CN116187713A

CN116187713A - Repairability maintenance model for equipment guarantee and implementation method thereof

Info

Publication number: CN116187713A
Application number: CN202310286741.XA
Authority: CN
Inventors: 黄鑫; 黄绪环
Original assignee: Shenzhen Prospect Internet Information Technology Co ltd
Current assignee: Shenzhen Prospect Internet Information Technology Co ltd
Priority date: 2023-03-15
Filing date: 2023-03-15
Publication date: 2023-05-30

Abstract

The invention discloses a repairability maintenance model for equipment guarantee, which comprises the following components: the system comprises a simulation input unit, a task generation unit, a task execution unit, a repairability maintenance unit, a data collection unit and a task evaluation unit; the task generating unit includes a task queue generating sub-model and an equipment calling sub-model. The repairability maintenance unit comprises a transportation event execution sub-model, an equipment fault occurrence mathematical sub-model, a maintenance guarantee process sub-model, a fault event generation sub-model, a maintenance task execution sub-model and a simulation time propeller. The invention also provides an implementation method of the repairability maintenance model for equipment guarantee.

Description

Repairability maintenance model for equipment guarantee and implementation method thereof

Technical Field

The invention relates to the technical field of simulation model application, in particular to a repairability maintenance model for equipment guarantee and an implementation method thereof.

Background

Along with the rapid development of informatization, the existing equipment using system is gradually changed from single-type equipment using to multi-system equipment combining using, aiming at a novel using mode, the restriction of various factors on task execution is considered, and the using efficiency of the equipment guaranteeing system is improved and needs to be considered in multiple aspects. How does a task that evaluates the use of multiple models of equipment succeed? How does consider the impact of equipment failure on the performance of the use of an equipment architecture? This is a problem that needs to be solved.

Frequent use of the assembled equipment, equipment wear, part aging, artificial improper operation, environmental influence and the like can cause the problems of large maintenance amount, low maintenance efficiency and the like of the equipment. The equipment maintenance is to take fault equipment or equipment needing regular maintenance and inspection as an object, and establish a maintenance point to develop corresponding maintenance behavior. The existing method mainly focuses on analyzing the maintenance method and mode of single equipment, and determines how to quickly realize the maintenance of the single equipment through an analysis algorithm, so that the maintenance of multiple equipment cannot be decided and judged. The simulation system is applied to the field of equipment maintenance, and provides an optimization decision for quick and efficient equipment maintenance, so that the research on a maintenance simulation model of equipment and an implementation method thereof becomes important.

Disclosure of Invention

Aiming at the problems, the invention provides a repairability maintenance model for equipment guarantee and an implementation method thereof. According to one aspect of the present invention, a restorative repair model for equipment assurance is presented, comprising: the system comprises a simulation input unit, a task generation unit, a task execution unit, a repairability maintenance unit, a data collection unit and a task evaluation unit; the task generating unit comprises a task queue generating sub-model and an equipment calling sub-model, wherein the task generating unit calls the task queue generating sub-model according to the input information input by the input unit to generate a task queue list; simultaneously starting the equipment calling sub-model; the equipment calling sub-model jointly determines equipment, due quantity and minimum quantity required by each task in the task queue table according to the simulation input unit; the task execution unit executes tasks according to the task queue table; in the equipment required by the task, judging whether the task is started or not if the number of the 'standby' states is/is larger than the minimum equipment number required by the task, and entering a task execution unit when the task is started, wherein the task cannot start to record the failure data of the task; after the task is started, the repairability maintenance unit generates fault queue information according to the task queue list and the related equipment data information; based on the fault queue information, the repairability maintenance unit executes repairability simulation calculation; the data collection unit collects intermediate data, result data, equipment state data and simulation time data of the repairability maintenance unit in simulation calculation, and sends the intermediate data, the result data, the equipment state data and the simulation time data into the task evaluation unit to carry out statistical analysis and evaluation on equipment guarantee performance.

Preferably, the repair unit includes: a transportation event execution sub-model, an equipment fault occurrence mathematical sub-model, a maintenance and guarantee process sub-model, a fault event generation sub-model, a maintenance task execution sub-model and a simulation time propeller.

Preferably, the input unit includes: fight planning, guarantee organization, maintenance model, maintenance work, guarantee resource, equipment model and environment information system.

Preferably, the combat design comprises combat unit and mission profile information; the equipment model includes component information and equipment structure; the guarantee organization comprises site information and a supply structure; the maintenance model comprises preventive maintenance information and repairable maintenance information; maintenance work includes maintenance activities and resource requirements; the guarantee resources comprise resource types and resource deployment; the equipment model and the combat thinking model form an equipment unit task model in the input unit, and the equipment system task input and the equipment unit task intensity are taken as constraints; the guarantee organization model comprises two levels of guarantee organization, namely a base level and a site level; the support resource model comprises personnel deployment and spare part deployment.

Preferably, the equipment failure occurrence mathematical submodel is:

the equipment s is composed of m component units, and the composition logic is as follows:

s＝{z ₁ ,z ₁ ,…,z _i ,…,z _m }

the failure probability distribution function of the basic component unit is known as:

F _i (t) (i＝1,2,…m)

the state variable expression of the component unit i is:

the state variable logic expression at the moment t of the equipment is as follows:

X(t)＝[b ₁ (t),b ₂ (t),…b _i (t),…b _m (t)]

the state variables at the time of the equipment t are:

the equipment state variable structure function is:

φ(X(t))＝φ(t)。

preferably, the maintenance assurance process submodel includes the following calculation process: finding a fault unit; judging whether the current stage can be repaired, if so, judging whether the maximum maintenance times are reached, and if not, directly sending the current stage to a maintenance base of the previous stage; if the maximum maintenance times are not reached, carrying out direct maintenance and replacement maintenance sampling; based on the multi-service-desk queuing model, applying for maintenance resources, and judging whether the maintenance resources are met; if the resource arrives and the maintenance resource is satisfied, sampling the direct maintenance time or the replacement maintenance time; sampling the repair time of the component; the repair maintenance process ends.

According to another aspect of the present invention, there is provided a method for implementing a restorative maintenance model for equipment assurance, including:

An input unit is first constructed, which contains design parameters and evaluation targets of a serviceable maintenance model, and which comprises: fight planning, organization assurance, maintenance activities, maintenance work, resource assurance, equipment model, and environmental information system; a task generating unit comprising an equipment calling sub-model is constructed, a task queue is generated based on the output information of the input unit, the task queues are ordered, and an event queue list is established; constructing a task execution unit; constructing a repairability maintenance unit; constructing a data collection unit, wherein the data collection unit records intermediate data, result data, equipment state data and simulation time data of a processing driving event; and constructing a task evaluation unit and evaluating equipment guarantee performance.

Preferably, the process of constructing a repair unit includes: 1) Establishing a mathematical submodel for equipment failure occurrence; 2) Based on the repair time service desk model, a maintenance and guarantee process sub-model is established; 3) Based on a fault event generation algorithm, a fault event generation sub-model is established, and a fault event table is generated; 4) Establishing a maintenance task execution sub-model; 5) Setting a simulation time propeller; the fault event generation sub-model carries out random test on the event queue table, determines the fault event with the fault first, starts the simulation time propeller, and adjusts the simulation time to the moment when the fault event has the fault; the repairability maintenance unit determines driving events which should be processed after the fault event occurs, wherein the driving events comprise starting time, duration and delay time of spare part supply, repairability maintenance, replacement part maintenance and processing driving events.

Preferably, the repair time service desk model is as follows:

the equipment average service rate μ is:

μ＝(m1×μ1+m2×μ2+…+mn×μn)/(m1+m2+…+mn)

the total number of service desks C is:

C＝m1+m2+…+mn；

assuming that the time-to-arrival strength of the damaged equipment is λ, where the fault belongs to the fly-away class, the proportion of repair by the fly-away class repair service counter is k1 (0<k1<1) The fault belongs to the ad hoc class and the proportion of repair by the ad hoc class repair service desk is k2 (0<k2<1) And so on, wherein normalization conditions need to be met, i.e

The actual arrival intensity of the fly-away fault component is k1λ, and the arrival intensity of the ad hoc fault component is k2λ, then there are:

Ws＝max(Ws1，Ws2，...，Wsn)

in the formula, if a service desk is not used in equipment fault repairing equipment, the corresponding average stay time Wsi item is zero, and the average repair time T of a single service desk _xl Is that

T _xl ＝MTTR+ΔT _rydk +ΔT _rycz +ΔT _ryst +ΔT _sbsl +ΔT _sssl +ΔT _bjql 。

Preferably, the process of building the fault event generation sub-model includes: determining the number of running devices and the number of parts of each device by using a vertical equipment logic mathematical model; determining random sampling data; generating a random number based on the random sample data; directly sampling the random number; recording the fault type of the equipment, writing a fault event table, and recording the fault-free time of the part.

Drawings

Various embodiments or examples ("examples") of the present disclosure are disclosed in the following detailed description and drawings. The drawings are not necessarily drawn to scale. In general, the disclosed products or methods may be performed in any order, unless otherwise specified in the claims. In the accompanying drawings:

FIG. 1 is a method of implementing a restorative maintenance model for equipment assurance according to the present disclosure;

FIG. 2 is a restorative repair model for equipment assurance according to the present disclosure;

FIG. 3 is a construction diagram of a simulation input unit for use with the present invention;

FIG. 4 is a block diagram of a repair unit according to the present invention;

FIG. 5 is a multiple service desk queuing sub-model for a restorative repair unit;

FIG. 6 is an equipment troubleshooting equipment queuing sub-model with specialized labor division;

FIG. 7 is a graph of guaranteed resource starvation versus repair time delay;

fig. 8 is a diagram showing a statistical structure of time data in computer simulation according to the present invention.

Detailed Description

Before explaining one or more embodiments of the disclosure in detail, it is to be understood that the embodiments are not limited in their application to the details of construction and to the steps or methods set forth in the following description or illustrated in the drawings. The system and method of the present invention will be described in detail below with reference to the attached drawings.

The implementation method of the repairability maintenance model for equipment guarantee is shown in fig. 1. In the implementation method of the repair model, an input unit is first constructed, and the input unit contains design parameters and evaluation targets of the repair model. Referring to fig. 3, the input unit includes: fight planning, organization assurance, maintenance activities, maintenance work, resource assurance, equipment model, and environmental information system; second, a task generating unit including a device call sub-model is constructed, which generates a task queue based on output information of the input unit, sorts the task queue, and establishes an event queue table. Thirdly, constructing a task execution unit.

Fourth, construct the repairability maintenance unit. As further shown in fig. 2, the process of constructing a repair unit includes: 1) Establishing a mathematical submodel for equipment failure occurrence; 2) Establishing a maintenance and guarantee process submodel; 3) Creating a fault event generation algorithm to generate a fault record table; 4) Establishing a maintenance task execution sub-model; 5) Setting a simulation time propeller. Thus, the constructed repair unit comprises: a transportation event execution sub-model, an equipment fault occurrence mathematical sub-model, a maintenance and guarantee process sub-model, a fault event generation sub-model, a maintenance task execution sub-model and a simulation time propeller. The fault event generation sub-model carries out random test on the event queue table, determines the fault event with the fault first, starts the simulation time propeller, and adjusts the simulation time to the moment when the fault event has the fault. The repair unit determines driving events to be processed after the occurrence of the fault event, such as spare part supply, repair, replacement repair, and start time, duration, delay time, etc. of the processing driving events.

Fifth, a data collection unit is constructed, which records intermediate data, result data, equipment status data, simulation time data of the process driving event. Finally, equipment guarantee performance evaluation is carried out: and constructing a task evaluation unit, and carrying out statistical analysis and evaluation on the equipment guarantee repairability maintenance model based on the intermediate data, the result data and the equipment state data.

Fig. 2 is a repairability maintenance model for equipment assurance according to the present invention, which includes a simulation input unit, a task generating unit, a task performing unit, a repairability maintenance unit, a data collecting unit, and a task evaluating unit. The task generating unit comprises a task queue generating sub-model and an equipment calling sub-model. The task generating unit calls a task queue generating sub-model according to the input information input by the input unit to generate a task queue list; and simultaneously starting the equipment call sub-model. The equipment calling sub-model jointly determines equipment, due quantity and minimum quantity required by each task in the task queue table according to the task execution section model, the combat unit model and the combat unit construction model; in the equipment required by the task, whether the number of the 'standby' states is greater than or equal to the minimum equipment number required by the task is judged to judge whether the task is started or not, the task is started to enter a task execution unit, and the task cannot be started to record the failure data of the task. Generating fault queue information by the fault event generation sub-model according to the task queue list and the related equipment data information; specifically, the fault event generation sub-model samples and calculates fault time points of the components according to the component fault rate distribution of the equipment; traversing all parts of all equipment, and sequencing the fault information of all parts in time sequence to form the fault queue information. The fault queue information contains earliest fault point information to serve as equipment fault event points, so that a maintenance guarantee process sub-model and a maintenance task execution sub-model are triggered. The data collection unit collects intermediate data and result data of the repairable repair unit in the simulation calculation.

As shown in FIG. 3, the simulation input unit of the present invention specifically comprises: fight planning, equipment model, guarantee organization, maintenance model, repair work, guarantee resource information and topography environment information. Fight contemplation includes fight cell and mission profile information; the equipment model includes component information and equipment structure; the guarantee organization comprises site information and a supply structure; the maintenance model comprises preventive maintenance information and repairable maintenance information; maintenance work includes maintenance activities and resource requirements; the guaranteed resources include resource types and resource deployments.

For the combat portrayal model in the simulation input unit, the present invention gives an example, which is only illustrative, comprising combat unit, deployment site, type of equipment required and number. For example, two combat units are disposed at

sites

1 and 2, respectively, and combat unit 1 has 18 TANK's and combat unit 2 has 15 TANK's. For the task profile, including the required equipment type and data, the minimum number of equipment to perform, duration, number of combat, for example, the basic task is to send 10 TANK executions each time, the minimum 8 starts or continues to execute tasks, and the task profile for each duration of 6 hours is 2 times per day for one year.

For the equipment model in the simulation input, the equipment model is composed of different equipment units to complete specific mission tasks, such as accurately striking the outside of a defense area of a navy base of a country, and the equipment units such as the active fire striking, the information support, the information fight, the command control and the like are required to cooperatively complete the fight tasks. Taking an information supporting equipment unit as an example, task targets comprise acquisition of target images, positions, surrounding situations and enemy air defense system information, air early warning monitoring and air command platform construction near an air defense area.

The equipment unit is composed of basic equipment, for example, including one piece of equipment: MAIN battery TANK comprising two components a01, a02, wherein the failure rate of a01 (10 ^-6 ) Failure rate of 13.96, A02 (10 ^-6 ) 12.62.

The equipment model and the combat thinking model form an equipment unit task model in the input unit, task input of an equipment system and task intensity of the equipment unit are taken as constraints, tasks of the basic equipment unit in a plurality of stages and logic conversion relations thereof are defined, and the aim of equipment support auxiliary decision is to compare and analyze different support schemes and propose implementation suggestions, so that the intensity requirements of the equipment unit can be met again with the fastest minimum resource consumption when the basic equipment unit fails. After the equipment unit task model is constructed, the preparation time before flight, the task flight time, the fault maintenance time, the fault waiting spare part time, the preparation time for restarting, the checking time after flight, the task success judging point and the like are defined. For example, basic aviation equipment units are mapped to a certain type of fighter, and during the task execution process of the fighter, a secondary system and components thereof forming the fighter generate faults or combat losses in a certain model, and the fighter must be returned to the field for maintenance or be disabled, so that the continuous progress of the task is affected. The base equipment unit performs a multi-stage mission with a pre-flight preparation time t1=t1-T0; starting maneuvering at the time T1, and executing a combat task when the time T2 reaches a task airspace, wherein maneuvering time T2=t2-t1=t4=t4-T3; effective support time t3=t3-T2 for the equipment unit to perform tasks; fault maintenance time t5=t5-T4; repair wait spare part time t6=t6-T5; again, the preparation time t7=t7-T6; the successful judgment point of the stage task is the moment when the equipment unit continuously meets the strength requirement and reaches stage conversion. During the task execution of the equipment system, the system does not pay attention to the intact state of a certain basic equipment unit, and only needs to pay attention to whether the task strength of the equipment unit is met, so that the comprehensive guarantee resources and the scheduled service objects are transferred from the traditional rough guarantee facing single equipment to the refined guarantee facing the equipment system, which is also a basic departure point and a foothold point of subsequent simulation optimization.

For a safeguard organization model in an input unit, it includes two levels of safeguard organization: DEPOT, the following two security sites: SITE1, SITE2, specific information is shown in tables 1 and 2.

TABLE 1 guarantee site location information

Site name	Longitude and latitude	Dimension(s)
			MAINDEPOT	13.83	63.07
SITE1	13.46	55.93
			SITE1	12.18	57.74
Task execution place	19.43	47.26

Table 2 site transport information

For maintenance information in the input unit, it includes:

and repairability maintenance information mainly describing maintenance places, maintenance time, maintenance modes and related maintenance activities.

Preventive maintenance information mainly describing the work type, maintenance interval, maintenance site, maintenance time, maintenance mode, and associated maintenance activities.

For a repair work model in an input unit, comprising:

repair activities, wherein a01 is a repairable piece, the repair is divided into two steps: firstly, disassembling and replacing at a SITE for 2 hours; and secondly, maintaining the maintenance time of the MAIN DEPOT site for 648 hours. A02 is a consumable part, and only the replacement part is maintained: the SITE was changed over for 2 hours.

The resource requirement is that the resource required by the MAIN battery TANK for disassembly and replacement maintenance work is 1 technician, and the resource required by the replacement maintenance work is 1 technician.

For a guaranteed resource model in an input unit, it includes:

Personnel deployment, technicians deploy 30 at MAIN DEPOT SITEs and 4 at SITE1 and SITE2 respectively.

When spare parts are deployed and the spare parts of the bottom-layer site are insufficient, an upper-level application is required, as shown in table 3. After the spare parts of the site are consumed, an application for purchase should be proposed, for example, after the a02 part is consumed, the purchase is applied 720.

Table 3 spare part deployment

IID (component ID)	STID (deployment site)	STSIZ (deployment number)
			A01	SITE1	3
A01	SITE2					2
			A01	MAIN DEPOT	3
A02	SITE1					2
			A02	SITE2			2
A02	MAIN DEPOT	5

The present invention focuses on the construction of a repair unit, which includes the following procedure.

1) Establishing equipment fault occurrence mathematical model

s＝{z ₁ ,z ₁ ,…,z _i ,…,z _m }

F _i (t) (i＝1,2,…m)

the state variable expression of the component unit i is:

X(t)＝[b ₁ (t),b ₂ (t),…b _i (t),…b _m (t)]

the state variables at the time of the equipment t are:

the equipment state variable structure function is:

φ(X(t))＝φ(t)。

2) Based on the repair time service desk model, a maintenance and guarantee process sub-model is established

A block diagram of the maintenance assurance process submodel is shown in fig. 4. Referring to fig. 4, the fault occurs only during the system execution task phase. The fault unit can be found by locating the fault by equipping the breakdown structure, such as products, sub-products, etc. For the failed unit, first, step S1 is performed: judging whether the current stage can be repaired, if so, performing step S2: and judging whether the maximum maintenance times are reached, and if the maximum maintenance times are not reached, directly sending the maintenance times to a maintenance base of the previous stage. If the maximum number of repairs is not reached, go to step S3: and (5) direct maintenance and replacement maintenance sampling. In step S4, whether the maintenance is direct or not is judged, and the process proceeds to step S5, wherein the maintenance resource application is carried out. For the application of the maintenance resource, step S6 is executed to judge whether the maintenance resource is satisfied. If the resource arrives and the maintenance resource is satisfied, on the one hand, the method goes to step S7, namely, the direct maintenance time sampling is carried out, and then, the method goes to step S13, namely, the repair maintenance process is finished. On the other hand, the process advances to step S9: the spare part application is performed, and then step S10 is entered: judging whether the spare part is satisfied, if the spare part is satisfied and the spare part has arrived, performing replacement repair time sampling S10, performing component repair time sampling S12, and finally, going to step S13, finishing the repair process. In the process of steps S6 to S11, communication with the resource waiting queue is repeatedly required to ensure that each step thereof operates in time series.

In fig. 4, the probability of occurrence of a failure of a failed unit follows a negative exponential distribution, and a system may have multiple failures in one task. However, any one sub-product can only fail once in a single task. Depending on the failure mode, the system failure may be repaired by direct repair or by replacement repair. The repairable maintenance of the equipment is generally carried out at a basic level, but in practice, replacement maintenance occupies the vast majority, which is beneficial to improving the usability of the equipment.

The systems that are returned in the task due to the fault or damage are all sent to the maintenance station. At a repair station, the failed product is removed and sent to the next repair station, the failed sub-product is removed and sent to another repair station, and so on. This series of activities is actually a driver of the demands of the logistics organization. Repair operations for systems and products have much in common and are handled in the same process, the same repair task in the same model. Repair of the system and product may be accomplished by replacing sub-products or direct repair. Similar to other repair operations, the repair operation may also be broken down into a series of sub-operations. The guaranteed resources for performing the repair job may be allocated to either the entire job or each sub-job. Repair operations for systems and products may be performed at different levels and at different sites. The failed product contains only one fault and one repair job at a time, while the failed system may have multiple faults and multiple repair jobs at the same time.

The replacement sub-operation refers to an operation of installing a good product. There must be spare parts available before the replacement sub-job can begin. If there are no spare parts available, the sub-job and the entire repair job will be forced to pause until a spare part arrives. The need for spare parts is immediately sent out at the beginning of the maintenance task. The desired sub-product type is determined at the turnaround stage after the mission.

The direct repair operation includes only repair sub-operations. If the sub-job is not explicitly specified, the sub-job is automatically generated within the model. In fact, a direct repair operation is a conventional repair operation that has only resource requirements and no other characteristics. After all sub-jobs for the restorative repair are completed, the repaired product or system will reenter the warranty organization. The system may have multiple repair operations at the same time. After all maintenance operations are completed, the repair work of the system is completed.

Since the queuing phenomenon caused by insufficient various guarantee resources is involved in the maintenance and guarantee process, the repairability maintenance model adopts a multi-service-desk queuing model to calculate maintenance and guarantee time, and the queuing model is shown in fig. 5. Following the theory of queuing theory, regarding equipment troubleshooting as queuing equipment, one needs to determine the next 3 parameters, namely the arrival strength of the equipment, the number of repair kiosks in the equipment and the average service rate of the equipment. Based on actual equipment failure, the repair equipment may analyze the 3 parameters as follows:

A) The arrival strength of the equipment. Since the failure equipment arrival strength is related to failure rate under actual equipment use and combat conditions, and is independent of maintenance and assurance resources, data can be generated from random sampling of failure rates of equipment components according to their distribution functions.

B) The number of service desks is modified in the equipment. The number of repair service desks is determined according to the requirements of a guarantee organization, the actual equipment fault condition and the existing military organization, and the number of repair service desks is only the number of service desks which are actually available and are needed to be considered, so that the repair service desks are related to reliability and utilization rate. It may be assumed that the composed repair benches are all available.

C) Average service rate of equipment. The average service rate of the equipment is based on the average service rate of the single repair service station, and the average service rate of the equipment can be obtained according to the queuing structure of the equipment as long as the average service rate of the single repair service station is known.

Through the above analysis, only the equipment average service rate in the 3 parameters needs to be solved. During modeling, the average service rate of an individual service station is known, and the equipment average service rate is calculated from the queuing structure of the equipment. The following assumptions can be made depending on the actual situation of the fault repair equipment:

A) The arrival profile of faulty equipment is determined from the actual equipment fault profile, the total number of faulty equipment is limited (over a time frame), and the manner of arrival to the repair facility is batched (including the case of a single faulty equipment);

b) The fault equipment arrives independently, and the arrival time interval obeys the random distribution;

c) When the fault equipment arrives at the repair mechanism, when all the repair service desks are occupied, the fault equipment waits for repair in a queue and is carried out according to the first-come first-serve principle;

d) The repair service desks are divided according to professions (such as flyers, special equipment and the like), n professions are arranged, each profession is provided with a plurality of service desks with the same functions and organized according to standards, and the number of the service desks is m1, m2, … and mn respectively;

e) The service time of the repair station is analyzed in two cases: when all maintenance guarantee resources are met, the repair time for a certain part is determined, namely MTTR; secondly, when the maintenance guarantee resources are insufficient, the maintenance time of the fault equipment is the sum of delay time caused by MTTR and various guarantee resources lack.

Based on the above assumptions and analysis, it can be considered that the equipment fault repair equipment satisfies the basic conditions of the multi-service window waiting queuing model M/M/n, and can be solved by using the mathematical formula of the M/M/n model.

Equipment troubleshooting equipment with specialized labor can thus be described as a queuing model as shown in fig. 6. According to fig. 6, the further analysis is calculated as follows:

in the equipment, m1 pieces of the fly-hair repair service desks with the same functions are arranged, and the average service rate of the single repair service desks is assumed to be known, for example, the average service rate of the fly-hair repair service desks is mu 1. Similarly, m2 special repair service desks with the same function are arranged in the equipment, the average service rate of the single repair service desks is mu 2, and by analogy, the method can be used for obtaining:

the equipment average service rate μ is:

μ＝(m1×μ1+m2×μ2+…+mn×μn)/(m1+m2+…+mn)

the total number of service desks C is:

C＝m1+m2+…+mn

It can be obtained that the arrival intensity of the actual flying fault-like component is k1λ, the arrival intensity of the ad hoc fault-like component is k2λ, and so on:

Ws＝max(Ws1，Ws2，…，Wsn)

in the formula, if a service desk is not used in the equipment troubleshooting equipment, the corresponding average stay time Wsi term is zero.

The foregoing calculates an equipment failure repair equipment multi-service bay total equipment average repair time, where the average service rate of a single repair service bay is assumed. The calculation method of the average service rate of the individual repair service desks is analyzed in detail below.

Assuming that the individual repair benches of the same specialty class are similar in nature and function, consider only the behavior of one of the repair benches, and analogize to the behavior of the other repair benches.

In the repair process, the sufficiency of various guarantee resources needs to be considered. Suppose that the repair process is only related to 4 types of guarantee resources, namely, manpower, supply guarantee, guarantee equipment and guarantee facilities. The other 4 kinds of guarantee resources (technical data, computer resource guarantee, training and training guarantee, package and storage) have little influence on the repairing process of the concrete service desk, and can be ignored for simplifying calculation.

The main factors of the unsatisfied human resources are the delay time caused by the fact that the profession is not in line with the mouth, the personnel are not in line with the job and the number of the personnel is not satisfied. The corresponding indexes are the contrast rate, the job title rate, the full-scale rate and the bit rate.

The condition that the equipment resources are not met is mainly characterized in that the quantity is not met and the variety is not met, if the variety is not met, the equipment cannot be unfolded, and only the repair unit at the previous level can be repaired. Therefore, the method is only caused by insufficient quantity, and is determined by quantity satisfaction rate or guarantee facility utilization rate.

The insufficient supply guarantee resources mainly refer to the shortage of the number and variety of spare parts. For a repair task, the deficiency of quantity and variety can lead to the requesting of spare parts, otherwise the repair task cannot be completed. Thus, a delay time of the spare parts is increased.

Therefore, a corresponding flow chart can be drawn according to the above-mentioned class 4 guaranteed resource satisfaction degree, as shown in fig. 7. FIG. 7 shows a diagram of guaranteed resource deficiency versus repair time delay. According to the situation shown in fig. 7, the following analytical calculations can be made:

a) When all kinds of guarantee resources are met, namely, manpower, supply guarantee, guarantee equipment and guarantee facilities are all met, the repair time is the average repair time MTTR under the standard condition. The average repair time for a single device may be obtained through past maintenance records or experience.

B) When the manpower personnel do not meet, mainly consider 3 factors: firstly, the time delay caused by the professional not to match the mouth is recorded as delta T _rydk Can be determined by the contrast ratio in the resource level parameter, and the professional contrast ratio symbol is marked as P _rydk The method comprises the steps of carrying out a first treatment on the surface of the Secondly, time delay caused by the improper job is recorded as DeltaT _rycz Can be determined by the resource level parameter of the job title, the symbol of the job title is denoted as P _rycz The method comprises the steps of carrying out a first treatment on the surface of the Thirdly, the number of people is insufficient and is recorded as delta T _rysl Can be determined by the personnel full-rate and personnel bit rate of resource level parameters, and the symbols are respectively marked as P _rymz And P _ryzw 。

C) When the guarantee equipment is not satisfied, mainly considering the time delay caused by insufficient quantity, and recording as delta T _sbsl The number of the guarantee devices is determined by the satisfaction rate of the number of the guarantee devices and the utilization rate of the guarantee devices, and the symbols are respectively marked as P _sbsl And P _sbly 。

D) When the safeguarding facilities are not satisfied, the time delay due to the insufficient quantity of the safeguarding facilities is denoted as DeltaT, similar to the consideration of the safeguarding facilities _sssl The symbol of the system is respectively marked as P and is determined by the quantity satisfaction rate of the guarantee facilities and the utilization rate of the guarantee facilities _rsssl And P _ssly 。

E) When the spare parts are not satisfied, the delay time caused by the claim of the spare parts is recorded as delta T after the process of applying and picking up the spare parts _bjql The parameters reflecting the satisfaction degree of the spare parts mainly comprise the spare part quantity satisfaction rate, the spare part variety completion rate and the spare part integrity rate. Since the delay time caused by requesting spare parts is not proportional to the number of spare parts, but is related to the process of requesting spare parts, the delta T is calculated _bjql The method should be singly used in the process of requesting the spare part.

Thus, an average repair time T of a single service desk can be obtained _xl Is that

T _xl ＝MTTR+ΔT _rydk +ΔT _rycz +ΔT _rysl +ΔT _sbsl +ΔT _sssl +ΔT _bjql (1)

The above equation is a general formula for calculating the average repair time for a single repair service desk, where when a certain class of resources is met, the corresponding Δt value is zero.

3) Based on a fault event generation algorithm, a fault event generation sub-model is established, and a fault event table is generated. The fault event table has the following data:

table 4 fault event table

The fault event generation sub-model adopts a Monte Carlo method, and the equipment guarantee efficiency is quantitatively evaluated through a statistical test and random simulation of random variables, and the basic idea of Monte Carlo is as follows: when the solved problem is the expected value of a certain random variable, the sample average value of the random variable can be obtained through sampling test and used as the solution of the problem. In the failure event generation sub-model, the number of devices being operated, and the number of components of each device, are first determined using the vertical equipment logical mathematical model. Second, randomly sampled data is determined. In the equipment security aid decision method, the data to be randomly sampled includes: the number of equipment, the number of equipment parts, the fault-free operation time of parts, the equipment state, the part replacement time and the part maintenance time.

Third, a random number is generated.

In the equipment support auxiliary decision platform, a Lehmer method is adopted for generating random numbers, the method is based on a multiplication congruence method, and the calculation formula is as follows:

u _i+1 ＝5 ^2p+1 ·u _i mod(2 ⁿ )

wherein the random seed number u0 is set by the user, in the platform:

p＝6，n＝31

the random number generation formula becomes:

u _i+1 ＝(1220703125u _i )mod(2147483648)

fourth, sampling of random variables

Sampling of random variables refers to generating simple random samples from a population of known distributions. Common random sampling methods include: the invention adopts a direct sampling method according to random seed numbers, a selective sampling method, a transformation sampling method, a compound sampling method, an approximate sampling method and the like. The basic principle is as follows:

if Z is [0,1]Random variable uniformly distributed on, ζ=f ^-1 (Z) ζ is a random variable having F (x) as a distribution function. For example, for an exponential distribution, the sample value generation process for its dependent variable is as follows:

f(t)＝λe ^-λt

/>

then

In the Monte Carlo-based guarantee maintenance process simulation, a random sampling method is adopted for failure rate, part replacement time, maintenance time, preventive maintenance time, spare part supply time and the like of the product. The main distribution types include: the index distribution, standard normal distribution, lognormal distribution, and weibull distribution are sampled as shown in table 5 below:

Table 5 random sampling formula for random variables of the primary distribution function

Fifthly, recording the equipment fault type and writing a fault event table; recording the part failure-free time which is equal to the fault event of the existing part object plus the simulation time increment.

4) Establishing maintenance task execution sub-model

The maintenance task execution sub-model of the equipment considers the use guarantee and the fault maintenance problem in task execution, and if the use guarantee cannot be completed, the task guarantee waiting in a limited time is carried out. In the use and maintenance process of the equipment, the use guarantee resources or the maintenance guarantee resources need to be analyzed to judge whether the requirements are met, and then an established resource allocation sub-model, a spare part shortage sub-model and a transportation event execution sub-model are needed. The maintenance task execution sub-model adopts a maintenance task execution algorithm, and the algorithm implementation process is outlined as follows:

5) Establishing a task evaluation unit, and executing repairability maintenance task evaluation

The task evaluation unit is responsible for carrying out statistical analysis and simulation output on the simulation of the repairability maintenance model, and comprises task capability evaluation, equipment capability evaluation, spare part capability evaluation and resource capability evaluation. After the equipment time data and the task related data are counted through the set simulation times and simulation periods, the following results can be obtained:

1) In the basic task setting, a judging rule of task completion can be given, and in the platform, the task is judged mainly according to the ratio of the task execution time to the task requirement time, if the task is required to be executed for 5 hours, the task success can be judged when the task is executed for 4.5 hours, and then the task success point can be set to be 0.9. The decision rule expression is as follows:

the method comprises the steps of simulating a guarantee scheme for a plurality of times in a platform, and counting the times of judging that the task is successful in the simulation, wherein the calculation method comprises the following steps:

P＝m/M

wherein M is the total number of times the device completes the task in the simulation period, and M is the number of times the device is scheduled to complete the task in the simulation period.

2) The task execution time ratio has specific task time arrangement in the whole simulation period, and the states of the equipment are divided into three types in all task time: executing tasks, fault (maintenance) and preventive maintenance, recording the time for executing the tasks in the simulation process, and then calculating the task execution time ratio according to the following formula:

where n is the number of simulations, T is the time to perform a task in the equipment per simulation cycle, and T is the task demand time of the equipment per simulation cycle.

3) The availability of use refers to the ratio of the actual working time to the time required to work in the whole life cycle under the specified condition, and is the capability of completing tasks under the continuous working condition of equipment and the utilization rate of the whole calendar time. The main data in the simulation platform is constituted by the structure diagram shown in fig. 8. As shown in FIG. 8, the programming time of the equipment includes both on-time and off-time throughout the simulation cycle. The operable time includes a dead time, a reaction time, a standby time, and a task execution time. The out-of-service time includes maintenance time, use guarantee time, improvement time, and delay time. The repair time includes repair time and preventive repair time, and the delay time includes guarantee resource delay time and management delay time.

The usage availability of the equipment is calculated as follows:

t in _Ui Is the internal energy working time of each simulation period, T _Di The time of incapacitation in each simulation period is shown, and n is the number of simulation times.

4) The number of spare parts shortage, the simulation time propeller records the number of the shortage caused by the shortage of spare parts in each set time interval, and the calculation mode is as follows:

wherein: n is the number of simulations, mi is the number of spare part shortages at a certain time interval, Δt=t ₂ -t ₁ A data collection time interval.

Although the invention has been described with reference to the embodiments shown in the drawings, equivalent or alternative means may be used without departing from the scope of the claims. The components described and illustrated herein are merely examples of systems/devices and methods that may be used to implement embodiments of the present disclosure and may be replaced with other devices and components without departing from the scope of the claims.

Claims

1. A restorative repair model for equipment assurance, comprising: the system comprises a simulation input unit, a task generation unit, a task execution unit, a repairability maintenance unit, a data collection unit and a task evaluation unit; the task generating unit comprises a task queue generating sub-model and an equipment calling sub-model, and is characterized in that,

The task generating unit calls a task queue generating sub-model according to the input information input by the input unit to generate a task queue list; simultaneously starting the equipment calling sub-model;

the equipment calling sub-model jointly determines equipment, due quantity and minimum quantity required by each task in the task queue table according to the simulation input unit;

the task execution unit executes tasks according to the task queue table; in the equipment required by the task, judging whether the task is started or not if the number of the 'standby' states is/is larger than the minimum equipment number required by the task, and entering a task execution unit when the task is started, wherein the task cannot start to record the failure data of the task;

after the task is started, the repairability maintenance unit generates fault queue information according to the task queue list and the related equipment data information; based on the fault queue information, the repairability maintenance unit executes repairability simulation calculation;

the data collection unit collects intermediate data, result data, equipment state data and simulation time data of the repairability maintenance unit in simulation calculation, and sends the intermediate data, the result data, the equipment state data and the simulation time data into the task evaluation unit to carry out statistical analysis and evaluation on equipment guarantee performance.

2. The restorative maintenance model for equipment assurance according to claim 1, wherein the restorative maintenance unit includes: a transportation event execution sub-model, an equipment fault occurrence mathematical sub-model, a maintenance and guarantee process sub-model, a fault event generation sub-model, a maintenance task execution sub-model and a simulation time propeller.

3. The restorative maintenance model for equipment assurance according to claim 1, wherein the input unit includes: fight planning, guarantee organization, maintenance model, maintenance work, guarantee resource, equipment model and environment information system.

4. A restorative maintenance model for equipment assurance according to claim 3, characterized in that the fight contemplation includes fight units and mission profile information; the equipment model includes component information and equipment structure; the guarantee organization comprises site information and a supply structure; the maintenance model comprises preventive maintenance information and repairable maintenance information; maintenance work includes maintenance activities and resource requirements; the guarantee resources comprise resource types and resource deployment; the equipment model and the combat thinking model form an equipment unit task model in the input unit, and the equipment system task input and the equipment unit task intensity are taken as constraints; the guarantee organization model comprises two levels of guarantee organization, namely a base level and a site level; the support resource model comprises personnel deployment and spare part deployment.

5. The restorative maintenance model for equipment assurance according to claim 2, wherein the equipment failure occurrence mathematical submodel is:

s＝{z ₁ ,z ₁ ,…,z _i ,…,z _m }

F _i (t) (i＝1,2,…m)

the state variable expression of the component unit i is:

X(t)＝[b ₁ (t),b ₂ (t),…b _i (t),…b _m (t)]

the state variables at the time of the equipment t are:

the equipment state variable structure function is:

φ(X(t))＝φ(t)。

6. the restorative repair model for equipment assurance according to claim 2, wherein the repair assurance process submodel comprises the following calculation process: finding a fault unit; judging whether the current stage can be repaired, if so, judging whether the maximum maintenance times are reached, and if not, directly sending the current stage to a maintenance base of the previous stage; if the maximum maintenance times are not reached, carrying out direct maintenance and replacement maintenance sampling; based on the multi-service-desk queuing model, applying for maintenance resources, and judging whether the maintenance resources are met; if the resource arrives and the maintenance resource is satisfied, sampling the direct maintenance time or the replacement maintenance time; sampling the repair time of the component; the repair maintenance process ends.

7. A method for implementing a restorative maintenance model for equipment assurance, comprising:

an input unit is first constructed, which contains design parameters and evaluation targets of a serviceable maintenance model, and which comprises: fight planning, organization assurance, maintenance activities, maintenance work, resource assurance, equipment model, and environmental information system;

a task generating unit comprising an equipment calling sub-model is constructed, a task queue is generated based on the output information of the input unit, the task queues are ordered, and an event queue list is established;

constructing a task execution unit;

constructing a repairability maintenance unit;

constructing a data collection unit, wherein the data collection unit records intermediate data, result data, equipment state data and simulation time data of a processing driving event;

and constructing a task evaluation unit and evaluating equipment guarantee performance.

8. The method for implementing a restorative maintenance model for equipment assurance according to claim 7, wherein the process of constructing a restorative maintenance unit includes:

1) Establishing a mathematical submodel for equipment failure occurrence; 2) Based on the repair time service desk model, a maintenance and guarantee process sub-model is established; 3) Based on a fault event generation algorithm, a fault event generation sub-model is established, and a fault event table is generated; 4) Establishing a maintenance task execution sub-model; 5) Setting a simulation time propeller;

The fault event generation sub-model carries out random test on the event queue table, determines the fault event with the fault first, starts the simulation time propeller, and adjusts the simulation time to the moment when the fault event has the fault; the repairability maintenance unit determines driving events which should be processed after the fault event occurs, wherein the driving events comprise starting time, duration and delay time of spare part supply, repairability maintenance, replacement part maintenance and processing driving events.

9. The method for implementing a restorative maintenance model for equipment assurance according to claim 8, wherein the repair time service desk model is as follows:

the equipment average service rate μ is:

μ＝(m1×μ1+m2×μ2+…+mn×μn)/(m1+m2+…+mn)

the total number of service desks C is:

C＝m1+m2+…+mn；

Ws＝max(Ws1，Ws2，…，Wsn)

T _xl ＝MTTR+ΔT _rydk +ΔT _rycz +ΔT _rysl +ΔT _sbsl +ΔT _sssl +ΔT _bjql 。

10. The method for implementing a restorative maintenance model for equipment assurance according to claim 8, wherein the process of building a failure event generation sub-model includes:

determining the number of running devices and the number of parts of each device by using a vertical equipment logic mathematical model; determining random sampling data; generating a random number based on the random sample data; directly sampling the random number; recording the fault type of the equipment, writing a fault event table, and recording the fault-free time of the part.