CN116258220A - Nuclear power plant spare part demand prediction method - Google Patents

Nuclear power plant spare part demand prediction method Download PDF

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Publication number
CN116258220A
CN116258220A CN202111461388.1A CN202111461388A CN116258220A CN 116258220 A CN116258220 A CN 116258220A CN 202111461388 A CN202111461388 A CN 202111461388A CN 116258220 A CN116258220 A CN 116258220A
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spare part
distribution
nuclear power
power plant
spare
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Inventor
姚昊
胡文勇
蔡胜武
吴宝华
胡琛
杨沥铭
李武平
熊伟
李志鹏
刘忠良
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CNNC Nuclear Power Operation Management Co Ltd
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CNNC Nuclear Power Operation Management Co Ltd
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    • 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
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
    • G06F17/10—Complex mathematical operations
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
    • G06F17/10—Complex mathematical operations
    • G06F17/15—Correlation function computation including computation of convolution operations
    • 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
    • G06Q10/08—Logistics, e.g. warehousing, loading or distribution; Inventory or stock management
    • G06Q10/087—Inventory or stock management, e.g. order filling, procurement or balancing against orders
    • 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
    • G06Q50/00—Systems or methods specially adapted for specific business sectors, e.g. utilities or tourism
    • G06Q50/06—Electricity, gas or water supply
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y04—INFORMATION OR COMMUNICATION TECHNOLOGIES HAVING AN IMPACT ON OTHER TECHNOLOGY AREAS
    • Y04S—SYSTEMS INTEGRATING TECHNOLOGIES RELATED TO POWER NETWORK OPERATION, COMMUNICATION OR INFORMATION TECHNOLOGIES FOR IMPROVING THE ELECTRICAL POWER GENERATION, TRANSMISSION, DISTRIBUTION, MANAGEMENT OR USAGE, i.e. SMART GRIDS
    • Y04S10/00—Systems supporting electrical power generation, transmission or distribution
    • Y04S10/50—Systems or methods supporting the power network operation or management, involving a certain degree of interaction with the load-side end user applications

Abstract

The invention belongs to the technical field of spare part management, and particularly relates to a spare part demand prediction method of a nuclear power plant. The method comprises the following steps: step 1: acquiring expected values of failure of spare parts in a given time interval (0, t) according to the spare part life distribution; step 2: inventory quota for the spare parts is determined based on the service level of the spare parts. The invention has the beneficial effects that: the quantitative calculation of the demand quantity and the probability of spare parts in a given time interval in the future can be realized, the manual subjective judgment is reduced, and the stock of the spare parts is reduced.

Description

Nuclear power plant spare part demand prediction method
Technical Field
The invention belongs to the technical field of spare part management, and particularly relates to a spare part demand prediction method of a nuclear power plant.
Background
In general, due to technical deficiency, economic limitations, etc., it is impossible to design a product to fully fulfill its intended function throughout its life cycle, which may lead to downtime for commercial equipment (e.g., nuclear power plants, airplanes, high-speed rails, etc.), at which point the assurance of spare parts is important. When the components are expensive, the inventory of spare parts must be properly managed, as a low inventory means an increased likelihood of waiting for spare parts, and a high inventory means too much money is spent. To ensure a certain safety stock to meet the demand for unplanned replacement of spare parts in field service work, nuclear power plants implement spare part quota management.
Spare part demand is an important input to spare part quota management, and its predictive accuracy plays an important role in reducing inventory and ensuring on-site operation. The spare part demand prediction methods generally adopted mainly have two types: the first is a reliability-based method, and the second is a black box method based on spare part consumption history data. In some cases, spare part requirements present a pattern that is not well predicted by conventional methods.
The existing method is that the nuclear power plant determines the inventory quota of spare parts manually and empirically, but the quota is conservative due to high subjectivity.
Disclosure of Invention
The invention aims to provide a nuclear power plant spare part demand prediction method, which can ensure the spare part consumption demand of a nuclear power plant in a certain time, rationalize the spare part stock and provide support for better developing the rated management work of the spare parts of the nuclear power plant.
The technical scheme of the invention is as follows: a nuclear power plant spare part demand prediction method comprises the following steps:
step 1: acquiring expected values of failure of spare parts in a given time interval (0, t) according to the spare part life distribution;
step 2: inventory quota for the spare parts is determined based on the service level of the spare parts.
The expected value in the step 1 is calculated as follows:
Figure BDA0003389933990000021
wherein M (t) is an expected value of spare part demand in a time interval (0, t), F (t) is an accumulated probability density function of life distribution, and the life distribution can be Weibull distribution, normal distribution, lognormal distribution, gamma distribution and the like.
In the step 1, the calculation of M (t) is performed as follows:
step 11: dividing the interval (0, t) into N equal parts, wherein the larger the length deltat of each part, namely t=N multiplied by deltat, the higher the calculation accuracy of M (t);
step 12: calculating the expected value of the failure times:
Figure BDA0003389933990000022
wherein t is i For the position of the ith part Deltat in the interval (0, t), t i ＝i×Δt；
Step 13: calculating the variance:
Figure BDA0003389933990000023
wherein: var [ N [ t ] ] is the variance of the number of failures that occur in the spare part during the time interval (0, t).
The step 2 of the method comprises the steps of,
step 21: assuming that there are S positions requiring the use of a spare part, the life of each spare part is L at the time of prediction i All bits after the time L has elapsedThe average demand of the spare parts is
Figure BDA0003389933990000024
Variance is
Figure BDA0003389933990000025
Step 22: calculating inventory quota D using poisson distribution p ＝P -1 (k％,M s ) Wherein P is -1 () An inverse function representing the Poisson's distribution cumulative density function, k being the service level to be achieved by the spare part, M s Parameters that are poisson distribution;
inventory quota D using normal distribution calculator N ＝N -1 (k％,M s ,var[N s (t)]) Wherein N is -1 () An inverse function representing a normal distribution cumulative density function, k being the service level to be achieved by the spare part, M s Is the mean value of normal distribution, var [ N ] s (t)]Is the variance of the normal distribution.
The invention has the beneficial effects that: the quantitative calculation of the demand quantity and the probability of spare parts in a given time interval in the future can be realized, the manual subjective judgment is reduced, and the stock of the spare parts is reduced.
Detailed Description
The present invention will be described in further detail with reference to specific examples.
The invention provides a method for predicting the demand of spare parts of a nuclear power plant, which comprises the following steps:
step 1: obtaining an expected value (or average value) of the spare part in failure in a given time interval (0, t) according to the spare part life distribution, wherein the general formula is calculated as follows:
Figure BDA0003389933990000031
wherein M (t) is an expected value of spare part demand in a time interval (0, t), F (t) is an accumulated probability density function of life distribution, and the life distribution can be Weibull distribution, normal distribution, lognormal distribution, gamma distribution and the like. The formula includes convolution, and in many cases, the calculation cannot be directly performed by using an analytic method, in this embodiment, the numerical calculation method is designed to calculate M (t), and the steps are as follows:
step 11: the interval (0, t) is divided into N equal parts, and the greater the length Δt of each part, i.e., t=n×Δt, the greater the calculation accuracy of M (t).
Step 12: calculating the expected value of the failure times:
Figure BDA0003389933990000032
wherein t is i For the position of the ith part Deltat in the interval (0, t), t i ＝i×Δt。
Step 13: calculating the variance:
Figure BDA0003389933990000041
/>
wherein: var [ N [ t ] ] is the variance of the number of failures that occur in the spare part during the time interval (0, t).
Step 2: determining inventory quota for spare parts based on service level of spare parts
Step 21: assuming that there are S positions requiring the use of a spare part, the life of each spare part is L at the time of prediction i The average demand of all the position spare parts after the time L is
Figure BDA0003389933990000042
Variance is
Figure BDA0003389933990000043
Step 22: optionally:
calculating inventory quota D using poisson distribution p ＝P -1 (k％,M s ) Wherein P is -1 () An inverse function representing the Poisson's distribution cumulative density function, k being the service level to be achieved by the spare part, M s Is a parameter of poisson distribution.
Inventory quota D using normal distribution calculator N ＝N -1 (k％,M s ,var[N s (t)]) Wherein N is -1 () An inverse function representing a normal distribution cumulative density function, k being the service level to be achieved by the spare part, M s Is the mean value of normal distribution, var [ N ] s (t)]Is the variance of the normal distribution.

Claims (7)

1. The nuclear power plant spare part demand prediction method is characterized by comprising the following steps of:
step 1: acquiring expected values of failure of spare parts in a given time interval (0, t) according to the spare part life distribution;
step 2: inventory quota for the spare parts is determined based on the service level of the spare parts.
2. The nuclear power plant spare part demand prediction method as claimed in claim 1, wherein: the expected value in the step 1 is calculated as follows:
Figure FDA0003389933980000011
wherein M (t) is an expected value of spare part demand in a time interval (0, t), F (t) is an accumulated probability density function of life distribution, and the life distribution can be Weibull distribution, normal distribution, lognormal distribution, gamma distribution and the like.
3. The nuclear power plant spare part demand prediction method as claimed in claim 2, wherein: in the step 1, the calculation of M (t) is performed as follows:
step 11: the interval (0, t) is divided into N equal parts, and the greater the length Δt of each part, i.e., t=n×Δt, the greater the calculation accuracy of M (t).
4. The nuclear power plant spare part demand prediction method as claimed in claim 2, wherein: in the step 1, the calculation of M (t) is performed as follows:
step 12: calculating the expected value of the failure times:
Figure FDA0003389933980000012
wherein t is i For the position of the ith part Deltat in the interval (0, t), t i ＝i×Δt。
5. The nuclear power plant spare part demand prediction method as claimed in claim 2, wherein: in the step 1, the calculation of M (t) is performed as follows:
step 13: calculating the variance:
Figure FDA0003389933980000021
wherein: var [ N [ t ] ] is the variance of the number of failures that occur in the spare part during the time interval (0, t).
6. The nuclear power plant spare part demand prediction method as claimed in claim 1, wherein: the step 2 of the method comprises the steps of,
step 21: assuming that there are S positions requiring the use of a spare part, the life of each spare part is L at the time of prediction i The average demand of all the position spare parts after the time L is
Figure FDA0003389933980000022
Variance is
Figure FDA0003389933980000023
7. The nuclear power plant spare part demand prediction method as set forth in claim 6, wherein: the step 2 of the method comprises the steps of,
step 22: calculating inventory quota D using poisson distribution p ＝P -1 (k％,M s ) Wherein P is -1 () An inverse function representing the Poisson's distribution cumulative density function, k being the service level to be achieved by the spare part, M s Parameters that are poisson distribution;
inventory quota D using normal distribution calculator N ＝N -1 (k％,M s ,var[N s (t)]) Wherein N is -1 () An inverse function representing a normal distribution cumulative density function, k being the service level to be achieved by the spare part, M s Is the mean value of normal distribution, var [ N ] s (t)]Is the variance of the normal distribution.
CN202111461388.1A 2021-12-03 2021-12-03 Nuclear power plant spare part demand prediction method Pending CN116258220A (en)

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Application Number Priority Date Filing Date Title
CN202111461388.1A CN116258220A (en) 2021-12-03 2021-12-03 Nuclear power plant spare part demand prediction method

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN202111461388.1A CN116258220A (en) 2021-12-03 2021-12-03 Nuclear power plant spare part demand prediction method

Publications (1)

Publication Number Publication Date
CN116258220A true CN116258220A (en) 2023-06-13

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Country Status (1)

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