技术领域technical field
本发明涉及装备保障领域，尤其是涉及一种基于蒙特卡洛法的航空机务保障过程评估方法。The invention relates to the field of equipment support, in particular to an evaluation method for aviation maintenance support process based on the Monte Carlo method.
背景技术Background technique
航空机务保障方案需要考虑的因素包括，器材筹备、维修方案、人员分配、保障设备分配等，这些因素是否满足保障飞机飞行完好率的需求是一个未知数。因此，航空机务保障方案是否合理可行，一直是困扰该领域技术人员的一个问题，所以，在航空机务保障方案实施之前，迫切需要一个工具，对评估航空机务装备保障方案的可行性进行评估。The factors that need to be considered in the aviation maintenance plan include equipment preparation, maintenance plan, personnel allocation, support equipment allocation, etc. It is unknown whether these factors can meet the requirements of ensuring the flight integrity of the aircraft. Therefore, whether the aviation maintenance support scheme is reasonable and feasible has always been a problem that plagues technical personnel in this field. Therefore, before the implementation of the aviation maintenance support scheme, a tool is urgently needed to evaluate the feasibility of the aviation maintenance equipment support scheme.
发明内容Contents of the invention
基于此，本发明的目的是针对现有技术的不足，提出了基于蒙特卡洛法的航空机务保障过程评估方法，并开发了相应的评估软件，评估飞机执行任务的成功性参数。Based on this, the purpose of the present invention is to address the deficiencies in the prior art, proposes a method for evaluating the aviation maintenance process based on the Monte Carlo method, and develops corresponding evaluation software to evaluate the success parameters of aircraft execution tasks.
为达到上述目的，本发明采用以下技术方案：To achieve the above object, the present invention adopts the following technical solutions:
基于蒙特卡洛法的航空机务保障过程评估方法，步骤如下：The evaluation method of aviation maintenance support process based on Monte Carlo method, the steps are as follows:
第一步：根据装备保障评估问题的物理性质建立仿真模型，在仿真开始时，首先进行初始化，然后按照未来时间表，建立时间序列，依据部件的失效率，进行随机抽样；未发生故障时，则时间按照步长往下推进，发生故障时，则触发后续的维修保障活动，进行数据记录然后按照时间步长往下推进；直至满足仿真停止条件，然后进行统计分析，得到仿真周期内的维修保障能力效能数据；所述后续的维修保障活动包括任务过程子模型、预防性维修子模型、修复性维修子模型和保障资源配置子模型；Step 1: Establish a simulation model based on the physical properties of the equipment support assessment problem. At the beginning of the simulation, it is initialized first, and then a time series is established according to the future schedule, and random sampling is carried out according to the failure rate of the components; when no failure occurs, Then the time is pushed down according to the step size. When a failure occurs, the subsequent maintenance support activities are triggered, data records are made and then pushed down according to the time step; until the simulation stop condition is met, statistical analysis is performed to obtain the maintenance within the simulation cycle. Support capability performance data; the subsequent maintenance support activities include task process sub-model, preventive maintenance sub-model, corrective maintenance sub-model and support resource allocation sub-model;
第二步：根据仿真模型中各随机变量的分布，利用线性同余法产生随机数，进行大次数的仿真实验，得出仿真的实验值；所述随机变量包括故障率，维修时间，换件时间和供应时间；The second step: according to the distribution of each random variable in the simulation model, use the linear congruence method to generate random numbers, carry out a large number of simulation experiments, and obtain the simulated experimental value; the random variables include failure rate, maintenance time, replacement parts time and availability;
第三步：根据仿真试验结果求它的统计特征量，从而获得问题的解和解的精度估计，得到装备的任务完成率、使用可用度等指标。Step 3: Find its statistical feature quantity according to the simulation test results, so as to obtain the solution of the problem and the accuracy estimation of the solution, and obtain the indicators such as the task completion rate and usability of the equipment.
进一步地，在仿真的过程中，事件随时间的处理是关键，即事件的触发都是以时间为基准轴，所以事件的队列处理是仿真的关键，所述事件的队列处理流程由仿真输入、事件队列处理和仿真输出三部分组成；由仿真输入模块提供仿真需要的各项数据，在队列处理模块中进行仿真，并由输出模块计算并统计各项输出数据；其中，队列处理模块是仿真核心模块，由此模块引发仿真器的各种功能的执行。Furthermore, in the process of simulation, the processing of events over time is the key, that is, the triggering of events is based on time, so the queue processing of events is the key to the simulation, and the queue processing flow of the events is determined by the simulation input, Event queue processing and simulation output are composed of three parts; the simulation input module provides various data required for simulation, performs simulation in the queue processing module, and the output module calculates and counts various output data; among them, the queue processing module is the core of the simulation A module by which the execution of various functions of the emulator is caused.
进一步地，事件队列中的事件包含了仿真初始化时生成的任务和预防性维修计划、以及仿真运行过程中产生的各种不同类型的事件；在每次处理完时间队列中事件后，事件队列模块判断是否又收到新的事件，如果收到新事件则按照队列中事件的时间先后顺序，对所有时间重新排序，然后将仿真时钟推进到事件队列中的第一个事件的时间并处理该事件，即根据该事件类型转入到相应的处理模块；判断队列处理模块中即将处理的当前时间类型，并为其定位到此事件类型的处理模块中。Further, the events in the event queue include the tasks and preventive maintenance plans generated during the simulation initialization, as well as various types of events generated during the simulation running; after processing the events in the time queue each time, the event queue module Determine whether a new event has been received, and if a new event is received, reorder all the time according to the chronological order of the events in the queue, and then advance the simulation clock to the time of the first event in the event queue and process the event , that is, transfer to the corresponding processing module according to the event type; judge the current time type to be processed in the queue processing module, and locate it in the processing module of this event type.
进一步地，所述的仿真模型为事件驱动模型，事件驱动模型中的任务、修复性维修、预防性维修和保障资源配置逻辑过程，由任务过程子模型、预防性维修过程子模型、修复性维修过程子模型和保障资源配置子模型决定。Further, the simulation model is an event-driven model, and the logic process of task, corrective maintenance, preventive maintenance and support resource allocation in the event-driven model is composed of task process sub-model, preventive maintenance process sub-model, corrective maintenance The process sub-model and the assurance resource allocation sub-model are determined.
进一步地，事件驱动模型中各事件的关系如下：首先由仿真发生器触发任务计划，按照任务要求调用装备；在整个任务过程中装备状态有三种可能：正常执行任务、故障和执行预防性维修，当其处于正常执行任务情况，则不触发其它时间序列，仿真往前推进，当故障时，需要开展修复性维修活动，此时会触发修复性维修模型，在维修过程中还会用到相关保障资源，则会触发保障资源配置子模型，当处于预防性维修时，则会触发预防性维修子模型，同时触发保障资源配置子模型；仿真计算器将所有数据记录在数据库中，然后按照时间步长继续往前推进，直至满足仿真结束条件，最后进行装备保障效能统计分析。Furthermore, the relationship between events in the event-driven model is as follows: first, the mission plan is triggered by the simulation generator, and the equipment is called according to the mission requirements; there are three possibilities for the equipment status during the entire mission process: normal execution of the mission, failure and preventive maintenance, When it is in the normal state of performing tasks, other time series will not be triggered, and the simulation will move forward. When a fault occurs, restorative maintenance activities need to be carried out. At this time, the restorative maintenance model will be triggered, and relevant guarantees will be used in the maintenance process. resources, it will trigger the guarantee resource configuration sub-model, and when it is in preventive maintenance, it will trigger the preventive maintenance sub-model and trigger the guarantee resource configuration sub-model; the simulation calculator will record all the data in the database, and then follow the time step Continue to move forward until the end conditions of the simulation are met, and finally carry out statistical analysis of equipment support effectiveness.
进一步地，预防性维修过程子模型为描述装备及相关的维修检查间隔时间、所需资源即预防性维修地点，并按照装备数量、持续飞行时间等安排预防性维修的间隔；在仿真周期内，如果装备达到预防性维修时机，此时装备处于不可用状态，并开展相关活动。Further, the preventive maintenance process sub-model is to describe the equipment and related maintenance inspection intervals, the required resources are the preventive maintenance locations, and arrange the preventive maintenance intervals according to the number of equipment, continuous flight time, etc.; in the simulation cycle, If the equipment reaches the preventive maintenance opportunity, the equipment is in an unusable state at this time, and related activities are carried out.
进一步地，修复性维修过程子模型为在装备执行任务过程中，可随机数发生器会依据零部件的失效率分布函数随机抽样，当发生故障时，则会启动修复性维修活动；故障件的修复可通过换件或直接维修来完成，装备的修复性维修一般在基层级进行，可分为换件维修和直接维修，但是实际中换件维修占了绝大多数，这有利于提高装备的使用可用度；部件的修复性维修是指对替换下来的故障件进行修复，在某些部队设有修理厂，可以对一部分部件进行修复，其余的部件送回生产厂修复，而在有些部队没有修理厂，不能对部件进行修复，部件估值后全部送回生产厂修复。Furthermore, the sub-model of the corrective maintenance process is that during the execution of the equipment task, the random number generator will randomly sample according to the failure rate distribution function of the parts, and when a failure occurs, the corrective maintenance activity will be started; Repairs can be done by replacing parts or direct repairs. The restorative repairs of equipment are generally carried out at the grassroots level, which can be divided into replacement repairs and direct repairs. However, in practice, replacement repairs account for the vast majority, which is conducive to improving equipment Availability of use; component remedial maintenance refers to the repair of replaced faulty parts. In some troops, there are repair shops, which can repair some parts, and the rest of the parts are sent back to the factory for repair. In some troops, there are no repair shops. The repair shop cannot repair the parts, and all parts will be sent back to the manufacturer for repair after valuation.
进一步地，保障资源配置过程子模型为在装备的使用和维修过程中，需要对使用保障资源或维修保障资源进行分析，判断是否满足要求。Furthermore, the support resource allocation process sub-model is that in the process of equipment use and maintenance, it is necessary to analyze the use support resources or maintenance support resources to judge whether they meet the requirements.
本发明还提供了一种基于蒙特卡洛法的航空机务保障过程评估方法的仿真试验，过程如下：The present invention also provides a kind of simulation test based on the Monte Carlo method for evaluating the aviation maintenance process evaluation method, the process is as follows:
1)装备逻辑数学模型1) Equipment logical mathematical model
设装备s由m个部件单元构成，其组成逻辑为：Equipment equipment s is composed of m component units, and its composition logic is as follows:
s＝{z1,z2,…,zi,…,zm}s={z1 ,z2 ,…,zi ,…,zm }
已知基本部件单元的失效概率分布函数为：The failure probability distribution function of the known basic component unit is:
Fi(t)(i＝1,2,…m)Fi (t) (i=1,2,...m)
部件单元i的状态变量表达式为：The state variable expression of component unit i is:
装备t时刻的状态变量逻辑表达式为：The logical expression of the state variable at time t of the equipment is:
X(t)＝[b1(t),b2(t),…bi(t),…bm(t)]X(t)＝[b1 (t), b2 (t),...bi (t),...bm (t)]
装备t时刻的状态变量为：The state variable at time t of the equipment is:
则装备状态变量结构函数为：Then the structure function of the equipment state variable is:
φ(X(t))＝φ(t)φ(X(t))=φ(t)
2)仿真计算2) Simulation calculation
通过设定的仿真次数和仿真周期，在统计了装备时间数据和任务相关数据后，可得到如下结果：Through the set simulation times and simulation cycle, after counting the equipment time data and task-related data, the following results can be obtained:
A)任务成功率，在基本任务设置中，可给出任务完成的判决规则，在平台中主要按照执行任务占任务要求时间的比值判定，如需要执行5小时的任务，当执行了4.5小时可判定任务成功，则可设置任务成功点为0.9；判决规则表达式如下：A) Task success rate. In the basic task settings, the judgment rules for task completion can be given. In the platform, it is mainly judged according to the ratio of the execution task to the required time of the task. To determine the success of the task, you can set the task success point to 0.9; the expression of the judgment rule is as follows:
在平台中会对一个保障方案仿真多次，并统计仿真中判定为任务成功的次数，其计算方法为：In the platform, a security plan will be simulated multiple times, and the number of missions judged to be successful in the simulation will be counted. The calculation method is:
P＝m/MP=m/M
式中m为仿真周期内装备完成任务的总次数，M是仿真周期内装备计划的任务次数。In the formula, m is the total number of times the equipment completes tasks in the simulation period, and M is the number of tasks planned by the equipment in the simulation period.
B)任务执行时间比，在整个仿真周期中，有具体的任务时间安排，在所有的任务时间中，装备的状态分为三种：执行任务、故障及故障维修、预防性维修，在仿真过程中记录下执行任务的时间，然后按下式计算任务执行时间比：B) Task execution time ratio. In the entire simulation cycle, there are specific task schedules. In all task time, the status of equipment is divided into three types: task execution, fault and fault maintenance, and preventive maintenance. During the simulation process Record the execution time of the task, and then calculate the task execution time ratio according to the following formula:
其中n是仿真次数，t是每次仿真周期内装备执行任务的时间，T是装备在每次仿真周期内的任务需求时间；Among them, n is the number of simulations, t is the time for the equipment to perform tasks in each simulation cycle, and T is the task requirement time of the equipment in each simulation cycle;
C)使用可用度，使用可用度是指在规定的条件下，整个寿命周期内实际工作时间与要求工作的时间之比，它是装备连续工作条件下完成任务的能力，是整个日历时间的利用率。因此装备的使用可用度计算公式如下：C) Use availability, use availability refers to the ratio of the actual working time to the required working time in the entire life cycle under specified conditions, it is the ability of the equipment to complete the task under continuous working conditions, and it is the utilization of the entire calendar time Rate. Therefore, the formula for calculating the usability of equipment is as follows:
式中TUi是每次仿真周期内能工作时间，TDi是每次仿真周期内不能工作时间，n是仿真次数；In the formula, TUi is the working time in each simulation cycle, TDi is the non-working time in each simulation cycle, and n is the number of simulations;
D)备件短缺数，仿真计算器会记录每个设定的时间间隔内，备件不足造成短缺的数量，计算的方式如下：D) The number of spare parts shortage, the simulation calculator will record the number of shortages caused by the lack of spare parts within each set time interval, the calculation method is as follows:
式中：n为仿真次数，mi为在某个时间间隔时备件短缺的数量，△t＝t2-t1为数据收集时间间隔；In the formula: n is the number of simulations, mi is the quantity of spare parts in short supply at a certain time interval, △t=t2 -t1 is the time interval of data collection;
3)运行结果：按照上述方法进行计算，得出了运行结果。3) Operation result: Calculate according to the above method, and obtain the operation result.
本发明所采用的仿真模型为事件驱动模型，在此模型中，其系统状态的改变仅仅由事件的发生引发，系统时间也由事件的发生时间来确定，由于事件发生时间的随机性，系统时间的推进步长也是随机的；由于两个相邻事件之间的时间内系统状态不会发生改变，所以系统时钟可以直接从一个事件发生的时间推进到下一个事件的发生时刻；因此事件驱动的仿真模型能够更精确地模拟复杂的系统，较周期驱动模型(时间驱动模型)具有更好的真实性。The simulation model adopted in the present invention is an event-driven model. In this model, the change of its system state is only caused by the occurrence of an event, and the system time is also determined by the occurrence time of the event. Due to the randomness of the event occurrence time, the system time The advance step size of t is also random; since the system state does not change during the time between two adjacent events, the system clock can be directly advanced from the time when one event occurs to the time when the next event occurs; therefore, event-driven Simulation models can more accurately simulate complex systems, and have better realism than cycle-driven models (time-driven models).
本发明的有益效果是：The beneficial effects of the present invention are:
本发明基于装备保障的现有机制体制特点，构建了装备保障方案的仿真模型，在此基础上，构建了保障过程中必须的任务规划及各类维修工作过程子模型，利用基于蒙特卡洛法的航空机务保障过程评估方法，对保障方案进行仿真计算，计算的结果真实可信，能为保障方案的制定者提供有力的决策支撑。Based on the characteristics of the existing mechanism and system of equipment support, the present invention constructs a simulation model of the equipment support scheme. On this basis, it constructs the necessary task planning in the support process and various maintenance work process The advanced aviation maintenance support process evaluation method is used to simulate and calculate the support plan. The calculation results are authentic and credible, which can provide strong decision-making support for support plan makers.
附图说明Description of drawings
图1为本发明的装备保障评估仿真模型；Fig. 1 is the equipment guarantee evaluation simulation model of the present invention;
图2基于事件驱动的保障指标评估；Figure 2. Evaluation of assurance indicators based on event-driven;
图3事件序列的触发关系；The trigger relationship of the event sequence in Figure 3;
图4预防性维修模型；Figure 4 preventive maintenance model;
图5修复性维修模型；Figure 5 Restorative maintenance model;
图6维修资源配置过程子模型；Figure 6 The sub-model of maintenance resource allocation process;
图7时间数据统计图；Figure 7 time data statistical chart;
图8某型飞机携行器材方案效费曲线；Fig. 8 The efficiency curve of the portable equipment scheme of a certain type of aircraft;
图9任务执行时间总计统计图；Figure 9 is a statistical chart of the total task execution time;
图10任务执行情况随时间变化示意图。Figure 10 Schematic diagram of task execution changing over time.
具体实施方式Detailed ways
下面结合附图和具体实施例对本发明作进一步描述。The present invention will be further described below in conjunction with the accompanying drawings and specific embodiments.
实施例1Example 1
如图1-10所示，基于蒙特卡洛法的航空机务保障过程评估方法，步骤如下：As shown in Figure 1-10, the evaluation method of aviation maintenance support process based on the Monte Carlo method, the steps are as follows:
第一步：根据装备保障评估问题的物理性质建立仿真模型，见图1，在仿真开始时，首先进行初始化，然后按照未来时间表，建立时间序列，依据部件的失效率，进行随机抽样；未发生故障时，则时间按照步长往下推进，发生故障时，则触发后续的维修保障活动，如任务过程子模型、预防性维修子模型、修复性维修子模型、保障资源配置子模型等进行数据记录然后按照时间步长往下推进；直至满足仿真停止条件，然后进行统计分析，得到仿真周期内的维修保障能力效能数据；Step 1: Establish a simulation model according to the physical properties of the equipment support assessment problem, as shown in Figure 1. At the beginning of the simulation, it is initialized first, and then a time series is established according to the future schedule, and random sampling is performed according to the failure rate of the components; When a failure occurs, the time will be advanced according to the step size. When a failure occurs, subsequent maintenance support activities will be triggered, such as task process sub-model, preventive maintenance sub-model, corrective maintenance sub-model, support resource allocation sub-model, etc. The data record is then pushed down according to the time step; until the simulation stop condition is met, and then statistical analysis is performed to obtain the maintenance support capability performance data within the simulation cycle;
第二步：根据仿真模型中各随机变量(如故障率，维修时间，换件时间，供应时间等)的分布，利用线性同余法产生随机数，进行大次数的仿真实验，得出仿真的实验值；Step 2: According to the distribution of random variables in the simulation model (such as failure rate, maintenance time, replacement time, supply time, etc.), use the linear congruence method to generate random numbers, conduct a large number of simulation experiments, and obtain the simulation experimental value;
第三步：根据仿真试验结果求它的统计特征量，从而获得问题的解和解的精度估计，得到装备的任务完成率、使用可用度等指标；Step 3: Calculate its statistical feature quantity according to the simulation test results, so as to obtain the solution of the problem and the accuracy estimation of the solution, and obtain the indicators such as the task completion rate and usability of the equipment;
在仿真的过程中，事件随时间的处理是关键，即事件的触发都是以时间为基准抽，所以事件的队列处理是仿真的关键，下面将说明事件的队列处理流程。In the process of simulation, the processing of events over time is the key, that is, the triggering of events is drawn based on time, so the queue processing of events is the key to the simulation. The following will explain the process of event queue processing.
2、事件的队列处理流程2. Event queue processing flow
事件的队列处理流程主要由仿真输入、事件队列处理和仿真输出三部分组成。由仿真输入模块提供仿真需要的各项数据，在队列处理模块中进行仿真，并由输出模块计算并统计各项输出数据。其中队列处理模块是仿真核心模块，由此模块引发仿真器的各种功能的执行。The event queue processing flow is mainly composed of three parts: simulation input, event queue processing and simulation output. The simulation input module provides various data required for simulation, performs simulation in the queue processing module, and calculates and counts various output data by the output module. Among them, the queue processing module is the core module of the simulation, and the module triggers the execution of various functions of the simulator.
事件队列中的事件包含了仿真初始化时生成的任务和预防性维修计划、以及仿真运行过程中产生的各种不同类型的事件。在每次处理完时间队列中事件后，事件队列模块判断是否又收到新的事件，如果收到新事件则按照队列中事件的时间先后顺序，对所有时间重新排序，然后将仿真时钟推进到事件队列中的第一个事件的时间并处理该事件，即根据该事件类型转入到相应的处理模块。判断队列处理模块中即将处理的当前时间类型，并为其定位到此事件类型的处理模块中。事件驱动的仿真流程如图2所示。The events in the event queue include the tasks and preventive maintenance plans generated when the simulation is initialized, as well as various types of events generated during the simulation run. After processing the events in the time queue each time, the event queue module judges whether a new event is received. If a new event is received, it reorders all the times according to the time sequence of the events in the queue, and then advances the simulation clock to Time of the first event in the event queue and process the event, that is, transfer to the corresponding processing module according to the event type. Determine the current time type to be processed in the queue processing module, and locate it in the processing module of this event type. The event-driven simulation process is shown in Figure 2.
事件驱动模型中的任务、修复性维修、预防性维修、保障资源配置逻辑过程，由任务过程子模型、预防性维修过程子模型、修复性维修过程子模型、保障资源配置子模型决定。The task, corrective maintenance, preventive maintenance, and support resource allocation logic process in the event-driven model are determined by the task process sub-model, preventive maintenance process sub-model, corrective maintenance process sub-model, and support resource allocation sub-model.
模型中的各事件关系如图3示，首先由仿真发生器触发任务计划，按照任务要求调用装备；在整个任务过程中装备状态有三种可能：正常执行任务、故障和执行预防性维修，当其处于正常执行任务情况，则不触发其它时间序列，仿真往前推进，当故障时，需要开展修复性维修活动，此时会触发修复性维修模型，在维修过程中还会用到相关保障资源，则会触发保障资源配置子模型，当处于预防性维修时，则会触发预防性维修子模型，同时触发保障资源配置子模型；仿真计算器将所有数据记录在数据库中，然后按照时间步长继续往前推进，直至满足仿真结束条件，最后进行装备保障效能统计分析。The relationship between each event in the model is shown in Figure 3. First, the mission plan is triggered by the simulation generator, and the equipment is called according to the mission requirements; there are three possibilities for the equipment status during the entire mission process: normal execution of the mission, failure and preventive maintenance. In the normal execution of tasks, other time series will not be triggered, and the simulation will move forward. When a fault occurs, restorative maintenance activities need to be carried out. At this time, the restorative maintenance model will be triggered, and relevant support resources will be used in the maintenance process. It will trigger the support resource configuration sub-model, and when it is in preventive maintenance, it will trigger the preventive maintenance sub-model, and trigger the support resource configuration sub-model at the same time; the simulation calculator will record all the data in the database, and then continue according to the time step Move forward until the end condition of the simulation is met, and finally carry out statistical analysis of equipment support effectiveness.
3、相关模型的建立3. Establishment of related models
3.1预防性维修过程子模型3.1 Preventive maintenance process sub-model
预防性维修模型描述装备及相关的维修检查间隔时间、所需资源即预防性维修地点，并按照装备数量、持续飞行时间等安排预防性维修的间隔。在仿真周期内，如果装备达到预防性维修时机，此时装备处于不可用状态，并开展相关活动。预防性维修过程子模型如图4所示。The preventive maintenance model describes the equipment and related maintenance inspection intervals, the required resources are the preventive maintenance locations, and arranges the preventive maintenance intervals according to the number of equipment and continuous flight time. During the simulation period, if the equipment reaches the preventive maintenance opportunity, the equipment is in an unusable state at this time, and related activities are carried out. The sub-model of the preventive maintenance process is shown in Figure 4.
3.2修复性维修过程子模型3.2 Submodel of corrective maintenance process
在装备执行任务过程中，可随机数发生器会依据零部件的失效率分布函数随机抽样，当发生故障时，则会启动修复性维修活动。故障件的修复可通过换件或直接维修来完成，装备的修复性维修一般在基层级进行，可分为换件维修和直接维修，但是实际中换件维修占了绝大多数，这有利于提高装备的使用可用度。部件的修复性维修是指对替换下来的故障件进行修复，在某些部队设有修理厂，可以对一部分部件进行修复，其余的部件送回生产厂修复，而在有些部队没有修理厂，不能对部件进行修复，部件估值后全部送回生产厂修复。建模过程如图5所示。During the execution of the equipment task, the random number generator will randomly sample according to the failure rate distribution function of the parts, and when a failure occurs, the corrective maintenance activity will be initiated. The repair of faulty parts can be completed by replacement or direct maintenance. The restorative maintenance of equipment is generally carried out at the grassroots level, which can be divided into replacement repairs and direct repairs. However, replacement repairs account for the vast majority in practice, which is conducive to Improve the usability of equipment. Restorative maintenance of components refers to the repair of replaced faulty parts. Some units have repair shops, which can repair part of the parts, and send the rest of the parts back to the factory for repair, while some units do not have repair shops and cannot The parts are repaired, and all parts are sent back to the factory for repair after valuation. The modeling process is shown in Figure 5.
3.3保障资源配置过程子模型3.3 Guaranteed resource allocation process sub-model
在装备的使用和维修过程中，需要对使用保障资源或维修保障资源进行分析，判断是否满足要求。建模过程如图6所示。In the process of equipment use and maintenance, it is necessary to analyze the use support resources or maintenance support resources to judge whether they meet the requirements. The modeling process is shown in Figure 6.
4、仿真实现4. Simulation implementation
4.1装备逻辑数学模型4.1 Equipment logic mathematical model
设装备s由m个部件单元构成，其组成逻辑为：Equipment equipment s is composed of m component units, and its composition logic is as follows:
s＝{z1,z2,…,zi,…,zm}s={z1 ,z2 ,…,zi ,…,zm }
已知基本部件单元的失效概率分布函数为：The failure probability distribution function of the known basic component unit is:
Fi(t)(i＝1,2,…m)Fi (t) (i=1,2,...m)
部件单元i的状态变量表达式为：The state variable expression of component unit i is:
装备t时刻的状态变量逻辑表达式为：The logical expression of the state variable at time t of the equipment is:
X(t)＝[b1(t),b2(t),…bi(t),…bm(t)]X(t)＝[b1 (t), b2 (t),...bi (t),...bm (t)]
装备t时刻的状态变量为：The state variable at time t of the equipment is:
则装备状态变量结构函数为：Then the structure function of the equipment state variable is:
φ(X(t))＝φ(t)φ(X(t))=φ(t)
4.2仿真计算4.2 Simulation calculation
通过设定的仿真次数和仿真周期，在统计了装备时间数据，和任务相关数据后，可得到如下结果：Through the set simulation times and simulation cycle, after counting the equipment time data and task-related data, the following results can be obtained:
A)任务成功率，在基本任务设置中，可给出任务完成的判决规则，在平台中主要按照执行任务占任务要求时间的比值判定，如需要执行5小时的任务，当执行了4.5小时可判定任务成功，则可设置任务成功点为0.9。判决规则表达式如下：A) Task success rate. In the basic task settings, the judgment rules for task completion can be given. In the platform, it is mainly judged according to the ratio of the execution task to the required time of the task. If the task is determined to be successful, the task success point can be set to 0.9. The judgment rule expression is as follows:
在平台中会对一个保障方案仿真多次，并统计仿真中判定为任务成功的次数，其计算方法为：In the platform, a security plan will be simulated multiple times, and the number of missions judged to be successful in the simulation will be counted. The calculation method is:
P＝m/MP=m/M
式中m为仿真周期内装备完成任务的总次数，M是仿真周期内装备计划的任务次数。In the formula, m is the total number of times the equipment completes tasks in the simulation period, and M is the number of tasks planned by the equipment in the simulation period.
B)任务执行时间比，在整个仿真周期中，有具体的任务时间安排，在所有的任务时间中，装备的状态分为三种：执行任务、故障(维修)、预防性维修、在仿真过程中记录下执行任务的时间，然后按下式计算任务执行时间比：B) Task execution time ratio. In the entire simulation cycle, there is a specific task schedule. In all task time, the state of the equipment is divided into three types: task execution, failure (maintenance), preventive maintenance, and during the simulation process. Record the execution time of the task, and then calculate the task execution time ratio according to the following formula:
其中n是仿真次数，t是每次仿真周期内装备执行任务的时间，T是装备在每次仿真周期内的任务需求时间。Among them, n is the number of simulations, t is the time for the equipment to perform tasks in each simulation cycle, and T is the task requirement time of the equipment in each simulation cycle.
C)使用可用度，使用可用度是指在规定的条件下，整个寿命周期内实际工作时间与要求工作的时间之比，它是装备连续工作条件下完成任务的能力，是整个日历时间的利用率。在仿真平台中主要的数据如图7所示。C) Use availability, use availability refers to the ratio of the actual working time to the required working time in the entire life cycle under specified conditions, it is the ability of the equipment to complete the task under continuous working conditions, and it is the utilization of the entire calendar time Rate. The main data in the simulation platform are shown in Figure 7.
因此装备的使用可用度计算公式如下：Therefore, the formula for calculating the usability of equipment is as follows:
式中TUi是每次仿真周期内能工作时间，TDi是每次仿真周期内不能工作时间，n是仿真次数。In the formula, TUi is the working time in each simulation cycle, TDi is the non-working time in each simulation cycle, and n is the number of simulations.
D)备件短缺数，仿真计算器会记录每个设定的时间间隔内，备件不足造成短缺的数量，计算的方式如下：D) The number of spare parts shortage, the simulation calculator will record the number of shortages caused by the lack of spare parts within each set time interval, the calculation method is as follows:
式中：n为仿真次数，mi为在某个时间间隔时备件短缺的数量，△t＝t2-t1为数据收集时间间隔。In the formula: n is the number of simulations, mi is the number of spare parts in short supply at a certain time interval, △t=t2 -t1 is the data collection time interval.
4.3运行结果4.3 Running results
以某型飞机为例，按照本发明的方法进行计算，得出了运行结果。Taking a certain type of aircraft as an example, the calculation is carried out according to the method of the present invention, and the operation result is obtained.
(1)效能优化曲线(1) Performance optimization curve
按照某型飞机携行方案分析模型，可按照系统法优化模型得到轮战任务某型飞机最优携行方案的效能优化曲线如图8所示。According to the analysis model of the carrying scheme of a certain type of aircraft, the performance optimization curve of the optimal carrying scheme of a certain type of aircraft in the round combat mission can be obtained according to the optimization model of the system method, as shown in Figure 8.
图8中横坐标为器材的采购投资费用，纵坐标为器材满足率，曲线中的每个点均代表一种最优库存方案，选中任一点，该点的横坐标代表此库存方案的器材采购费用，纵坐标代表此方案下系统能达到的最佳效能参数。In Figure 8, the abscissa is the purchase investment cost of equipment, and the ordinate is the equipment satisfaction rate. Each point in the curve represents an optimal inventory plan. If any point is selected, the abscissa of the point represents the equipment procurement of this inventory plan. Cost, the vertical axis represents the best performance parameters that the system can achieve under this scheme.
(2)系统效能指标(2) System performance index
生成的方案23中系统的效能指标如表1所示：The performance indicators of the generated system in Scheme 23 are shown in Table 1:
表1某型飞机携行任务总体效能指标Table 1 The overall performance index of a certain type of aircraft carrying missions
(3)最优备件库存方案(3) Optimal spare parts inventory plan
按照系统效能选择的最优方案23，可以生成相应的最优备件方案包括备件种类、备件数量等，库存方案示意如表2所示。According to the optimal scheme 23 selected by the system performance, the corresponding optimal spare parts scheme can be generated, including the type of spare parts, the quantity of spare parts, etc. The inventory scheme is shown in Table 2.
表2库存方案示意Table 2 Inventory plan schematic
(4)保障过程仿真验证(4) Guarantee process simulation verification
在完成方案的标准分析后，采用以蒙特卡洛的保障过程仿真模型为基础的器材保障方案分析系统进行仿真验证。仿真模型的仿真次数为100次，仿真周期为4392小时，结果收集间隔为24小时，携行标准的任务能力仿真结果如图9、10所示。图9为任务执行时间总计统计图，由图可知，已完成工作时间为98.27％，未完成工作时间为1.73％，任务执行时间满足实际的使用要求；图10为任务执行情况随时间变化示意图，其中横坐标为日历时间，纵坐标为任务执行时间，直线4对应的横坐标为要求执行任务的时间，直线2为实际执行任务的时间，直线3为未完成任务的时间，由任务执行仿真结果可看出按照携行标准优化的结果，实际执行任务的完成率为98.27％，满足任务要求。After completing the standard analysis of the scheme, the material support scheme analysis system based on the Monte Carlo support process simulation model is used for simulation verification. The number of simulations of the simulation model is 100 times, the simulation period is 4392 hours, and the result collection interval is 24 hours. The simulation results of the carrying standard task capability are shown in Figures 9 and 10. Figure 9 is a statistical chart of the total task execution time. It can be seen from the figure that the completed work time is 98.27%, and the unfinished work time is 1.73%. The task execution time meets the actual usage requirements; The abscissa is the calendar time, the ordinate is the task execution time, the abscissa corresponding to the line 4 is the time required to execute the task, the line 2 is the time for the actual task execution, and the line 3 is the time for the unfinished task, and the simulation results are executed by the task It can be seen that according to the optimization results of the carrying standard, the completion rate of the actual execution task is 98.27%, which meets the task requirements.
最后说明的是，以上实施例仅用以说明本发明的技术方案而非限制，本领域普通技术人员对本发明的技术方案所做的其他修改或者等同替换，只要不脱离本发明技术方案的精神和范围，均应涵盖在本发明的权利要求范围当中。Finally, it is noted that the above embodiments are only used to illustrate the technical solution of the present invention without limitation, other modifications or equivalent replacements made by those skilled in the art to the technical solution of the present invention, as long as they do not depart from the spirit and spirit of the technical solution of the present invention All should be included in the scope of the claims of the present invention.
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| CN201910452380.5ACN110188457A (en) | 2019-05-28 | 2019-05-28 | Evaluation method of aviation maintenance support process based on Monte Carlo method |
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| CN201910452380.5ACN110188457A (en) | 2019-05-28 | 2019-05-28 | Evaluation method of aviation maintenance support process based on Monte Carlo method |
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| CN110188457Atrue CN110188457A (en) | 2019-08-30 |
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| CN201910452380.5APendingCN110188457A (en) | 2019-05-28 | 2019-05-28 | Evaluation method of aviation maintenance support process based on Monte Carlo method |
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| RJ01 | Rejection of invention patent application after publication | Application publication date:20190830 | |
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