Method and system for predicting periodic maintenance cost of mechanical and electrical products
By using the Weibull distribution model to calculate the probability of maintenance frequency and cost satisfaction rate of electromechanical products, the uncertainty in predicting the cost of regular maintenance of electromechanical products is resolved, and more accurate and stable annual cost prediction is achieved.
Patent Information
- Application Number
- CN202511483364.4
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-10-17
- Publication Date
- 2025-11-14
- Estimated Expiration
- Not applicable · inactive patent
AI Technical Summary
Existing technologies struggle to accurately and reliably predict the annual costs of routine maintenance for batch electromechanical products, primarily due to the uncertainty and randomness of product aging.
A prediction method based on the Weibull distribution is adopted. By calculating the probability arrays of the number of maintenance and total maintenance times of the product in the new year, and combining them with the cost satisfaction rate, a cost prediction curve is plotted to accurately reflect the impact of product quantity and aging degree.
This enables more rational budgeting for the regular maintenance of electromechanical products, accurately reflects the impact of product quantity and aging, and improves the accuracy and stability of cost forecasting.
Smart Images
Figure CN120952276A_ABST
Abstract
Description
Technical Field
[0001] This application belongs to the field of electromechanical product maintenance forecasting, and more specifically, relates to a method and system for forecasting the periodic maintenance costs of electromechanical products. Background Technology
[0002] Many electromechanical products experience performance degradation due to gradual aging during use, thus requiring regular maintenance, such as recalibrating key product parameters. This maintenance does not involve replacing critical components. Although the content and cost of such maintenance are fixed, the randomness of product lifespan means the actual number of regular maintenance sessions is uncertain, making it difficult to accurately predict maintenance costs. Because the degree of aging varies from year to year and between different products, the common practice of "referring to actual costs in recent years and the subjective experience of relevant personnel" is insufficient for accurately and consistently predicting the annual cost of regular maintenance for mass-produced products. Summary of the Invention
[0003] In view of the shortcomings of the prior art, the purpose of this application is to provide a method and system for predicting the periodic maintenance of electromechanical products, aiming to solve the problem that the prior art is unable to accurately and stably predict the annual cost of periodic maintenance of batch products.
[0004] To achieve the above objectives, firstly, this application provides a method for predicting the periodic maintenance costs of electromechanical products, specifically as follows: Based on the cost prediction curve between the predicted cost array and the corresponding cost satisfaction rate array, the product maintenance cost is predicted; The method for obtaining the cost forecast curve specifically includes the following steps: Step 1: Based on the product lifetime following a Weibull distribution, iterate through and calculate the product lifetime. i Maintenance frequency in the new year j The probability of; where, ; The maximum number of maintenance services required for each product in the new year; Step Two: Based on the product i Maintenance frequency in the new year j The probability, before using convolution calculation i A probability array of the total number of repairs for each product; Step 3: Repeat Step 1 and Step 2 until the total number of repairs for all products in the current batch is obtained. J The probability array is iterated through to calculate the total number of maintenance and upkeep cycles for the current batch of products. J Predicted costs and corresponding cost satisfaction rates Obtain the cost forecast curve; among which, , This represents the quantity of products in the current batch.
[0005] More preferably, the product i Maintenance frequency in the new year j probability for:
[0006] in, For products i The time already worked; is the scale parameter of the Weibull distribution; Let be the shape parameter of the Weibull distribution; For product lifespan; Calculate the product based on its lifespan and the time it has been in operation. i Maintenance frequency in the new year ,when At the time of its establishment, ,otherwise ;
[0007] in, Is to take and The minimum value in; It is to take the sign of the integer down; yes The remainder value; Working hours for product planning in the new year; Represents the product's working time It will require maintenance once later.
[0008] More preferably, step three specifically involves: when Then the former i The probability array for the total number of repairs for each product is as follows: , Otherwise, convolution calculation and Before the update i A probability array of the total number of repairs for each product; where... Each product The number of maintenance visits during the new year is 0, 1, 2, ..., j, ... N The probability of.
[0009] More preferably, the total number of product maintenance and upkeep cycles. Required forecasting costs and the corresponding cost satisfaction rate They are respectively:
[0010]
[0011]
[0012] in, Cost fulfillment rate; This is a probability array representing the total number of repairs for the current batch of products. The maximum number of maintenance services required for each product in the new year; This represents the quantity of products in the current batch. This refers to the cost of each maintenance session for the product.
[0013] Secondly, this application provides a system for predicting the periodic maintenance costs of electromechanical products, comprising: The cost forecasting module is used to forecast product maintenance costs based on the cost forecasting curve between the forecast cost array and the corresponding cost fulfillment rate array. The first data processing module is used to iterate and calculate the product lifespan based on the Weibull distribution. i Maintenance frequency in the new year j The probability of; where, ; The maximum number of maintenance services required for each product in the new year; The second data processing module is used to process data according to the product. i Maintenance frequency in the new year j The probability, before using convolution calculation i A probability array of the total number of repairs for each product; The third data processing module is used to analyze the total number of repairs for all products in the current batch. J The probability array is iterated through to calculate the total number of maintenance and upkeep cycles for the current batch of products. J Predicted costs and corresponding cost satisfaction rates Obtain the cost forecast curve; among which, , This represents the quantity of products in the current batch.
[0014] More preferably, the product in the first data processing module i Maintenance frequency in the new year j probability for:
[0015] in, For products i The time already worked; For product dimensional parameters; For product shape parameters; For product lifespan; Calculate the product based on its lifespan and the time it has been in operation. i Maintenance frequency in the new year ,when At the time of its establishment, ,otherwise ;
[0016] in, Is to take and The minimum value in; It is to take the sign of the integer down; yes The remainder value; Working hours for product planning in the new year; Represents the product's working time It will require maintenance once later.
[0017] More preferably, in the third data processing module, when Then the former i The probability array for the total number of repairs for each product is as follows: , Otherwise, convolution calculation and Before the update i A probability array of the total number of repairs for each product; where... Each product Maintenance frequency during the new year: 0, 1, 2, ... j , ..., N The probability of.
[0018] More preferably, in the third data processing module, the total number of product maintenance and upkeep operations... Required forecasting costs and the corresponding cost satisfaction rate They are respectively:
[0019]
[0020]
[0021] in, Cost fulfillment rate; This is a probability array representing the total number of repairs for the current batch of products. The maximum number of maintenance services required for each product in the new year; This represents the quantity of products in the current batch. This refers to the cost of each maintenance session for the product.
[0022] Thirdly, this application provides an electronic device, comprising: at least one memory for storing a program; and at least one processor for executing the program stored in the memory, wherein when the program stored in the memory is executed, the processor is configured to execute the method described in the first aspect or a further preferred embodiment of the first aspect.
[0023] Fourthly, this application provides a computer-readable storage medium storing a computer program that, when run on a processor, causes the processor to perform the method described in the first aspect or a further preferred embodiment of the first aspect.
[0024] Fifthly, this application provides a computer program product that, when run on a processor, causes the processor to perform the method described in the first aspect or a further preferred embodiment of the first aspect.
[0025] It is understood that the beneficial effects of the second to fifth aspects mentioned above can be found in the relevant descriptions in the first aspect mentioned above, and will not be repeated here.
[0026] Overall, the technical solutions conceived in this application have the following beneficial effects compared with the prior art: This application provides a method for predicting the periodic maintenance costs of electromechanical products. It fully considers the product's age and, based on the product's lifespan following a Weibull distribution, calculates the probability of the number of maintenance visits required in the new year, while simultaneously obtaining the total number of maintenance visits for the current batch of products. J The projected costs and cost fulfillment rates can accurately reflect the impact of product quantity and product aging, enabling more reasonable budget budgeting. Attached Figure Description
[0027] Figure 1 This is a flowchart of a method for predicting the periodic maintenance costs of electromechanical products provided in an embodiment of this application; Figure 2 This is a cost prediction curve diagram between budgeted cost and cost fulfillment rate provided in the embodiments of this application; Figure 3 This is a probability distribution diagram of the total number of maintenance operations provided in the embodiments of this application; Figure 4 This is a comparison chart of cost prediction curves for the aging levels of various products provided in the embodiments of this application. Detailed Implementation
[0028] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this application.
[0029] In this application, the term "and / or" describes the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent three cases: A exists alone, A and B exist simultaneously, and B exists alone. The symbol " / " in this document indicates that the related objects are in an "or" relationship; for example, A / B means A or B.
[0030] The terms "first" and "second," etc., used in the specification and claims of this application are used to distinguish different objects, rather than to describe a specific order of objects.
[0031] In the embodiments of this application, the terms "exemplary" or "for example" are used to indicate that something is an example, illustration, or description. Any embodiment or design that is described as "exemplary" or "for example" in the embodiments of this application should not be construed as being more preferred or advantageous than other embodiments or design. Specifically, the use of the terms "exemplary" or "for example" is intended to present the relevant concepts in a specific manner.
[0032] In the description of the embodiments in this application, unless otherwise stated, "multiple" means two or more.
[0033] The embodiments of this application are described below with reference to the accompanying drawings.
[0034] This application provides a method for predicting the periodic maintenance costs of electromechanical products. It can comprehensively consider the quantity of electromechanical products and the aging degree of each product, and provide support for cost prediction by predicting the probability that various costs meet maintenance needs (hereinafter referred to as cost satisfaction rate).
[0035] In engineering, the Weibull distribution is used to describe electromechanical products exhibiting aging phenomena, such as engines, air turbine engines, batteries, hydraulic pumps, relays, gyroscopes, and electric motors. For the Weibull distribution... , For scale parameters; Given the shape parameter, the probability density function is: ; like Figure 1 As shown, this application provides a method for predicting the periodic maintenance costs of electromechanical products, specifically including the following steps: Step S1: Determine known information: The product's lifespan follows a Weibull distribution. The quantity of products is nAt the start of the new year, products i The working hours are Working hours for product planning in the new year Product working time Afterwards, maintenance is required, and the cost of each maintenance is... ; Step S2: Calculate the maximum number of maintenance visits for each product in the new year. , for The floor value is used to initialize the product serial number. ; Step S3: Iterate through and calculate products Maintenance frequency in the new year probability , ; According to the lifetime variable in the formula and It can calculate the number of maintenance services required in the new year. ,when At the time of its establishment, ,otherwise ;in, For product lifespan;
[0036] in, Is to take and The minimum value in; It is to take the sign of the integer down; yes The remainder value; Step S4: If Then the current batch before The probability array for the total number of repairs for each product is as follows: , Otherwise, convolution calculation and Then update the probability array with the results. , that is to say , This is the symbol for convolution calculation; Step S5: Update ,like If yes, proceed to step S3; otherwise, proceed to step S6. Step S6: Iterate through and calculate the total number of maintenance and upkeep cycles for this batch of products. Required forecasting costs and the corresponding cost satisfaction rate ,make , , ; Step S7: Plot the array of predicted costs and its corresponding cost satisfaction rate array The cost forecast curves between these curves can assist in the annual forecasting of maintenance costs. For example, the corresponding forecast cost can be found based on the forecast cost fulfillment rate, or the corresponding cost fulfillment rate can be found based on the existing forecast cost value.
[0037] Example 1 There are 10 units of a certain product. The operating time of each product is shown in Table 1. The planned operating time for the products in the new year is 500 hours. The product lifespan follows a Weibull distribution. The product undergoes maintenance every 100 hours of operation, with each maintenance costing 100 yuan. Using the above method, calculate the maintenance cost and its cost satisfaction rate, and plot the cost prediction curve.
[0038] Table 1
[0039] This application provides a method for predicting the periodic maintenance costs of electromechanical products, specifically including the following steps: Step S1: Determine known information: Product lifespan distribution parameters , The quantity of products is n At the start of the new year, products The working hours are Working hours for product planning in the new year =500 hours, per working time of the product =Maintenance is required every 100 hours. The cost of each maintenance is as follows. =100 yuan; Step S2: Maximum number of maintenance services for a single product within the new year. Initialize product serial number ; Step S3: Iterate through and calculate products Maintenance frequency in the new year probability The probability results of maintenance frequency for each product are shown in Table 2. Table 2
[0040] Step S4: Before the update i Probability array of the total number of repairs for each product ; Step S5: Update ,like If yes, proceed to step S3; otherwise, proceed to step S6. Step S6: Iterate through and calculate the total number of maintenance and upkeep cycles for this batch of products. Required forecasting costs and the corresponding cost satisfaction rate , The results are shown in sub-tables 3-1 and 3-2; Step S7: Plot the array of predicted costs and its corresponding cost satisfaction rate array The cost prediction curves between these two points are shown in the figure. Figure 2 The results show that if the cost satisfaction rate is required to be no less than 0.9, the predicted cost must be no less than 4,300 yuan, which can satisfy the condition that the total number of maintenance times does not exceed 43 times; if the predicted cost that can be invested is 4,000 yuan, the cost satisfaction rate is 0.748, and there is a 25.2% possibility of insufficient cost.
[0041] Subtable 3-1
[0042] Subtable 3-2
[0043] The accuracy of the above method can be verified using simulation. The key to the above method lies in accurately calculating the probability array of the total number of maintenance operations. , Figure 3 The above examples respectively employ simulation methods and the probability arrays of this application. result, Figure 3 This indicates that the two results are highly consistent.
[0044] Figure 4 The simulation verification of the cost prediction curves for the above example considering the aging of each product is shown, and the cost prediction curves for products that are not considered (treated as new products) are also plotted (when the cost satisfaction rate is not less than 0.9, the predicted cost is 5000 yuan). Figure 4 This indicates that the degree of product obsolescence has a significant impact on the predicted cost, and ignoring this factor will lead to an "inflated" predicted cost. This application accurately reflects the impact of product quantity and product obsolescence, and can formulate predicted costs more reasonably.
[0045] Example 2 This application provides a system for predicting the periodic maintenance costs of electromechanical products, including: The cost forecasting module is used to forecast product maintenance costs based on the cost forecasting curve between the forecast cost array and the corresponding cost fulfillment rate array. The first data processing module is used to iterate and calculate the product lifespan based on the Weibull distribution. i Maintenance frequency in the new year j The probability of; where, ; The maximum number of maintenance services required for each product in the new year; The second data processing module is used to process data according to the product. i Maintenance frequency in the new year j The probability, before using convolution calculation i A probability array of the total number of repairs for each product; The third data processing module is used to analyze the total number of repairs for all products in the current batch. J The probability array is iterated through to calculate the total number of maintenance and upkeep cycles for the current batch of products. J Predicted costs and corresponding cost satisfaction rates Obtain the cost forecast curve; among which, , This represents the quantity of products in the current batch.
[0046] More preferably, the product in the first data processing module i Maintenance frequency in the new year j probability for:
[0047] in, For products i The time already worked; For product dimensional parameters; For product shape parameters; For product lifespan; Calculate the product based on its lifespan and the time it has been in operation. i Maintenance frequency in the new year ,when At the time of its establishment, ,otherwise ;
[0048] in, Is to take and The minimum value in; It is to take the sign of the integer down; yes The remainder value; Working hours for product planning in the new year; Represents the product's working time It will require maintenance once later.
[0049] More preferably, in the third data processing module, when Then the former i The probability array for the total number of repairs for each product is as follows: , Otherwise, convolution calculation and Before the update i A probability array of the total number of repairs for each product; where... Each product Maintenance frequency during the new year: 0, 1, 2, ... j , ..., N The probability of.
[0050] More preferably, in the third data processing module, the total number of product maintenance and upkeep operations... Required forecasting costs and the corresponding cost satisfaction rate They are respectively:
[0051]
[0052]
[0053] in, Cost fulfillment rate; This is a probability array representing the total number of repairs for the current batch of products. The maximum number of maintenance services required for each product in the new year; This represents the quantity of products in the current batch. This refers to the cost of each maintenance session for the product.
[0054] It is understood that the detailed functional implementation of each of the above units / modules can be found in the description in the aforementioned method embodiments, and will not be repeated here.
[0055] It should be understood that the above-described device is used to execute the methods in the above embodiments. The implementation principle and technical effect of the corresponding program modules in the device are similar to those described in the above methods. The working process of the device can be referred to the corresponding process in the above methods, and will not be repeated here.
[0056] Based on the methods in the above embodiments, this application provides an electronic device that may include a processor, a communications interface, a memory, and a communication bus, wherein the processor, communications interface, and memory communicate with each other via the communication bus. The processor may invoke logical instructions stored in the memory to execute the methods in the above embodiments.
[0057] Furthermore, the logical instructions in the aforementioned memory can be implemented as software functional units and, when sold or used as independent products, can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or a portion of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this application.
[0058] Based on the methods in the above embodiments, this application provides a computer-readable storage medium storing a computer program that, when run on a processor, causes the processor to execute the methods in the above embodiments.
[0059] Based on the methods in the above embodiments, this application provides a computer program product that, when run on a processor, causes the processor to execute the methods in the above embodiments.
[0060] It is understood that the processor in the embodiments of this application can be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, transistor logic devices, hardware components, or any combination thereof. A general-purpose processor can be a microprocessor or any conventional processor.
[0061] The method steps in this application embodiment can be implemented in hardware or by a processor executing software instructions. The software instructions can consist of corresponding software modules, which can be stored in random access memory (RAM), flash memory, read-only memory (ROM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), registers, hard disks, portable hard disks, CD-ROMs, or any other form of storage medium known in the art. An exemplary storage medium is coupled to the processor, enabling the processor to read information from and write information to the storage medium. Of course, the storage medium can also be a component of the processor. The processor and the storage medium can reside in an ASIC.
[0062] In the above embodiments, implementation can be achieved entirely or partially through software, hardware, firmware, or any combination thereof. When implemented using software, it can be implemented entirely or partially as a computer program product. The computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, all or part of the processes or functions described in the embodiments of this application are generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium or transmitted through the computer-readable storage medium. The computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via wired (e.g., coaxial cable, fiber optic, digital subscriber line (DSL)) or wireless (e.g., infrared, wireless, microwave, etc.) means. The computer-readable storage medium can be any available medium that a computer can access or a data storage device such as a server or data center that integrates one or more available media. The available medium can be a magnetic medium (e.g., floppy disk, hard disk, magnetic tape), an optical medium (e.g., DVD), or a semiconductor medium (e.g., solid-state disk (SSD)).
[0063] It is understood that the various numerical designations used in the embodiments of this application are merely for the convenience of description and are not intended to limit the scope of the embodiments of this application.
[0064] Those skilled in the art will readily understand that the above description is merely a preferred embodiment of this application and is not intended to limit this application. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of this application should be included within the scope of protection of this application.
Claims
1. A method for predicting the periodic maintenance costs of electromechanical products, characterized in that, Specifically: Based on the cost prediction curve between the predicted cost array and the corresponding cost satisfaction rate array, the product maintenance cost is predicted; The method for obtaining the cost forecast curve specifically includes the following steps: Step 1: Based on the product lifetime following a Weibull distribution, iterate through and calculate the product lifetime. i Maintenance frequency in the new year j The probability of; where, ; The maximum number of maintenance services required for each product in the new year; Step Two: Based on the product i Maintenance frequency in the new year j The probability before using convolution calculation i A probability array of the total number of repairs for each product; Step 3: Repeat Step 1 and Step 2 until the total number of repairs for all products in the current batch is obtained. J The probability array is iterated through to calculate the total number of maintenance and upkeep cycles for the current batch of products. J Predicted costs and corresponding cost satisfaction rates Obtain the cost forecast curve; among which, , This represents the quantity of products in the current batch.
2. The method for predicting the periodic maintenance cost of electromechanical products according to claim 1, characterized in that, Product in Step One i Maintenance frequency in the new year j probability for: in, For products i The time already worked; is the scale parameter of the Weibull distribution; Let be the shape parameter of the Weibull distribution; For product lifespan; Calculate the product based on its lifespan and the time it has been in operation. i Maintenance frequency in the new year ,when At the time of its establishment, ,otherwise ; in, Is to take and The minimum value in; It is to take the sign of the integer down; yes The remainder value; Working hours for product planning in the new year; Represents the product's working time It will require maintenance once later.
3. The method for predicting the periodic maintenance cost of electromechanical products according to claim 2, characterized in that, Step three specifically involves: when Then the former i The probability array for the total number of repairs for each product is as follows: , Otherwise, convolution calculation and Before the update i A probability array of the total number of repairs for each product; where... Each product Maintenance frequency during the new year: 0, 1, 2, ... j , ..., N The probability of.
4. The method for predicting the periodic maintenance cost of electromechanical products according to claim 3, characterized in that, Total number of product maintenance and upkeep operations Required forecasting costs and the corresponding cost satisfaction rate They are respectively: in, Cost fulfillment rate; This is a probability array representing the total number of repairs for the current batch of products. The maximum number of maintenance services required for each product in the new year; This represents the quantity of products in the current batch. This refers to the cost of each maintenance session for the product.
5. A system for predicting the periodic maintenance costs of electromechanical products, characterized in that, include: The cost forecasting module is used to forecast product maintenance costs based on the cost forecasting curve between the forecast cost array and the corresponding cost fulfillment rate array. The first data processing module is used to iterate and calculate the product lifespan based on the Weibull distribution. i Maintenance frequency in the new year j The probability of; where, ; The maximum number of maintenance services required for each product in the new year; The second data processing module is used to process data according to the product. i Maintenance frequency in the new year j The probability before using convolution calculation i A probability array of the total number of repairs for each product; The third data processing module is used to analyze the total number of repairs performed on all products in the current batch. J The probability array is iterated through to calculate the total number of maintenance and upkeep cycles for the current batch of products. J Predicted costs and corresponding cost satisfaction rates Obtain the cost forecast curve; among which, , This represents the quantity of products in the current batch.
6. The system for predicting the periodic maintenance costs of electromechanical products according to claim 5, characterized in that, Products in the first data processing module i Maintenance frequency in the new year j probability for: in, For products i The time already worked; For product dimensional parameters; For product shape parameters; For product lifespan; Calculate the product based on its lifespan and the time it has been in operation. i Maintenance frequency in the new year ,when At the time of its establishment, ,otherwise ; in, Is to take and The minimum value in; It is to take the sign of the integer down; yes The remainder value; Working hours for product planning in the new year; Represents the product's working time It will require maintenance once later.
7. The system for predicting the periodic maintenance costs of electromechanical products according to claim 6, characterized in that, In the third data processing module, when Then the former i The probability array for the total number of repairs for each product is as follows: , Otherwise, convolution calculation and Before the update i A probability array of the total number of repairs for each product; where... Each product Maintenance frequency during the new year: 0, 1, 2, ... j , ..., N The probability of.
8. The system for predicting the periodic maintenance costs of electromechanical products according to claim 7, characterized in that, Total number of product maintenance and upkeep operations Required forecasting costs and the corresponding cost satisfaction rate They are respectively: in, Cost fulfillment rate; This is a probability array representing the total number of repairs for the current batch of products. The maximum number of maintenance services required for each product in the new year; This represents the quantity of products in the current batch. This refers to the cost of each maintenance session for the product.
9. An electronic device, characterized in that, include: At least one memory for storing computer programs; At least one processor is configured to execute a program stored in the memory, wherein when the program stored in the memory is executed, the processor is configured to perform a method for predicting the periodic maintenance costs of electromechanical products as described in any one of claims 1-4.
10. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is run on the processor, the processor performs the method for predicting the periodic maintenance costs of electromechanical products as described in any one of claims 1-4.