, returning to the step 32 to start the next task simulation, and if the number of times of simulation is equal to the total number of times of simulation, directly entering the step 4.
And step 36, judging whether all parallel subtasks in the current stage are traversed completely, if not, counting the subtasks by +1 and returning to the step 34, if yes, judging whether the current stage number is smaller than the total number of stages, if yes, counting the stage by +1, initializing the subtask number and returning to the step 33, if not, returning to the step 32, otherwise, ending the simulation, recording simulation process data, and entering the step 4.
Step 4, sorting simulation evaluation results through simulation data;
and (3) recording the equipment failure condition and the success probability of the key task node in each simulation by operating the simulation evaluation process in the step (3), and finally outputting a simulation-based statistical result, wherein the statistical result is closer to a true value when the simulation times are more. Num times of simulation operation of the multi-stage task reliability model, N times of simulation circulation each time, N times of task failure are counted, and the success rate R of the system task when the simulation model is operated for the ith time is calculated i And counting the times Mx (i) of system faults caused by the faults of the equipment x when the simulation model is operated for the ith time, wherein the total number of the parts is Ncom. The data are sorted to obtain:
1. task success rate mean of system
2. Mean success rate of tasks
Variance of (2)
3. Mean success rate of tasks
Has a 95% confidence interval of
4. The importance of the device x is
Wherein, standard deviation
z c Obtaining z by querying a normal distribution table c ＝1.96。
Example 1:
the technical scheme of the invention is further explained by combining the attached drawings.
As shown in fig. 1, the method for evaluating reliability of a multi-stage task in an aerospace launch site of the invention comprises the following steps:
aiming at the description of the space launching task by the primary space launching field, a specific multi-stage task reliability model can be established. Due to the confidential particularity of the specific space launching mission, the embodiment only focuses on several stages of the specific space launching mission, and reasonably analyzes the stages, and a model diagram of the embodiment is shown in fig. 3.
The multi-stage task reliability model M is defined by a quadruple M ═ { MS, MSM, MP, MRBD }. The MS stage is divided into three stages of vertical final assembly and system test, vertical transfer and filling launching day, which are called stage one, stage two and stage three for convenience of description, and each stage completes respective specified tasks in sequence. Each stage can comprise a plurality of parallel subtasks MSM which can run in a set time without mutual interference, the stage one comprises three subtasks of product transportation admission, vertical assembly and work test, the stage two comprises three subtasks of oil engine system work, air conditioning system work and driving mechanism system work, and the stage three comprises two subtasks of kerosene filling and online gas supply. In this model, each subtask consists of a single device, forming a corresponding task profile MP. Each device is composed of a respective reliability block diagram MRBD, and a specific schematic diagram is shown in fig. 4, which includes serial, parallel and other structures.
Step 2, inputting parameters required to be used in the multi-stage task reliability model of the space launch site;
inputting parameters specifically used in a model, wherein the total simulation times are 10000 times; the simulation times are repeated for 20 times; the total number of stages is 3; the number of the sub tasks in the stage is (3, 3 and 4); the start time and the end time of the three key tasks are respectively (2022.03.04-2022.03.25), (2022.03.26-2022.04.04) and (2022.04.08-2022.04.10); the total task time of each stage is (22, 10, 3); the continuous running time of the three subtasks in the first stage is (8, 8, 15) and the starting time is (2022.03.04, 2022.03.11, 2022.03.11); the continuous running time of the three subtasks in the second stage is (8, 8, 8) and the starting time is (2022.03.26, 2022.03.26, 2022.03.26); the two subtasks in the third stage have a duration of (3, 3) and a start time of (2022.04.08 ), all equipment failure and maintenance distribution parameters are shown in table 1 below, and the number of simulations, the number of task failures, the number of stages and the number of subtasks are initialized.
TABLE 1 Equipment failure and maintenance distribution parameters
Step 3, performing simulation analysis on the whole multi-stage task by using a Monte Carlo simulation method, determining the time sequence relation of all equipment fault events and maintenance events along with the lapse of a simulation clock, and advancing the simulation clock by scheduling and processing the discrete events, wherein the operation steps for the case are as follows:
step 31, the number of initialization simulation times and the number of task failure times are all 0.
And 32, executing one-time simulation, wherein the simulation times are +1, and the number of the initialization current task stages is 1.
And step 33, reading the information of the current subtask. For this embodiment, since all the device failure distribution types are exponential distributions, and have the characteristic of no aftereffect, it is considered that all the participating devices are perfectly new each time the subtask is simulated, that is, the run time is 0, and the number of the initialization subtasks is 1.
And step 34, performing failure time simulation on all the devices participating in the subtask, wherein the failure type of the devices in the embodiment is exponential distribution, and the failure time is directly generated through simulation according to the exponential distribution parameters of the devices. Inputting the failure time of all equipment into a reliability block diagram structure to further obtain the failure time of the whole subtask, wherein the embodiment only has a series-parallel connection structure, and for the series connection structure, the minimum failure time of the constituent elements is taken as the failure time of the structure; for a parallel structure, the maximum failure time of a constituent element is taken as the failure time of the structure.
And step 35, judging whether the simulation of the subtask is finished, wherein the judgment is based on the failure time of the current reliability block diagram, namely whether the fault arrival time of the subtask is greater than the stage task time, if so, the subtask represents that the subtask meets the requirement of successful continuous work and enters the next simulation before each simulated device fails, namely, step 36, and if not, the subtask fails and needs to generate corresponding maintenance activities. And regarding the equipment with the simulation failure time less than the fault arrival time of the subtask, considering that the equipment has a fault in the simulation, and updating the working time of the rest equipment for normal working.
The one-time maintenance time of the equipment can be simulated according to the maintenance type and the parameters of the fault equipment, the maintenance type of the equipment of the embodiment has fixed maintenance time and non-repairable type, the maintenance time can be considered as a large enough constant in the simulation of the non-repairable type of equipment, and the fixed maintenance time equipment can directly use the time parameters to push a simulation clock. Taking the maximum value of the maintenance time of all fault equipment as the maintenance time of the next subtask, pushing the simulation clock and judging whether the current simulation clock exceeds the stage task time, if not, returning to the step 34, if so, the task simulation fails, recording the number of times of task failure plus 1, if the number of times of simulation is less than the total number of times of simulation, returning to the step 32 to start the next task simulation, and if the number of times of simulation is equal to the total number of times of simulation, directly entering the step 4.
And step 36, judging whether all parallel subtasks in the current stage are completely traversed, if not, counting the subtasks by +1 and returning to the step 34, if all the subtasks are traversed, judging whether the current stage number is smaller than the total stage number, if so, counting the stage by +1, initializing the subtask number and returning to the step 33, if not, counting the simulation times by less than the total simulation times, returning to the step 32, otherwise, finishing the simulation, recording simulation process data, and entering the step 4.
Step 4, sorting and analyzing simulation evaluation results;
and (3) running the simulation evaluation model in the step (3) and recording the equipment failure condition and the success probability of the key task node in each simulation, so that a simulation-based statistical result can be finally output. The multi-stage task system simulation calculation model is operated for 20 times, the simulation cycle number of each time is 10000, the task failure number is counted, and the final task success rate result obtained by calculating the system task success rate and the confidence coefficient of 95% in each simulation is 0.9557, as shown in the following table two.
Watch two
| Phase name | Probability of stage success | Cumulative probability of success | Upper confidence limit | Lower confidence limit |
| Vertical final assembly and system testing | 0.9751 | 0.9751 | 0.9887 | 0.9615 |
| Vertical transport | 0.985 | 0.9605 | 0.9876 | 0.9338 |
| Filling and launching day | 0.995 | 0.9557 | 0.9963 | 0.9162 |
The total unit number is 12, the equipment number is 8, the times of system faults caused by each equipment fault are counted, and the relative importance, namely the task influence degree, can be obtained by sorting. And calculating the mean time between failures of the equipment according to the reliability block diagram of each piece of equipment and the unit reliability parameters, and displaying the result in the table III, wherein the task influence degree is calculated to be 0, which means that the equipment does not fail in multiple simulations and the task is not influenced.
Watch III
| Device name | Degree of influence of task | Mean time between failures (day) |
| Product transportation approach | 0.3065 | 252.0762 |
| Air conditioning system | 0.253 | 211.8441 |
| Tower crane | 0.2202 | 1063.874 |
| Drive control | 0.1339 | 454.9744 |
| Gas supply | 0.0863 | 197.0603 |
| Oil engine system | 0 | 1995.6402 |
| Test system | 0 | 783.3328 |
| Kerosene oil | 0 | 292.7087 |
Example 2:
a multi-stage task reliability simulation evaluation method for an aerospace launch site comprises the following steps:
step 2, inputting parameters required to be used in the multi-stage task reliability model of the space launch site;
step 3, carrying out simulation analysis on the whole multi-stage task by using a Monte Carlo simulation method, effectively processing the condition that equipment participating in the task is repairable or unrepairable, determining the time sequence relation of fault events and maintenance events of all equipment units through simulation, scheduling and processing discrete events to advance a simulation clock, and further repeatedly completing multiple times of simulation;
and 4, sorting the simulation evaluation result through the simulation data, and outputting and displaying.
Further, defining that the space launching multi-stage task successfully completes respective tasks in each stage, and enabling the subtasks in each stage to continuously work for a certain time. In addition, the method focuses on the dependency of the system equipment unit between stages, namely, the working/failure state of the unit of the later stage is dependent on the working/failure state of the unit of the previous stage. A maintainable space launch multi-stage task reliability model M is defined by a quadruple M ═ { MS, MSM, MP, MRBD }.
Stage (2): the MS represents a set of stages of multi-stage task partitioning, each stage has a corresponding critical task time node, and the task success can be described as: each stage completes the respective specified task in sequence.
And (3) subtasks: MSM represents a subtask set in a phase, each phase may contain multiple parallel subtasks, which can run in a specified time without mutual interference, where the success of one subtask can be described as: under serviceable conditions, the subtasks are run for a specified time and the total elapsed time does not exceed the deadline for this phase. When all subtasks at this stage are completed, the decision stage task is completed.
Subtask profile: the MP represents the set of devices used in the subtask profile, and in general, the subtask can only be continuously performed when the devices participating in the subtask are all in a normal operating state, so that all devices participating in the subtask are processed in a serial configuration. And when the duration of the subtask reaches the specified length, the current subtask is considered to be completed. When the subtask fails in the midway, if the current stage is not finished, the equipment participating in the subtask can be put into use again after being maintained, and the subtask is restarted until the specified time length is reached; and if the time reaches the phase end time and the subtask is not completed yet, judging that the subtask fails.
And (3) equipment model: MRBD represents the set of component parts U and the reliability block structure of the devices participating in the subtasks. When the devices participating in the subtasks cannot be split into smaller components, the MRBD represents a reliability block diagram of a single component whose information in turn contains the failure distribution type and parameters and the maintenance distribution type and parameters. When the interior of the large-scale equipment participating in the subtasks can be continuously split into smaller components, the reliability block diagram is used for representing the structures among the components, including series connection, parallel connection, series-parallel connection, voting structure and backup structure.
Through the description of the multi-stage task, a universal space launching multi-stage task reliability model is established, the reliability model can comprise a plurality of stage tasks, each stage task is provided with a key task time node and a different number of subtasks, different subtasks are represented by a structure formed by connecting single or a plurality of devices in series, and the time required to continuously run can be different.
Further, special conditions such as repairable or non-repairable equipment conditions, exponential distribution, normal distribution, uniform distribution, gamma distribution, lognormal distribution, weibull distribution and non-failure types of equipment failure types, exponential distribution, normal distribution, uniform distribution, gamma distribution, lognormal distribution, weibull distribution, fixed maintenance duration and non-repairable types of equipment maintenance types and the like can be reasonably and effectively dealt with through simulation.
The simulation method comprises the following specific steps:
step 31, the number of initialization simulation times and the number of task failure times are all 0.
And 32, executing one-time simulation, wherein the simulation times are plus 1, and the number of the current task initialization stages is 1.
Step 33, reading the information of the current subtask: subtask duration run time, current phase time node, reliability block structure of this subtask, used equipment failure, repair distribution parameters, and run time. And the number of initialization subtasks is 1.
And step 34, performing failure time simulation on all the devices participating in the subtask, regarding the failure time as a sufficiently large constant in the simulation for the devices which do not fail, if the failure distribution type of the devices is exponential distribution, directly simulating to generate the failure time according to exponential distribution parameters of the devices, if the failure distribution type of the devices is not exponential distribution, continuously simulating a numerical value according to the distribution parameters of the devices until the numerical value is greater than the running time, and subtracting the running time from the numerical value to serve as the failure time of the devices. When the subtask is simulated for the first time, the devices are considered as being perfect as new, namely the running time is 0; when the subtask is not simulated for the first time, the simulation information of the previous time is read, and the used time of the subtask and the running time of the equipment are updated.
Inputting the failure time of all the devices into a reliability block diagram structure to further obtain the failure time of the whole subtask, wherein for a serial structure, the minimum failure time of the composition elements is taken as the failure time of the structure; for a parallel structure, taking the maximum failure time of the constituent elements as the failure time of the structure; for a k-out-of-n voting structure (a k-out-of-n system is a system which is provided with n devices, and when k devices are normal, the whole system can work normally), sequencing simulation failure time of all the n devices from large to small, and taking the kth failure time as the failure time of the structure; and for the backup structure, taking the sum of the failure time of all cold backups and the maximum failure time of all hot backups as the failure time of the structure.
And step 35, judging whether the simulation of the subtask is finished, wherein the judgment is based on the failure time of the current reliability block diagram, namely whether the fault arrival time of the subtask is greater than the stage task time, if so, the subtask represents that the subtask meets the requirement of successful continuous work and enters the next simulation before each simulated device fails, namely, step 36, and if not, the subtask fails and needs to generate corresponding maintenance activities. And regarding the equipment with the simulation failure time less than the fault arrival time of the subtask, the equipment is considered to have a fault in the simulation, and the rest equipment works normally and updates the working time of the equipment.
The one-time maintenance time of the equipment can be simulated according to the maintenance type and the parameters of the fault equipment, the maintenance time can be considered as a large enough constant in the simulation of the equipment of the non-repairable type, and the rest of the maintenance type equipment can use the corresponding random number generation operators to complete the maintenance time simulation of the equipment. Taking the maximum maintenance time of all fault equipment as the maintenance time of the subtask, pushing the simulation clock and judging whether the current simulation clock exceeds the stage task time, if not, returning to the step 34, if so, the task fails in simulation, recording the number of times of failure of the task plus 1, if the number of times of simulation is less than the total number of times of simulation, returning to the step 32 to start the next task simulation, and if the number of times of simulation is equal to the total number of times of simulation, directly entering the step 4.
And step 36, judging whether all parallel subtasks in the current stage are completely traversed, if not, counting the subtasks by +1 and returning to the step 34, if all the subtasks are traversed, judging whether the current stage number is smaller than the total stage number, if so, counting the stage by +1, initializing the subtask number and returning to the step 33, if not, counting the simulation times by less than the total simulation times, returning to the step 32, otherwise, finishing the simulation, recording simulation process data, and entering the step 4.
The invention has the beneficial effects that: the method provided by the invention provides a multi-stage task reliability analysis modeling and evaluation method for the space launching task, can perform simulation evaluation calculation on the task under repairable and non-repairable conditions, outputs the success probability of a key task node and key equipment influencing the task by means of statistical analysis, helps a decision maker to intuitively analyze the success rate of the task and places needing improvement, and thus is beneficial to the full success of the whole space launching task.
The above description is only a preferred embodiment of the present invention, and it should be noted that, for those skilled in the art, it is possible to make various improvements and modifications without departing from the technical principle of the present invention, and those improvements and modifications should be considered as the protection scope of the present invention.
Claims (10)
1. A multi-stage task reliability simulation evaluation method for an aerospace launch site is characterized by comprising the following steps:
s1, constructing a multi-stage task reliability model of the space launch site;
step S2, inputting parameters required to be used in the space launch field multi-stage task reliability model;
step S3, performing simulation analysis on the whole multi-stage task by using a Monte Carlo simulation method, determining the time sequence relation of all equipment fault events and maintenance events along with the lapse of a simulation clock, and advancing the simulation clock by scheduling and processing the discrete events so as to repeatedly complete multiple simulations;
and step S4, sorting the simulation evaluation result through the simulation data.
2. The spacecraft launch site multi-stage task reliability simulation evaluation method according to claim 1, wherein the step S1 specifically comprises: defining the success of the multi-stage task as that each stage successfully finishes the respective task, and the subtasks in each stage continuously work for a certain time; the unit operating/failing state of the latter stage depends on the operating/failing state of the unit of the preceding stage; defining a maintainable space launch multi-stage task reliability model M by using a quadruple M ═ { MS, MSM, MP, MRBD }; MS is stage, MSM is subtask, MP is subtask section, and MRBD is device model.
3. The aerospace launch site multi-stage mission reliability simulation assessment method of claim 2, wherein the stages: the MS represents a multi-stage task division stage set, each stage has a corresponding key task time node, and the task success description is as follows: each stage completes the respective specified tasks in sequence;
and (3) subtasks: MSM represents a subtask set in a phase, each phase comprises a plurality of parallel subtasks, the subtasks run in a specified time without mutual interference, and the success description of one subtask is as follows: under the condition of maintainability, the subtasks are continuously operated for a specified time, and the total consumed time does not exceed the cut-off time of the phase; when all subtasks in the stage are completed, judging that the stage task is completed;
subtask profile: the MP represents an equipment set used in the subtask profile, and when the equipment participating in the subtask is in a normal working state, the subtask can be continuously carried out, and all the equipment participating in the subtask is processed according to a series structure; when the duration of the subtask reaches the specified length, the current subtask is considered to be completed; when the subtask fails in the midway, if the current stage is not finished, the equipment participating in the subtask is put into use again after maintenance operation is carried out on the equipment, and the subtask is restarted until the specified time length is reached; if the time reaches the stage end time and the subtask is not completed, judging that the subtask fails;
and (3) equipment model: MRBD represents a component assembly U and a reliability block structure of equipment participating in subtasks; when the equipment participating in the subtask cannot be split into smaller components, the MRBD represents a reliability block diagram of a single component, and the information of the component also comprises a failure distribution type and parameters and a maintenance distribution type and parameters; when the interior of the large-scale equipment participating in the subtasks is continuously split into smaller components, the reliability block diagram is used for representing the structures among the components, including series connection, parallel connection, series-parallel connection, voting structures and backup structures.
4. The aerospace launch site multi-stage task reliability simulation assessment method according to claim 2 or 3, wherein the step S2 specifically includes: establishing a customized space launching multi-stage task reliability model according to the step 1, inputting parameters used specifically, including total simulation times, repeated simulation times, total stage numbers, number of subtasks in the stage, key task node time, total task time, continuous operation time of the subtasks, failure of all equipment and maintenance distribution parameters, and initializing the simulation times and the task failure times.
5. The spacecraft launch site multi-stage task reliability simulation evaluation method according to claim 4, wherein the step S3 specifically comprises the steps of:
step 31, initializing the simulation times and the task failure times to be 0;
step 32, executing one-time simulation, wherein the simulation times are +1, and the number of the initialization current task stages is 1;
step 33, reading the information of the current subtask: the subtask continuous operation time, the current stage time node, the reliability block diagram structure of the subtask, the used equipment failure, the maintenance distribution parameter and the operated time; and the number of initialization subtasks is 1;
step 34, performing failure time simulation on all the devices participating in the subtask to obtain failure time; when the subtask is simulated for the first time, the devices are considered as being perfect as new, namely the running time is 0; when the subtask is not simulated for the first time, reading the simulation information of the previous time, and updating the used time of the subtask and the running time of the equipment; inputting the failure time of all the devices into a reliable block diagram structure to further obtain the failure time of the whole subtask;
step 35, judging whether the subtask is finished in simulation, wherein the judgment basis is failure time of the current reliability block diagram, that is, whether the fault arrival time of the subtask is greater than stage task time, if so, the subtask meets the requirement of successful continuous work and enters the next simulation before each simulated device fails, that is, step 36, and if not, the subtask fails and needs to generate corresponding maintenance activities; regarding the equipment of which the simulation failure time is less than the fault arrival time of the subtask, the equipment is considered to have a fault in the simulation, and other equipment works normally and updates the working time of the equipment; simulating one-time maintenance time of the equipment according to the maintenance type and parameters of the fault equipment, taking the maximum value of the maintenance time of all the fault equipment as the maintenance time of the next subtask, pushing the simulation clock and judging whether the current simulation clock exceeds the stage task time, if not, returning to the step 34, if so, the task simulation fails, recording the task failure times +1, if the simulation times are less than the total simulation times, returning to the step 32 to start the next task simulation, and if the simulation times are equal to the total simulation times, directly entering the step 4;
and step 36, judging whether all parallel subtasks in the current stage are completely traversed, if not, counting the subtasks by +1 and returning to the step 34, if all the subtasks are traversed, judging whether the current stage number is smaller than the total stage number, if so, counting the stage by +1, initializing the subtask number and returning to the step 33, if not, counting the simulation times by less than the total simulation times, returning to the step 32, otherwise, finishing the simulation, recording simulation process data, and entering the step 4.
6. The spacecraft launch site multi-stage task reliability simulation evaluation method according to claim 5, wherein in the step S34, the failure type of the device is exponential distribution, normal distribution, uniform distribution, gamma distribution, log-normal distribution, Weibull distribution or non-failure type, wherein the failure time is considered to be a sufficiently large constant in the simulation for the non-failure device; if the failure distribution type of the equipment is exponential distribution, directly simulating to generate failure time according to exponential distribution parameters of the equipment; if the failure distribution type of the equipment is not exponential, the simulation value is continuously calculated according to the distribution parameters of the equipment until the simulation value is greater than the running time, and the running time is subtracted from the simulation value to serve as the failure time of the equipment.
7. The spacecraft launch site multi-stage task reliability simulation evaluation method according to claim 5, wherein in the step S34, for a series structure, the minimum failure time of a constituent element is taken as the failure time of the structure; for a parallel structure, taking the maximum failure time of the constituent elements as the failure time of the structure; for a k-out-of-n voting structure, sorting the simulation failure times of all n devices from large to small, and taking the kth failure time as the failure time of the structure; and for the backup structure, taking the sum of the failure time of all cold backups and the maximum failure time of all hot backups as the failure time of the structure.
8. The method according to claim 5, wherein in step S36, the maintenance type of the device is exponential distribution, normal distribution, uniform distribution, gamma distribution, log-normal distribution, Weibull distribution, fixed maintenance duration or non-repairable type, the maintenance time is considered to be a sufficiently large constant for the non-repairable type of device, the remaining maintenance type devices use the corresponding random number generation operators to complete the maintenance time simulation of the device, and in the simulation, once the maintenance of the device occurs, the device is replaced with a new device, that is, the maintenance is good as before, and there is no working time.
9. The spacecraft launch site multi-stage task reliability simulation evaluation method according to any one of claims 5 to 8, wherein the step S4 specifically comprises: recording the equipment failure condition and the success probability of the key task node in each simulation by operating the simulation evaluation process in the step 3, and finally outputting a simulation-based statistical result, wherein the statistical result is closer to a true value when the simulation times are more; num times of simulation operation of the multi-stage task reliability model, N times of simulation circulation each time, N times of task failure are counted, and the success rate R of the system task when the simulation model is operated for the ith time is calculated i And (N-N)/N, wherein the total number of parts is Ncom, and the times Mx (i) of system failure caused by the failure of the device x when the simulation model is operated for the ith time are counted.
10. The method for simulation evaluation of reliability of the multi-stage task of the space launch site according to claim 9, wherein in the step S4, the data are further sorted, specifically including:
the mean success rate of the system is
Mean success rate of tasks
Variance of (2)
Mean success rate of tasks
Has a 95% confidence interval of
The importance of the device x is
Wherein, standard deviation
z c Obtaining z by querying normal distribution table c ＝1.96。
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