Turbine and roller skating oil pump spare part importance degree evaluation method based on TOPSIS analysis

Abstract

The invention belongs to the technical field of steam and roller skating oil pump spare part importance degree evaluation, discloses a steam and roller skating oil pump spare part importance degree evaluation method based on TOPSIS analysis, and aims at solving the problems that steam and roller skating oil pump spare parts are multiple in variety, spare part reserve influence factor degrees are multiple, and spare part reserve decision making difficulty is large. The spare part reserve variety is preliminarily determined based on maintenance work analysis, a spare part importance degree evaluation index system is constructed on the basis of research and analysis of steam and roller skating oil pump spare part importance degree influence factors, and spare part importance degree evaluation is performed by adopting a TOPSIS analysis method. When the TOPSIS analysis method is used, an analytic hierarchy process and an entropy weight method are combined to determine the index weight, subjective and objective weights are combined to make up for the respective insufficiency of the two methods, and a satisfactory result is obtained. The method can provide reference for spare part reserve optimization of other equipment, and can also provide thoughts for other multi-attribute decision-making problems.

Description

Technical Field

[0001] This invention belongs to the technical field of turbine lubricating oil pump spare parts management, and particularly relates to a method for assessing the importance of turbine lubricating oil pump spare parts based on TOPSIS analysis. Background Technology

[0002] Spare parts refer to the parts or accessories reserved to ensure the normal operation, maintenance, or replacement of equipment. The turbine lubricating oil pump is a key piece of equipment in the turbine power system, and its normal operation directly affects the stability of the entire power system. The turbine lubricating oil pump has a complex structure, including multiple subsystems such as the turbine, reducer, and regulation and control system, with over 500 parts and a wide variety of spare parts. Whether equipment can be repaired promptly after a failure largely depends on the appropriate variety and quantity of spare parts in reserve. Insufficient reserve variety and quantity will lead to the inability to replace and repair parts in a timely manner after equipment failure, affecting system operation; excessive reserve will increase inventory burden and waste resources. Therefore, scientifically assessing the importance of turbine lubricating oil pump spare parts and determining a reasonable spare parts reserve plan is a key issue in equipment maintenance management.

[0003] Many factors influence spare parts inventory decisions, including failure rate, vulnerability, impact of failure, repair time, and price. Different decision factors need to be considered for different usage scenarios. Therefore, the spare parts inventory decision is a multi-attribute comprehensive decision problem, usually described as spare parts importance assessment. Among existing spare parts importance assessment methods, the Analytic Hierarchy Process (AHP), as a subjective weighting method, relies on expert judgment and is easily influenced by human factors, leading to biased weighting results. The entropy weighting method, as an objective weighting method, relies only on the characteristics of the data itself, and the weighting results may be distorted when the sample data is insufficient. In addition, the determination of spare parts inventory types must consider not only importance but also the actual needs of maintenance work.

[0004] Based on the above analysis, the problems and shortcomings of the existing technology are as follows:

[0005] (1) The determination of spare parts types needs to be combined with the results of maintainability analysis and maintenance work analysis, such as maintenance items, their repair levels and maintenance intervals, to meet the requirements of actual maintenance conditions. Current methods for determining the importance of spare parts do not take this issue into account.

[0006] (2) The Analytic Hierarchy Process (AHP) is a subjective weighting method. When the AHP is used alone to determine the importance of spare parts, the weights are based on the decision-maker's subjective judgment, which may lead to unreasonable weights due to judgment bias.

[0007] (3) The entropy weight method is an objective weighting method. Although the entropy weight method alone is used to determine the importance of spare parts, it does not rely on human judgment and the weight is determined entirely by the data itself. However, the weight results may be distorted when the sample is insufficient.

[0008] Therefore, there is an urgent need for a method to assess the importance of turbine lubricating pump spare parts that can integrate the advantages of subjective and objective weights and combine them with the actual equipment maintenance. Summary of the Invention

[0009] To address the problem that existing technologies for managing turbine lubricating pump spare parts rely heavily on experience-based judgment and lack unified quantitative standards, which can easily lead to the loss of critical spare parts or redundant reserves of low-importance spare parts, resulting in downtime risks or excessive inventory costs, this invention provides a TOPSIS spare parts importance assessment method based on maintenance work analysis and a fusion of subjective and objective weights. This method is used to scientifically quantify and rank the importance of turbine lubricating pump spare parts, providing a reliable basis for spare parts reserve decisions.

[0010] The technical solution adopted in this invention is: a method for assessing the importance of turbine lubricating oil pump spare parts based on TOPSIS analysis, comprising the following steps:

[0011] First, a maintenance analysis was conducted on eight subsystems of the steam turbine lubricating oil pump: steam turbine, reducer, regulation and control system, vibration damping system, electric auxiliary oil pump system, five-screw lubricating oil pump, pipeline accessories, and instrument panel and alarm signal board. Typical failure causes, repair levels, and maintenance intervals for each component were statistically analyzed. The maintenance interval was used as a quantitative representation of the frequency of spare parts demand. Components with maintenance intervals less than or equal to a preset threshold were screened as important spare parts and used as subsequent evaluation targets, thereby achieving pre-screening of key spare parts.

[0012] Secondly, a spare parts importance evaluation index system is constructed. With spare parts importance assessment as the target layer, the failure probability level, detection difficulty level, average repair time, price, and severity of failure impact are selected as the evaluation criteria layer. The solution layer consists of important spare parts that have been screened, and an evaluation matrix is ​​formed through multi-source data collection.

[0013] Next, the weights of the indicators are determined. An analytic hierarchy process (AHP) is used to construct a judgment matrix, and each indicator is compared pairwise. Subjective weights are obtained through normalization, eigenvector calculation, and consistency checks. Simultaneously, the original data is standardized, and the information entropy of each indicator is calculated using the entropy weight method to obtain objective weights. Based on this, subjective and objective weights are combined in a certain proportion to obtain a comprehensive weight, thus taking into account both expert experience and objective data characteristics, improving the rationality and stability of the weighting results.

[0014] Then, the importance of spare parts is ranked based on the TOPSIS method. The evaluation matrix is ​​dimensionless, a standardized weighted decision matrix is ​​constructed, positive and negative ideal solutions are determined, and the distance between each spare part and the positive and negative ideal solutions is calculated. Based on this, the relative proximity is obtained, and the importance of spare parts is ranked according to the magnitude of the proximity. The larger the proximity, the higher the importance of the spare part under the comprehensive index, and the more priority should be given to allocating reserve and maintenance resources.

[0015] The present invention also provides a system for evaluating the importance of turbine lubricating oil pump spare parts based on the above method, including an evaluation index module, an index weight module, and an importance ranking module; and further provides a computer device, a computer-readable storage medium, and an information data processing terminal for implementing the above method or system.

[0016] This invention offers the following advantages: By employing a technical approach of "maintenance work analysis and screening of key spare parts + TOPSIS evaluation integrating subjective and objective weights," it achieves a shift from experience-based management to data-driven management. This avoids the omission of key spare parts or redundant reserves of low-importance spare parts, effectively reducing inventory costs and downtime losses while ensuring equipment operational safety, and improving the scientific rigor, precision, and reliability of spare parts management. Furthermore, this method boasts advantages such as a clear indicator system, standardized calculation process, quantifiable results, and strong reproducibility. It is applicable to the evaluation and optimization of spare parts reserves for turbine lubricating pumps and other complex power equipment, demonstrating significant engineering practical value and promising prospects for wider application. Attached Figure Description

[0017] Figure 1 This is a flowchart of the method for assessing the importance of turbine lubricating oil pump spare parts based on TOPSIS analysis provided in this embodiment of the invention.

[0018] Figure 2 This is a block diagram of the importance assessment system for turbine lubricating oil pump spare parts based on TOPSIS analysis provided in an embodiment of the present invention.

[0019] Figure 3 This is a schematic diagram of the working principle of the turbine lubricating oil pump provided in an embodiment of the present invention.

[0020] Figure 4 This is a diagram of the importance evaluation index system for turbine lubricating oil pump spare parts provided in the embodiments of the present invention.

[0021] Figure 5 This is a flowchart of the TOPSIS method provided in an embodiment of the present invention.

[0022] Figure 6 This is a flowchart of the analytic hierarchy process provided in an embodiment of the present invention.

[0023] Figure 7 This is a flowchart of the entropy weight method provided in an embodiment of the present invention. Detailed Implementation

[0024] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention.

[0025] like Figure 1 As shown, taking a certain type of steam turbine lubricating oil pump as the research object, this embodiment of the invention constructs a spare part importance assessment method based on TOPSIS analysis, which is used to quantitatively analyze the importance of different spare parts in terms of reliability, safety, and economy. First, maintenance work analysis is carried out on the eight subsystems of the steam turbine lubricating oil pump, the maintenance interval of each component is statistically analyzed, and seven important spare parts with a maintenance interval of no more than 3A / U are selected as evaluation objects. These include the steam turbine support bearing, the steam turbine support thrust bearing, the steam turbine thrust bearing, the reducer support bearing, the reducer support thrust bearing, the lubricating bellows of the regulating control system, and the screw bushing of the five-screw pump, thus ensuring that the evaluation objects are representative and have practical engineering significance.

[0026] Based on this, raw data on the above-mentioned spare parts in five indicators—probability of occurrence, difficulty of detection, average repair time, price, and severity of impact—were obtained through equipment maintenance records, manufacturer technical data, and expert surveys, forming an evaluation matrix. To address the uncertainty of indicator weights, a combination of subjective and objective methods was used to determine the weights: on the one hand, a judgment matrix was constructed based on expert experience, and subjective weights were obtained through normalization, eigenvector calculation, and consistency checks; on the other hand, information entropy was calculated on the standardized indicator data to obtain objective weights, thus reflecting the impact of indicator differences on the weights. By merging subjective and objective weights in a 50 / 50 ratio, a comprehensive weight was obtained, effectively avoiding the bias caused by a single weighting method and making the weighting results more robust and reasonable.

[0027] The original index data was then standardized to construct positive and negative ideal solutions. The distance of each spare part to the ideal solution was calculated, and the proximity was further determined to rank the importance of each spare part. The calculation results show that there are significant differences among the spare parts in terms of comprehensive risk, maintenance difficulty, and economic impact. Among them, the screw bushing of the five-screw pump and the lubricating bellows of the regulating control system have higher importance and should be given priority in inventory and maintenance resources, while the importance of spare parts such as the turbine thrust bearing is relatively low, and the reserve level can be appropriately reduced.

[0028] The above methods enable tiered allocation and optimized management of spare parts resources while ensuring the safe and stable operation of turbine lubricating oil pumps. This reduces capital occupation and inventory backlog, lowers maintenance costs, and improves support efficiency. The evaluation method provided by this invention has advantages such as clear data sources, standardized calculation processes, quantifiable results, and strong repeatability. It provides scientific and reliable technical support for spare parts management of turbine lubricating oil pumps and support decisions for similar equipment, and has good engineering applicability and promotional value.

[0029] The evaluation index system for the importance of spare parts provided in this embodiment of the invention:

[0030] (1) Occurrence probability level; the occurrence probability level represents the actual likelihood of a certain failure mode occurring;

[0031] (2) Detection difficulty level; The detection difficulty level represents the difficulty of detecting the specific cause of the fault;

[0032] (3) Mean repair time; the average time required to successfully complete restorative repairs under specific conditions and time periods;

[0033] (4) Price; Price affects the overall cost of spare parts inventory, so it is necessary to select parts with low cost and high cost performance as much as possible;

[0034] (5) The impact of the failure is severe; an important measure to determine the severity of the consequences caused by the failure mode.

[0035] The spare parts importance ranking method based on TOPSIS provided in this embodiment of the invention:

[0036] (1) Data standardization processing

[0037] Let the index data matrix of each scheme to be evaluated be:

[0038] (1)

[0039] In the formula: m is the number of schemes to be evaluated, and n is the number of indicators;

[0040] The indicators are dimensionless to obtain the standard matrix X′:

[0041] Positive indicators: (2)

[0042] Negative indicators: (3)

[0043] In the formula: x'ij is the data after normalization of the j-th evaluation index by the i-th evaluator, xij is the original data of the j-th evaluation index by the i-th evaluator, min xij is the minimum value of the original data, and max xj and min xj are the maximum and minimum values ​​of the j-th evaluation index, respectively.

[0044] (2) Establish a weighted decision evaluation matrix

[0045] The index weights WJ, obtained by combining the analytic hierarchy process (AHP) and the entropy weight method, form a weight vector W. Based on the standardized x'ij, the standardized weighting matrix V is obtained, calculated using the following formula:

[0046] (4)

[0047] (3) Establish a weighted decision evaluation matrix

[0048] (5)

[0049] The original setting of Equation (5) is for the calculation of positive indicators; since negative indicators have been transformed in the early data standardization process, in the subsequent process of determining the positive and negative ideal solutions, regardless of the original indicator type, the calculation method of positive indicators can be uniformly adopted.

[0050] (4) Calculate the closeness

[0051] When calculating the distance between the positive and negative ideal solutions of each scheme, it is first necessary to clarify the standardized data format so as to facilitate the subsequent distance calculation. After standardization, the data of each indicator is converted into a unified dimension to ensure the comparability between different indicators. The distance between each scheme and the positive and negative ideal solutions can be calculated by equation (6).

[0052] (6)

[0053] The construction of ideal and negative ideal solutions provides a basis for the calculation of relative closeness. Ideal solutions represent the best performance of each indicator, while negative ideal solutions represent the worst performance. This definition allows the evaluation to be carried out within a relative framework. The relative closeness value reflects the comprehensive value of the indicators and provides decision-makers with a clear basis for ranking.

[0054] (7).

[0055] The method for determining index weights provided in this embodiment of the invention

[0056] (1) Determination of subjective weights of indicators based on the analytic hierarchy process;

[0057] (2) Determination of objective weights of indicators based on entropy weight method.

[0058] The present invention provides a method for determining the subjective weights of indicators based on the analytic hierarchy process.

[0059] (1) Construct the judgment matrix

[0060] Constructing a judgment matrix is ​​the process of comparing the importance of elements at the same level with respect to a certain criterion at the next higher level. The core is to mathematically quantify this relative importance, typically using a 1-9 degree scale.

[0061] Inviting experts to make pairwise comparisons of the evaluation criteria indicators and determine their importance values ​​constitutes the judgment matrix A=(anm);

[0062] (8)

[0063] (2) Weight calculation

[0064] Hierarchical single ranking focuses on indicators of adjacent levels, aiming to determine the weight ranking of lower-level indicators relative to upper-level targets; the core step is to solve for the eigenvalues ​​λ of the constructed judgment matrix and its corresponding eigenvectors W; when the judgment matrix A satisfies the characteristic equation AW=λmaxWi, the resulting vector W is the eigenvector of the maximum eigenvalue λmax of matrix A, and the element values ​​of this vector directly reflect the relative importance of the lower-level indicators.

[0065] Normalize each column of the judgment matrix, that is...

[0066] (9)

[0067] In the formula: bij is an element in the normalized judgment matrix;

[0068] After normalizing the matrix, summing it row-wise yields:

[0069] (10)

[0070] In the formula, Bi is the sum of the i-th row of the normalized judgment matrix;

[0071] Normalization process:

[0072] (11)

[0073] W=(W1,W2,…,Wn)T is the eigenvector of the judgment matrix. The formula for calculating the largest eigenvalue λ of the judgment matrix is:

[0074] (12)

[0075] (3) Consistency check

[0076] The calculation results need to be checked for consistency. The consistency check formula is as follows:

[0077] (13)

[0078] In the formula This is the scalar value for the matrix consistency test;

[0079] (14)

[0080] In the formula: RI is the random consistency index, which is a coefficient related to n; according to the judgment criteria, if CI=0, the judgment matrix has consistency. The smaller CI is, the greater the consistency. When CR<0.1, it means that the consistency test is passed; when CR>0.1, the consistency test is not passed, and the relevant element values ​​of the judgment matrix need to be adjusted and the test needs to be repeated until it passes.

[0081] (4) Overall Hierarchical Sorting

[0082] Using the hierarchical single-sorting results as the basis for calculation, the system collects single-sorting data of all elements at a certain level, comprehensively considers the weight ratio of each element to the overall goal, as well as the logical relationship with the elements at the upper level, and finally accurately determines the combined weight value of each element at that level. This process combines quantitative analysis and logical deduction to ensure the scientificity and reliability of weight assignment.

[0083] The objective weight determination method for indicators based on entropy weight method provided in this embodiment of the invention:

[0084] (1) Data standardization processing:

[0085] (15)

[0086] Where x'ij represents the standardized value of the j-th indicator of the i-th sample, xij represents the original data, and μj and σj represent the mean and standard deviation of the j-th indicator, respectively.

[0087] (2) Calculate the relative entropy of the i-th sample under the j-th index:

[0088] (16)

[0089] Where pij represents the probability value of the j-th indicator of the i-th sample, and n represents the total number of samples;

[0090] (3) Calculate the information entropy of each indicator:

[0091] (17)

[0092] (3) Calculate the entropy weight of the j-th index:

[0093] (18).

[0094] like Figure 2 As shown in the figure, the turbine lubricating oil pump spare parts importance assessment system based on TOPSIS analysis provided by this embodiment of the invention includes:

[0095] The evaluation index module is used to construct an evaluation index system for the importance of spare parts;

[0096] Importance ranking module, used for ranking spare parts importance based on the TOPSIS method;

[0097] The indicator weight module is used to determine the indicator weights.

[0098] like Figure 2 As shown, the turbine lubricating oil pump spare parts importance assessment system based on TOPSIS analysis described in this invention realizes the quantitative assessment and ranking of spare parts importance through a modular collaborative approach. Its overall working principle is as follows.

[0099] The system first analyzes the spare parts involved in the operation and maintenance of the turbine lubricating oil pump using an evaluation index module. Based on equipment operational reliability, maintenance support needs, and economic requirements, an evaluation index system for the importance of spare parts is constructed. This evaluation index system is used to characterize the comprehensive impact of different spare parts on operational safety, failure risk, and support costs from multiple dimensions, providing a unified data foundation for subsequent assessments. The evaluation index module collects and organizes the raw data of each spare part under the corresponding index, forming an index data set for calculation.

[0100] After the indicator system is constructed, the indicator data is transferred to the indicator weighting module. This module analyzes the importance of the evaluation indicators and determines the indicator weights from two perspectives: expert judgment and sample data distribution characteristics. Firstly, based on expert judgments of the relative importance of each indicator, a weighting result reflecting subjective experience is generated. Secondly, based on the information distribution of each indicator in the spare parts sample, a weighting result reflecting objective differences is determined. Subsequently, the indicator weighting module integrates these subjective and objective weights to obtain a comprehensive indicator weight for importance assessment, thus avoiding biases caused by a single weighting source while taking into account both engineering experience and actual data characteristics.

[0101] After obtaining the comprehensive indicator weights, the system calculates and ranks the importance of each spare part through the importance ranking module. This module first standardizes the evaluation indicator data to eliminate differences in the dimensions and value ranges of different indicators, making the data comparable. Based on this, a weighted decision evaluation matrix is ​​constructed by combining the comprehensive indicator weights, and the ideal and negative ideal evaluation states are determined within a unified evaluation space. The importance ranking module calculates the degree to which each spare part is close to the above evaluation states, quantifying the comprehensive performance level of different spare parts under the overall indicator system, and outputs the spare part importance ranking results accordingly.

[0102] Through data interaction and functional collaboration among the above modules, this invention can transform multi-source evaluation indicators into quantifiable and sortable spare parts importance results, providing objective and reliable technical support for the inventory configuration, priority management, and maintenance support decisions of turbine lubricating pump spare parts.

[0103] The specific implementation method of this invention is as follows:

[0104] 1. Composition of the steam turbine lubricating oil pump and selection of key spare parts

[0105] The working principle of the turbine lubricating oil pump is as follows: Figure 3 As shown, steam heat energy is converted into mechanical energy inside the steam turbine, which drives the lubricating oil pump to do work through the reducer, pressurizing the lubricating oil in the system oil tank and pumping it to the lubricating oil main pipe.

[0106] The turbine lubricating oil pump mainly consists of eight subsystems: turbine, reducer, regulating and control system, vibration damping system, electric auxiliary oil pump system, five-screw lubricating oil pump, pipeline accessories, instrument panel, and alarm signal board. Its structure is relatively complex, with over 500 parts. It is neither possible nor necessary to stock all parts as spares; only those closely related to maintenance work need to be considered. Therefore, to formulate a spare parts reserve plan for the turbine lubricating oil pump, the first step is to analyze its maintenance work to determine the objects of the spare parts reserve analysis. Taking the turbine as an example, its main maintenance work is shown in Table 1. Typical faults include wear of the support shaft, wear of the support thrust bearing, wear of the thrust bearing shaft, gear wear, and impeller blade damage. To prevent these faults, a scheduled disassembly and repair maintenance strategy is adopted. The shorter the disassembly and repair interval, the greater the possibility of part damage and the more urgent the need for spare parts. The maintenance intervals for the support bearing, support thrust bearing, and thrust bearing are relatively short, and the demand for spare parts is relatively large, so they can be considered as key components for reserve. The maintenance intervals for gears and impeller blades are relatively long, and the demand for spare parts is relatively small, so they are not considered as key components for analysis.

[0107] Table 1 Analysis of Steam Turbine Maintenance Work

[0108]

[0109] *The maintenance interval A represents "years", and U represents restorative maintenance.

[0110] Using the same method, important spare parts were screened for the other 7 subsystems. The parts identified for spare parts reserve analysis included: the turbine's support bearing, support thrust bearing, and thrust bearing; the reducer's support bearing and thrust bearing; the lubricating bellows in the operating condition setpoint of the regulation control system; and the screw bushing of the five-screw pump.

[0111] Based on extensive data collection and organization, the following criteria were selected for evaluating the importance of spare parts: failure probability level, detection difficulty level, average repair time, price, and severity of impact. The constructed evaluation index system is as follows: Figure 4 As shown, the evaluation index system is divided into three layers: the target layer, the criterion layer, and the alternative layer. The target layer is the core of the evaluation and the benchmark of the assessment system; the criterion layer breaks down the overall target into actionable subdivisions; and the alternative layer covers specific alternatives that can be selected, achieving specific measurement and evaluation of the criterion layer targets through quantitative or qualitative analysis.

[0112] (1) Occurrence probability level. The occurrence probability level represents the actual likelihood of a certain failure mode occurring.

[0113] (2) Detection difficulty level. The detection difficulty level represents the difficulty of detecting the specific cause of the fault.

[0114] (3) Mean Repair Time. The average time required to successfully complete restorative repairs under specific conditions and time periods.

[0115] (4) Price. Price affects the overall cost of spare parts inventory, so it is necessary to select parts with low cost and high cost performance as much as possible.

[0116] (5) Severe impact of the failure. An important measure to determine the severity of the consequences caused by the failure mode.

[0117] TOPSIS, as a multi-objective decision analysis method, operates around the processing of raw data and the comparison of solutions, such as... Figure 5 As shown, the original data matrix is ​​first oriented to unify the direction of index measurement and eliminate interference from differences in data properties. Then, the distances of each evaluation object to the ideal optimal solution and the non-ideal worst solution are calculated, and the ranking is determined accordingly. The ideal solution should be as close as possible to the optimal solution while staying far away from the worst solution, thereby achieving the decision-making objective.

[0118] (1) Data standardization processing

[0119] Let the index data matrix of each scheme to be evaluated be:

[0120] (1)

[0121] In the formula: m is the number of schemes to be evaluated, and n is the number of indicators.

[0122] The indicators are dimensionless to obtain the standard matrix X′:

[0123] Positive indicators: (2)

[0124] Negative indicators: (3)

[0125] In the formula: x'ij is the data after normalization of the j-th evaluation index by the i-th evaluator, xij is the original data of the j-th evaluation index by the i-th evaluator, min xij is the minimum value of the original data, and max xj and min xj are the maximum and minimum values ​​of the j-th evaluation index, respectively.

[0126] (2) Establish a weighted decision evaluation matrix

[0127] The index weights WJ, obtained by combining the analytic hierarchy process (AHP) and the entropy weight method, form a weight vector W. Based on the standardized x'ij, the standardized weighting matrix V is obtained, calculated using the following formula:

[0128] (4)

[0129] (3) Establish a weighted decision evaluation matrix

[0130] (5)

[0131] Equation (5) was originally designed for positive indicators. Since negative indicators have already been converted in the early data standardization process, the calculation method for positive indicators can be uniformly used to determine the ideal solutions for positive and negative indicators, regardless of the original indicator type.

[0132] When calculating the distance between the positive and negative ideal solutions of each scheme, it is first necessary to clarify the standardized data format to facilitate subsequent distance calculations. After standardization, the data of each indicator is converted into a unified dimension, ensuring the comparability between different indicators. The distance between each scheme and the positive and negative ideal solutions can be calculated using equation (6).

[0133] (6)

[0134] The construction of ideal and negative ideal solutions provides the foundation for calculating relative closeness. Ideal solutions represent the best performance of each indicator, while negative ideal solutions represent the worst performance; this definition allows evaluations to be conducted within a relative framework. The relative closeness value reflects the comprehensive value of the indicators and provides decision-makers with a clear basis for ranking.

[0135] (7)

[0136] Constructing a judgment matrix is ​​the process of comparing the importance of elements at the same level with respect to a certain criterion at the next higher level. The core is to use mathematics to quantify this relative importance, usually using a 1-9 degree scale, as shown in Table 2.

[0137] Table 2. Scale Methods and Their Meanings

[0138]

[0139] Inviting experts to compare and judge the evaluation criteria indicators pairwise and determine the importance values ​​constitutes the judgment matrix A=(anm).

[0140] (8)

[0141] Hierarchical single ranking focuses on indicators at adjacent levels, aiming to determine the weight ranking of lower-level indicators relative to higher-level objectives. The core step is to solve for the eigenvalues ​​λ of the constructed judgment matrix and their corresponding eigenvectors W. When the judgment matrix A satisfies the characteristic equation AW = λmaxWi, the resulting vector W is the eigenvector of the largest eigenvalue λmax corresponding to matrix A, and the element values ​​of this vector directly reflect the relative importance of the lower-level indicators.

[0142] Normalize each column of the judgment matrix, that is...

[0143] (9)

[0144] In the formula: bij is an element in the normalized judgment matrix.

[0145] After normalizing the matrix, summing it row-wise yields:

[0146] (10)

[0147] In the formula, Bi is the sum of the i-th row of the normalized judgment matrix.

[0148] Normalization process:

[0149] (11)

[0150] W=(W1,W2,…,Wn)T is the eigenvector of the judgment matrix. The formula for calculating the largest eigenvalue λ of the judgment matrix is:

[0151] (12)

[0152] The calculation results need to be checked for consistency. The consistency check formula is as follows:

[0153] (13)

[0154] In the formula This is the scalar value for the matrix consistency test.

[0155] (14)

[0156] In the formula: RI is the random consistency index, which is a coefficient related to n. According to the judgment criteria, if CI=0, the judgment matrix has consistency. The smaller the CI, the greater the consistency. When CR<0.1, it means that the consistency test has been passed; when CR>0.1, the consistency test has not been passed, and the relevant element values ​​of the judgment matrix need to be adjusted and the test needs to be repeated until it passes.

[0157] Using the hierarchical single-sorting results as the basis for calculation, the system collects single-sorting data of all elements at a certain level, comprehensively considers the weight ratio of each element to the overall goal, and its logical relationship with elements at the higher level, and finally accurately determines the combined weight values ​​of each element at that level. This process combines quantitative analysis and logical deduction to ensure the scientific nature and reliability of the weight assignment.

[0158] A method for determining the objective weights of indicators based on the entropy weight method:

[0159] (1) Data standardization processing:

[0160] (15)

[0161] Where x'ij represents the standardized value of the j-th indicator of the i-th sample, xij represents the original data, and μj and σj represent the mean and standard deviation of the j-th indicator, respectively.

[0162] (2) Calculate the relative entropy of the i-th sample under the j-th index:

[0163] (16)

[0164] Where pij represents the probability value of the j-th indicator of the i-th sample, and n represents the total number of samples.

[0165] (3) Calculate the information entropy of each indicator:

[0166] (17)

[0167] (3) Calculate the entropy weight of the j-th index:

[0168] (18)

[0169] Case Study on Importance Assessment of Steam Turbine Lubricating Oil Pump Components

[0170] The basic approach to assessing the importance of turbine lubricating oil pump components is as follows: Based on the constructed component importance evaluation index system, the analytic hierarchy process (AHP) and entropy method are used to determine the comprehensive subjective and objective weights of the indicators. On this basis, the approximation ideal ranking method is used to obtain the importance ranking of six components: turbine support bearing, turbine support thrust bearing, turbine thrust bearing, reducer support bearing, reducer support thrust bearing, regulating control system lubricating oil bellows, and five-screw pump screw bushing.

[0171] 3.1 Raw Data Processing

[0172] The raw data for the schemes at the criterion level were obtained through statistical analysis and surveys. The raw data were then processed according to Table 3 to obtain the quantitative data for the evaluation schemes, as shown in Table 4.

[0173] Table 3. Fuzzy rating levels and corresponding fuzzy numbers

[0174]

[0175] Table 4 Quantitative Table of Evaluation Scheme

[0176]

[0177] 3.2 Determination of Indicator Weights

[0178] 3.2.1 Determining Subjective Weights of Indicators Based on the Analytic Hierarchy Process

[0179] Relevant personnel were invited to assess the weights of the evaluation indicators for the turbine lubricating oil pump. The obtained scores for each indicator were summarized, integrated, and normalized to obtain the indicator importance judgment matrix, as shown in Table 5.

[0180] Table 5. Matrix for Judging the Importance of Indicators

[0181]

[0182] The weights, maximum eigenvalues, and CR values ​​of each indicator are obtained according to equations (8)-(12), as shown in Table 4. CR values ​​< 0.1 pass the consistency test.

[0183] Table 6 Subjective Weights of Indicators

[0184]

[0185] 3.2.2 Determining the Objective Weights of Indicators Based on the Entropy Method

[0186] The data in Table 4 are normalized according to formula (15), and the objective weights of the indicators are calculated according to formulas (16)-(18), as shown in Table 7.

[0187] Table 7 Objective Weights of Indicators

[0188]

[0189] 3.3.3 Calculation of the comprehensive weight of indicators

[0190] The overall weight of importance evaluation is obtained by giving 50% to both subjective and objective evaluation results, as shown in Table 8.

[0191] Table 8. Overall Weight of Indicators

[0192]

[0193] 3.3 Ranking of Spare Parts Importance

[0194] Based on equations (2) and (3), Table 4 is normalized, and then the spare parts proximity ranking is calculated according to equations (4)-(7) and the data in Table 8, as shown in Table 9.

[0195] Table 9. Ranking by Relative Proximity

[0196]

[0197] Based on the calculation and ranking of proximity, the following order of fit is determined: five-screw pump screw bushing > regulating control system lubricating bellows > turbine support thrust bearing > reducer support thrust bearing > turbine support bearing > reducer support bearing > turbine thrust bearing. Based on this ranking, an optimal spare parts reserve strategy can be selected to reduce costs and increase efficiency, enhancing the scientific and practical nature of turbine lubricating pump spare parts management.

[0198] This invention can be widely applied to spare parts management scenarios for turbine lubricating pump manufacturers, power system operation and maintenance service providers, and end users such as energy / ships / power plants.

[0199] The embodiments of the invention show that this invention selects important spare parts through maintenance work analysis to ensure the relevance of the assessment; it integrates the subjective experience of the analytic hierarchy process (AHP) with the objective data advantages of the entropy weight method to improve the scientific nature of weight determination; and it uses the TOPSIS method to quantitatively rank the importance of spare parts, providing a precise decision-making basis for the stockpiling of turbine lubricating pump spare parts. Furthermore, this method can be extended to spare parts importance assessment and multi-attribute decision-making scenarios for other industrial equipment, demonstrating broad practicality and promotional value.

[0200] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any modifications, equivalent substitutions, and improvements made by those skilled in the art within the scope of the technology disclosed in the present invention, and within the spirit and principles of the present invention, should be covered within the scope of protection of the present invention.

Claims

1. A method for assessing the importance of turbine lubricating oil pump spare parts, characterized in that, The minimum closed-loop evaluation process includes the following steps: Construct a multi-dimensional importance evaluation index system for turbine lubricating oil pump spare parts. The index system should at least simultaneously reflect the fault risk characteristics, maintenance support characteristics and economic impact characteristics of the spare parts. Based on the evaluation index system, the corresponding raw index data are obtained for multiple spare parts samples; The original indicator data is standardized to eliminate differences in the units and ranges of values ​​of different indicators, so that the data of each indicator are comparable. The comprehensive weight of each evaluation indicator is determined based on the dispersion of each indicator among the samples and expert experience. Under the comprehensive weight constraint, a weighted decision evaluation matrix is ​​constructed, and positive ideal evaluation state and negative ideal evaluation state are determined in a unified evaluation space; Calculate the relative closeness of each spare part sample to the positive ideal evaluation state and the negative ideal evaluation state; The importance of turbine lubricating oil pump spare parts is ranked and output based on their relative proximity. This enables quantitative assessment and hierarchical decision support for the importance of turbine lubricating pump spare parts.

2. The method as described in claim 1, characterized in that, The evaluation index system includes at least: A probability index characterizing the likelihood of a failure mode occurring; A detection difficulty index that characterizes the ease with which a fault can be identified and located; Average repair time, a metric representing the level of maintenance resource occupancy; Price indicators that characterize the economic efficiency of spare parts inventory; The severity index of failure impact, which characterizes the severity of the consequences of a failure.

3. The method as described in claim 1, characterized in that, The standardization process includes: Based on the statistical characteristics of each indicator in the sample set, the original data is transformed into a dimensionless form so that the standardized indicator data reflects the relative superiority or inferiority relationship on the same numerical scale.

4. A method for determining the weight of indicators for assessing the importance of turbine lubricating oil pump spare parts, characterized in that, include: Based on pairwise comparisons of the relative importance of each evaluation indicator by experts, a judgment matrix reflecting the relationship of subjective judgment is constructed. The judgment matrix is ​​subjected to consistency verification to obtain subjective weight results that meet the consistency requirements; Based on the information distribution characteristics of each indicator in the spare parts sample, the objective weight reflecting the degree of objective difference of the indicators is calculated. The subjective weights and objective weights are then fused together to obtain a comprehensive index weight for assessing the importance of spare parts.

5. The method as described in claim 4, characterized in that, The judgment matrix is ​​constructed by comparing the importance of the same level evaluation index with respect to the upper level target in pairs, and the relative importance is quantified by using a proportional scale.

6. The method as described in claim 4, characterized in that, The calculation of the objective weights includes: Based on the uniformity of the distribution of each indicator in different spare parts samples, the amount of information contained in the indicator is determined. The smaller the amount of information, the higher the corresponding weight of the indicator.

7. A system for assessing the importance of turbine lubricating oil pump spare parts, characterized in that, include: The data acquisition module is used to collect raw data of spare parts under multiple evaluation indicators; The data processing module is used to standardize the raw data; The weight calculation module is used to generate the comprehensive weights of each evaluation indicator; The evaluation and analysis module is used to calculate the relative proximity of spare parts based on a weighted decision evaluation matrix. The sorting output module is used to output the importance ranking results of turbine lubricating pump spare parts.

8. The system as described in claim 7, characterized in that, The evaluation and analysis module distinguishes importance by constructing positive and negative ideal evaluation states and calculating the distance relationship between each spare part and the evaluation state.

9. The method or system according to any one of claims 1 to 8, characterized in that, The results of the spare parts importance assessment are used to guide the inventory allocation, priority management, or maintenance support strategy of turbine lubricating pump spare parts.

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