Maintenance decision-making method for hydroelectric equipment based on FMEA

Abstract

An FMEA-based hydropower equipment-oriented maintenance decision-making method comprises the following steps: firstly, establishing a hydropower equipment model, and realizing unified management and sharing of information through an FMEA analysis framework and a maintenance decision-making framework; secondly, taking a risk analysis result of the FMEA as a recommendation strategy of maintenance items, and automatically generating a maintenance item list with priority ranking according to a risk priority number (RPN value); thirdly, based on the selected maintenance items, the maintenance level of the equipment is recommended, and the effectiveness and pertinence of maintenance measures are ensured; and finally, making a scheduling strategy of an equipment maintenance plan, and reasonably arranging maintenance time by considering factors such as resource optimization and operation influence. According to the method, scientificity and efficiency of maintenance decision making are improved, and safe and stable operation of the hydroelectric equipment is guaranteed.

Description

Technical Field

[0001] The invention belongs to the field of hydropower equipment, and in particular relates to a maintenance decision method for hydropower equipment based on FMEA. Background Art

[0002] In the operation and maintenance of hydropower equipment, it is crucial to ensure the safe and stable operation of the equipment. Traditional hydropower equipment maintenance decision-making methods often rely on experience judgment and regular maintenance, which has many limitations. On the one hand, the maintenance method based on experience judgment lacks scientificity and systematicness. Maintenance personnel mainly determine maintenance items and time based on past experience, and it is difficult to accurately assess the actual status and potential risks of the equipment. This method may lead to over-maintenance or under-maintenance. Excessive maintenance will waste resources, increase costs, and may cause unnecessary interference to the equipment; while insufficient maintenance may cause the equipment to continue to operate in the presence of hidden dangers, increase the probability of failure, and seriously affect the reliability of hydropower equipment and the stability of power supply. On the other hand, the regular maintenance mode cannot respond to the dynamic changes of the equipment in a timely manner. Regardless of the actual status of the equipment, maintenance is carried out at fixed time intervals. This may result in unnecessary maintenance when the equipment is in good condition, and failure to timely discover and deal with potential problems in the equipment. With the continuous development and complexity of hydropower equipment, its operating status is affected by many factors, such as equipment aging, changes in the operating environment, and load fluctuations. Traditional periodic maintenance methods make it difficult to accurately grasp these changes and cannot meet the needs of modern hydropower equipment for efficient and precise maintenance.

[0003] In summary, traditional maintenance methods also have shortcomings in resource allocation and planning. It is difficult to reasonably arrange maintenance time and tasks under limited resource conditions, which easily leads to resource waste or maintenance progress delays. At the same time, there is a lack of scientific basis for determining the maintenance priority of high-risk equipment, which cannot effectively reduce the risk of major failures. Summary of the invention

[0004] The technical problem to be solved by the present invention is to provide a maintenance decision-making method for hydropower equipment based on FMEA. By introducing FMEA analysis, the analysis unit of hydropower equipment is subdivided, the analysis granularity is increased, the equipment analysis level is improved, the defect recognition rate and the equipment operation risk recognition rate are improved, and the maintenance strategy recommendation and maintenance plan scheduling functions are realized by combining the status assessment and defect analysis related analysis results.

[0005] In order to solve the above technical problems, the technical solution adopted by the present invention is:

[0006] A maintenance decision method for hydropower equipment based on FMEA, the steps are:

[0007] Step 1: Connect the FMEA analysis framework and maintenance decision framework to achieve unified information management and sharing, laying the foundation for subsequent decision-making.

[0008] Step 1.1: Build a hydropower equipment model.

[0009] Build a hierarchical equipment structure, covering the unit level, equipment level, component level and other levels, forming a unified logical equipment tree and instantiation tree, clearly presenting the hierarchical relationship and functional association between equipment. Add attribute information such as name, model, operating parameters, historical fault records, etc. to the equipment node, and comprehensively record the basic characteristics and past conditions of the equipment. Formulate a unified data format to ensure the compatibility and sharing of business information of different systems, and realize seamless flow and interaction of data. Define the data mapping relationship, accurately connect FMEA and maintenance decision information, so that the data of the two frameworks can accurately correspond, and provide a solid foundation for subsequent analysis and decision-making.

[0010] Step 1.2: Standardize the processing measures library and maintenance project library.

[0011] Classify and refine FMEA treatment measures, including preventive maintenance, corrective maintenance, improvement measures and other types, and clarify the applicable scenarios and specific requirements of different measures. Configure and connect treatment measures with equipment models, and introduce configuration tables and associated fields in the hydropower equipment model to accurately link each treatment measure with specific equipment components and failure modes in the equipment model. Establish a standard maintenance project library, formulate detailed project classification standards and standard operating procedures, etc., to ensure the standardization and efficiency of maintenance projects. Through matching strategies and data update mechanisms, associate FMEA treatment measures with maintenance projects, and realize automatic association and synchronous update of treatment measures and maintenance projects.

[0012] Step 2: Determine the maintenance items based on the FMEA analysis results and generate a priority list of maintenance items.

[0013] Step 2.1: Obtain risk analysis results.

[0014] Calculate the RPN value and use the commonly used risk assessment indicators in FMEA, such as severity (Severity, S), occurrence (Occurrence, O), and detection (Detection, D), to accurately assess the risk level of the failure mode and provide a quantitative basis for subsequent decision-making. Obtain the corresponding treatment measures from the treatment measure library according to the RPN value to ensure that appropriate treatment methods can be taken for failure modes of different risk levels.

[0015] Step 2.2: Build a recommendation strategy.

[0016] Divide the risk levels and divide the RPN values ​​into high, medium and low risk levels, and clarify the risk level and response priorities of each level. Set thresholds and key equipment levels, and determine the threshold standards for different risk levels and the recommended priorities of key equipment based on the enterprise's risk tolerance and the degree of key equipment maintenance. Build a multi-dimensional weight analysis model, comprehensively consider factors such as equipment criticality, historical failure data, operating environment and equipment aging, and determine the recommended priority. For high-risk failure modes, directly recommend corresponding maintenance items, and prioritize them according to RPN values ​​and risk strategy results. For medium and low-risk failure modes, screen and evaluate based on the equipment's operating status and historical data to determine whether maintenance measures need to be taken immediately or can be included in the next maintenance plan. Generate a recommendation list through the rule engine and algorithm process to ensure the accuracy and rationality of the recommendation.

[0017] Step 3: Determine the maintenance level based on the selected items in FMEA and comprehensively recommend the maintenance level of the equipment.

[0018] Step 3.1: Analyze risk data.

[0019] Read the priority list to obtain the maintenance project priority list generated in step 2, which includes the RPN value and risk strategy score of the maintenance project. Perform a detailed analysis of the RPN value and risk strategy score to determine the dynamic adjustment analysis strategy for the minimum maintenance level of the maintenance project, and scientifically determine the adjustment method for the minimum maintenance level of the maintenance project according to different RPN value intervals and risk score intervals.

[0020] Step 3.2: Dynamically update the maintenance level.

[0021] Based on the analysis strategy, the minimum maintenance level of each maintenance project is updated and adjusted to ensure that the maintenance level matches the actual risk situation. The adjustment is automatically triggered based on the RPN value and risk strategy value recalculated when the maintenance task is started, ensuring the real-time and accuracy of the maintenance level.

[0022] Step 3.3: Apply the comprehensive condition assessment and defect analysis results.

[0023] Collect key equipment status parameters. Through the status monitoring system, comprehensively collect key equipment status parameters such as wear degree, temperature rise, energy consumption, etc., and accurately evaluate the comprehensive status of the equipment. According to the defect analysis results, classify equipment defects into three levels: slight, medium, and severe. For medium or severe defects, increase the priority of maintenance projects to ensure safe and stable operation of the equipment.

[0024] Step 3.4: Comprehensively recommend maintenance level.

[0025] According to the results of equipment status assessment, defect analysis and FMEA analysis, the maintenance level standards are scientifically divided, and equipment maintenance is divided into four levels: A, B, C, and D. A dynamic recommendation model is constructed to update the recommended equipment maintenance level in real time according to equipment status changes, historical data accumulation, defect analysis feedback, and FMEA analysis feedback. Through a specific calculation method, the final level score = w1×comprehensive status score + w2×defect analysis score + w3×minimum maintenance level score, where w1, w2, and w3 are weight coefficients of each score, and the weights are set according to the importance of the equipment, the risk tolerance of the enterprise, and historical maintenance data. A trigger judgment strategy is used to determine the recommended maintenance level based on the final score. If the final score exceeds the set high-risk threshold, the recommended maintenance level is A / B maintenance; if the score is in the middle range, the recommended level C maintenance; if the final score is lower than the set safety threshold, the recommended level is D maintenance.

[0026] Step 4: Schedule the maintenance plan based on the maintenance level determined by FMEA. Arrange the maintenance time reasonably.

[0027] Step 4.1: Design multi-objective functions and constraints.

[0028] A multi-objective function is designed with the goal of minimizing the downtime of the entire plant and giving priority to the maintenance of high-risk units. The downtime function Ttotal is defined as the sum of the maintenance time of each unit. The goal is to minimize this time to reduce the impact on power supply. At the same time, a high-risk priority function Rhigh_risk is defined to represent the unit priority under the high-risk strategy to ensure that the highest priority maintenance is completed. Constraints such as the maximum number of units to be maintained at the same time, the maximum number of A / B maintenance items, and the high-risk maintenance time are set to ensure that maintenance resources are reasonably allocated under limited resources to avoid causing instability in the operation of the entire plant.

[0029] Step 4.2: Establish calculation formula and adopt combinatorial optimization algorithm.

[0030] Establish multi-objective scheduling calculation formulas and constraint formulas, comprehensively consider multi-objective optimization needs, and ensure the scientificity and rationality of maintenance scheduling. Use combined optimization algorithms such as genetic algorithms or particle swarm optimization algorithms to optimize maintenance scheduling, and find the optimal maintenance time schedule through population initialization, selection, crossover, mutation and other processes or each particle continuously iteratively adjusts under objective functions and constraints to meet multi-objective needs and complex constraints.

[0031] The present invention can achieve the following beneficial effects:

[0032] 1. The hydropower equipment maintenance decision model disclosed in the present invention realizes the intelligent analysis of equipment failure maintenance decision-making, conducts online monitoring of the health status of hydropower equipment, provides a unit defect prediction algorithm, establishes a fault prediction model, conducts online monitoring of the equipment operation status through historical operation data and regular detection data, analyzes whether the equipment has operation hazards, and conducts early warning analysis. At the same time, an equipment failure diagnosis expert system is established to analyze the causes of equipment failures based on the equipment failure information obtained through online monitoring, and take measures to realize automatic intelligent fault diagnosis and maintenance management of equipment.

[0033] 2. The technical solution of the present invention breaks the old maintenance system characterized by regular planned maintenance and post-maintenance, introduces the awareness of risk maintenance and condition-based maintenance, optimizes the maintenance strategy, provides data support for the scheduling of maintenance plans, provides reasonable maintenance units, and realizes full-range, full-process, and full-flow information communication for the maintenance of hydropower equipment. BRIEF DESCRIPTION OF THE DRAWINGS

[0034] The present invention will be further described below in conjunction with the accompanying drawings and embodiments:

[0035] Figure 1 It is a flow chart of the present invention. DETAILED DESCRIPTION

[0036] The preferred solution is Figure 1 As shown in the figure, a maintenance decision-making method for hydropower equipment based on FMEA is firstly implemented by establishing a hydropower equipment model, connecting the FMEA analysis framework and the maintenance decision-making framework, so as to realize the unified management and sharing of information. Secondly, the risk analysis results of FMEA are used as the recommended strategy for the maintenance project, and a priority list of maintenance projects is automatically generated according to the risk priority number (RPN value). Then, based on the selected maintenance project, the maintenance level of the equipment is recommended to ensure the effectiveness and pertinence of the maintenance measures; finally, a scheduling strategy for the equipment maintenance plan is formulated, and factors such as resource optimization and operation impact are considered to reasonably arrange the maintenance time. This method improves the scientificity and efficiency of maintenance decisions and ensures the safe and stable operation of hydropower equipment. The specific steps are:

[0037] Step 1: Connect the FMEA analysis framework and maintenance decision framework.

[0038] First, by establishing a hydropower equipment model, the two management frameworks are connected to achieve sharing and management of all information based on a unified equipment model. Specifically, the hierarchical structure and attribute information of the equipment are constructed to ensure that the FMEA analysis results and maintenance decision data are seamlessly connected in the same model. Secondly, the FMEA treatment measure library is standardized and connected with the standard maintenance project and special maintenance project library. Through a unified standardization system and mapping relationship, the treatment measures in the FMEA are linked to the corresponding maintenance projects to ensure the effective implementation and management of the maintenance measures. This step lays a key foundation for the subsequent maintenance project recommendation based on the risk analysis results. Maintenance level recommendation and intelligent planning and scheduling have laid a key foundation.

[0039] 1.1. Establish hydropower equipment model:

[0040] Equipment hierarchy model: Build a hierarchical model of equipment, including unit level, equipment level, component level, etc., to form a unified logical equipment tree and instantiation tree for hydropower.

[0041] Device attribute information: Add attribute information for each device node, such as device name, model, operating parameters, historical fault records, etc.

[0042] Unified data format: Establish unified data formats and standards to ensure that business information between FMEA management tools and maintenance decision-making advanced application systems can be compatible and shared with each other.

[0043] Data mapping relationship: Define the mapping relationship between the failure mode, failure impact, RPN value and other information in FMEA and the maintenance item name, maintenance item level and other information in maintenance decision.

[0044] 1.2 Standardize the FMEA treatment measures library and the standard maintenance project / special maintenance project library:

[0045] 1.2.1 Standardized FMEA processing measures library:

[0046] Classification of treatment measures: Classify the treatment measures proposed in FMEA, such as preventive maintenance, corrective maintenance, improvement measures, etc.

[0047] Measure refinement: refine the treatment measures and clarify the specific operating steps, required tools and personnel skill requirements.

[0048] The treatment measures are configured and linked to the hydropower equipment model: by introducing configuration tables and associated fields in the hydropower equipment model for linkage, each treatment measure is linked to specific equipment components and failure modes in the equipment model.

[0049] 1.2.2 Establishment of standard maintenance project library:

[0050] Project classification: Maintenance projects are divided into standard maintenance projects and special maintenance projects.

[0051] Project standardization: formulate standard operating procedures (SOP), working hours quota, quality standards, etc. for each maintenance project.

[0052] Association mapping: Establish an association table between FMEA treatment measures and maintenance items, and match them by maintenance item name.

[0053] 1.2.3 Standardized penetration method:

[0054] Information matching strategy: Develop matching strategies to automatically match corresponding maintenance items based on the characteristics of FMEA treatment measures.

[0055] Data update mechanism: Set up data update and synchronization mechanism to ensure the consistency and real-time nature of information in the two databases.

[0056] Step 2: Determine maintenance items based on FMEA analysis results:

[0057] The results of FMEA risk analysis are used as recommended strategies for maintenance projects. By calculating the risk priority number (RPN value) of each failure mode, it is divided into different risk levels (high, medium, and low), and high-priority failure modes are screened according to the set threshold as recommended maintenance projects. Then, through mapping relationships and matching algorithms, these high-risk failure modes are automatically matched to corresponding maintenance projects, and a priority-sorted maintenance project list is generated to ensure that equipment problems with higher risks are handled first.

[0058] 2.1 Obtaining FMEA risk analysis results:

[0059] RPN risk assessment index: Commonly used risk assessment indicators in FMEA include severity (Severity, S), occurrence (Occurrence, O), and detection (Detection, D); Calculation of risk priority number (RPN): RPN = S × O × D By calculating the RPN value, the risk level of each failure mode is evaluated.

[0060] Acquisition of treatment measures for different failure modes and RPN values: By evaluating the RPN value of each failure mode, the treatment measures corresponding to the risk level are automatically obtained from the treatment measure library, so as to perform lean maintenance for different risk levels.

[0061] 2.2 Method for constructing recommended strategies for maintenance projects:

[0062] 2.2.1 Determine the risk threshold:

[0063] Risk classification management: RPN values ​​are divided into different risk levels (high, medium, and low).

[0064] Set threshold standards: Set RPN thresholds based on the enterprise’s risk tolerance. For example, failure modes with RPN>600 (medium risk and above) require special attention.

[0065] Set the level of key equipment: according to the degree of key equipment maintenance, set the recommended priority, such as key equipment > main equipment > secondary equipment > auxiliary equipment;

[0066] Set risk strategy: Based on equipment criticality (the importance of equipment in the overall system), historical failure data (failure frequency and maintenance cost), operating environment (such as water flow, temperature, etc.) and equipment aging, a multi-dimensional weight analysis model is constructed. Different weights are assigned to these factors, and the priority of each maintenance project is calculated through a comprehensive scoring formula. The final level is divided into immediate maintenance, recommended monitoring, or inclusion in the next maintenance.

[0067] 2.2.2 Recommended maintenance items:

[0068] High risk failure mode handling:

[0069] Direct recommendation: For high-risk failure modes, the corresponding maintenance items are directly recommended.

[0070] Priority sorting: Prioritize maintenance items according to RPN value.

[0071] Criticality sorting: Prioritize maintenance projects based on risk strategy results.

[0072] Medium and low risk failure mode handling:

[0073] Condition screening: Screen low- and medium-risk failure modes based on equipment operating status, historical data, etc.

[0074] Necessity assessment: Evaluate whether immediate maintenance measures are needed or whether they can be included in the next maintenance plan.

[0075] 2.2.3 Implementation of recommendation algorithm:

[0076] Rules Engine: Build a rule-based recommendation system to associate risk levels with maintenance items.

[0077] Algorithm flow:

[0078] (1) Input the FMEA analysis results and obtain the RPN value of each failure mode.

[0079] (2) Classify maintenance project priorities according to the set thresholds and risk strategies.

[0080] (3) Match the corresponding maintenance items through mapping relationships.

[0081] (4) Generate a recommended list of maintenance items and sort them by priority.

[0082] For example: Failure mode A: RPN = 900 (high risk), risk strategy score 0.98, immediate maintenance is recommended.

[0083] Failure mode B: RPN = 600 (medium risk), risk strategy score 0.62, it is recommended to monitor or include it in the next maintenance.

[0084] Step 3: Determine the maintenance level based on the selected items of FMEA:

[0085] The selected maintenance items are used as the basis for determining the maintenance level. First, obtain the maintenance item priority list generated in step 2, and conduct an in-depth analysis of the RPN values ​​and risk strategy scores in the list. Based on these data, dynamically update the maintenance level for each maintenance item. Next, combined with the comprehensive status assessment results, defect analysis results, and FMEA analysis results of the equipment, comprehensively recommend the final maintenance level of the equipment, thereby ensuring the appropriate maintenance frequency and intensity of the equipment, so as to achieve the rational allocation of maintenance resources and efficient management of equipment.

[0086] 3.1 Obtain a priority list of maintenance projects and analyze risk data:

[0087] 3.1.1 Read the priority list:

[0088] Data source: Get the maintenance project priority list generated in step 2. The list includes the RPN value and risk strategy score of the maintenance project.

[0089] 3.1.2 Detailed analysis of RPN value and risk strategy score:

[0090] The dynamic adjustment analysis strategy of the minimum maintenance level of maintenance items is shown in Table 1:

[0091] Table 1 Dynamic adjustment analysis strategy table for the minimum maintenance level of maintenance items

[0092] RPN(Low) RPN(Medium) RPN(High) Risk score 0.7-1.0 No adjustment Adjust up one level Adjust up two levels Risk score 0.4-0.7 Adjust up one level Adjust up one level Adjust up two levels Risk score 0.0-0.4 No adjustment No adjustment Adjust up one level

[0093] 3.2 Dynamic update method of the minimum maintenance level of the maintenance project itself:

[0094] 3.2.1 Dynamic update rules:

[0095] Update strategy: Based on the dynamic adjustment analysis strategy of the minimum maintenance level of maintenance items in 3.12, the minimum maintenance level of each maintenance item is updated and adjusted.

[0096] 3.2.2 Trigger conditions for dynamic adjustment:

[0097] Calculation of RPN value and risk strategy value: When the maintenance task is started, the RPN value and risk strategy value are recalculated.

[0098] Rule judgment: When the dynamic adjustment analysis strategy of the minimum maintenance level of the maintenance project exceeds a specific threshold, the maintenance level upgrade or plan adjustment is automatically triggered.

[0099] 3.3 Application of comprehensive status assessment and defect analysis results:

[0100] 3.3.1 The role of comprehensive status assessment:

[0101] Status indicator collection: collect key status parameters of equipment (such as wear degree, temperature rise, energy consumption, etc.) through the status monitoring system.

[0102] Evaluation method: Use the weighted average method to combine the status parameters with the comprehensive health score of the equipment to form a comprehensive status evaluation value.

[0103] 3.3.2 Application of defect analysis results:

[0104] Defect severity classification: Based on the defect analysis results, equipment defects are classified into three levels: minor, moderate, and severe.

[0105] Defect-related maintenance: If a defect is defined as medium or severe, the priority of the corresponding maintenance project will be increased accordingly.

[0106] 3.4 Comprehensive recommendation method based on equipment maintenance level:

[0107] 3.4.1 Maintenance level classification basis and standards:

[0108] Equipment level definition: The maintenance level of the equipment will be adjusted according to the lowest maintenance item level of the equipment itself. The equipment maintenance is divided into four levels: A / B / C / D.

[0109] Classification basis: Comprehensive status assessment results - using the weighted average method, the status parameters are combined with the comprehensive health score of the equipment to form a comprehensive status assessment value; defect analysis results: based on the defect analysis results, equipment defects are divided into three levels: slight, moderate, and severe; based on FMEA analysis results - when the dynamic adjustment analysis strategy of the minimum maintenance level of the maintenance project exceeds a specific threshold, it automatically triggers the upgrade of the maintenance level or adjustment of the plan.

[0110] 3.4.2 Recommended strategy:

[0111] Dynamic recommendation strategy model: Build a dynamic update model to update the recommended equipment maintenance level in real time based on equipment status changes, historical data accumulation, feedback from defect analysis, and feedback from FMEA analysis.

[0112] Calculation method: Final grade score = w1 × comprehensive status score + w2 × defect analysis score + w3 × minimum maintenance grade score; w1, w2, w3 are the weight coefficients of each score, and the weights are set according to the importance of the equipment, the company's risk tolerance and historical maintenance data to ensure that key factors receive appropriate attention.

[0113] Trigger judgment strategy:

[0114] High score strategy: If the final score exceeds the set high risk threshold, the recommended maintenance level is A / B maintenance.

[0115] Medium score strategy: If the score is in the middle range, level C is recommended.

[0116] Low score strategy: If the final score is lower than the set safety threshold, the recommended level is D repair.

[0117] Step 4: Schedule the maintenance plan based on the maintenance level determined by FMEA:

[0118] Based on the risk analysis results of FMEA, a maintenance schedule for hydropower equipment that meets multi-objective optimization is formulated. By designing a multi-objective function, the aim is to minimize the downtime of the entire plant while giving priority to the maintenance of high-risk units. In addition, a series of constraints are set up, including the maximum number of units to be maintained simultaneously, the maximum number of A / B maintenance projects, and the earliest and latest maintenance times for high-risk units, to ensure the rational allocation of maintenance resources. In order to optimize the maintenance schedule under complex constraints, a combinatorial optimization algorithm (such as a genetic algorithm or a particle swarm optimization algorithm) is used to obtain the optimal maintenance schedule while meeting the goals and constraints.

[0119] 4.1 Scheduling conditions setting:

[0120] 4.1.1. Multi-objective function design:

[0121] The core of the multi-objective function is to achieve the minimum downtime of the whole plant and give priority to the maintenance of the units with the highest risk strategy. In order to meet the dual needs of minimizing the impact of downtime and minimizing risks of hydropower equipment, the following multi-objective function is designed:

[0122] Objective 1: Minimize the downtime of the entire plant. Define the downtime function Ttotal as the sum of the maintenance time of each unit. The goal is to minimize this time to reduce the impact on power supply.

[0123] Ttotal definition: The objective function aims to minimize the sum of all unit maintenance time, defined as Ttotal. By reducing the downtime of each unit and coordinating the maintenance sequence, the impact on the production of the entire plant can be reduced.

[0124] Objective 2: Prioritize maintenance of high-risk units. To reduce major risks, equipment that has been assessed as high risk is given priority for maintenance. The high-risk priority function Rhigh_risk is defined to represent the unit priority under the high-risk strategy, ensuring that maintenance is completed with the highest priority. Rhigh_risk definition: To reduce risk events during operation, units with higher risk levels are given priority for maintenance. The objective function is defined as the high-risk priority function Rhigh_risk, which represents the priority weight of high-risk units in the maintenance schedule. By adjusting the weights, the units with the highest risk can be given priority in the plan. Define the multi-objective optimization function:

[0125] min(Ttotal,Rhigh_risk)

[0126] This optimization function takes into account both minimizing the total equipment downtime and prioritizing maintenance of high-risk equipment when scheduling.

[0127] The constraints are designed as follows:

[0128] During maintenance scheduling, a series of constraints need to be considered to ensure reasonable allocation of resources. The main constraints include:

[0129] Maximum number of units under maintenance at the same time: The number of units under maintenance at the same time shall not exceed the set upper limit to avoid causing instability in the operation of the entire plant.

[0130] Maximum number of A / B maintenance projects: There is also an upper limit on the number of A and B maintenance projects that can be carried out simultaneously, so as to avoid affecting plant operations due to excessive concentration of maintenance time and resources.

[0131] Earliest and latest high-risk maintenance time: To ensure timely maintenance of high-risk units, it is necessary to set the earliest and latest maintenance time to ensure that the maintenance cycle is completed within a reasonable range.

[0132] In order to meet multi-objective optimization and complex constraints, the present invention introduces a combinatorial optimization algorithm, such as a genetic algorithm or a particle swarm optimization algorithm, to optimize the maintenance schedule.

[0133] Genetic algorithm: Through population initialization, selection, crossover, mutation and other processes, the optimal maintenance schedule is gradually evolved. Genetic algorithm can globally optimize under complex constraints to achieve the goal of minimum downtime and high-risk priority maintenance.

[0134] Particle swarm optimization algorithm: finds the optimal solution by iteratively adjusting each particle (i.e., maintenance scheduling solution) under the objective function and constraints. Particle swarm optimization algorithm has a fast convergence speed and is suitable for maintenance scheduling problems with multiple objectives and complex constraints.

[0135] Through iterative calculation of the scheduling algorithm, the optimal maintenance scheduling plan that meets multi-objective requirements and constraints is finally obtained.

[0136] The above embodiments are only preferred technical solutions of the present invention and should not be regarded as limiting the present invention. The protection scope of the present invention shall be the technical solutions recorded in the claims, including equivalent replacement solutions of the technical features in the technical solutions recorded in the claims. That is, equivalent replacement improvements within this scope are also within the protection scope of the present invention.

Claims

1. A maintenance decision-making method for hydropower equipment based on FMEA, characterized by: The steps include: Step 1: Establish FMEA analysis framework and maintenance decision framework; Step 2: Determine maintenance items based on FMEA analysis results; generate a priority list of maintenance items; Step 3: Determine the maintenance level based on the selected items of FMEA; comprehensively recommend the maintenance level of equipment; Step 4: Schedule maintenance plans based on the maintenance levels determined by FMEA; arrange maintenance time reasonably.

2. A maintenance decision-making method for hydropower equipment based on FMEA according to claim 1; characterized in that: The sub-steps of step 1 are: Step 1.1: Establish a hydropower equipment model: Construct a hierarchical equipment structure; cover the unit level, equipment level, component level and other levels; form a unified logical equipment tree and instantiation tree; clarify the hierarchical relationship and functional association between equipment; add attribute information including name, model, operating parameters, and historical fault records to the equipment node; comprehensively record the basic characteristics and past conditions of the equipment; formulate a unified data format; ensure the compatibility and sharing of business information of different systems; realize seamless flow and interaction of data; define data mapping relationships; accurately connect FMEA and maintenance decision information; enable the data of the two frameworks to accurately correspond; provide a solid foundation for subsequent analysis and decision-making; Step 1.2: Standardize and connect the treatment measures library and maintenance project library: Classify and refine the FMEA treatment measures; cover various types including preventive maintenance, corrective maintenance, and improvement measures; clarify the applicable scenarios and specific requirements of different measures; configure and connect the treatment measures with the equipment model; introduce configuration tables and associated fields in the hydropower equipment model; achieve accurate links between each treatment measure and specific equipment components and failure modes in the equipment model; establish a standard maintenance project library; formulate detailed project classification standards and standard operating procedures; ensure the standardization and efficiency of maintenance projects; through matching strategies and data update mechanisms; associate FMEA treatment measures with maintenance projects; and achieve automatic association and synchronous update of treatment measures and maintenance projects.

3. A maintenance decision-making method for hydropower equipment based on FMEA according to claim 1; characterized in that: The sub-steps of step 2 are: Step 2.1: Obtain risk analysis results: calculate the RPN value; use the severity, occurrence, and detection of risk assessment indicators commonly used in FMEA to accurately assess the risk level of the failure mode; provide a quantitative basis for subsequent decision-making; obtain the corresponding treatment measures from the treatment measure library according to the RPN value; Ensure that appropriate measures can be taken for failure modes with different risk levels; Step 2.2: Construct a recommendation strategy: divide the risk levels; Divide the RPN value into high, medium and low risk levels; clarify the risk level and response priorities for each level; Set thresholds and key equipment levels; determine threshold standards for different risk levels and recommended priorities for key equipment based on the enterprise's risk tolerance and key equipment maintenance level; build a multi-dimensional weight analysis model; comprehensively consider factors including equipment criticality, historical failure data, operating environment, and equipment aging; Determine the recommendation priority; for high-risk failure modes; directly recommend the corresponding maintenance items; and prioritize them according to the RPN value and risk strategy results; for medium and low-risk failure modes; Combine equipment operation status and historical data to conduct screening and evaluation; Determine whether immediate maintenance measures are required or can be included in the next maintenance plan; generate a recommendation list through rule engines and algorithm processes; Ensure the accuracy and rationality of recommendations.

4. A maintenance decision-making method for hydropower equipment based on FMEA according to claim 1; characterized in that: The sub-steps of step 3 are: Step 3.1: Analyze risk data; Step 3.2: Dynamically update the maintenance level; Step 3.3: Apply the comprehensive condition assessment and defect analysis results; Step 3.4: Comprehensively recommend maintenance level.

5. A maintenance decision-making method for hydropower equipment based on FMEA according to claim 1; characterized in that: In step 3.1, the method for analyzing risk data is: read the priority list; obtain the maintenance project priority list generated in step 2; which includes the RPN value and risk strategy score of the maintenance project; conduct a detailed analysis of the RPN value and risk strategy score; determine the minimum maintenance level of the maintenance project and dynamically adjust the analysis strategy; according to different RPN value ranges and risk score ranges; determine the adjustment method of the minimum maintenance level of the maintenance project.

6. A maintenance decision-making method for hydropower equipment based on FMEA according to claim 1, characterized in that: In step 3.2, the method of dynamically updating the maintenance level is: based on the analysis strategy; Update and adjust the minimum maintenance level for each maintenance item to ensure that the maintenance level matches the actual risk situation; Based on the RPN value and risk strategy value recalculated when the maintenance task is started; automatic triggering of adjustments; ensuring the real-time and accuracy of the maintenance level.

7. A maintenance decision-making method for hydropower equipment based on FMEA according to claim 1, characterized in that: In step 3.3, the method of applying the comprehensive status assessment and defect analysis results is: collect key status parameters of the equipment; comprehensively collect key status parameters including the degree of wear, temperature rise, and energy consumption of the equipment through the status monitoring system; accurately assess the comprehensive status of the equipment; classify equipment defects according to the defect analysis results; divide them into three levels: minor, medium, and severe; for medium or severe defects; associate and improve the priority of maintenance items; ensure the safe and stable operation of the equipment.

8. A maintenance decision-making method for hydropower equipment based on FMEA according to claim 1, characterized in that: In step 3.4, the method for comprehensively recommending maintenance levels is: based on equipment status assessment, defect analysis and FMEA analysis results; scientifically divide the maintenance level standards; divide equipment maintenance into four levels: A, B, C, and D; Build a dynamic recommendation model; based on equipment status changes, historical data accumulation, feedback from defect analysis, and feedback from FMEA analysis; update the recommended equipment maintenance level in real time; by calculation: That is, the final grade score = w1 × comprehensive status score + w2 × defect analysis score + w3 × minimum maintenance grade score; Among them, w1, w2, and w3 are the weight coefficients of each score; the weights are set according to the importance of the equipment, the company's risk tolerance, and historical maintenance data; a trigger judgment strategy is adopted; the recommended maintenance level is determined based on the final score; if the final score exceeds the set high-risk threshold, the recommended maintenance level is A / B; if the score is in the middle range, the recommended maintenance level is C; if the final score is lower than the set safety threshold, the recommended maintenance level is D.

9. A maintenance decision-making method for hydropower equipment based on FMEA according to claim 1, characterized in that: The sub-steps of step 4 are: Step 4.1: Design multi-objective functions and constraints: Design multi-objective functions with the goal of minimizing the downtime of the entire plant and giving priority to the maintenance of high-risk units; define the downtime function Ttotal as the sum of the maintenance time of each unit; The goal is to minimize that time; To reduce the impact on power supply; at the same time; define the high-risk priority function Rhigh_risk to represent the unit priority under the high-risk strategy; ensure that the highest priority maintenance is completed; set the constraints including the maximum number of units to be maintained at the same time, the maximum number of A / B maintenance items and the high-risk maintenance time; Ensure reasonable allocation of maintenance resources under limited resources; avoid causing instability in the operation of the entire plant; Step 4.2: Establish calculation formulas and adopt combinatorial optimization algorithms: Establish multi-objective scheduling calculation formulas and constraint formulas; comprehensively consider multi-objective optimization requirements; ensure the scientificity and rationality of maintenance scheduling; use combinatorial optimization algorithms such as genetic algorithms or particle swarm optimization algorithms to optimize maintenance scheduling; through population initialization, selection, crossover, mutation and other processes or each particle continuously iterates and adjusts under objective functions and constraints; find the optimal maintenance time schedule; meet multi-objective requirements and complex constraints.

10. A maintenance decision system for hydropower equipment based on FMEA; characterized by: A maintenance decision method for hydropower equipment based on FMEA according to any one of claims 1 to 9 is adopted.

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