CN108304971B - Method for calculating spare part demand of multiple sets of equipment - Google Patents
Method for calculating spare part demand of multiple sets of equipment Download PDFInfo
- Publication number
- CN108304971B CN108304971B CN201810124671.7A CN201810124671A CN108304971B CN 108304971 B CN108304971 B CN 108304971B CN 201810124671 A CN201810124671 A CN 201810124671A CN 108304971 B CN108304971 B CN 108304971B
- Authority
- CN
- China
- Prior art keywords
- spare part
- calculating
- equal
- equipment
- unit
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/04—Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem"
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
- G06Q10/063—Operations research, analysis or management
- G06Q10/0639—Performance analysis of employees; Performance analysis of enterprise or organisation operations
Landscapes
- Business, Economics & Management (AREA)
- Engineering & Computer Science (AREA)
- Human Resources & Organizations (AREA)
- Economics (AREA)
- Strategic Management (AREA)
- Entrepreneurship & Innovation (AREA)
- Development Economics (AREA)
- Marketing (AREA)
- Game Theory and Decision Science (AREA)
- Operations Research (AREA)
- Quality & Reliability (AREA)
- Tourism & Hospitality (AREA)
- Physics & Mathematics (AREA)
- General Business, Economics & Management (AREA)
- General Physics & Mathematics (AREA)
- Theoretical Computer Science (AREA)
- Educational Administration (AREA)
- Hardware Redundancy (AREA)
- Management, Administration, Business Operations System, And Electronic Commerce (AREA)
Abstract
The invention provides a method for calculating the demand of spare parts of multiple sets of equipment, which initializes a spare part scheme and calculates the guarantee effect of the spare part scheme by traversing and calculating the failure degree of each type of unit, and if P0 is more than or equal to PsIf yes, the calculation is terminated; if P0<PsTraversing and generating candidate spare part schemes, calculating corresponding marginal benefit, and calculating dV from all marginal benefitsjFinding out the serial number Jm corresponding to the maximum value, traversing within the range that d is more than or equal to 1 and less than or equal to Nd to obtain an updated spare part scheme, and obtaining a final result through circular traversal calculation. The method has high accuracy, and under the condition of the same spare part demand, the spare part guarantee effect under the repair and overhaul strategy can be regarded as the upper limit result of all other overhaul strategies, so that when no method for accurately calculating the spare part guarantee effect under the repair and overhaul strategy exists, the method for accurately calculating the spare part guarantee effect under the repair and overhaul strategy can provide an 'optimistic' estimation result of the guarantee effect for other overhaul strategies.
Description
Technical Field
The invention relates to the field of spare part demand calculation, in particular to a method for calculating the spare part demand of multiple sets of equipment.
Background
Devices typically have a hierarchical structure. For example, a device may be divided into, from top to bottom: devices, components, plates, components, and the like. The device consists of various types of components, each of which consists of various plates, and so on. In this context, we refer to the replaceable/repairable product at the bottom most layer as a unit. Since the cells are at the bottom layer and the composition is relatively simple and pure, the distribution pattern of the lifetime is closer to the theoretical "standard" distribution pattern, such as the common exponential distribution, gamma distribution, normal distribution, weibull distribution, etc.
Generally, the life of units such as electronic parts follows an exponential distribution, such as:printed circuit board inserts, electronic components, resistors, capacitors, integrated circuits, etc. An exponential cell is a cell whose lifetime X follows an exponential distribution, denoted X to Exp (λ), with the density function of X being f (X) ═ λ e-λx。
The service life of mechanical parts generally follows normal distribution, such as collector rings, gear boxes, speed reducers and the like. Normal type unit refers to a unit whose lifetime follows normal distribution, and lifetime X distribution is recorded as X-N (mu, sigma)2) Where μ is the mean of the lifetimes, σ2As a function of the variance of the lifetime, the density of X is
Electromechanical component life generally follows a weibull-type distribution, such as: ball bearings, relays, batteries, hydraulic pumps, gears, material fatigue, etc., the profile being suitable for describing aging-induced failures. The Weibull type cell refers to a cell whose lifetime follows Weibull distribution, and the lifetime X distribution is denoted as X-W (alpha, b), wherein the scale parameter alpha is more than 0, the shape parameter b is more than or equal to 1 in engineering, and the density function of X is
In the text, a plurality of sets of equipment are same-type equipment, are simultaneously put into use and have similar working environments, the working accumulated time of each equipment is also relatively close, and the working accumulated time of each equipment is simplified to be equal in the text. The device has multiple units to configure spare parts. These units are in independent relationship, namely: when a certain unit fails, the working state of other units cannot be influenced, other units cannot stop working, and other units cannot be caused to fail. The appearance of these units when they fail is not obvious, and it can only be known whether the units are intact through special overhaul work. Therefore, when the use of these sets of this type of equipment is resumed, all the equipment needs to be overhauled, replacing the faulty unit with spare parts, to ensure the integrity of the equipment when it is resumed.
In the above convention, the common scenes corresponding to reality are as follows:
1) the device in a stored state. During the storage period, the reliability relations among the equipment and the units in the equipment of the batch are independent, and the fault of certain equipment or the fault of certain unit in the equipment has no influence on other equipment or units. When the batch of equipment is enabled, the equipment/unit integrity status needs to be determined through overhaul work, and the expected number of intact equipment is reached under the support of certain spare part resources.
2) The unit fault is a soft fault, namely when the performance of the unit continuously drops to a specified value, the unit can be determined to be in fault, but the unit does not stop working at the moment, the fault effect affects equipment in a comprehensive mode together with other units, and the fault unit can only be searched and repaired in a maintenance mode afterwards.
In this context, the term "repair strategy" refers to that after all the equipment is inspected, all the intact units and spare parts are used to assemble the largest possible number of pieces of equipment. For example: the apparatus consisted of a model A, B, C unit. A, C in the equipment 1 has a fault, B in the equipment 2 has a fault, A and B in the equipment 3 have faults, the number of spare parts of the current A, B, C three-type unit is 1, 1 and 0, and because the number of the three-type sound units (including the spare parts) is 2, 2 and 2 respectively, 2 sound equipment can be obtained at the moment according to the repair strategy.
The splicing repair and overhaul strategy is a strategy of making the best use of the things, can utilize intact units and spare parts to the maximum extent, is an overhaul strategy with the highest efficiency compared with other overhaul strategies, and is particularly suitable for equipment with high modularization degree. Under the condition of the same spare part demand, the spare part guarantee effect under the repair and overhaul strategy can be regarded as the upper limit result of all other overhaul strategies, so that when no accurate spare part guarantee effect calculation method under other overhaul strategies exists, the method for accurately calculating the spare part guarantee effect under the repair and overhaul strategy can provide an 'optimistic' estimation result of the guarantee effect for other overhaul strategies.
Disclosure of Invention
In view of this, the present invention provides a method for accurately calculating the demand of spare parts for multiple sets of equipment.
The technical scheme of the invention is realized as follows: the invention provides a method for calculating the demand of spare parts of a plurality of sets of equipment, which comprises the following steps,
s1, traversing and calculating the failure degree Pf of each type of unitd，PfdDescribing the probability of the unit failing in the period of 0-T, wherein T is the accumulated working time of the equipment, d is more than or equal to 1 and less than or equal to Nd, d is the serial number of the unit type, and Nd is the number of the unit types;
s2, initializing spare part scheme [ Nb1 ]1 Nb12 …Nb1d …Nb1Nd]Traversing within the range of d being more than or equal to 1 and less than or equal to Nd to ensure that Nb1d0; setting the judgment flag to 1;
s3, calculating a spare part scheme [ Nb1 Nb2 …Nbd …NbNd]Ensuring effect P (N)ok≥Nmin)，P(Nok≥Nmin) Number of intact devices NokGreater than or equal to NminProbability of (A), NminTo minimize the number of devices that are guaranteed to be intact; nbdIs 0;
s4, if flag>0, let P0 become P (N)ok≥Nmin) If P0 is not less than PsIf the calculation is terminated, PsTo guarantee a probability threshold, Nb1dSpare part requirements for each type of unit; if P0<PsGo to step S5;
s5, traversing and generating candidate spare part schemes, and calculating corresponding marginal benefit;
s6, dV from all marginal benefitsjFinding out a serial number jm corresponding to the maximum value, wherein j is a candidate spare part scheme serial number;
s7, traversing within the range that d is more than or equal to 1 and less than or equal to Nd
Let Nb bei＝Nb1iObtaining an updated spare part scheme [ Nb1 Nb2 …Nbd …NbNd](ii) a The determination flag is set to 1, and the process goes to step S3.
In addition to the above technical solution, preferably, in step S1,
if the life of the d-th unit follows the exponential distribution Exp (λ), the degree of failure Pf isd＝1-e-λT；
If the life of the d-th type unit follows normal distribution N (mu, sigma)2) Degree of failure
Degree of failure if the life of the d-th cell follows the Weibull distribution W (α, b)
On the basis of the above technical solution, preferably, step S3 includes,
s3.1, initializing d, and making d equal to 1;
s3.2, calculating the number of d-th type sound units not less than NminProbability P ofd，
Wherein p is Pfd，NzThe number of the table sleeves of the same type equipment;
s3.3, updating d, making d equal to d +1, if d > Nd, go to step S3.4, otherwise go to step S3.2;
s3.4, calculating P (N)ok≥Nmin) Let us order
S3.5, if flag>0, let P0 become P (N)ok≥Nmin) Go to step S4; otherwise, let Pj be P (N)ok≥Nmin) Go to step 5.4.
On the basis of the above technical solution, preferably, step S5 includes,
s5.1, initializing j, j being a candidate spare part scheme serial number, setting j to 1, and setting a judgment flag to 0;
s5.2, generating a j number candidate spare part scheme [ Nb2 ]j1 Nb2j2 …Nb2jd …Nb2jNd]，
Order to
S5.3, traversing within the range that d is more than or equal to 1 and less than or equal to Nd, so as to lead Nb tod＝Nb2jdTurning to step S3, calculating a guarantee effect Pj corresponding to the candidate spare part scheme number j;
s5.4, calculating marginal benefit dV corresponding to the j candidate spare part schemej；
Order to
MjFor purchase price of candidate spare part scheme of number j, marginal benefit dVjThe improvement degree of each dollar spent on the jth unit spare part on the guarantee effect is reflected;
s5.5, let j equal j +1, if j > Nd go to step S6, otherwise go to step S5.2.
Compared with the prior art, the method for calculating the spare part demand of the multiple sets of equipment has the following beneficial effects:
(1) the guarantee probability results calculated by the method are compared with the guarantee probability results calculated by the simulation method, the guarantee probability results obtained by the two methods are highly consistent, and the accuracy of the method is displayed;
(2) under the condition of the same spare part demand, the spare part guarantee effect under the repair and overhaul strategy can be regarded as the upper limit result of all other overhaul strategies, so that when no accurate spare part guarantee effect calculation method under other overhaul strategies exists, the method for accurately calculating the spare part guarantee effect under the repair and overhaul strategy can provide an 'optimistic' estimation result of the guarantee effect for other overhaul strategies.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be obtained by a person skilled in the art without any inventive step based on the embodiments of the present invention, are within the scope of the present invention.
Example 1
A type 3 unit is arranged in a certain type of equipment, the service life of the type 1 unit is subject to exponential distribution Exp (0.002), and the price is 100 yuan; type 2 unit life obeys normal distribution N (600, 130)2) The price is 150 yuan; the life of the type 3 unit is subject to Weibull distribution W (600, 1.8), and the price is 200 yuan. The total number of the equipment is 5, and the accumulated working time T of the equipment is 500 h. The 5 devices are about to be overhauled, and the number N of intact devices is required after the overhaul is finishedokProbability P (N) of not less than 4okNot less than 4) and not less than 0.85. And calculating the spare part demand required by various types of units of the maintenance work.
The calculation steps are as follows:
step S1, the failure degree Pf of each type unit is calculated in a traversing mannerdAnd d is more than or equal to 1 and less than or equal to Nd, and d is a unit type serial number.
Degree of failure Pf of type 1 exponential cell1＝1-e-0.002×500＝0.632；
Degree of failure of type 2 normal type unit
Degree of failure of type 3 Weibull cell
In step S2, the spare part scenario [ 000 ] is initialized, and the determination flag is set to 1.
Step S3, calculating spare part scheme [ 000 ]]Ensuring effect P (N)ok≥4)。P0＝P(Nok≥4)＝0.0077。
In step S4, since P0 is 0.0077, it is smaller than PsTherefore, the process goes to step S5.
And step S5, traversing and generating candidate spare part schemes, and calculating corresponding marginal benefit. The calculation results are shown in Table 1.
TABLE 1 candidate spare part scheme and results thereof
Step S6, at all marginal benefits dVjIn (1), the number corresponding to the maximum value is 1;
step S7, obtaining an updated spare part scheme [ 100 ]; the determination flag is set to 1, and the process goes to step S3.
The above process was repeated, and the spare part schemes obtained in sequence are shown in table 2.
TABLE 2 spare parts scheme and its safeguard effect
As can be seen from Table 2, the securing effect P (N) is obtained when the spare part requirements of the three-type unit are 4, 1 and 3 respectivelyokNot less than 4) ═ 0.892, satisfying P (N)okNot less than 4) greater than 0.85.
Table 2 also shows that: the spare part scheme guarantee effect evaluation method provided by the invention has the advantages that the evaluation result is highly consistent with that of a simulation method, and the engineering application requirements are met.
The simulation method in Table 2 employs a simulation model for simulating the number of spare parts of each type of unit as [ N ]1N2 …Nd …NNd]And (5) carrying out maintenance on all the devices.
(1) Initializing d, and making d equal to 1.
(2) According to the life distribution rule of the d-type unit, Nz random numbers are generated and are marked as [ simT ]1 simT2 …simTz…simTNz]For simulating the life of this type of cell on Nz devices.
(3) In [ simT ]1 simT2 …simTz …simTNz]To find the working accumulation larger than the equipmentRandom numbers of time T, if the random numbers have n in totaldNumber of good units of the type dyNd＝nd+Nd。
(4) D is updated, and d is equal to d + 1. If d is>NdAnd (5) turning to the step (5), otherwise, turning to the step (2).
(5) At all dyNdD is more than or equal to 1 and less than or equal to Nd, and the minimum value is taken as the number N of intact equipmentok. If N is presentok≥NminIf not, the flag is made to be 0.
After the maintenance process is simulated for many times, the mean value of the flag is that the number of intact equipment is not less than NminThe probability is the guarantee effect obtained by the simulation method.
The above description is only for the purpose of illustrating the preferred embodiments of the present invention and is not to be construed as limiting the invention, and any modifications, equivalents, improvements and the like that fall within the spirit and principle of the present invention are intended to be included therein.
Claims (4)
1. A method for calculating the demand quantity of spare parts of a plurality of sets of equipment is characterized by comprising the following steps: comprises the following steps of (a) carrying out,
s1, traversing and calculating the failure degree Pf of each type of unitd，PfdDescribing the probability of the unit failing in the period of 0-T, wherein T is the accumulated working time of the equipment, d is more than or equal to 1 and less than or equal to Nd, d is the serial number of the unit type, and Nd is the number of the unit types;
s2, initializing spare part scheme [ Nb1 ]1 Nb12...Nb1d...Nb1Nd]Traversing within the range of d being more than or equal to 1 and less than or equal to Nd to ensure that Nb1d0; setting the judgment flag to 1;
s3, calculating a spare part scheme [ Nb1 Nb2...Nbd...NbNd]Ensuring effect P (N)ok≥Nmin)，P(Nok≥Nmin) Number of intact devices NokGreater than or equal to NminProbability of (A), NminTo minimize the number of devices that are guaranteed to be intact; nbdIs 0;
s4, if flag >0, let P0 become P (N)ok≥Nmin) If P0 is not less than PsIf the calculation is terminated, PsTo guarantee a probability threshold, Nb1dSpare part requirements for each type of unit; if P0 < PsGo to step S5;
s5, traversing and generating candidate spare part schemes, and calculating corresponding marginal benefit;
s6, dV from all marginal benefitsjFinding out a serial number jm corresponding to the maximum value, wherein j is a candidate spare part scheme serial number;
s7, traversing within the range that d is more than or equal to 1 and less than or equal to Nd
Let Nb bei＝Nb1iObtaining an updated spare part scheme [ Nb1 Nb2...Nbd...NbNd](ii) a The determination flag is set to 1, and the process goes to step S3.
2. The method of calculating the spare part demand of a plurality of sets of equipment as set forth in claim 1, wherein: in the step S1, in the above step,
if the life of the d-th unit follows the exponential distribution Exp (λ), the degree of failure Pf isd＝1-e-λT；
If the life of the d-th type unit follows normal distribution N (mu, sigma)2) Degree of failure
Degree of failure if the life of the d-th cell follows the Weibull distribution W (α, b)
3. The method of calculating the spare part demand of a plurality of sets of equipment as set forth in claim 1, wherein: the step S3 includes the steps of,
s3.1, initializing d, and making d equal to 1;
s3.2, calculating the number of d-th type sound unitsAmount of not less than NminProbability P ofd，
Wherein p is Pfd，NzThe number of the table sleeves of the same type equipment;
s3.3, updating d, making d equal to d +1, if d > Nd, going to step S3.4, otherwise going to step S3.2;
s3.4, calculating P (N)ok≥Nmin) Let us order
S3.5, if flag > 0, let P0 be P (N)ok≥Nmin) Go to step S4; otherwise, let Pj be P (N)ok≥Nmin) Go to step 5.
4. The method of calculating the spare part demand of a plurality of sets of equipment as set forth in claim 1, wherein: the step S5 includes the steps of,
s5.1, initializing j, j being a candidate spare part scheme serial number, setting j to 1, and setting a judgment flag to 0;
s5.2, generating a j number candidate spare part scheme [ Nb2 ]j1 Nb2j2...Nb2jd...Nb2jNd]，
Order to
S5.3, traversing within the range that d is more than or equal to 1 and less than or equal to Nd, so as to lead Nb tod＝Nb2jdTurning to step S3, calculating a guarantee effect Pj corresponding to the candidate spare part scheme number j;
s5.4, calculating marginal benefit dV corresponding to the j candidate spare part schemej；
Order to
MjIs jPurchase price, marginal benefit dV of number candidate spare part schemejThe improvement degree of each dollar spent on the jth unit spare part on the guarantee effect is reflected;
s5.5, let j equal j +1, if j > Nd go to step S6, otherwise go to step S5.2.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201810124671.7A CN108304971B (en) | 2018-02-07 | 2018-02-07 | Method for calculating spare part demand of multiple sets of equipment |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201810124671.7A CN108304971B (en) | 2018-02-07 | 2018-02-07 | Method for calculating spare part demand of multiple sets of equipment |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN108304971A CN108304971A (en) | 2018-07-20 |
| CN108304971B true CN108304971B (en) | 2021-07-16 |
Family
ID=62864881
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201810124671.7A Active CN108304971B (en) | 2018-02-07 | 2018-02-07 | Method for calculating spare part demand of multiple sets of equipment |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN108304971B (en) |
Families Citing this family (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109388861B (en) * | 2018-09-17 | 2023-05-12 | 中国人民解放军海军工程大学 | Exponential cell life distribution parameter estimation method |
| CN109388860B (en) * | 2018-09-17 | 2023-05-16 | 中国人民解放军海军工程大学 | Gamma type unit service life distribution parameter estimation method |
| CN109446481B (en) * | 2018-09-17 | 2023-05-12 | 中国人民解放军海军工程大学 | Method for estimating life distribution parameters of log-normal unit |
| CN109376334B (en) * | 2018-09-17 | 2023-05-12 | 中国人民解放军海军工程大学 | Method for estimating life distribution parameters of normal unit |
| CN109583017B (en) * | 2018-10-24 | 2022-10-25 | 中国人民解放军海军工程大学 | Method for calculating loss quantity of normal unit spare parts |
| CN109523160A (en) * | 2018-11-15 | 2019-03-26 | 青岛航讯网络技术服务有限公司 | The more normal state assembly of elements spare parts demand amounts of the long-term support mission of larger cargo ships determine method |
| CN110147906B (en) * | 2019-05-16 | 2022-12-30 | 中国人民解放军海军工程大学 | Optimization method for preventive maintenance scheme of partially-replaced parts |
| CN110287523B (en) * | 2019-05-16 | 2023-07-18 | 中国人民解放军海军工程大学 | Spare part scheme optimization method and device for multi-batch parts in modularized storage mode |
Citations (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104766247A (en) * | 2015-04-14 | 2015-07-08 | 国家电网公司 | Full voltage class power grid reliability calculation method |
| CN105608506A (en) * | 2015-12-23 | 2016-05-25 | 中国人民解放军空军第一航空学院 | Margin effect analysis based equipment inventory scheme optimization method and system |
| CN105787245A (en) * | 2014-12-25 | 2016-07-20 | 北京电子工程总体研究所 | Repair rate and inventory based spare part optimization method |
| CN106530134A (en) * | 2016-11-16 | 2017-03-22 | 国家电网公司 | Influence index marginal benefit analysis method and device based on reliability calculation model |
| CN106845109A (en) * | 2017-01-20 | 2017-06-13 | 中国人民解放军海军工程大学 | A kind of exponential type has the computational methods of longevity part spare parts demand amount |
| CN106874238A (en) * | 2017-01-20 | 2017-06-20 | 中国人民解放军海军工程大学 | A kind of computational methods of Weibull type unit spare parts demand amount |
| CN106971048A (en) * | 2017-04-14 | 2017-07-21 | 中国人民解放军海军工程大学 | A kind of gamma type has longevity part spare parts demand amount computational methods |
| CN107437116A (en) * | 2017-08-02 | 2017-12-05 | 中国航空工业集团公司西安飞机设计研究所 | It is a kind of towards using and maintenance task Equipment Indemnificatory analysis method |
| CN107480862A (en) * | 2017-07-19 | 2017-12-15 | 天津大学 | A kind of Distribution Network Reliability marginal cost marginal analysis method based on big data |
-
2018
- 2018-02-07 CN CN201810124671.7A patent/CN108304971B/en active Active
Patent Citations (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105787245A (en) * | 2014-12-25 | 2016-07-20 | 北京电子工程总体研究所 | Repair rate and inventory based spare part optimization method |
| CN104766247A (en) * | 2015-04-14 | 2015-07-08 | 国家电网公司 | Full voltage class power grid reliability calculation method |
| CN105608506A (en) * | 2015-12-23 | 2016-05-25 | 中国人民解放军空军第一航空学院 | Margin effect analysis based equipment inventory scheme optimization method and system |
| CN106530134A (en) * | 2016-11-16 | 2017-03-22 | 国家电网公司 | Influence index marginal benefit analysis method and device based on reliability calculation model |
| CN106845109A (en) * | 2017-01-20 | 2017-06-13 | 中国人民解放军海军工程大学 | A kind of exponential type has the computational methods of longevity part spare parts demand amount |
| CN106874238A (en) * | 2017-01-20 | 2017-06-20 | 中国人民解放军海军工程大学 | A kind of computational methods of Weibull type unit spare parts demand amount |
| CN106971048A (en) * | 2017-04-14 | 2017-07-21 | 中国人民解放军海军工程大学 | A kind of gamma type has longevity part spare parts demand amount computational methods |
| CN107480862A (en) * | 2017-07-19 | 2017-12-15 | 天津大学 | A kind of Distribution Network Reliability marginal cost marginal analysis method based on big data |
| CN107437116A (en) * | 2017-08-02 | 2017-12-05 | 中国航空工业集团公司西安飞机设计研究所 | It is a kind of towards using and maintenance task Equipment Indemnificatory analysis method |
Non-Patent Citations (3)
| Title |
|---|
| Fractional Order Discrete Grey Model and Its Application in Spare Parts Demand Forecasting;潘显俊等;《兵工学报》;20170430;全文 * |
| 一种基于历史数据的备件需求量计算方法;王学伟等;《舰船科技学术》;20091130;全文 * |
| 面向任务的作战单元携行备件配置优化方法研究;阮旻智等;《兵工学报》;20170630;全文 * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN108304971A (en) | 2018-07-20 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN108304971B (en) | Method for calculating spare part demand of multiple sets of equipment | |
| CN108304665B (en) | Method for calculating spare part demand of multiple sets of equipment | |
| CN108509389B (en) | Spare part demand calculation method for Weibull serial parts | |
| CN108470250B (en) | Spare part demand calculation method for normal series connection component | |
| CN106874238B (en) | Method for calculating required quantity of Weibull unit spare parts | |
| CN108520145B (en) | Method for calculating demand of Weibull unit spare parts under risk of storage failure | |
| CN106971048B (en) | Method for calculating spare part demand of gamma type service-life part | |
| CN103592214B (en) | A kind of evaluation methodology of corrosion state of carbon steel material in atmospheric environment | |
| CN108536960B (en) | Method for calculating demand of exponential type unit spare parts under risk of storage failure | |
| CN108509390A (en) | A kind of spare parts demand amount computational methods of gamma type series components | |
| CN115310048B (en) | Method and system for calculating repair probability of equipment in expected time | |
| CN108564270A (en) | A kind of gamma type unit spare parts demand amount computational methods under store failure risk | |
| CN109523160A (en) | The more normal state assembly of elements spare parts demand amounts of the long-term support mission of larger cargo ships determine method | |
| CN110298071B (en) | Modularized component maintenance planning method and device | |
| CN115270078B (en) | Method and system for calculating average repair time of electromechanical equipment | |
| CN110287523B (en) | Spare part scheme optimization method and device for multi-batch parts in modularized storage mode | |
| CN115759479B (en) | Complex equipment fault positioning optimization method and system based on comprehensive value | |
| CN110298477B (en) | Preventive maintenance plan making method | |
| CN110147906B (en) | Optimization method for preventive maintenance scheme of partially-replaced parts | |
| CN108564224A (en) | A kind of time-optimized method and apparatus of environmental stress screening | |
| CN115358562A (en) | Quality testing method and device for printed circuit board and electronic equipment | |
| CN109543276B (en) | Method for determining spare part demand of multi-Gamma unit of long-term guarantee task of large cargo ship | |
| CN109388860B (en) | Gamma type unit service life distribution parameter estimation method | |
| CN109583017B (en) | Method for calculating loss quantity of normal unit spare parts | |
| CN110119820B (en) | Method for making integral replacement preventive maintenance scheme |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |