Spare part optimization method, device and equipment for key parts of centrifugal compressor and medium

Abstract

The invention provides a spare part optimization method, device and equipment for key parts of a centrifugal compressor and a medium. The method comprises the steps that use information of the key parts of a key part standby system of the centrifugal compressor in the current use state and spare part information of the key parts in the current standby state are obtained; when the number of the spare parts is smaller than a preset value, using information of the key parts in the current using state and spare part information of the key parts in the current standby state are input into a spare part optimization model, and the spare part optimization model outputs spare part purchasing conditions of the key parts of the key part standby system; the spare part optimization model comprises an average cost rate calculation model, a reliability evaluation model and constraint conditions of the key part standby system. On the premise of ensuring the reliability requirement of the system, the ordering quantity and ordering time interval of the spare parts are optimized, and the inventory cost and the management cost are reduced.

Description

Technical Field

[0001] This invention relates to the field of centrifugal compressor technology, and in particular to a method, apparatus, equipment and medium for optimizing spare parts of key components of a centrifugal compressor. Background Technology

[0002] Natural gas pipeline systems are large, complex, and open systems composed of numerous units, and their reliability directly affects the stable supply of natural gas. Centrifugal compressors, as core equipment in natural gas pipeline systems, undertake the crucial task of pressurizing and transporting natural gas; their stable and continuous operation is vital to ensuring the safety of the natural gas pipeline system's supply. However, in actual operation, key components of centrifugal compressors inevitably malfunction, leading to compressor shutdowns, affecting the normal operation of the natural gas pipeline system, and causing economic losses.

[0003] To ensure uninterrupted operation of a centrifugal compressor in the event of a failure in a critical component, backups of these components are typically prepared in advance. However, current backup methods involve either bulk purchasing of spare parts or ordering or warehousing only one spare part at a time. Bulk purchasing leads to surplus spare parts, which can degrade in performance during storage, increasing costs and complexity. Ordering only one spare part may result in supply delays, potentially causing even greater downtime losses. Summary of the Invention

[0004] To address the problems existing in the prior art, this invention provides a method, apparatus, equipment, and medium for optimizing spare parts of key components of a centrifugal compressor.

[0005] This invention provides a spare parts optimization method for a critical component of a centrifugal compressor, applicable to the spare parts system of any critical component of a centrifugal compressor. The method includes: Acquire key component data of the critical component backup system of the centrifugal compressor during the current service cycle. The component data includes usage information of the key components in the current service state and spare parts information of the key components in the current standby state. The spare parts information includes the quantity of spare parts. When the number of spare parts is less than a preset value, the usage information of the key components in the current use state and the spare parts information of the key components in the current standby state are input into the spare parts optimization model. The spare parts optimization model outputs the spare parts procurement status of the key components in the key component standby system. The spare parts optimization model includes an average cost rate calculation model, a reliability evaluation model, and constraints for the key component standby system, which are constructed based on the usage information of the key components in the current use state, the spare parts information of the key components in the current standby state, and the procurement information of the key components in the predicted state.

[0006] According to the present invention, a spare parts optimization method for a key component of a centrifugal compressor is provided, the method further comprising constructing a spare parts optimization model, including: Based on the usage information of key components under current use, the spare parts information of key components under current standby status, and the procurement information of key components under predicted status, a calculation model for the total working life of the key component standby system and a life probability density function are constructed. Based on the total working life calculation model and life probability density function of the critical component backup system, an average life distribution model and a reliability evaluation model of the critical component backup system are constructed. Based on the average life distribution model and reliability evaluation model of the critical component backup system, a model for the expected time interval of spare parts ordering for the critical component backup system is constructed. Obtain the expected cost model of the backup system for critical components over a single service life; Based on the expected spare parts ordering interval model and the expected cost model within a single usage cycle of the critical component backup system, an average cost rate calculation model for the critical component backup system is constructed.

[0007] A spare parts optimization model is established with constraints that the reliability of the spare parts for critical components is higher than the reliability threshold and the maximum spare parts order quantity for critical components is less than or equal to the maximum inventory quantity. The spare parts ordering interval and spare parts ordering quantity of the spare parts system for critical components are used as decision variables, and the minimum average cost rate is used as the optimization objective.

[0008] According to the present invention, a spare parts optimization method for a critical component of a centrifugal compressor is provided, the method further comprising constructing an expected cost model of the spare parts system for the critical component within a single service life, including: Based on the spare parts information of key components in the current standby state and the procurement information of key components in the predicted state, determine the inventory holding cost and spare parts ordering cost of the key component standby system. Based on the pre-defined failure maintenance cost estimation strategy and preventive maintenance cost estimation strategy, as well as the reliability evaluation model of the critical component backup system, the system maintenance cost of the critical component backup system is determined. Based on the inventory holding cost, spare parts ordering cost, and system maintenance cost of the critical component backup system, construct the expected cost model of the critical component backup system within a single service life.

[0009] According to the present invention, a spare parts optimization method for a key component of a centrifugal compressor is provided, wherein the expected cost model of the spare parts system for the key component within a single service life includes: in, The expected cost of a backup system for critical components over a single service life. It is the total cost of the backup system for critical components. Order the quantity of spare parts for critical components. For the time interval of spare parts ordering, For system maintenance costs, For inventory holding costs, To cover the cost of ordering spare parts, For the estimation of failure-related repair costs, For the estimation of preventive maintenance costs, For reliability evaluation models, Inventory holding cost for each spare part, Basic costs for ordering spare parts, The unit price for each spare part.

[0010] According to the present invention, a spare parts optimization method for key components of a centrifugal compressor is provided, wherein the average cost rate calculation model includes: in, The average cost rate for backup systems of critical components. Expected time interval for spare parts ordering The average lifespan distribution of the backup system for critical components. The total service life of the backup system for critical components.

[0011] According to the present invention, a spare parts optimization method for a key component of a centrifugal compressor is provided, wherein the expected model for the spare parts ordering time interval includes: in, Expected time interval for spare parts ordering This refers to the expected spare parts ordering interval when the total service life of the critical component backup system is less than the predetermined spare parts ordering interval. This refers to the expected spare parts ordering interval when the total service life of the critical component backup system is greater than or equal to the predetermined spare parts ordering interval. The total service life of the backup system for critical components is greater than or equal to the predetermined spare parts ordering interval. T The probability of occurrence.

[0012] According to the present invention, a spare parts optimization method for a key component of a centrifugal compressor is provided, wherein the reliability evaluation model includes: in, The reliability of the backup system for critical components during its service life q. For a key component within a usage cycle q and nThe lifetime probability density function of a critical component backup system, consisting of ordered spare parts for key components. It uses time points within the period q. To use a point in time within the period q, This represents the time difference between ordering the previous spare part and the next spare part within the usage period q.

[0013] This invention also provides a spare parts optimization device for key components of a centrifugal compressor, applicable to the spare parts system of any key component of a centrifugal compressor. The device includes: The acquisition module is used to acquire key component data of the critical component backup system of the centrifugal compressor during the current service cycle. The component data includes the usage information of the key components in the current service state and the spare parts information of the key components in the current standby state. The spare parts information includes the number of spare parts. The processing module is used to input the usage information of key components in the current use state and the spare parts information of key components in the current standby state into the spare parts optimization model when the number of spare parts is less than a preset value. The spare parts optimization model outputs the spare parts procurement status of key components in the key component standby system. The spare parts optimization model includes an average cost rate calculation model, a reliability evaluation model, and constraints for the key component standby system, which are constructed based on the usage information of key components in the current use state, the spare parts information of key components in the current standby state, and the procurement information of key components in the predicted state.

[0014] The present invention also provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the spare parts optimization method for key components of the centrifugal compressor as described above.

[0015] The present invention also provides a non-transitory computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the spare parts optimization method for key components of a centrifugal compressor as described above.

[0016] The present invention also provides a computer program product, including a computer program that, when executed by a processor, implements the spare parts optimization method for key components of a centrifugal compressor as described above.

[0017] This invention provides a method, apparatus, equipment, and medium for optimizing spare parts for key components of a centrifugal compressor. By inputting the usage information of the key components in their current operating state and the spare parts information of the key components in their current standby state into a spare parts optimization model when the number of spare parts is less than a preset value, the spare parts optimization model outputs the spare parts procurement status of the key components in the standby system. This enables the optimization model based on reliability thresholds to combine the inventory management of multiple spare parts with the degradation effect, optimizing the ordering quantity and ordering interval of spare parts while ensuring system reliability requirements, thereby reducing inventory and management costs. Attached Figure Description

[0018] To more clearly illustrate the technical solutions in this invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of this invention. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0019] Figure 1 This is a flowchart illustrating the method for optimizing spare parts for key components of a centrifugal compressor provided by the present invention.

[0020] Figure 2 This is a schematic diagram of the construction process of the spare parts optimization model provided by the present invention.

[0021] Figure 3 This is a schematic diagram showing the relationship between the lifespan of the backup system and the lifespan of key components provided by the present invention.

[0022] Figure 4 This is a comparison graph of the lifetime probability density function of the backup system provided by the present invention with the results obtained by statistical simulation when the number of spare parts is 0 and 4.

[0023] Figure 5 This is a comparison chart of system lifespan provided by the present invention, considering the degradation of spare parts in inventory and not considering the degradation of spare parts in inventory.

[0024] Figure 6 This is a schematic diagram of the spare parts optimization device for key components of a centrifugal compressor provided by the present invention.

[0025] Figure 7 This is a schematic diagram of the structure of the electronic device provided by the present invention. Detailed Implementation

[0026] To make the objectives, technical solutions, and advantages of this invention clearer, the technical solutions of this invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of this invention. All other embodiments obtained by those skilled in the art based on the embodiments of this invention without creative effort are within the scope of protection of this invention.

[0027] The following is combined with Figures 1-7 This invention describes a method, apparatus, equipment, and medium for optimizing spare parts for key components of a centrifugal compressor.

[0028] Figure 1 This invention provides a flowchart illustrating a method for optimizing spare parts for a key component of a centrifugal compressor. (See attached diagram.) Figure 1 This method can be applied to the backup system of any critical component of a centrifugal compressor. The backup system of a critical component of a centrifugal compressor is a redundant or emergency protection system designed to improve the reliability of equipment operation and avoid downtime caused by sudden failures. These backup systems are usually configured for vulnerable or critical components to ensure that the backup plan can be quickly switched on or activated when the main system fails, maintaining continuous operation of the compressor or safely shutting it down.

[0029] In actual operation, key components of centrifugal compressors inevitably malfunction, causing the centrifugal compressor to shut down, affecting the normal operation of the natural gas pipeline system, and resulting in economic losses.

[0030] Research on spare parts management for key components of centrifugal compressors mainly focuses on two aspects. Firstly, many studies assume an ample supply of spare parts, eliminating the need to consider the risk of shortages. While this simplifies the problem, it often leads to higher costs and poorer economics. Secondly, some studies employ a (0,1) type inventory management strategy, ordering or receiving only one spare part at a time. While this method reduces the risk of spare parts surplus, it can lead to delays in spare parts supply, resulting in greater downtime losses.

[0031] In practice, both insufficient and excessive spare parts can negatively impact the stable operation of centrifugal compressors and increase operating costs. Therefore, optimizing spare parts reserves is crucial for centrifugal compressor maintenance.

[0032] In this invention, the method includes the following steps: Step 11: Obtain the critical component data of the critical component backup system of the centrifugal compressor during the current service cycle. The critical component data includes the usage information of the critical components in the current service state and the spare parts information of the critical components in the current standby state. The spare parts information includes the quantity of spare parts.

[0033] Step 12: When the number of spare parts is less than the preset value, input the usage information of the key components in the current use state and the spare parts information of the key components in the current standby state into the spare parts optimization model. The spare parts optimization model outputs the spare parts procurement status of the key components in the key component standby system. The spare parts optimization model includes an average cost rate calculation model, a reliability evaluation model, and constraints for the key component standby system, which are constructed based on the usage information of the key components in the current use state, the spare parts information of the key components in the current standby state, and the procurement information of the key components in the predicted state.

[0034] It should be noted that for a critical component backup system of a centrifugal compressor (such as a dry gas seal backup system, where the critical component is the dry gas seal), multiple usage cycles can be defined within the overall service life of the centrifugal compressor. Within each usage cycle, replacement of the critical component's spare parts may or may not occur. Therefore, within each usage cycle, it is necessary to ensure that there are enough spare parts available to meet the predicted normal replacement frequency. This requires estimating the procurement of spare parts for the critical component in each usage cycle. Therefore, it is necessary to obtain critical component data for the centrifugal compressor's critical component backup system within the current usage cycle. This critical component data includes usage information for the critical component in its current operating state and spare part information for the critical component in its current standby state, including the quantity of spare parts. The usage information for the critical component includes basic information such as the model, initial lifespan, and remaining lifespan of the critical component already in use, as well as the model, initial lifespan, and remaining lifespan of the critical component already stored in the inventory (typically, one spare part is stored in the inventory). In other words, any information that can be used to predict the procurement of spare parts for critical components can be collected. This spare parts information for critical components includes the quantity of spare parts, the optimal ordering cycle for spare parts, and the corresponding average cost rate.

[0035] When the number of spare parts is less than the preset value, it indicates that failure to replenish spare parts in a timely manner may affect the function of the critical component backup system for the centrifugal compressor. Therefore, it is necessary to input the usage information of critical components in their current operational state and the spare parts information of critical components in their current standby state into the spare parts optimization model. The spare parts optimization model then outputs the spare parts procurement status of the critical component backup system. This spare parts procurement status includes the predicted number of spare parts to be prepared, the optimal ordering cycle for the spare parts, and the corresponding average cost rate.

[0036] In this invention, since the data involves predicting spare parts for critical components, the ordering cycle required for ordering spare parts, and even the ordering costs, the spare parts optimization model employed aims to construct an average cost rate calculation model and a reliability evaluation model for the critical component spare parts system based on the usage information of critical components under current operating conditions, the spare parts information of critical components under current standby conditions, and the procurement information of critical components under predicted conditions. The main purpose of the constructed model is to provide a calculation formula with constraints. By calculating the model, the number of spare parts for critical components, the optimal ordering cycle for spare parts, and the corresponding average cost rate can be obtained. Therefore, the spare parts optimization model of this invention uses the constraints of critical component standby reliability exceeding a reliability threshold and the maximum spare parts ordering quantity for critical components being less than or equal to the maximum inventory level. It uses the spare parts ordering time interval and spare parts ordering quantity of the critical component spare parts system as decision variables, and the minimum average cost rate as the optimization objective. The specific mathematical representation is as follows: ; in, The average cost rate of the backup system for critical components of the centrifugal compressor. For reliability evaluation models, The average cost rate function for the backup system of critical components. The reliability threshold for backup systems of critical components; This represents the maximum inventory level of spare parts that can be stored in the inventory. Order the quantity of spare parts for critical components.

[0037] In a further method of this invention, the main focus is on explaining the process of constructing a spare parts optimization model; see [link to relevant documentation]. Figure 2 The details are as follows: Based on the usage information of key components under current use, the spare parts information of key components under current standby status, and the procurement information of key components under predicted status, a calculation model for the total working life of the key component standby system and a life probability density function are constructed. Based on the total working life calculation model and life probability density function of the critical component backup system, an average life distribution model and a reliability evaluation model of the critical component backup system are constructed. Based on the average life distribution model and reliability evaluation model of the critical component backup system, a model for the expected time interval of spare parts ordering for the critical component backup system is constructed. Obtain the expected cost model of the backup system for critical components over a single service life; Based on the expected spare parts ordering interval model and the expected cost model within a single usage cycle of the critical component backup system, an average cost rate calculation model for the critical component backup system is constructed.

[0038] A spare parts optimization model is established with constraints that the reliability of the backup system for critical components is higher than the reliability threshold and the maximum spare parts order quantity for critical components is less than or equal to the maximum inventory quantity. The spare parts ordering interval and spare parts ordering quantity for the backup system for critical components are used as decision variables, and the minimum average cost rate is used as the optimization objective.

[0039] It should be noted that, in this invention, the various models mentioned above can be regarded as functional formulas.

[0040] The spare parts of the critical components of a centrifugal compressor's backup system follow a Weibull distribution, which is applicable to critical components of centrifugal compressors, systems with characteristic lifespans, and their shape parameters... β It can describe the rate of increase in the failure rate.

[0041] The probability density function of the Weibull distribution is: ; The reliability function of the Weibull distribution is: ; in, t It is a time variable. η β is the characteristic lifetime parameter, and β is the shape parameter. The shape parameter β determines the shape of the distribution, while the characteristic lifetime parameter... η It is related to the average lifespan and determines the extent of the distribution.

[0042] In the Weibull distribution, the characteristic lifetime parameter is closely related to the component lifetime. Therefore, a decay coefficient is introduced. Each time a spare part is replaced, its characteristic life parameter, which follows a Weibull distribution, decays. Let this decay coefficient have a mean of... The variance is The normal distribution, i.e. .

[0043] The probability density function of the initial working components (which may include critical components in use and those in inventory) of the critical component backup system is: ; The critical component backup system i The probability density function of a spare part in operation can be iteratively expressed as: ; In the formula, where The probability density function representing the initial working component lifetime; The characteristic lifetime parameters of the initial working component that follow a Weibull distribution; Indicates the first i The lifetime probability density function of a spare part during operation; For the first i -1 is a characteristic lifetime parameter of a spare part that follows a Weibull distribution.

[0044] In this invention, the optimization is based on an initial working component and n A backup system for critical components of a centrifugal compressor, consisting of spare parts. For example... Figure 3 As shown, the lifespan of this system is the sum of the lifespan of the initial working component and the lifespan of each spare component during operation.

[0045] Because in this backup system, the initial working components and n The service life of each spare part is an independent random variable, and the probability density of the service life of each part is as follows: ; The total service life of the backup system is: ; in, For the lifespan of the backup system; The initial working life of the backup system's components; For the backup system i The service life of each spare part.

[0046] Then the lifetime probability density function of the backup system can be expressed as follows: ; Then it consists of an initial working part and n The probability density function of the spare system lifespan of the key component of a centrifugal compressor composed of spare parts can be iteratively expressed as: ; in, For a single initial working component and n The lifetime probability density function of a backup system composed of spare parts; For a single initial working component and n The lifetime probability density function of a backup system consisting of -1 spare parts.

[0047] The reliability evaluation model for the backup system of the key component of the centrifugal compressor is as follows: in, The reliability of the backup system for critical components during its service life q. For a key component within a usage cycle q and n The lifetime probability density function of a critical component backup system, consisting of ordered spare parts for key components. It uses time points within the period q. To use a point in time within the period q, This represents the time difference between ordering the previous spare part and the next spare part within the usage period q.

[0048] At this point, the average lifespan of the backup system can be expressed as: ; Because spare parts degrade while in stock, and may also fail when a spare part is replaced and put into operation, the lifespan of a backup system may be shorter than the planned spare parts ordering interval. In this case, the spare parts ordering interval is actually the lifespan of the backup system. At this point, the above formula can be expressed as: ; in, This represents the expected time interval between actual spare parts orders. This refers to the actual time interval for ordering spare parts.

[0049] Further solving the above equation yields: ; in, Expected time interval for spare parts ordering This refers to the expected spare parts ordering interval when the total service life of the critical component backup system is less than the predetermined spare parts ordering interval. This refers to the expected spare parts ordering interval when the total service life of the critical component backup system is greater than or equal to the predetermined spare parts ordering interval. The total service life of the backup system for critical components is greater than or equal to the predetermined spare parts ordering interval. T The probability of occurrence.

[0050] In a further step of the present invention, the process of processing the expected cost model for constructing a backup system for critical components within a single service life is explained and illustrated as follows: Based on the spare parts information of key components in the current standby state and the procurement information of key components in the predicted state, determine the inventory holding cost and spare parts ordering cost of the key component standby system. Based on the pre-defined failure maintenance cost estimation strategy and preventive maintenance cost estimation strategy, as well as the reliability evaluation model of the critical component backup system, the system maintenance cost of the critical component backup system is determined. Based on the inventory holding cost, spare parts ordering cost, and system maintenance cost of the critical component backup system, construct the expected cost model of the critical component backup system within a single service life.

[0051] It should be noted that in this invention, the solution is a multi-spare-parts inventory optimization problem, so the spare parts in the inventory state include... n Each spare part incurs inventory holding costs, including the cost of storage space and the associated costs of handling and maintaining the spare part. Furthermore, ordering spare parts incurs additional costs, including the purchase price of the spare part and basic fees incurred with each order, such as shipping costs. The inventory holding costs and spare part ordering costs of the critical component spare system can be expressed by the following formulas: ; ; in, Inventory holding costs; Cost of ordering spare parts; Inventory holding cost for each spare part; Quantity for spare parts order; Basic costs for ordering spare parts; The unit price for each spare part.

[0052] In addition to the aforementioned inventory costs and spare parts ordering costs, the maintenance costs of the critical spare system for centrifugal compressors should also be considered. This cost covers the expenses incurred in performing regular inspections, maintenance, and repairs on the spare system, primarily including failure-based repair costs and preventative maintenance costs. Failure-based repair, often referred to as reactive maintenance, is a maintenance strategy based on the actual failure situation of the dry gas seal spare system. Implement a strategy for estimating the cost of fault-based repairs; preventative maintenance refers to maintenance that involves stopping and inspecting the machine at predetermined time intervals based on experience to prevent damage. Implement a strategy for predicting the cost of preventative maintenance.

[0053] Based on preventive maintenance costs and fail-safe maintenance costs, and combining the reliability and unreliability functions of the dry gas seal backup system, a reliability-based maintenance cost model for the dry gas seal backup system is established as follows: ; in, For reliability function; For unreliability functions, ; This represents the total cost of system maintenance.

[0054] Based on the three types of costs mentioned above, the expected cost within a single period can be obtained. : in, The expected cost of a backup system for critical components over a single service life. It is the total cost of the backup system for critical components. Order the quantity of spare parts for critical components. For the time interval of spare parts ordering, For system maintenance costs, For inventory holding costs, To cover the cost of ordering spare parts, For the estimation of failure-related repair costs, For the estimation of preventive maintenance costs, For reliability evaluation models, Inventory holding cost for each spare part, Basic costs for ordering spare parts, The unit price for each spare part, This refers to a point in time within the spare parts ordering interval.

[0055] Expected time interval for completing actual spare parts order and expected cost within a single cycle After that, you can obtain the following: n The formula for calculating the average cost rate of the spare system for critical components of a centrifugal compressor is as follows: in, The average cost rate for backup systems of critical components. Expected time interval for spare parts ordering The average lifespan distribution of the backup system for critical components. The total service life of the backup system for critical components.

[0056] In this invention, the spare parts optimization model is constrained by the following conditions: the reliability of the spare parts for critical components is higher than the reliability threshold, and the maximum spare parts order quantity for critical components is less than or equal to the maximum inventory quantity. The model uses the spare parts ordering interval and the order quantity for the spare parts in the critical component spare parts system as decision variables, and the optimization objective is to minimize the average cost rate. The specific mathematical representation is as follows: ; in, The average cost rate of the backup system for critical components of the centrifugal compressor. For reliability evaluation models, The average cost rate function for the backup system of critical components. The reliability threshold for backup systems of critical components; This represents the maximum inventory level of spare parts that can be stored in the inventory. Order the quantity of spare parts for critical components.

[0057] The spare parts optimization method for key components of centrifugal compressors provided by this invention, when the number of spare parts is less than a preset value, inputs the usage information of the key components in the current use state and the spare parts information of the key components in the current standby state into the spare parts optimization model. The spare parts optimization model outputs the spare parts procurement status of the key components in the key component standby system. This enables the optimization model based on reliability thresholds to combine the inventory management of multiple spare parts with the degradation effect. Under the premise of ensuring system reliability requirements, it optimizes the ordering quantity and ordering time interval of spare parts, thereby reducing inventory costs and management costs.

[0058] This invention considers the degradation that spare parts undergo during storage and proposes a Weibull distribution model for spare part performance decline. Unlike existing technologies that simply assume the spare parts are intact, this degradation-considering model more realistically reflects the actual situation and improves the accuracy of spare system life assessment.

[0059] The multi-spare parts inventory optimization model proposed in this invention can take into account the degradation effect of spare parts under different inventory durations, scientifically optimize the reserve status of multiple spare parts, minimize the risk of inventory backlog and shortage, and ensure system reliability, thus achieving good economic benefits.

[0060] This invention proposes a spare parts ordering strategy with the goal of minimizing the average cost rate by calculating expected costs and combining them with inventory holding costs, ordering costs, and maintenance costs. This optimization method not only considers costs but also ensures that system reliability thresholds are met, thereby optimizing the overall strategy for spare parts ordering and inventory management. This model effectively solves the reliability assessment problem when multiple spare parts are sequentially activated in a standby system, enhancing the system's practical operability.

[0061] First, the backup system lifetime distribution model established in this invention is verified using statistical simulation. The core idea of ​​statistical simulation is to approximate the actual results of the problem through extensive random sampling and repeated experiments, and then use statistical analysis to derive the desired conclusions, such as probability distribution, expected value, variance, or system reliability. Statistical histograms for different numbers of spare parts are obtained by statistically analyzing the failure time intervals of a large number of components. Figure 4 The yellow histogram is shown in the figure. In this invention, the maximum inventory of spare parts is 8. Figure 4 The results shown are a comparison between the lifetime probability density function of the backup system with 0 and 4 spare parts and the results obtained by statistical simulation. The blue line represents the model results established in this invention.

[0062] As can be seen from the results in the figure above, there is a good consistency between the life distribution model of the dry gas sealed backup system and the results obtained by the statistical simulation method, which demonstrates the accuracy of the model established in this invention.

[0063] To further validate the spare system lifetime distribution model, this paper compares and analyzes the cases that consider spare parts degradation within inventory with those that do not. For example... Figure 5 As shown, the spare system lifetime PDF curve considering spare parts degradation is represented by a blue line, while the lifetime PDF curve not considering spare parts degradation is represented by a red line.

[0064] Analysis results show that considering spare parts degradation in inventory reduces the lifespan of the backup system compared to not considering it. Furthermore, as the number of spare parts increases, the performance degradation of spare parts used later becomes more pronounced due to prolonged storage, thus making the lifespan difference between the two scenarios more significant. This indicates that the lifespan of the backup system may be overestimated when spare parts degradation is not considered, especially with the maximum number of spare parts. n When the value is 8, the difference in the average lifespan of the backup system is most significant in the two cases.

[0065] After verifying the accuracy of the backup system life distribution model established in this invention, we can further analyze the optimization model of the spare parts reserve scheme for key components of centrifugal compressors based on the reliability threshold. Taking the dry gas seal backup system as an example, the relevant parameters calculated from the dry gas seal data are shown in Table 1 below.

[0066] Table 1 shows the relevant parameters derived from the dry gas seal data.

[0067] In the spare parts inventory optimization model of this invention, the optimal spare parts ordering quantity is found to be 6, and the optimal spare parts ordering interval is 7833.00h, obtained by solving the problem using the grid search method. Table 2 summarizes the optimal spare parts ordering interval, optimal spare parts ordering quantity, and average cost rate of this spare parts inventory optimization model.

[0068] Table 2 shows the optimization results of the spare parts reserve plan.

[0069] The electrocardiogram (ECG) monitoring device provided by the present invention is described below. The ECG monitoring device described below can be referred to in correspondence with the ECG monitoring method described above.

[0070] Figure 6 This invention provides a schematic flowchart of a spare parts optimization device for a key component of a centrifugal compressor. (See attached diagram.) Figure 6 This device is applied to the backup system of any critical component of a centrifugal compressor, and includes an acquisition module 61 and a processing module 62, wherein: The acquisition module is used to acquire key component data of the critical component backup system of the centrifugal compressor during the current service cycle. The component data includes the usage information of the key components in the current service state and the spare parts information of the key components in the current standby state. The spare parts information includes the number of spare parts. The processing module is used to input the usage information of key components in the current use state and the spare parts information of key components in the current standby state into the spare parts optimization model when the number of spare parts is less than a preset value. The spare parts optimization model outputs the spare parts procurement status of key components in the key component standby system. The spare parts optimization model includes an average cost rate calculation model, a reliability evaluation model and constraints for the key component standby system, which are constructed based on the usage information of key components in the current use state, the spare parts information of key components in the current standby state and the procurement information of key components in the predicted state.

[0071] Since the apparatus of this embodiment is based on the same principle as the method of the above embodiment, more detailed explanations will not be repeated here.

[0072] It should be noted that, in the embodiments of the present invention, the relevant functional modules can be implemented by a hardware processor.

[0073] The spare parts optimization device for key components of centrifugal compressors provided by this invention, when the number of spare parts is less than a preset value, inputs the usage information of the key components in the current use state and the spare parts information of the key components in the current standby state into the spare parts optimization model. The spare parts optimization model outputs the spare parts procurement status of the key components in the key component standby system. This enables the optimization model based on reliability thresholds to combine the inventory management of multiple spare parts with the degradation effect. Under the premise of ensuring system reliability requirements, it optimizes the ordering quantity and ordering time interval of spare parts, thereby reducing inventory costs and management costs.

[0074] Figure 7 An example is a schematic diagram of the physical structure of an electronic device, such as... Figure 7As shown, the electronic device may include: a processor 71, a communication interface 72, a memory 73, and a communication bus 74. The processor 71, communication interface 72, and memory 73 communicate with each other via the communication bus 74. The processor 71 can call logical instructions in the memory 73 to execute a spare parts optimization method for critical components of the centrifugal compressor. This method includes: acquiring critical component data of the centrifugal compressor's critical component backup system within the current usage cycle; the critical component data includes usage information of critical components in the current usage state and spare parts information of critical components in the current standby state, the spare parts information including the quantity of spare parts; when the quantity of spare parts is less than a preset value, inputting the usage information of critical components in the current usage state and the spare parts information of critical components in the current standby state into a spare parts optimization model; and outputting the spare parts procurement status of the critical components in the critical component backup system from the spare parts optimization model. The spare parts optimization model includes an average cost rate calculation model, a reliability evaluation model, and constraints for the critical component backup system, constructed based on the usage information of critical components in the current usage state, the spare parts information of critical components in the current standby state, and the procurement information of critical components in the predicted state.

[0075] Furthermore, the logical instructions in the aforementioned memory 73 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 the present invention, essentially, or the part that contributes to the prior art, or a part 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 the present invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0076] On the other hand, the present invention also provides a computer program product, which includes a computer program that can be stored on a non-transitory computer-readable storage medium. When the computer program is executed by a processor, the computer can execute the spare parts optimization method for key components of a centrifugal compressor provided by the above methods. The method includes: acquiring key component data of the key component backup system of the centrifugal compressor in the current service life, the key component data including usage information of key components in the current service state and spare parts information of key components in the current standby state, the spare parts information including the quantity of spare parts; when the quantity of spare parts is less than a preset value, inputting the usage information of key components in the current service state and the spare parts information of key components in the current standby state into a spare parts optimization model, and outputting the spare parts procurement status of key components of the key component backup system by the spare parts optimization model; wherein, the spare parts optimization model includes an average cost rate calculation model, a reliability evaluation model and constraints of the key component backup system constructed based on the usage information of key components in the current service state, the spare parts information of key components in the current standby state and the procurement information of key components in the predicted state.

[0077] In another aspect, the present invention also provides a non-transitory computer-readable storage medium storing a computer program thereon. When executed by a processor, the computer program implements a spare parts optimization method for critical components of a centrifugal compressor provided by the methods described above. The method includes: acquiring critical component data of a critical component backup system of a centrifugal compressor during the current usage cycle; the critical component data includes usage information of critical components in the current usage state and spare parts information of critical components in the current standby state, wherein the spare parts information includes the quantity of spare parts; when the quantity of spare parts is less than a preset value, inputting the usage information of critical components in the current usage state and the spare parts information of critical components in the current standby state into a spare parts optimization model, and outputting the spare parts procurement status of critical components in the critical component backup system from the spare parts optimization model; wherein the spare parts optimization model includes an average cost rate calculation model, a reliability evaluation model, and constraints for the critical component backup system constructed based on the usage information of critical components in the current usage state, the spare parts information of critical components in the current standby state, and the procurement information of critical components in the predicted state.

[0078] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs. Those skilled in the art can understand and implement this without any creative effort.

[0079] Through the above description of the embodiments, those skilled in the art can clearly understand that each embodiment can be implemented by means of software plus necessary general-purpose hardware platforms, and of course, it can also be implemented by hardware. Based on this understanding, the above technical solutions, in essence or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, magnetic disk, optical disk, etc., and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute the methods described in the various embodiments or some parts of the embodiments.

[0080] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for optimizing spare parts for key components of a centrifugal compressor, characterized in that, A backup system for any critical component of a centrifugal compressor, the method comprising: Acquire key component data of the backup system for critical components of the centrifugal compressor during the current service cycle. The key component data includes usage information of key components in the current service state and spare parts information of key components in the current standby state, including the number of spare parts. When the number of spare parts is less than a preset value, the usage information of the key components in the current use state and the spare parts information of the key components in the current standby state are input into the spare parts optimization model. The spare parts optimization model outputs the spare parts procurement status of the key components in the key component standby system. The spare parts optimization model includes an average cost rate calculation model, a reliability evaluation model, and constraints for the key component standby system, which are constructed based on the usage information of the key components in the current use state, the spare parts information of the key components in the current standby state, and the procurement information of the key components in the predicted state.

2. The spare parts optimization method for key components of a centrifugal compressor according to claim 1, characterized in that, The method also includes constructing a spare parts optimization model, including: Based on the usage information of key components under current use, the spare parts information of key components under current standby status, and the procurement information of key components under predicted status, a calculation model for the total working life of the key component standby system and a life probability density function are constructed. Based on the total working life calculation model and life probability density function of the critical component backup system, an average life distribution model and a reliability evaluation model of the critical component backup system are constructed. Based on the average life distribution model and reliability evaluation model of the critical component backup system, a model for the expected time interval of spare parts ordering for the critical component backup system is constructed. Obtain the expected cost model of the backup system for critical components over a single service life; Based on the expected spare parts ordering interval model and the expected cost model within a single usage cycle of the critical component backup system, an average cost rate calculation model for the critical component backup system is constructed. A spare parts optimization model is established with constraints that the reliability of the backup system for critical components is higher than the reliability threshold and the maximum spare parts order quantity for critical components is less than or equal to the maximum inventory quantity. The spare parts ordering interval and spare parts ordering quantity for the backup system for critical components are used as decision variables, and the minimum average cost rate is used as the optimization objective.

3. The spare parts optimization method for key components of a centrifugal compressor according to claim 1, characterized in that, The method also includes constructing an expected cost model for the backup system of critical components over a single service life, including: Based on the spare parts information of key components in the current standby state and the procurement information of key components in the predicted state, determine the inventory holding cost and spare parts ordering cost of the key component standby system. Based on the pre-defined failure maintenance cost estimation strategy and preventive maintenance cost estimation strategy, as well as the reliability evaluation model of the critical component backup system, the system maintenance cost of the critical component backup system is determined. Based on the inventory holding cost, spare parts ordering cost, and system maintenance cost of the critical component backup system, construct the expected cost model of the critical component backup system within a single service life.

4. The spare parts optimization method for key components of a centrifugal compressor according to claim 3, characterized in that, The expected cost model for the critical component backup system over a single service life includes: in, The expected cost of a backup system for critical components over a single service life. It is the total cost of the backup system for critical components. Order the quantity of spare parts for critical components. For the time interval of spare parts ordering, For system maintenance costs, For inventory holding costs, To cover the cost of ordering spare parts, For the estimation of failure-related repair costs, For the estimation of preventive maintenance costs, For reliability evaluation models, Inventory holding cost for each spare part, Basic costs for ordering spare parts, The unit price for each spare part, This refers to a point in time within the spare parts ordering interval.

5. The spare parts optimization method for key components of a centrifugal compressor according to claim 2, characterized in that, The average expense ratio calculation model includes: in, The average cost rate for backup systems of critical components. Expected time interval for spare parts ordering The average lifespan distribution of the backup system for critical components. The total service life of the backup system for critical components.

6. The spare parts optimization method for key components of a centrifugal compressor according to claim 5, characterized in that, The expected model for spare parts ordering time intervals includes: in, Expected time interval for spare parts ordering This refers to the expected spare parts ordering interval when the total service life of the critical component backup system is less than the predetermined spare parts ordering interval. This refers to the expected spare parts ordering interval when the total service life of the critical component backup system is greater than or equal to the predetermined spare parts ordering interval. The total service life of the backup system for critical components is greater than or equal to the predetermined spare parts ordering interval. T The probability of occurrence.

7. The spare parts optimization method for key components of a centrifugal compressor according to claim 6, characterized in that, The reliability evaluation model includes: in, The reliability of the backup system for critical components during its service life q. For a key component within a usage cycle q and n The lifetime probability density function of a critical component backup system, consisting of ordered spare parts for key components. It uses time points within the period q. To use a point in time within the period q, This represents the time difference between ordering the previous spare part and the next spare part within the usage period q.

8. A spare parts optimization device for a key component of a centrifugal compressor, characterized in that, A backup system for any critical component of a centrifugal compressor, the device comprising: The acquisition module is used to acquire key component data of the critical component backup system of the centrifugal compressor during the current service cycle. The component data includes the usage information of the key components in the current service state and the spare parts information of the key components in the current standby state. The spare parts information includes the number of spare parts. The processing module is used to input the usage information of key components in the current use state and the spare parts information of key components in the current standby state into the spare parts optimization model when the number of spare parts is less than a preset value. The spare parts optimization model outputs the spare parts procurement status of key components in the key component standby system. The spare parts optimization model includes an average cost rate calculation model, a reliability evaluation model, and constraints for the key component standby system, which are constructed based on the usage information of key components in the current use state, the spare parts information of key components in the current standby state, and the procurement information of key components in the predicted state.

9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the program, it implements the spare parts optimization method for key components of the centrifugal compressor as described in any one of claims 1 to 7.

10. A non-transitory computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the spare parts optimization method for key components of the centrifugal compressor as described in any one of claims 1 to 7.

Images