CN114219129A - Task and MTBF-based weapon system accompanying spare part demand prediction and evaluation system - Google Patents
Task and MTBF-based weapon system accompanying spare part demand prediction and evaluation system Download PDFInfo
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Abstract
The invention provides a method and a system for forecasting and evaluating requirements of weapon system accompanied spare parts based on task and time between failures (MTBF), wherein the method comprises the following steps: organizing each Line Replaceable Unit (LRU) MTBF of the weapon system; calculating the damage number of the LRU under a certain guarantee probability to predict the number of spare parts required for reaching a certain guarantee probability; combining the spare parts which are not selected and influence the operation, and considering whether the spare parts are in a double redundancy design, and selecting the LRU which influences the operation and is not in the double redundancy as the spare parts; the spare part configuration of the final weapon system is evaluated. The method predicts the requirements of the spare parts, reduces the configuration of the spare parts as much as possible on the premise of ensuring the operation of a weapon system, and adopts a reasonable evaluation method to enhance the scientificity and the evaluability of the configuration of the spare parts. The method does not need fitting and direct prediction based on the product state, improves the accuracy of army guarantee, reduces the guarantee pressure, and ensures that spare parts are guaranteed more accurately and efficiently.
Description
Technical Field
The invention relates to a spare part selecting and evaluating method in a weapon system, in particular to a task and MTBF (mean time between failure) based weapon system accompanying spare part demand predicting and evaluating system. MTBF, Mean Time Between Failure, Mean Time Between failures, i.e., Time Between failures.
Background
With the development of science and technology, the requirements of weapon systems on light weight and accuracy of guarantee are higher and higher. If the spare parts are arranged too high, a large number of spare parts are carried, especially in field operations or ocean navigation, occupying a part of the operating space, increasing the logistical burden on the troops and also increasing the capital investment. If the spare parts are not configured enough, the weapon equipment cannot be maintained and put into use in time, and the continuous combat capability cannot be maintained. Therefore, the accurate and reasonable spare part configuration reduces the number of spare parts on the premise of ensuring the normal operation and maintenance requirements of the weapon equipment, and the scientific and evaluable property of the spare part configuration is enhanced by adopting a reasonable evaluation method according to the actual use data of the spare parts, so that the method has very important significance.
In the prior art, the demand forecasting method of the existing weapon system is mainly obtained by engineering experience of designers or a machine learning method, and the fighting demand, the fault interval time of spare parts and equipment design are not fully considered. Machine learning algorithms, such as neural networks, support vector regression, and other methods, are mainly based on prediction of large data and are difficult to adapt to small-range and small-sample weapon systems. The spare parts of the weapon system are high-reliability spare parts, the spare parts have specific service life distribution, and the requirement is generated when the spare parts reach the service life limit, so that the method only depending on machine learning or engineering experience needs a large amount of time or data as support, and the accuracy is low.
For example, chinese granted patent document CN108710905B discloses a method and a system for predicting the number of spare parts based on multi-model union, the method includes: the method comprises the steps of constructing a database of historical use quantity of spare parts, selecting a training set, constructing a time sequence characteristic for each training sample, respectively training a GPR model, a GMR model and a RBFN model for the training set, carrying out optimal model label calibration on the training samples according to sample prediction deviation, respectively carrying out GMM model training on the calibrated data set, inputting the time sequence characteristic of a sample to be tested into different GMM models to obtain three probability values, comparing the probability values to select an optimal model label, inputting the time sequence characteristic of the sample to be tested into a corresponding optimal model to retrain, and predicting the use quantity of the sample to be tested in the next month by using the retrained optimal model. However, it adopts sample training and is difficult to adapt to a small-range small-sample weapon system.
Disclosure of Invention
In view of the defects in the prior art, the invention aims to provide a task and MTBF-based weapon system spare part demand prediction and evaluation system and system.
The invention provides a task and MTBF-based weapon system spare part demand prediction and evaluation method, which comprises the following steps:
step S1: acquiring data of a Line Replaceable Unit (LRU) selected as a spare part;
step S2: calculating the required quantity of spare parts of each LRU under the set guarantee probability according to the data of the line replaceable unit LRU;
step S3: screening LRU supplements from the LRUs not selected as spare parts; setting the demand quantity of the remaining LRUs which are not selected as spare parts as a default value;
step S4: and evaluating the guarantee probability of the configured spare parts to obtain an evaluation result, wherein the configured spare parts comprise the spare parts configured in the steps S2 and S3.
Preferably, in the step S1:
the data of the line replaceable unit LRU includes: mean time between failures MTBF for ith LRUiI th LRU operating time twiInstallation number N of ith LRUi，i＝1,2,…,Ns，NsRepresenting the number of the types of spare parts;
operating time tw of ith LRUiHistorical operating time of ith LRU + predicted operating time tp of task of ith LRUi(ii) a Wherein:
the historical working time of the ith LRU is the working time of the ith LRU after installation;
predicted task work time tp of ith LRUi(life of ith LRU/age of ith LRU) × task time of ith LRU.
Preferably, in step S2:
based on determining MTBF for each LRUiOperating time twiAnd the number N of different spare partsiTo calculate the actual configuration number n of the spare parts required by each type of spare parts under a certain protection probability Pi；
λ＝1/MTBFi
P is guarantee probability;
s is the configuration quantity of spare parts; taking the calculated value of S as the actual configuration number n of spare partsi；
Lambda is the spare part failure rate;
j is a number from 0 to S in sequence;
symbol! Is a factorial.
Preferably, in the step S3:
step S3.1: acquiring the LRU which is not selected as the spare part;
step S3.2: screening the LRU which is not selected as a spare part to obtain the LRU which influences the battle;
step S3.3: judging whether each LRU influencing the battle is in a double redundancy design;
step S3.4: if yes, the LRU influencing the battle is discarded and is not used as a spare part; if not, determining the LRU influencing the battle as a spare part, namely the newly added spare part;
step S3.5: updating the number N of the types of the spare parts according to the newly added spare partss。
Preferably, in the step S4:
evaluation satisfaction rate B1：
NsRepresenting the number of the types of spare parts;
neirepresenting the theoretical demand number of the ith actual configuration spare part;
Nzrepresenting the number of types of theoretical spare parts;
nlirepresenting the ith theoretical spare part allocation number
NliThe installed number of the ith URL is represented;
evaluating utilization factor B2：
The invention provides a task and MTBF-based weapon system spare part demand prediction and evaluation system, which comprises:
module M1: acquiring data of a Line Replaceable Unit (LRU) selected as a spare part;
module M2: calculating the required quantity of spare parts of each LRU under the set guarantee probability according to the data of the line replaceable unit LRU;
module M3: screening LRU supplements from the LRUs not selected as spare parts; setting the demand quantity of the remaining LRUs which are not selected as spare parts as a default value;
module M4: and evaluating the guarantee probability of the configured spare parts to obtain an evaluation result, wherein the configured spare parts comprise spare parts configured by the modules M2 and M3.
Preferably, in said module M1:
the data of the line replaceable unit LRU includes: mean time between failures MTBF for ith LRUiI th LRU operating time twiInstallation number N of ith LRUi，i＝1,2,…,Ns，NsRepresenting the number of the types of spare parts;
operating time tw of ith LRUiHistorical operating time of ith LRU + predicted operating time tp of task of ith LRUi(ii) a Wherein:
the historical working time of the ith LRU is the working time of the ith LRU after installation;
predicted task work time tp of ith LRUi(life of ith LRU/age of ith LRU) × task time of ith LRU.
Preferably, in module M2:
based on determining MTBF for each LRUiOperating time twiAnd the number N of different spare partsiTo calculate the actual configuration number n of the spare parts required by each type of spare parts under a certain protection probability Pi；
λ＝1/MTBFi
P is guarantee probability;
s is the configuration quantity of spare parts; taking the calculated value of S as the actual configuration number n of spare partsi；
Lambda is the spare part failure rate;
j is a number from 0 to S in sequence;
symbol! Is a factorial.
Preferably, in said module M3:
module M3.1: acquiring the LRU which is not selected as the spare part;
module M3.2: screening the LRU which is not selected as a spare part to obtain the LRU which influences the battle;
module M3.3: judging whether each LRU influencing the battle is in a double redundancy design;
module M3.4: if yes, the LRU influencing the battle is discarded and is not used as a spare part; if not, determining the LRU influencing the battle as a spare part, namely the newly added spare part;
module M3.5: updating the number N of the types of the spare parts according to the newly added spare partss。
Preferably, in said module M4:
evaluation satisfaction rate B1：
NsRepresenting the number of the types of spare parts;
neirepresenting the theoretical demand number of the ith actual configuration spare part;
Nzrepresenting the number of types of theoretical spare parts;
nlirepresenting the ith theoretical spare part allocation number
NliThe installed number of the ith URL is represented;
evaluating utilization factor B2：
Compared with the prior art, the invention has the following beneficial effects:
1. according to the method, the requirement of the spare parts is predicted by a task and mean fault interval time-based weapon system spare part requirement prediction method, the configuration of the spare parts is reduced as much as possible on the premise of guaranteeing the battle of a weapon system, and the scientificity and the evaluability of the spare part configuration are enhanced by adopting a reasonable evaluation method.
2. The method does not need fitting, saves calculated amount, improves the accuracy of army guarantee, reduces guarantee pressure, and ensures that spare parts are guaranteed more accurately and efficiently.
3. The method improves the accuracy of the spare part demand prediction of the weapon system, provides powerful method support for spare part planning and evaluation during task execution, and accelerates the turnover of spare parts by evaluating the configuration level of the spare parts.
Drawings
Other features, objects and advantages of the invention will become more apparent upon reading of the detailed description of non-limiting embodiments with reference to the following drawings:
FIG. 1 is a flow chart of steps of a method for forecast assessment of weapon system spare part demand based on mission and fault interval time.
FIG. 2 is a system level spare part requirement simulation diagram according to an embodiment of the invention.
FIG. 3 is a summary analysis of selected spare parts according to an embodiment of the present invention.
FIG. 4 is a flow chart of the reselection of unselected spare parts in the present invention.
Detailed Description
The present invention will be described in detail with reference to specific examples. The following examples will assist those skilled in the art in further understanding the invention, but are not intended to limit the invention in any way. It should be noted that it would be obvious to those skilled in the art that various changes and modifications can be made without departing from the spirit of the invention. All falling within the scope of the present invention.
The invention relates to a system for forecasting and evaluating requirements of weapon system accompanied spare parts based on task and time between failures (MTBF), which comprises the following steps: organizing each Line Replaceable Unit (LRU) MTBF of the weapon system; calculating the damage number of the LRU under a certain guarantee probability based on the historical working time of the weapons and equipment, the predicted working time of the tasks and the MTBF of each LRU to predict the number of spare parts required for reaching a certain guarantee probability; selecting and selecting the LRU which affects the battle and is not dual-redundancy as the spare part by combining the unselected spare parts which affect the battle and considering whether the spare parts are dual-redundancy design, and setting the required quantity of the remaining LRUs which are not selected as the spare parts as a default value, for example, the default value is 1; the spare part configuration of the final weapon system is evaluated.
As shown in fig. 1, first, data of each LRU is sorted. LRU, Line Replaceable Unit. Specifically, according to an embodiment of the present invention, MTBF, historical operating time data, installed number associated with LRU may be collected. Wherein the MTBF means an average time between failures in a repair replacement record of the LRU, the historical operating time data includes a time that the LRU has been operating, and the installed number means a corresponding configuration of a single LRU. For example, if the LRU is a power module of an engine control device, the installed number of the power module may be 3, which means that the power module totals 3 blocks in the weapon system. Taking a certain model of spare parts as an example, the related data is arranged as the following table 1:
TABLE 1 data information table of certain model LRU
| Serial number | LRU name | Number of machines | MTBF(h) | Working time (h) | Remarks for note |
| 1 | LRU 1 | 2 | 50000 | 1500 | |
| 2 | LRU 2 | 5 | 40000 | 1500 | |
| 3 | |
1 | 80000 | 1500 | |
| 4 | LRU 4 | 1 | 85000 | 1500 | |
| 5 | |
3 | 60000 | 1500 | |
| 6 | LRU 6 | 4 | 55000 | 1500 | |
| 7 | |
3 | 35000 | 1500 | |
| 8 | LRU 8 | 2 | 50000 | 1500 | |
| 9 | LRU 9 | 8 | 100000 | 1500 | |
| 10 | |
3 | 70000 | 1500 | |
| … |
The working time of the weapon equipment of the type is 20 years and the service life is 20000 hours according to the working time of each LRU, the service life of the weapon equipment is 24 months, and the operation time of each LRU in 24 months is estimated according to the service life and the service life, wherein the task estimated working time of each LRU is the service life/service life task time. As exemplified above, the average annual operating time is 1000 hours, and the two year run time is 2000 hours.
Secondly, setting the working time to be 0-5000 hours according to the calculation formula of the guarantee probability P and the MTBF of each LRU, and establishing a weapon system spare part requirement three-dimensional model for simulation calculation of the following spare parts, as shown in the attached figure 2.
Next, based on the working time 3500 hours, in the case that the guarantee probability of a single spare part is greater than 90%, the required quantity of spare parts is as follows, and we select spare part LRU1, LRU2, LRU5, LRU6, LRU7, LRU8, LRU9 and LRU10, the configuration quantity is 1, 2 and 1, as shown in fig. 3.
Again, based on the weapon system usage requirements, other operational affecting and non-dual redundant LRU supplements are selected as spare parts according to fig. 2, as follows:
TABLE 2 other data information tables affecting the operational LRU
| Serial number | LRU name | Number of machines | MTBF(h) | Working time (h) | Remarks for note |
| 1 | |
1 | 80000 | 1500 |
The list of spare parts of the weapon system is thus derived as follows:
TABLE 1 data information table of certain model LRU
| Serial number | LRU name | Number of machines | MTBF(h) | Spare number (number) | Remarks for note |
| 1 | LRU 1 | 2 | 50000 | 1 | |
| 2 | LRU 2 | 5 | 40000 | 1 | |
| 3 | |
1 | 80000 | 1 | |
| 4 | |
3 | 60000 | 1 | |
| 5 | LRU 6 | 4 | 55000 | 1 | |
| 6 | |
3 | 35000 | 1 | |
| 7 | LRU 8 | 2 | 50000 | 1 | |
| 8 | LRU 9 | 8 | 100000 | 2 | |
| 9 | |
3 | 70000 | 1 |
Next, the number of theoretical spare part requirements for each LRU is calculated. The details are shown in the following table:
TABLE 4 theoretical spare parts data table of certain model
Finally, according to the 9 spare parts configured, the number of spare parts to be provided is calculated Sne:
Sne＝0.14+0.4375+0.04375+0.175+0.254545455+0.3+0.14+0.28+0.15＝1.920795455
the number of kinds of theoretical spare parts that should be configured is calculated from all 10 LRUs:
Snl＝1.961971925
evaluating satisfaction of spare parts
Evaluating the utilization of spare parts
Those skilled in the art will appreciate that, in addition to implementing the systems, apparatus, and various modules thereof provided by the present invention in purely computer readable program code, the same procedures can be implemented entirely by logically programming method steps such that the systems, apparatus, and various modules thereof are provided in the form of logic gates, switches, application specific integrated circuits, programmable logic controllers, embedded microcontrollers and the like. Therefore, the system, the device and the modules thereof provided by the present invention can be considered as a hardware component, and the modules included in the system, the device and the modules thereof for implementing various programs can also be considered as structures in the hardware component; modules for performing various functions may also be considered to be both software programs for performing the methods and structures within hardware components.
The foregoing description of specific embodiments of the present invention has been presented. It is to be understood that the present invention is not limited to the specific embodiments described above, and that various changes or modifications may be made by one skilled in the art within the scope of the appended claims without departing from the spirit of the invention. The embodiments and features of the embodiments of the present application may be combined with each other arbitrarily without conflict.
Claims (10)
1. A task and MTBF-based weapon system spare part demand prediction and evaluation method is characterized by comprising the following steps:
step S1: acquiring data of a Line Replaceable Unit (LRU) selected as a spare part;
step S2: calculating the required quantity of spare parts of each LRU under the set guarantee probability according to the data of the line replaceable unit LRU;
step S3: screening LRU supplements from the LRUs not selected as spare parts; setting the demand quantity of the remaining LRUs which are not selected as spare parts as a default value;
step S4: and evaluating the guarantee probability of the configured spare parts to obtain an evaluation result, wherein the configured spare parts comprise the spare parts configured in the steps S2 and S3.
2. The task and MTBF-based weapon system spare part demand prediction and evaluation method according to claim 1, characterized in that in step S1:
the data of the line replaceable unit LRU includes: mean time between failures MTBF for ith LRUiI th LRU operating time twiInstallation number N of ith LRUi，i＝1,2,…,Ns，NsRepresenting the number of the types of spare parts;
operating time tw of ith LRUiHistorical operating time of ith LRU + task predicted operating time of ith LRUtpi(ii) a Wherein:
the historical working time of the ith LRU is the working time of the ith LRU after installation;
predicted task work time tp of ith LRUi(life of ith LRU/age of ith LRU) × task time of ith LRU.
3. The task and MTBF-based weapon system spare part demand prediction and evaluation method according to claim 2, characterized by, in step S2:
based on determining MTBF for each LRUiOperating time twiAnd the number N of different spare partsiTo calculate the actual configuration number n of the spare parts required by each type of spare parts under a certain protection probability Pi；
λ＝1/MTBFi
P is guarantee probability;
s is the configuration quantity of spare parts; taking the calculated value of S as the actual configuration number n of spare partsi；
Lambda is the spare part failure rate;
j is a number from 0 to S in sequence;
symbol! Is a factorial.
4. The task and MTBF-based weapon system spare part demand prediction and evaluation method according to claim 1, characterized in that in step S3:
step S3.1: acquiring the LRU which is not selected as the spare part;
step S3.2: screening the LRU which is not selected as a spare part to obtain the LRU which influences the battle;
step S3.3: judging whether each LRU influencing the battle is in a double redundancy design;
step S3.4: if yes, the LRU influencing the battle is discarded and is not used as a spare part; if not, determining the LRU influencing the battle as a spare part, namely the newly added spare part;
step S3.5: updating the number N of the types of the spare parts according to the newly added spare partss。
5. The task and MTBF-based weapon system spare part demand prediction and evaluation method according to claim 2, characterized in that in step S4:
evaluation satisfaction rate B1：
NsRepresenting the number of the types of spare parts;
neirepresenting the theoretical demand number of the ith actual configuration spare part;
Nzrepresenting the number of types of theoretical spare parts;
nlirepresenting the ith theoretical spare part allocation number
NliThe installed number of the ith URL is represented;
evaluating utilization factor B2：
6. A task and MTBF-based weapon system spare part demand prediction and evaluation system, comprising:
module M1: acquiring data of a Line Replaceable Unit (LRU) selected as a spare part;
module M2: calculating the required quantity of spare parts of each LRU under the set guarantee probability according to the data of the line replaceable unit LRU;
module M3: screening LRU supplements from the LRUs not selected as spare parts; setting the demand quantity of the remaining LRUs which are not selected as spare parts as a default value;
module M4: and evaluating the guarantee probability of the configured spare parts to obtain an evaluation result, wherein the configured spare parts comprise spare parts configured by the modules M2 and M3.
7. The task and MTBF-based weapon system spare part demand prediction and evaluation system according to claim 6, characterized in that in said module M1:
the data of the line replaceable unit LRU includes: mean time between failures MTBF for ith LRUiI th LRU operating time twiInstallation number N of ith LRUi，i＝1,2,…,Ns，NsRepresenting the number of the types of spare parts;
operating time tw of ith LRUiHistorical operating time of ith LRU + predicted operating time tp of task of ith LRUi(ii) a Wherein:
the historical working time of the ith LRU is the working time of the ith LRU after installation;
predicted task work time tp of ith LRUi(life of ith LRU/age of ith LRU) × task time of ith LRU.
8. The task and MTBF-based weapons system accessory demand prediction and evaluation system of claim 7, wherein in module M2:
based on determining MTBF for each LRUiOperating time twiAnd the number N of different spare partsiTo calculate the actual configuration number n of the spare parts required by each type of spare parts under a certain protection probability Pi：
λ＝1/MTBFi
P is guarantee probability;
s is the configuration quantity of spare parts; taking the calculated value of S as the actual configuration number n of spare partsi；
Lambda is the spare part failure rate;
j is a number from 0 to S in sequence;
symbol! Is a factorial.
9. The task and MTBF-based weapon system spare part demand prediction and evaluation system according to claim 6, characterized in that in said module M3:
module M3.1: acquiring the LRU which is not selected as the spare part;
module M3.2: screening the LRU which is not selected as a spare part to obtain the LRU which influences the battle;
module M3.3: judging whether each LRU influencing the battle is in a double redundancy design;
module M3.4: if yes, the LRU influencing the battle is discarded and is not used as a spare part; if not, determining the LRU influencing the battle as a spare part, namely the newly added spare part;
module M3.5: updating the number N of the types of the spare parts according to the newly added spare partss。
10. The task and MTBF-based weapon system spare part demand prediction and evaluation system according to claim 7, characterized in that in said module M4:
evaluation satisfaction rate B1：
NsRepresenting the number of the types of spare parts;
neirepresenting the theoretical demand number of the ith actual configuration spare part;
Nzrepresenting the number of types of theoretical spare parts;
nlirepresenting the ith theoretical spare part allocation number
NliThe installed number of the ith URL is represented;
evaluating utilization factor B2：
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Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN115858403A (en) * | 2023-03-01 | 2023-03-28 | 中国电子科技集团公司第十研究所 | False alarm rate prediction method of electronic system |
| CN116596263A (en) * | 2023-05-26 | 2023-08-15 | 中国人民解放军93184部队 | Aircraft spare part demand determining method and device based on flight task |
| CN117151281A (en) * | 2023-08-16 | 2023-12-01 | 北京创奇视界科技有限公司 | Optimization method and device for equipment spare part scheme and related equipment |
| CN117236783A (en) * | 2023-10-19 | 2023-12-15 | 北京归一科技有限公司 | Method and system for calculating various evaluation indexes for guaranteeing effectiveness of armored equipment |
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- 2021-11-22 CN CN202111385852.3A patent/CN114219129A/en active Pending
Cited By (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN115858403A (en) * | 2023-03-01 | 2023-03-28 | 中国电子科技集团公司第十研究所 | False alarm rate prediction method of electronic system |
| CN115858403B (en) * | 2023-03-01 | 2023-06-02 | 中国电子科技集团公司第十研究所 | False alarm rate prediction method of electronic system |
| CN116596263A (en) * | 2023-05-26 | 2023-08-15 | 中国人民解放军93184部队 | Aircraft spare part demand determining method and device based on flight task |
| CN117151281A (en) * | 2023-08-16 | 2023-12-01 | 北京创奇视界科技有限公司 | Optimization method and device for equipment spare part scheme and related equipment |
| CN117151281B (en) * | 2023-08-16 | 2024-03-26 | 北京创奇视界科技有限公司 | Optimization method and device for equipment spare part scheme and related equipment |
| CN117236783A (en) * | 2023-10-19 | 2023-12-15 | 北京归一科技有限公司 | Method and system for calculating various evaluation indexes for guaranteeing effectiveness of armored equipment |
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