A method for forecasting spare parts demand in nuclear power plants

Abstract

This invention belongs to the field of spare parts management technology, specifically relating to a method for forecasting spare parts demand in nuclear power plants. It includes the following steps: Step 1: Obtain the expected value of spare parts failing within a given time interval (0, t) based on the spare parts lifespan distribution; Step 2: Determine the spare parts inventory quota based on the spare parts' service level. The beneficial effects of this invention are: it enables quantitative calculation of the demand and probability of spare parts within a given future time interval, reducing subjective human judgment and lowering spare parts inventory.

Description

Technical Field

[0001] This invention belongs to the field of spare parts management technology, specifically relating to a method for predicting spare parts demand in nuclear power plants. Background Technology

[0002] Generally, due to technological limitations and economic constraints, it's impossible to design a product that will fully perform its intended function throughout its entire lifecycle. For commercial equipment (such as nuclear power plants, airplanes, and high-speed trains), this could lead to downtime, making spare parts availability crucial. When components are expensive, proper spare parts inventory management is essential. Low inventory increases the likelihood of waiting for spare parts, while excessive inventory ties up too much capital. To ensure a certain safety stock to meet the needs of unplanned spare parts replacements during on-site maintenance, nuclear power plants implement spare parts quota management.

[0003] Spare parts demand is a crucial input for spare parts quota management, and its forecasting accuracy plays a vital role in reducing inventory and ensuring on-site maintenance. There are generally two main types of spare parts demand forecasting methods: the first is reliability-based methods, and the second is black-box methods based on historical spare parts consumption data. In some cases, spare parts demand exhibits patterns that traditional methods cannot accurately predict.

[0004] The existing method is for nuclear power plants to determine spare parts inventory quotas manually based on experience, but this method is too subjective and the quotas tend to be conservative. Summary of the Invention

[0005] The purpose of this invention is to provide a method for predicting the demand for spare parts in nuclear power plants. This method can ensure the consumption of spare parts in nuclear power plants within a certain period of time, rationalize spare parts inventory, and provide support for better management of spare parts quotas in nuclear power plants.

[0006] The technical solution of the present invention is as follows: A method for predicting the demand for spare parts in nuclear power plants, comprising the following steps:

[0007] Step 1: Obtain the expected value of spare parts failing within a given time interval (0, t) based on the spare parts life distribution;

[0008] Step 2: Determine the inventory quota for spare parts based on their service level.

[0009] The expected value in step 1 is calculated as follows:

[0010]

[0011] In the formula, M(t) is the expected value of spare parts demand within the time interval (0, t), and F(t) is the cumulative probability density function of the lifetime distribution. The lifetime distribution can be a Weibull distribution, a normal distribution, a log-normal distribution, a gamma distribution, etc.

[0012] The calculation of M(t) in step 1 is as follows:

[0013] Step 11: Divide the interval (0, t) into N equal parts, each with a length Δt, i.e., t = N × Δt. The larger N is, the higher the accuracy of M(t) calculation.

[0014] Step 12: Calculate the expected number of failures:

[0015]

[0016] In the formula, t i Let t be the position of the i-th Δt in the interval (0, t). i =i×Δt;

[0017] Step 13: Calculate the variance:

[0018]

[0019] In the formula: var[N[t]] is the variance of the number of times the spare part fails in the time interval (0, t).

[0020] Step 2 includes,

[0021] Step 21: Assume there are S locations that require a certain spare part, and the lifespan of each spare part is L during prediction. i Then, after time L, the average demand for spare parts at all locations is:

[0022] variance is

[0023] Step 22: Calculate the inventory quota D using the Poisson distribution. p =P -1 (k%,M s ), where P -1 () represents the inverse function of the Poisson cumulative density function, k is the service level that the spare part needs to achieve, and M s represents the parameters of the Poisson distribution;

[0024] Calculate the inventory quota D using the normal distribution. N =N -1 (k%,M s ,var[N s (t)]), where N -1() represents the inverse function of the cumulative density function of the normal distribution, k is the service level that the spare part needs to achieve, and M s Let N be the mean of a normal distribution. s [(t)] represents the variance of the normal distribution.

[0025] The beneficial effects of this invention are: it enables quantitative calculation of the demand for spare parts and its probability within a given future time interval, reducing subjective human judgment and lowering spare parts inventory. Detailed Implementation

[0026] The present invention will be further described in detail below with reference to specific embodiments.

[0027] The present invention provides a method for predicting the demand for spare parts in nuclear power plants, comprising the following steps:

[0028] Step 1: Obtain the expected (or average) value of spare parts failure within a given time interval (0, t) based on the spare parts life distribution. The general formula for calculation is:

[0029]

[0030] In the formula, M(t) represents the expected value of spare parts demand within the time interval (0, t), and F(t) is the cumulative probability density function of the lifetime distribution, which can be a Weibull distribution, a normal distribution, a log-normal distribution, a gamma distribution, etc. This formula involves convolution, and in many cases, it cannot be directly calculated analytically. In this embodiment, a numerical calculation method is designed to calculate M(t), and the steps are as follows:

[0031] Step 11: Divide the interval (0, t) into N equal parts, each with a length Δt, i.e., t = N × Δt. The larger N is, the higher the accuracy of M(t) calculation.

[0032] Step 12: Calculate the expected number of failures:

[0033]

[0034] In the formula, t i Let t be the position of the i-th Δt in the interval (0, t). i = i × Δt.

[0035] Step 13: Calculate the variance:

[0036]

[0037] In the formula: var[N[t]] is the variance of the number of times the spare part fails in the time interval (0, t).

[0038] Step 2: Determine the inventory quota for spare parts based on their service level.

[0039] Step 21: Assume there are S locations that require a certain spare part, and the lifespan of each spare part is L during prediction. i Then, after time L, the average demand for spare parts at all locations is:

[0040] variance is

[0041] Step 22: Optional:

[0042] Calculate the inventory quota D using the Poisson distribution. p =P -1 (k%,M s ), where P -1 () represents the inverse function of the Poisson cumulative density function, k is the service level that the spare part needs to achieve, and M s represents the parameters of the Poisson distribution.

[0043] Calculate the inventory quota D using the normal distribution. N =N -1 (k%,M s ,var[N s (t)]), where N -1 () represents the inverse function of the cumulative density function of the normal distribution, k is the service level that the spare part needs to achieve, and M s Let N be the mean of a normal distribution. s [(t)] represents the variance of the normal distribution.

Claims

1. A method for predicting spare parts demand in nuclear power plants, characterized in that, Includes the following steps: Step 1: Obtain the expected value of spare parts failing within a given time interval (0, t) based on the spare parts life distribution; The expected value in step 1 is calculated as follows: In the formula, M(t) is the expected value of spare parts demand within the time interval (0, t), and F(t) is the cumulative probability density function of the lifetime distribution. The lifetime distribution can be a Weibull distribution, a normal distribution, a log-normal distribution, or a gamma distribution. The calculation of M(t) in step 1 is as follows: Step 11: Divide the interval (0, t) into N equal parts, each with a length Δt, i.e., t = N × Δt. The larger N is, the higher the accuracy of M(t) calculation. Step 12: Calculate the expected number of failures: In the formula, t i Let t be the position of the i-th Δt in the interval (0, t). i =i×Δt; Step 13: Calculate the variance: In the formula: var[N[t]] is the variance of the number of times the spare part fails in the time interval (0, t); Step 2: Determine the inventory quota for spare parts based on their service level; Step 2 includes, Step 21: Assume there are S locations that require a certain spare part, and the lifespan of each spare part is L during prediction. i Then, after time L, the average demand for spare parts at all locations is: variance is Step 22: Calculate the inventory quota D using the Poisson distribution. p =P -1 (k%,M s ), where P -1 () represents the inverse function of the Poisson cumulative density function, k is the service level that the spare part needs to achieve, and M s represents the parameters of the Poisson distribution; Calculate the inventory quota D using the normal distribution. N =N -1 (k%,M s ,var[N s (t)]), where N -1 () represents the inverse function of the cumulative density function of the normal distribution, k is the service level that the spare part needs to achieve, and M s Let N be the mean of a normal distribution. s [(t)] represents the variance of the normal distribution.

Images