Multi-level decision model construction and analysis method for prolonging life of mechanical and electrical products

Abstract

The invention relates to the technical field of reliability engineering, in particular to a multi-level decision model construction and analysis method for life extension of mechanical and electrical products, which comprises the following steps: according to product tree construction and life extension information collection, analyzing life restricting factors, designing factors, environmental factors and management factors, and establishing a multi-level decision model for life extension of mechanical and electrical products. Factors and reasons influencing the life are found out, a multi-level life extension decision model is constructed around the core of the life extension method, five factor decisions including a measure feasibility decision, a weak link decision, an opportunity decision, an effect decision and a cost decision are analyzed, and finally a comprehensive decision life extension scheme is formed. The method can objectively provide a product life extension decision-making method with higher practical operability and perspectiveness, and realizes collaborative improvement of resource utilization efficiency, operation cost control and market competitive advantages.

Description

Technical Field

[0001] This invention belongs to the field of reliability engineering technology, specifically relating to a multi-level decision model construction and analysis method for extending the lifespan of electromechanical products. Background Technology

[0002] In today's highly competitive market environment, product life extension is of significant practical importance and economic value for enterprises in reducing operating costs, improving resource utilization efficiency, and enhancing market competitiveness. With the continuous advancement of science and technology, the rate of product updates and iterations is accelerating. However, many products, even after reaching their designed lifespan, can still maintain normal operation in some functions. Directly discarding such products would not only result in serious resource waste but also significantly increase production costs and operational burdens for enterprises. While existing product life extension analysis methods have made some progress, they still have many significant technical shortcomings in practical applications, making it difficult to meet enterprises' needs for accurate and efficient life extension decisions. Specifically, these include... (1) Existing life extension analysis methods generally suffer from the problem of single analysis dimensions and lack of systematic consideration. Such methods usually only conduct life extension assessment from a single dimension, such as judging whether the product has the feasibility of life extension based on the product's cumulative usage time or simple performance parameters. They do not conduct a systematic analysis of the coupling effect of multiple factors throughout the product's entire life cycle, and cannot comprehensively and objectively reflect the product's actual operating status and potential lifespan.

[0003] (2) Existing life extension decision-making methods have limitations in adaptability and scientific rigor. In the life extension decision-making process, relevant technical solutions often rely on simple means such as experience-based judgment or linear analysis, which are difficult to adapt to the complex and ever-changing product operating environment and diverse life extension needs. This leads to a significant deviation between the decision results and actual engineering requirements, and fails to provide a reliable basis for the formulation of life extension solutions. Taking life extension assessment in typical fields such as complex electromechanical products as an example, traditional technical solutions often neglect the comprehensive consideration of multiple factors, resulting in low decision accuracy. Existing assessment processes often focus on single technical indicators such as the degree of mechanical wear, without fully considering multi-level key factors such as environmental impact, design characteristics, cost input, and maintenance conditions. This directly leads to inaccurate life extension decision results, failing to achieve the core objectives of product performance optimization and effective life extension, and may even cause safety hazards or economic losses due to decision-making errors.

[0004] (3) Existing life extension decision-making models still suffer from structural defects and poor operability. These models lack clear hierarchical and systematic design, making it difficult to comprehensively weigh multiple factors such as technical feasibility, economic rationality, and safety and reliability. As a result, the models are not very operable in actual engineering applications and cannot provide enterprises with standardized and implementable life extension decision support solutions.

[0005] In view of this, the present invention is hereby proposed. Summary of the Invention

[0006] To address the aforementioned technical problems in the existing technology, this invention provides a multi-level decision-making model construction and analysis method for extending the service life of electromechanical products, solving the problems existing in current product service life extension decisions and providing more effective product service life extension decision support.

[0007] To achieve the above objectives, the technical solution of the present invention is as follows: A multi-level decision-making model construction and analysis method for life extension of electromechanical products includes: S1. Product tree construction and life extension information collection: Based on the characteristics of electromechanical products, product levels are divided and a product tree is constructed to collect life extension information of electromechanical products. S2. Lifespan limiting factor analysis: This involves analyzing environmental, design, and management-related factors that affect the lifespan of electromechanical products, studying product failure modes and mechanisms, and determining the limiting factors of product lifespan by combining life extension elements based on life extension methods. S3. Construction of a multi-level life extension decision model: Based on the analysis results of the life-limiting factors and life extension elements, a multi-level life extension decision model is constructed, which includes decision on weak links, decision on the feasibility of measures, decision on effects, decision on timing, and decision on costs. S4. Form a comprehensive decision-making life extension plan, integrate the decision results of each level of the multi-level life extension decision model, perform comprehensive calculation and screening on each life extension measure, and form a life extension plan for electromechanical products.

[0008] Furthermore, the product tree is an information skeleton and information container built on the basis of the electromechanical product configuration; after the product tree is built, the life extension information of the electromechanical products obtained includes: product configuration, expected life of each level of product, delivery time and usage guarantee information.

[0009] Furthermore, the lifespan constraint analysis includes: studying environmental factors, design factors, and management factors in relation to the product's service environment; analyzing typical material failure modes and mechanisms; and further subdividing lifespan extension methods into three categories and eight lifespan extension elements based on their implementation paths, thereby comprehensively determining the product's lifespan constraint factors.

[0010] Furthermore, the multi-level life extension decision-making model is constructed based on the analysis of life-limiting factors and life extension elements, including a calculation and judgment mechanism with five dimensions: weak link decision, feasibility decision, effect decision, timing decision and cost decision.

[0011] Furthermore, the product tree is divided into equipment level, component level, and part level, and each level and the complexity of the electromechanical products are adapted to management needs.

[0012] Furthermore, the life extension information includes: Basic product information includes product number, delivery time, ideal cost basis, storage life, working life, life type, remarks, and spare fields, and remains unchanged after the electromechanical product design is finalized; Product maintenance and support information includes product name, product code, failure date, failure type, failure description, failure handling status, and remarks. This information is updated cumulatively as the product is used and maintained. Lifetime basic data: including product name, product code, failure rate, lifetime, probability model, parameters 1-3, and remarks fields. It is derived from basic product information, product maintenance and support information, and industrial-level statistical analysis, and is iteratively corrected as maintenance and support information becomes available.

[0013] Furthermore, the classification of the life extension elements includes: Design changes include: design optimization, process improvement, and material selection; Enhanced protection measures include replacing worn-out parts, improving storage / transportation conditions, and extending service life through repairs. Optimization deployment category: including condition-based maintenance decision optimization and usage pattern optimization.

[0014] Furthermore, the specific process for decision-making regarding the weak link is as follows: Based on the failure modes derived from FMECA analysis or subjective judgments based on experience, each component of the product is assigned a 0 / 1 judgment result, where 1 indicates that the component has a failure mode that affects its lifespan, and 0 indicates that it does not. For components with a judgment result of 1, analyze the failure mechanism and rank them according to risk, then calculate the weakness factor using a formula. The specific formula is as follows:

[0015] in, For the rank of the failure mechanism, The weak factor ranges from 0 to 1, representing the total number of failure mechanisms that affect lifespan.

[0016] Furthermore, the comprehensive decision-making life extension scheme utilizes comprehensive decision-making... The specific formula for construction is as follows:

[0017]

[0018] in, As an effect factor, For cost factors, , The cost factor is calculated based on the pre-processed funds. For timing-based or feasibility-based decisions, the result is either 0 or 1. The balance coefficient for weak links, This is the cost equilibrium coefficient. A fairness coefficient is used for research and protection.

[0019] Furthermore, the lifespan limiting factor analysis also includes the lifespan extension effect of improving the storage / transportation environment. The Arrhenius model is used to calculate the lifespan extension effect, and the specific formula is as follows:

[0020]

[0021] in, The lifespan of electromechanical products at a certain temperature. Here, k represents the activation energy of the fault mechanism, and k is the Boltzmann constant. It is a constant. For ambient temperature, For the product lifespan before temperature improvement, To improve product lifespan after temperature adjustment, This is a value that enhances the lifespan extension effect.

[0022] Compared with existing technologies, the present invention provides a multi-level decision-making model construction and analysis method for extending the service life of electromechanical products. The method includes: based on the product tree construction and service life information collection, analyzing the factors limiting service life from the aspects of design factors, environmental factors, and management factors, identifying the factors and causes affecting service life, and constructing a multi-level service life extension decision-making model around the core of the service life extension method. The model analyzes the decision-making of five factors: feasibility of measures, weak link decision, timing decision, effect decision, and cost decision, and finally forms a comprehensive service life extension plan. The present invention can objectively provide a more practical and forward-looking product service life extension decision-making method, and achieve synergistic improvement in resource utilization efficiency, operating cost control, and market competitive advantage. Attached Figure Description

[0023] Figure 1 A schematic diagram illustrating the multi-level decision model construction and analysis method provided in an embodiment of the present invention; Figure 2 A schematic diagram of a product tree for extending product life provided in an embodiment of the present invention; Figure 3 A schematic diagram illustrating the construction of a multi-level lifetime extension decision model provided in an embodiment of the present invention; Figure 4 A schematic diagram of a weak link decision diagram provided in an embodiment of the present invention; Figure 5 A schematic diagram illustrating the similarity of life-extending effects provided in embodiments of the present invention; Figure 6 This is a schematic diagram illustrating the comprehensive decision-making process provided in an embodiment of the present invention. Detailed Implementation

[0024] The technical solution of the present invention will be clearly described below with reference to the accompanying drawings. Obviously, the described embodiments are not all embodiments of the present invention. All other embodiments obtained by those skilled in the art without creative effort are within the protection scope of the present invention.

[0025] It should be noted that, unless otherwise specifically stated, the relative arrangement and numerical expressions of the components and steps described in these embodiments should not be construed as limiting the scope of the invention.

[0026] The following description of exemplary embodiments is merely illustrative and is not intended to limit the invention or its application or use in any way. Techniques, methods, and apparatus known to those skilled in the art may not be discussed in detail herein, but where applicable, such techniques, methods, and apparatus should be considered part of this specification.

[0027] Example 1 See Figure 1 , Figure 1 This invention proposes a multi-level decision-making model construction and analysis method for extending the lifespan of electromechanical products; specific steps may include: S1. Product Tree Construction and Life Extension Information Collection: Based on the characteristics of electromechanical products, product levels are divided and a product tree is constructed to collect life extension information for electromechanical products; specifically including: S11. Based on the complexity of electromechanical products and management needs, classify products into different levels, such as... Figure 2 As shown, electromechanical products are divided into different levels such as equipment, components, and parts.

[0028] S12. Obtain life extension information for electromechanical products. Life extension requires various types of product information, including product configuration, expected lifespan at each product level, delivery time, and usage support information. To effectively collect and utilize life extension information, it is divided into three categories: basic product information, product maintenance and support information, and basic lifespan data. The specific fields for each type of information include: Basic product information mainly consists of model attribute information, specifically including: product name, product number, ideal cost basis, storage life, working life, lifespan type, remarks, and spare fields. Model attribute information remains unchanged after the electromechanical product design is finalized, including information on lifespan of spare parts and ideal cost. Model attribute information can be determined and entered after delivery, while product attribute information is entered after delivery and updated cumulatively during subsequent use and maintenance. Lifespan basic information is derived from model attribute information and product attribute information, combined with industrial-level statistical analysis. Product maintenance and support information: mainly product attribute information, specifically including: product name, product code, failure date, failure type, failure description, failure handling status, and remarks field, which are cumulatively updated as the product is used and maintained. Product attribute information is closely related to each product and is different for each product, such as delivery date and maintenance information. Lifespan baseline data: This data is derived by combining product maintenance and support information with basic industrial data. Once a model is defined, the product design, production, and manufacturers at each level are determined. Lifespan baseline data needs to be collected, accumulated, and statistically analyzed. It is then continuously iterated and corrected based on maintenance and support information during subsequent use. Specifically, it includes fields such as product name, product code, failure rate, lifespan, probability model, parameter 1, parameter 2, parameter 3, and remarks. It is derived from basic product information, product maintenance and support information, and industrial-level statistical analysis, and is iteratively corrected as maintenance and support information becomes available.

[0029] S2. Lifespan Limitation Factor Analysis: This section analyzes environmental, design, and management-related factors affecting the lifespan of electromechanical products, studies product failure modes and mechanisms, and, in conjunction with life extension elements segmented based on life extension methods, identifies the limiting factors of product lifespan. It mainly includes limiting factor analysis, lifespan cause analysis, and life extension element analysis, and is implemented as follows: S21. Analysis of limiting factors, specifically including: S211. Environmental Factor Analysis: It is necessary to identify the factors affecting the service life of electromechanical products within the service life profile, including temperature, humidity, salt spray, water pressure, vibration, shock, etc., and study the product failure modes and failure mechanisms under the action of the main factors, and analyze the limiting factors of product life.

[0030] S212. Design Factor Analysis: This mainly includes material selection, structural design, protection design, and selection of electronic components. Among these, material selection and structural design are the most important limiting factors.

[0031] S213. Management Factor Analysis: This includes regular maintenance and upkeep of electromechanical products, operation management of operators, preservation of maintenance records, performance testing, etc. Among these, maintenance and upkeep and operation management are the most influential limiting factors on the lifespan of electromechanical products.

[0032] S22. Analysis of factors affecting lifespan, mainly divided into internal causes of failure and external causes of failure, specifically including: S221. Internal causes of failure: These refer to the direct causes of product failure due to inherent design flaws, manufacturing defects, or insufficient performance. They can generally be categorized as follows: The product performance did not meet the usage requirements. For example, the product experienced overload (insufficient strength design), low-cycle fatigue (insufficient fatigue strength), deformation (insufficient stiffness), creep (material selection), corrosion (insufficient corrosion resistance design), and wear (insufficient hardness design).

[0033] Defects in the product's own materials. For example, internal defects can easily lead to stress concentration, resulting in fatigue and overload failure; uneven, volatile, and uncoordinated structure can easily lead to high-cycle fatigue; poor surface integrity (caused by materials or processing) can easily cause local stress concentration; reduced corrosion and wear resistance can easily lead to high-cycle fatigue, wear, corrosion, etc.

[0034] The material has poor environmental adaptability. For example, it is prone to corrosion, creep, and aging failure.

[0035] The materials have poor compatibility. For example, there is wear failure (adhesive wear, abrasive wear), diffusion of intermetallic compounds, etc.

[0036] S222. External Causes of Failure: These refer to indirect causes of product failure besides its own inherent factors, such as failures of other products, environmental factors, and human factors. They can generally be categorized as follows: The workload factor refers to the various loads that a product experiences during operation, such as liquid pressure, gas pressure, current, and voltage. It also takes into account the different stages of the task, such as operation, storage, and transportation.

[0037] External environmental conditions, such as climatic factors including temperature (high and low temperature environments), temperature changes, humidity, solar radiation, etc.; mechanical environmental factors including periodic vibration, random vibration, impact (including free fall, tilt fall, etc.), steady-state acceleration, and static load, etc.

[0038] Human factors refer to damage or loss of a product beyond expectations caused by human error, transportation, storage errors, or improper behavior during the product's life cycle.

[0039] Unexpected factors refer to the impact of uncontrollable factors on products, such as bird strikes on aircraft or earthquakes on the ground.

[0040] S23. Life Extension Factor Analysis: This involves design improvements (including product and process design) targeting the inherent weaknesses of electromechanical products to extend their lifespan. Life extension can also be achieved by reducing consumption; considering lifespan as a specific value, damage accumulates, and adjustments to usage patterns reduce damage and consumption, thus extending the service life. Furthermore, life extension can be achieved through condition recovery; electromechanical products inevitably suffer some damage during use, and scientific maintenance and repair can prevent or curb further damage, ensuring the expected lifespan is achieved. Corresponding life extension activities include only three methods: enhanced protection, optimized deployment, and design modification. These can be further subdivided into eight types based on their implementation, called life extension factors. Each life extension factor has different specific implementation methods, as detailed below: S231. Design Modification: Analyze the factors affecting service life and the mechanisms of failure from three life-extending elements: optimized design, process improvement, and material selection. Specific details are as follows: S2311. Optimized Design: Improvements are made to areas with design defects through structural optimization, mechanism matching, and circuit simulation optimization. S2312. Process Improvement: For defects in components caused by manufacturing reasons, process improvement, production quality control, application of testing methods, and improvement of process reliability can be considered to improve the reliability of the manufacturing process. S2313, Material Selection: Given the limitations of the technology at the time that caused the problems, we can now consider analyzing ways to improve the lifespan of electromechanical products from aspects such as component upgrades, material selection, and the application of new technologies and processes.

[0041] S232. Enhanced Support: This section analyzes ways to improve product lifespan from the perspectives of maintenance support types, maintenance intervals, maintenance content, and support conditions. Specific details are as follows: S2321. Replacement of life-limiting parts: In the product composition, the components that affect the life-limiting parts are re-identified in conjunction with the work content of intermediate and major overhauls. The list of life-limiting parts for electromechanical products is sorted out and improved, and the life is extended by replacing the life-limiting parts. S2322. Improve storage / transportation environment: Based on the product storage profile and transportation profile, analyze the unreasonable aspects of protection and optimize them, and take measures such as heat insulation, sealing, temperature and humidity control, and buffering measures during transportation to extend service life; S2323, Maintenance and Service Extension: By analyzing the service life of each component of the product, identify components with a high failure rate, conduct comprehensive inspections of products with out-of-tolerance indicators or near the technical requirement boundaries, replace or repair them, analyze the rationality of existing maintenance procedures, and propose reasonable maintenance intervals and maintenance operations.

[0042] S233. Optimized Deployment: This section analyzes ways to extend the lifespan of electromechanical products from the perspectives of decision-making optimization and usage mode optimization. Specific details are as follows: S2331. Condition-based maintenance decision optimization: Enhance condition prediction and condition-based maintenance, provide condition monitoring capabilities, monitor products in real time, and promptly detect and repair issues.

[0043] S2332. Usage Mode Optimization: The transportation process is the main source of vibration and shock, and the environment is complex and highly uncertain. By reducing the frequency of transfers during the product's lifespan, and by adopting the ability of in-situ maintenance and remote support, the frequency and distance of transportation caused by maintenance can be reduced; by optimizing resource allocation and the principle of task sharing, the frequency of product loading can be reduced; and by improving the level of online status monitoring and fault isolation, the frequency of disassembly and assembly caused by false alarms can be reduced.

[0044] S3. Construction of a multi-level life extension decision-making model: Based on the analysis results of lifespan limiting factors and life extension elements, a multi-level life extension decision-making model is constructed, including decisions on weak links, feasibility of measures, effects, timing, and costs; for example... Figure 3 The diagram shows a computational judgment mechanism, the core of the lifespan extension method, comprising five aspects: weak link decision-making, feasibility decision-making, effect decision-making, timing decision-making, and cost decision-making. Specifically, it includes: S31. Decision-making regarding weak links: Decision-making regarding weak links involves two levels of judgment, and the specific steps are as follows: S311. After obtaining the product composition, firstly, based on the structural analysis / functional analysis of the product, the failure modes obtained from the FMECA analysis are distinguished to obtain the components that include failure modes affecting lifespan (if the early model did not undergo FMECA, subjective judgment can also be made based on experience to determine the relatively weak components or parts in terms of lifespan, and the relatively weak components are obtained), and each component is assigned a 0 / 1 judgment result. S312. Further analysis and judgment are made on the components identified by 0 / 1, based on the analysis of the effective mechanism, failure rate, and severity, and a ranking of the effective mechanisms is given to identify weak links and sensitive loads. Then, the weakness factor is calculated by median rank. See the decision-making process. Figure 4 .

[0045] Therefore, the concept of a weak factor is introduced. In this invention, it is calculated based on the median rank of the failure mechanism according to the risk ranking, and is defined as follows:

[0046] in, The weak factor is between 0 and 1, with smaller values ​​indicating more severe weaknesses. The rank of the failure mechanism; This represents the total number of failure mechanisms that affect lifespan.

[0047] Taking a certain piece of equipment as an example, this equipment consists of 7 components. Through weak link analysis, components 1 to 5 have no failure modes that affect lifespan (or rarely reach their lifespan based on historical data), so the 0 / 1 judgment result is 0. Components 6 and 7 have failure modes that affect lifespan, so the 0 / 1 judgment result is 1. Further failure mechanism analysis of components 6 and 7 revealed that component 6 has 3 failure mechanisms and component 7 has 1 failure mechanism. Based on engineering experience, the 4 failure mechanisms are ranked, and the weak factor is obtained according to the formula. The specific results are shown in Table 1. Table 1 Decision Table for Weakness Factors

[0048] S32. Feasibility Decision-Making for Measures: Based on the analysis of life extension measures and life extension principles, it is known that both life extension principles and measures have certain categorization attributes. Any life extension measure belongs to one of the three major categories and eight life extension elements. Therefore, this invention sets eight tags: "Optimized Design," "Process Improvement," "Material Selection," "Replacement of Service-Dependent Parts," "Maintenance Life Extension," "Improvement of Storage / Transportation Environment," "Condition-Based Maintenance Optimization Decision," and "Usage Mode Optimization." Feasibility decision-making for measures involves judging the feasibility of life extension measures based on life extension principles, with a result of 0 or 1. The decision-making mechanism determines the tags for this life extension activity based on the life extension principles selected by the user, and then filters the life extension measures according to the tags. Measures that conform to the life extension principles are judged with a decision result of 1, and those that do not conform are judged with a result of 0. Specific results are shown in Table 2: Table 2: Comparison Table of Feasibility Decisions for Measures

[0049] S33. Effectiveness Decision: For the eight life extension factors in design modification, enhanced protection, and optimized deployment, the life extension effects follow different patterns. However, some life extension factors exhibit similar patterns. For example, the three life extension factors in design modification show nearly identical patterns, and the life extension patterns of replacing worn-out components and maintenance in enhanced protection are similar. Therefore, the effects of all eight life extension factors should be studied together. Figure 5 As shown. The specific effects of the life-extending elements are as follows: S331. Life Extension Effects of Design Modifications: These are categorized into three life extension elements: optimized design life extension, process improvement life extension, and material selection life extension. The effects of these life extensions are both disruptive and similar. The disruptive aspect is that, if the measures are effective, the lifespan after implementation can not only weaken or eliminate lifespan shortcomings but may even exceed the target lifespan. The similarity lies in the consistent pattern observed in the life extension effects among the three elements.

[0050] Product life indicators and target lifespans vary significantly across different components. For example, some life indicators are measured in time, while others are measured in the number of cycles. Target lifespans can be 5 years, 10 years, 20 years, or 100 or 2000 cycles, etc. To standardize the representation of life extension effects, the concept of relative lifespan is introduced, which is the ratio of the expected lifespan of the measure to the target lifespan. Effectiveness decision-making quantifies the life extension effect produced by the life extension measures. To measure the merits of a life extension effect, an effectiveness factor is introduced. The specific formula is:

[0051]

[0052] in, The expected lifespan of a related product resulting from a certain life extension measure. This measure may not be validated; validation is recommended before extending the lifespan. for The ratio of the product lifespan index to the corresponding product lifespan index is recommended to be between 0.1 and 10 to simplify engineering processes; The effect factor is a normalization of relative lifespan, ranging from 0 to 1. A larger value indicates a better life extension effect.

[0053] S332. The effect of replacing life-limited parts on life extension versus repair-based life extension: Given the distribution type and parameters of life-limited parts, the probability density function and life distribution function are determined. The replacement time for life-limited parts is determined based on the failure rate (3% or 5%), such as replacing life-limited parts in year N. The improvement in life extension effect is related to the service life of the new and old parts, as shown in the following formula:

[0054]

[0055] in, To improve reliability, For the initial reliability of new life-limited components, Reliability of existing components after N years of use; The relative increase in lifespan after replacing with high-life-value parts. This is the baseline lifespan of components with a lifespan. For the failure rate of new life parts, The failure rate of existing lifespan components; S333. Life Extension Effect of Improving Storage / Transportation Environment: Temperature has a significant impact on the lifespan of electromechanical products, and it is most commonly used as an accelerating stress in accelerated testing of these products. This is because high temperatures can accelerate internal chemical reactions, leading to premature product failure. Therefore, it is essential to study the quantitative relationship between temperature and lifespan. The Arrhenius model is frequently used to describe the relationship between temperature stress and the lifespan of electromechanical products; changing the temperature can have a significant effect on extending lifespan. The specific formula is as follows:

[0056]

[0057] in, The lifespan of electromechanical products at a certain temperature; The activation energy of the fault mechanism is expressed in eV; k is the Boltzmann constant. , It is a constant. For ambient temperature, For the product lifespan before temperature improvement, To improve product lifespan after temperature adjustment, This is a value that enhances the lifespan extension effect.

[0058] S334. The Life Extension Effect of Condition-Based Maintenance: Based on research into condition monitoring and life prediction technologies, a comprehensive study of costs and effects is conducted. Through maintenance decision modeling and dual-objective decision optimization, a reasonable maintenance support plan is formulated to extend the life of electromechanical products. Condition-based maintenance of electromechanical products is modeled with the dual objectives of maximizing lifespan and minimizing cost.

[0059] The objective function for maximizing lifespan is as follows:

[0060] The objective function with the goal of minimizing cost is as follows:

[0061] in, To allow preparation time, For preventative maintenance time, Let L be the fault repair time, and L be the decision variable. To cover preventative maintenance costs, For failure costs.

[0062] Based on the above model, decision optimization will be performed with the dual objectives of maximizing lifespan and minimizing cost. The satisfaction functions for the two objectives are defined as follows:

[0063]

[0064] in, To achieve life expectancy target satisfaction, This refers to the lifespan index corresponding to the maintenance time L. This represents the maximum value of the lifespan index. This represents the minimum value of the lifespan index. To achieve cost target satisfaction, This refers to the cost indicator corresponding to the repair time L. The maximum value of the cost indicator. This represents the minimum value of the cost indicator. The overall satisfaction function u(d) is constructed as follows:

[0065]

[0066]

[0067] in, The distance from the current point to the optimal target point is the square of the distance. It is the squared maximum distance between the worst point and the best point within the feasible region; By defining the allowable ranges for u(A(L)) and u(C(L)), the bi-objective decision optimization problem is transformed into a single-objective programming problem, as shown below:

[0068]

[0069] in, The objective function is to maximize overall satisfaction. Constrained by lifespan satisfaction. To meet cost satisfaction constraints, The feasible range of maintenance time is defined; the optimal maintenance time L can be obtained through model calculation.

[0070] S335. Life Extension Effect of Optimized Usage Modes: Considering environmental and management factors affecting the lifespan of electromechanical products, such as impact and vibration during transportation, collisions during loading, and static electricity and excessive mechanical stress during disassembly, reasonable planning and optimized usage modes should be implemented during the usage phase, and scientific usage plans should be formulated. In conjunction with the usage process of electromechanical products, the focus is on three main usage phases: transportation or transshipment, commissioning and disassembly, and loading and unloading. In the transportation and transshipment phase, improving on-site maintenance and remote support capabilities reduces the frequency and distance of transportation due to maintenance. In the commissioning and disassembly phase, improving online status monitoring and fault isolation levels reduces the frequency of disassembly and assembly caused by false alarms. In the loading and unloading phase, optimizing resource allocation reduces the frequency of product loading.

[0071] Optimizing these processes to extend lifespan is essentially a proportional stretching of lifespan. The specific lifespan extension effect of each measure is not easily generalized; the same measure will have different effects depending on the specific application scenario. For example, optimizing "transportation from the cave to the dock" to "transportation from the workshop to the dock" will result in different proportions of short transportation distances and different road grades at different bases. This invention, based on engineering practice, lists commonly used usage mode optimization types and provides recommended values ​​for lifespan extension effects. See Table 3 for details: Table 3. Life Extension Effect of Optimized Usage Mode

[0072] S34. Timing Decision: The inputs to the timing decision are information such as product delivery time, replacement frequency, and lifespan distribution. The output is a determination of whether the lifespan extension is within the appropriate timeframe, which is a product attribute. Lifespan extension timing is a product attribute. After product delivery, the lifespan or reliability follows a specific distribution and shows a downward trend. When the probability of achieving the target (PSZ) drops below a threshold, lifespan extension activities are triggered. The timing decision determines "when" to implement lifespan extension after the delivery of electromechanical products; it is also a 0 or 1 decision.

[0073] To better describe the operational readiness of electromechanical products, the concept of compliance probability is introduced, taking into account the characteristics of these products. The compliance probability is defined as: the probability that the number n of existing electromechanical products (including those in storage and use) in good condition is not less than M, i.e. This is denoted as PSZ. The probability of meeting the standard directly answers the question that the military cares about most—when required to perform a mission, how many electromechanical products have tactical and technical specifications that meet the design and finalization requirements and can be put into use directly.

[0074] From a statistical perspective, the quantity n of intact existing electromechanical products follows a binomial distribution. Let N be the total number of all existing electromechanical products involved, M be the lower limit of the acceptable number of intact products, and p be the reliability of a single electromechanical product for duration t. Then the storage compliance probability PSZ is:

[0075] Without external intervention, the overall probability of compliance for electromechanical products decreases with the number of years since delivery. It remains at a relatively high probability value in the first few years, above 0.9, then declines sharply to below 0.5, and finally remains at a very low probability value. Combat readiness generally requires maintaining a value above a certain threshold, i.e., the acceptable threshold. If it falls below the acceptable lower limit, preventative maintenance is necessary. If the product is above the acceptable threshold in year n and below it in year n+1, then taking measures in year n is scientifically reasonable.

[0076] S35. Cost Decision-Making: Cost estimation models are proposed for each of the eight life extension factors. Furthermore, cost information is specific to particular electromechanical products; it is meaningless to study a single cost of a particular type of electromechanical product without cross-sectional comparisons. Therefore, an ideal benchmark cost is proposed as a reference for other costs.

[0077] S351. Ideal baseline cost refers to the cost incurred by the military if it were to purchase a completely new set of established equipment. This mainly involves acquisition costs (including manufacturing costs, period costs, and profit), maintenance costs for replacing the old equipment with the new, and decommissioning costs. It can serve as a benchmark for other life extension costs. The ideal baseline cost model is as follows:

[0078] in, For purchase cost, For the guarantee fee, For retirement disposal fees, For manufacturing costs, For period fees, For profit.

[0079] S352. Costs for design life extension primarily involve development costs and technological improvement costs. Among the three methods of design life extension—optimized design life extension, process improvement life extension, and material selection life extension—all incur technological improvement costs. The main difference lies in the specific details of the development costs; all require design fees, material costs, and testing fees, but the proportions of each sub-item differ. A unified cost model can be used for design life extension costs. The general model is as follows:

[0080] in, For research and development costs, For technical improvement expenses; S353. The cost of extending the service life of replacement parts mainly involves repair costs (including repair personnel costs, repair material costs, repair equipment / facilities costs, etc.) and support costs (including supply support costs, support material costs, support personnel costs, etc.). The cost model is as follows:

[0081] in, For manufacturing costs, For repair costs, For the guarantee fee, For maintenance personnel costs, For repair equipment costs, For equipment / facilities repair costs, For supply guarantee fees, To ensure facility costs, To ensure the cost of materials, To ensure personnel expenses; S354. The cost of extending service life due to improved storage / transportation environment mainly involves the research and development costs, manufacturing costs, and support costs (including supply support costs, support data costs, support facility costs, and support personnel costs) of support elements. Its cost model is as follows:

[0082] in, For research and development costs, For the guarantee fee, For manufacturing costs, Research and development expenses for environmental improvement equipment, For supply guarantee fees, To ensure facility costs, To ensure the cost of materials, To ensure personnel expenses; S355. Maintenance and life extension costs mainly involve demonstration fees, maintenance fees (including maintenance personnel fees, maintenance equipment fees, maintenance equipment / facilities fees, etc.), and support fees (including supply support fees, support data fees, support personnel fees, etc.). The maintenance and life extension cost model is as follows:

[0083] in, For the cost of argumentation, For repair costs, For the guarantee fee, For maintenance personnel costs, For repair equipment costs, For equipment / facilities repair costs; S356. The cost of extending service life through condition-based maintenance optimization decisions mainly involves feasibility study fees, research and development fees, procurement fees, and maintenance fees. Its cost model is as follows:

[0084] in, For the cost of argumentation, Research and development expenses for environmental improvement equipment, For purchase cost, For the guarantee fee; S357. Life extension due to usage mode optimization involves changes in usage mode and primarily affects coverage premiums. The cost model for enabling technology-based life extension is as follows:

[0085] in, For the guarantee fee; The economic constraints on extending the lifespan of electromechanical products ultimately boil down to a trade-off between costs. Cost decision-making involves quantifying the expenses incurred by life-extending measures. To measure the cost advantages and disadvantages of a life-extending measure, a cost factor is introduced. Based on the analysis of economic constraints, the cost of any life-extending measure can be calculated using a corresponding cost model, and the benchmark cost of any product can be calculated using an ideal benchmark cost model. Therefore, the cost factor is defined as the corresponding cost divided by the ideal benchmark cost. The specific formula is as follows:

[0086]

[0087] in, This is a cost factor, ranging from 0 to 1, with a larger value for smaller costs. The cost incurred for a certain life extension measure is an attribute of the measure; The product cost calculated based on the ideal benchmark model is an attribute of the product; see Table 4 for details: Table 4. Life Extension Effect of Optimized Usage Mode

[0088] S4. Formulate a comprehensive life extension plan, integrating the decision results of each level of the multi-level life extension decision model, performing comprehensive calculations and screening of various life extension measures to form a life extension plan for electromechanical products. Specifically, this includes: The aforementioned comprehensive decision-making life extension plan is based on a multi-level life extension decision-making model. Taking each life extension measure as an object, it further analyzes the results of decisions based on five factors: weakness factors, effectiveness factors, cost factors, etc., and performs weighted operations to ultimately assign a decision score to each measure. See the comprehensive decision-making principle diagram below. Figure 6 The decision score serves as the basis for subsequent optimization of measures to determine the life extension plan.

[0089] This invention needs to explain the determination principles of the weighting coefficients W1, W2, and W3, as well as the scope of the timing decision.

[0090] Weak link balance coefficient W1: Weak link is an attribute of the product and is a measure of the degree of weakness of electromechanical products and their components. The value of weak link balance coefficient W1 is between 0 and 1. If the coefficient is too large, the life extension plan will select measures that tend to target all electromechanical products. If the coefficient is too small, it will help to select measures that target weak links.

[0091] Cost Equalization Coefficient W2: Life extension measures can be one-time improvement measures targeting specific objects, or a one-time investment that affects multiple objects of electromechanical products. If it is the latter, the corresponding life extension cost should be amortized. There are two methods: First, obtain the cost when entering the measure and perform preprocessing, i.e., cost amortization, and then make cost decisions and comprehensive decisions; Second, make cost decisions directly, and then introduce a weighting coefficient W2 when making comprehensive decisions, which is named the cost equalization coefficient.

[0092] The research-support-application fairness coefficient W3: The eight life extension elements are divided into three categories: design modification, enhanced support, and optimized deployment, corresponding to research and development, support, and use, respectively. If any one of the three categories is adopted as a life extension measure, the decision score is fair to the corresponding life extension element. However, if two or three categories are selected simultaneously, it is unfair. To resolve the fairness issue among the three categories, a weighted coefficient W3 is introduced, named the research-support-application fairness coefficient. The research-support-application fairness coefficient W3 is a three-dimensional array [a,b,c], where a, b, and c all take values ​​from 0 to 1, representing the weights of design modification, enhanced support, and optimized deployment, respectively. A higher coefficient value is generally more favorable for that type of measure to be included in the life extension technology solution.

[0093] The scope of timing decisions: Timing decisions are 0 / 1 decisions. As product delivery time increases, reliability gradually decreases and lifespan is rapidly worn down. They are instructive for extending lifespan through replacement of dead parts and maintenance, but not applicable to other lifespan extension factors. The default setting is 1, meaning that it is reasonable and feasible to take these lifespan extension factors at any time.

[0094] The comprehensive decision-making calculation formula is as follows:

[0095]

[0096] in, This represents the cost factor calculated using the pre-processed funds; The result is 0 or 1 to indicate whether the decision is based on timing or feasibility. The balancing coefficient for weak links is set to 0.3 by default. The fairness coefficient for research and development is set by default to [0.5, 0.8, 0.6].

[0097] In summary, this invention, through research on life extension technology, analyzes life-limiting factors from the perspectives of design, environmental, and management factors to identify the factors and causes affecting lifespan. Combining the characteristics of electromechanical products and their development and usage patterns, it proposes a set of life extension methods, explaining product tree construction and life extension information collection. Around the core of the life extension method, a multi-level life extension decision-making model is constructed, analyzing five factors: feasibility of measures, weak link decisions, timing decisions, effectiveness decisions, and cost decisions. Based on multi-level life extension decisions, a comprehensive life extension solution is ultimately formed.

[0098] The above specific embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to examples, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the scope of the technical solutions of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.

Claims

1. A method for constructing and analyzing a multi-level decision-making model for extending the lifespan of electromechanical products, characterized in that, include: S1. Product tree construction and life extension information collection: Based on the characteristics of electromechanical products, product levels are divided and a product tree is constructed to collect life extension information of electromechanical products. S2. Lifespan limiting factor analysis: This involves analyzing environmental, design, and management-related factors that affect the lifespan of electromechanical products, studying product failure modes and mechanisms, and determining the limiting factors of product lifespan by combining life extension elements based on life extension methods. S3. Construction of a multi-level life extension decision model: Based on the analysis results of the life-limiting factors and life extension elements, a multi-level life extension decision model is constructed, which includes decision on weak links, decision on the feasibility of measures, decision on effects, decision on timing, and decision on costs. S4. Form a comprehensive decision-making life extension plan, integrate the decision results of each level of the multi-level life extension decision model, perform comprehensive calculation and screening on each life extension measure, and form a life extension plan for electromechanical products.

2. The method for constructing and analyzing a multi-level decision-making model for extending the lifespan of electromechanical products according to claim 1, characterized in that, The product tree is an information skeleton and information container built on the basis of the electromechanical product configuration. After the product tree is built, the life extension information of the electromechanical products obtained includes: product configuration, expected life of each level of product, delivery time and usage guarantee information.

3. The method for constructing and analyzing a multi-level decision-making model for extending the lifespan of electromechanical products according to claim 1, characterized in that, The analysis of lifespan limiting factors includes: studying environmental factors, design factors, and management factors in the product's service environment; analyzing typical material failure modes and mechanisms; and subdividing lifespan extension methods into three categories and eight lifespan extension elements based on their implementation paths, thereby comprehensively determining the product's lifespan limiting factors.

4. The method for constructing and analyzing a multi-level decision-making model for extending the lifespan of electromechanical products according to claim 1, characterized in that, The multi-level life extension decision-making model is constructed based on the analysis of life-limiting factors and life extension elements, and includes a calculation and judgment mechanism with five dimensions: weak link decision, feasibility of measures decision, effect decision, timing decision and cost decision.

5. The method for constructing and analyzing a multi-level decision-making model for extending the lifespan of electromechanical products according to claim 1, characterized in that, The product tree is divided into equipment level, component level, and part level, and each level and the complexity of the electromechanical products are adapted to management needs.

6. The method for constructing and analyzing a multi-level decision-making model for extending the lifespan of electromechanical products according to claim 1, characterized in that, The life extension information includes: Basic product information includes product number, delivery time, ideal cost basis, storage life, working life, life type, remarks, and spare fields, and remains unchanged after the electromechanical product design is finalized; Product maintenance and support information includes product name, product code, failure date, failure type, failure description, failure handling status, and remarks. This information is updated cumulatively as the product is used and maintained. Lifetime basic data: including product name, product code, failure rate, lifetime, probability model, parameters 1-3, and remarks fields. It is derived from basic product information, product maintenance and support information, and industrial-level statistical analysis, and is iteratively corrected as maintenance and support information becomes available.

7. The method for constructing and analyzing a multi-level decision-making model for extending the lifespan of electromechanical products according to claim 1, characterized in that, The classification of the life extension factors includes: Design changes include: design optimization, process improvement, and material selection; Enhanced protection measures include replacing worn-out parts, improving storage / transportation conditions, and extending service life through repairs. Optimization deployment category: including condition-based maintenance decision optimization and usage pattern optimization.

8. The method for constructing and analyzing a multi-level decision-making model for extending the lifespan of electromechanical products according to claim 1, characterized in that, The specific process for making decisions regarding the weak links is as follows: Based on the failure modes derived from FMECA analysis or subjective judgments based on experience, each component of the product is assigned a 0 / 1 judgment result, where 1 indicates that the component has a failure mode that affects its lifespan, and 0 indicates that it does not. For components with a judgment result of 1, analyze the failure mechanism and rank them according to risk, then calculate the weakness factor using a formula. The specific formula is as follows: in, For the rank of the failure mechanism, The weak factor ranges from 0 to 1, representing the total number of failure mechanisms that affect lifespan.

9. The method for constructing and analyzing a multi-level decision-making model for extending the lifespan of electromechanical products according to claim 1, characterized in that, The comprehensive decision-making life extension scheme uses comprehensive decision-making... The specific formula for construction is as follows: in, As an effect factor, For cost factors, , The cost factor is calculated based on the pre-processed funds. For timing-based or feasibility-based decisions, the result is either 0 or 1. The balance coefficient for weak links, This is the cost equilibrium coefficient. A fairness coefficient is used for research and protection.

10. The method for constructing and analyzing a multi-level decision-making model for extending the lifespan of electromechanical products according to claim 1, characterized in that, The lifespan limiting factor analysis also includes the lifespan extension effect of improving the storage / transportation environment. The Arrhenius model is used to calculate the lifespan extension effect, and the specific formula is as follows: in, The lifespan of electromechanical products at a certain temperature. Here, k represents the activation energy of the fault mechanism, and k is the Boltzmann constant. It is a constant. For ambient temperature, For the product lifespan before temperature improvement, To improve product lifespan after temperature adjustment, This is a value that enhances the lifespan extension effect.

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