Method and system for estimating spare parts consumption quantity of mechatronic components considering repair time consumption

Abstract

The application discloses a kind of electromechanical spare parts consumption quantity estimation method and system considering maintenance time consumption, belong to electromechanical maintenance support technical field.It includes:Step0: obtain task time, electromechanical life obeys Weibull distribution scale parameter and shape parameter, maintenance time consumption obeys normal distribution parameter and spare parts quantity s;Step1: Weibull distribution scale parameter and shape parameter are converted into gamma approximate shape parameter and approximate scale parameter, initialize spare parts consumption quantity r=1;Step2: using gamma approximate shape parameter and approximate scale parameter, multiple integral is used to calculate the probability of success of r spare parts consumption task in task time, guarantee success probability;Step3: update r=r+1, if r≤s, execute Step2, otherwise, execute Step4;Step4: double integral is used to calculate the probability of guarantee failure in task time;Step5: three types of probability are integrated, and the electromechanical spare parts consumption quantity is calculated.The application first proposes the estimation of spare parts consumption quantity considering maintenance time consumption, which is practical and reduces error.

Description

Technical Field

[0001] This invention belongs to the field of electromechanical component maintenance and support technology, and more specifically, relates to a method and system for estimating the quantity of spare parts consumed by electromechanical components, taking into account maintenance time. Background Technology

[0002] Spare parts are an important maintenance resource and the material foundation for maintenance work. The quantity of spare parts consumed reflects the degree of "preparedness without use." Accurately estimating spare parts consumption is a prerequisite for the scientific management of many spare parts. For example, combining spare parts consumption quantity with spare parts price information can estimate the turnover rate of spare parts procurement funds; combining current spare parts inventory information with estimates of spare parts consumption over a future period can predict future spare parts availability, thereby scientifically selecting appropriate timing and quantities for replenishment procurement.

[0003] Electromechanical components, such as ball bearings, relays, switches, circuit breakers, magnetrons, potentiometers, gyroscopes, electric motors, aircraft generators, batteries, hydraulic pumps, air turbine engines, gears, valves, and fatigue-prone parts, generally follow a Weibull distribution in their lifespan. Spare parts warehousing management requires the estimation of spare parts consumption for these components. For example, for electromechanical components with long production cycles, advance ordering is often necessary, necessitating accurate estimation of spare parts consumption over a given period.

[0004] Current methods for estimating spare parts consumption ignore the impact of maintenance time and are only suitable for situations where maintenance time is very short. When maintenance time is long, these methods will introduce large estimation errors, and most of them are equipment-level, lacking methods for estimating spare parts consumption at the electromechanical component level. Summary of the Invention

[0005] In view of the shortcomings of the prior art, the purpose of this invention is to provide a method and system for estimating the consumption of spare parts for electromechanical components that takes into account maintenance time, in order to solve the problems of large estimation errors and lack of estimation at the electromechanical component level.

[0006] To achieve the above objectives, in a first aspect, the present invention provides a method for estimating the consumption quantity of electromechanical spare parts considering maintenance time, the method comprising:

[0007] Step 0: Obtain the Weibull distribution scale and shape parameters of the task time and electromechanical component lifespan, the normal distribution parameters of the maintenance time, and the number of spare parts (s).

[0008] Step 1: Convert the Weibull distribution scale parameters and shape parameters into gamma approximate shape parameters and approximate scale parameters, and initialize the spare parts consumption quantity r = 1;

[0009] Step 2: Using the gamma approximation shape parameter and approximation scale parameter, calculate the probability of success and the probability of ensuring success of the task within the time limit by consuming r spare parts through multiple integrations.

[0010] Step 3: Update r = r + 1. If r ≤ s, execute Step 2; otherwise, execute Step 4.

[0011] Step 4: Calculate the probability of failure within the time limit of the double integral calculation task;

[0012] Step 5: Combine the probabilities of the three types to determine the number of computer electronic component spare parts consumed.

[0013] Preferably, in Step 1, the calculation formula is as follows:

[0014]

[0015]

[0016] Where Γ() is the gamma function, a is the gamma approximation shape parameter, b is the gamma approximation scale parameter, u is the Weibull distribution scale parameter, and v is the Weibull distribution shape parameter.

[0017] Preferably, the probability of a task succeeding within the time limit is ps, which consumes r spare parts. r The calculation formula is as follows:

[0018]

[0019] Where T is the task time, c is the average maintenance time of electromechanical components, d is the root variance of maintenance time of electromechanical components, a is the gamma approximation shape parameter, and b is the gamma approximation scale parameter.

[0020] Preferably, the probability of success is pb if r spare parts are consumed within the task time. r The calculation formula is as follows:

[0021]

[0022] Where T is the task time, c is the average maintenance time of electromechanical components, d is the root variance of maintenance time of electromechanical components, a is the gamma approximation shape parameter, and b is the gamma approximation scale parameter.

[0023] Preferably, the probability pf of failure is calculated within the task time:

[0024]

[0025] Where T is the task time, c is the average maintenance time of electromechanical components, d is the root variance of maintenance time of electromechanical components, a is the gamma approximation shape parameter, and b is the gamma approximation scale parameter.

[0026] Preferably, the combined probability of the three types and the consumption quantity of computer electronic component spare parts are as follows:

[0027]

[0028] Where nx represents the quantity of spare parts consumed for electromechanical components; pf represents the probability of failure to ensure service availability within the task time; and ps represents the probability of failure to ensure service availability within the task time. r pb represents the probability of a task succeeding within a given time frame by consuming r spare parts. r The probability of success is achieved by consuming r spare parts within the task time.

[0029] To achieve the above objectives, in a second aspect, the present invention provides a system for estimating the consumption quantity of electromechanical spare parts considering maintenance time, comprising: a processor and a memory; the memory for storing computer execution instructions; and the processor for executing the computer execution instructions, such that the method described in the first aspect is executed.

[0030] To achieve the above objectives, in a third aspect, the present invention provides a computer-readable storage medium storing a computer program that, when executed on a processor, causes the processor to perform the method described in the first aspect.

[0031] In summary, the technical solutions conceived by this invention have the following beneficial effects compared with the prior art:

[0032] This invention discloses a method and system for estimating the consumption quantity of spare parts for electromechanical components, taking into account maintenance time. The method includes: Step 0: Obtaining the task time, the Weibull distribution scale and shape parameters of the electromechanical component's lifespan, the normal distribution parameters of the maintenance time, and the spare part quantity s; Step 1: Converting the Weibull distribution scale and shape parameters into gamma approximation shape and scale parameters, and initializing the spare part consumption quantity r = 1; Step 2: Using the gamma approximation shape and scale parameters, performing multiple integrations to calculate the probability of successful task completion and the probability of successful maintenance within the task time, consuming r spare parts; Step 3: Updating r = r + 1. If r ≤ s, proceed to Step 2; otherwise, proceed to Step 4; Step 4: Performing double integrations to calculate the probability of maintenance failure within the task time; Step 5: Combining the three types of probabilities to calculate the spare part consumption quantity. This invention is the first to propose a spare part consumption quantity estimation method that considers maintenance time, which is realistic, reduces errors, fills the gap in spare part consumption quantity estimation at the electromechanical component level, and lays the foundation for scientific maintenance and maintenance support. Attached Figure Description

[0033] Figure 1This is a flowchart of a method for estimating the consumption of spare parts for electromechanical components, taking into account maintenance time, provided by the present invention.

[0034] Figure 2 The results are obtained by using the method of this invention and simulation method to measure the average number of spare parts consumed when the number of spare parts is 1 to 5. Detailed Implementation

[0035] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention.

[0036] like Figure 1 As shown, the present invention provides a method for estimating the consumption of spare parts for electromechanical components considering maintenance time. The method includes:

[0037] Step 0: Obtain the Weibull distribution scale and shape parameters of the task time and electromechanical component lifespan, the normal distribution parameters of the maintenance time, and the number of spare parts s.

[0038] Task time is defined as the planned working time of equipment within a certain period. If equipment malfunctions during the task period and requires repair, the repair time will occupy the original task time.

[0039] If a random variable follows a Weibull distribution w(u,v), where u is the scale parameter and v is the shape parameter, its probability density function is: x is a random variable. In this invention, the lifespan and maintenance time of electromechanical components are both considered random variables.

[0040] Step 1: Convert the Weibull distribution scale parameters and shape parameters into gamma approximate shape parameters and approximate scale parameters, and initialize the spare parts consumption quantity r = 1.

[0041] Preferably, in Step 1, the calculation formula is as follows:

[0042]

[0043]

[0044] Where Γ() is the gamma function, a is the gamma approximation shape parameter, b is the gamma approximation scale parameter, u is the Weibull distribution scale parameter, and v is the Weibull distribution shape parameter.

[0045]

[0046] Step 2: Using the gamma approximation shape parameter and approximation scale parameter, calculate the probability of success and the probability of ensuring success of the task within the time limit by consuming r spare parts through multiple integration.

[0047] "Mission success" is defined as the electromechanical components being able to operate until the very last moment.

[0048] "Successful guarantee" is defined as having spare parts available when a failure occurs.

[0049] "Guarantee failure" is defined as when a failure occurs and no spare parts are available.

[0050] Preferably, the probability of a task succeeding within the time limit is ps, which consumes r spare parts. r The calculation formula is as follows:

[0051]

[0052] Where T is the task time, c is the average maintenance time of electromechanical components, d is the root variance of maintenance time of electromechanical components, a is the gamma approximation shape parameter, and b is the gamma approximation scale parameter.

[0053] Preferably, the probability of success is pb if r spare parts are consumed within the task time. r The calculation formula is as follows:

[0054]

[0055] Where T is the task time, c is the average maintenance time of electromechanical components, d is the root variance of maintenance time of electromechanical components, a is the gamma approximation shape parameter, and b is the gamma approximation scale parameter.

[0056] Step 3: Update r = r + 1. If r ≤ s, execute Step 2; otherwise, execute Step 4.

[0057] Step 4: Calculate the probability of failure within the task time using double integral calculation.

[0058] Preferably, the probability pf of failure is calculated within the task time:

[0059]

[0060] Where T is the task time, c is the average maintenance time of electromechanical components, d is the root variance of maintenance time of electromechanical components, a is the gamma approximation shape parameter, and b is the gamma approximation scale parameter.

[0061] Step 5: Combine the probabilities of the three types to determine the number of computer electronic component spare parts consumed.

[0062] Preferably, the combined probability of the three types and the consumption quantity of computer electronic component spare parts are as follows:

[0063]

[0064] Where nz represents the quantity of spare parts consumed for electromechanical components; pf represents the probability of failure to ensure service availability within the task time; and ps represents the probability of failure to ensure service availability within the task time. r pb represents the probability of a task succeeding within a given time frame by consuming r spare parts. r The probability of success is achieved by consuming r spare parts within the task time.

[0065] Example 1

[0066] The lifespan of a certain electromechanical component follows a Weibull distribution W(100,2.2), the task duration is 300 hours, and it is equipped with 3 spare parts. The repair time follows a normal distribution N(10,2). Calculate the average number of spare parts consumed.

[0067] Initialize by setting the spare parts consumption quantity r = 1, parameter a = 4.343, and parameter b = 20.39.

[0068] Calculate the probability ps for different r. r pb r The calculation results are shown in Table 1.

[0069] Table 1

[0070] 1 0.000 0.000 2 0.330 0.013 3 0.401 0.052

[0071] The probability of failure is calculated as pf = 0.159.

[0072] The average number of spare parts consumed is calculated to be nx = 2.52.

[0073] Example 2

[0074] The lifespan of a certain electromechanical component follows a Weibull distribution W(100,2.2), the task duration is 300 hours, and the time to repair the fault follows a normal distribution N(10,2).

[0075] A spare parts support simulation model was established to verify the method of this invention through simulation. Figure 2 It can be seen that the results of the method of the present invention are in excellent agreement with the simulation results.

[0076] Those skilled in the art will readily understand that the above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A method for estimating the consumption of spare parts for electromechanical components, taking into account maintenance time, characterized in that, The method includes: Step 0: Obtain the Weibull distribution scale and shape parameters of the task time and electromechanical component lifespan, the normal distribution parameters of the maintenance time, and the number of spare parts (s). Step 1: Convert the Weibull distribution scale parameters and shape parameters into gamma approximate shape parameters and approximate scale parameters, and initialize the spare parts consumption quantity. ; Step 2: Using the gamma approximation shape parameter and approximation scale parameter, calculate the probability of success and the probability of ensuring success of the task within the time limit by consuming r spare parts through multiple integrations. Step 3: Update ,like If the above conditions are met, proceed to Step 2; otherwise, proceed to Step 4. Step 4: Calculate the probability of failure within the time limit of the double integral calculation task; Step 5: Combine the probabilities of the three types to determine the number of computer electronic component spare parts consumed; The probability of a task succeeding within a given time frame by consuming r spare parts. The calculation formula is as follows: in, For the task time, This represents the average repair time for electromechanical components. The root variance of maintenance time for electromechanical components. The shape parameters are approximate by gamma. The gamma approximation scale parameter; Consuming r spare parts within the task time guarantees the probability of success. The calculation formula is as follows: Calculate the probability of failure within the task time. : The combined probability of the three types of computer electronic component spare parts consumption is as follows: in, The quantity of spare parts consumed for electromechanical components; Calculate the probability of failure within the task time. The probability of a task succeeding within the specified time frame by consuming r spare parts. The probability of success is achieved by consuming r spare parts within the task time.

2. The method as described in claim 1, characterized in that, In Step 1, the conversion formula is as follows: in, For gamma function, Let be the scaling parameter of the Weibull distribution. Let be the shape parameter of the Weibull distribution.

3. A system for estimating the consumption of spare parts for electromechanical components, taking into account maintenance time, characterized in that, include: Processor and memory; The memory is used to store computer-executed instructions; The processor is configured to execute the computer execution instructions, causing the method of claim 1 or 2 to be executed.

4. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program that, when run on a processor, causes the processor to perform the method of claim 1 or 2.

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