Method and system for determining RCM maintenance strategy of hydroelectric generating set based on risk priority

Abstract

The invention belongs to the field of hydroelectric generating set RCM maintenance, and discloses a hydroelectric generating set RCM maintenance strategy determination method and system based on risk priority, and the method comprises the steps: building an FMEA analysis database based on the historical operation data, fault event ledger and equipment state monitoring data of a hydroelectric generating set; performing quantitative evaluation on each fault mode of the FMEA analysis database from occurrence degree O, severity S and undetectability D, and calculating a risk priority number RPN of each fault mode; risk grades are divided according to the risk priority number RPN, a dynamic maintenance strategy is generated based on the risk grades, the dynamic maintenance strategy is output to the operation and maintenance management system, the actual operation condition and fault risks of equipment are fully considered, the reliability and safety of the hydroelectric generating set are effectively improved, the maintenance cost is reduced, and the non-planned downtime is shortened.

Description

Technical Field

[0001] This invention belongs to the field of RCM maintenance technology for hydropower units, and relates to a method and system for determining RCM maintenance strategies for hydropower units based on risk priority. Background Technology

[0002] Hydropower, as a crucial component of renewable energy, occupies a key position in the power supply system. Hydropower units, as the core equipment of hydropower generation, directly affect the stability, economic efficiency, and ecological benefits of power supply. With the continuous growth of electricity demand and the development of smart grids, higher demands are placed on the safe and reliable operation of hydropower units. Scientific and efficient maintenance strategies have become a core element in ensuring the stable operation of these units.

[0003] Currently, the mainstream maintenance strategies for hydropower units mainly include periodic maintenance and reactive maintenance. Periodic maintenance involves comprehensive inspection and maintenance of the equipment according to a pre-set time cycle. While this method can prevent failures to some extent, it is prone to over-maintenance or under-maintenance because it does not fully consider the actual operating conditions and individual differences of the equipment. Over-maintenance not only wastes manpower, material resources, and financial resources, but frequent disassembly operations may also damage the equipment and reduce its service life; while under-maintenance fails to detect potential faults in time, which may lead to unplanned downtime and cause significant economic losses. Reactive maintenance, on the other hand, involves repairing equipment after a failure has occurred. Although it saves on routine maintenance costs, the suddenness of failures may lead to further damage to the equipment, power outages, and even safety accidents and environmental pollution, making it difficult to meet the stringent reliability and stability requirements of modern power systems.

[0004] Reliability-centered maintenance (RCM) is increasingly being applied in equipment maintenance. By analyzing equipment failure modes and their impacts, targeted maintenance strategies are developed, improving the scientific nature of equipment maintenance to some extent. However, existing RCM-based maintenance methods for hydropower units still have many shortcomings. On the one hand, the quantitative assessment of the occurrence, severity, and undetectability of failure modes during risk assessment lacks systematicity and accuracy, and fails to fully consider the impact of complex factors such as seasonal variations in water flow, water quality parameters, and equipment geographical location on failures. On the other hand, the matching between risk level classification and maintenance strategies is not refined enough, making it difficult to provide dynamic and accurate maintenance plans based on the actual risk status of the equipment. Summary of the Invention

[0005] To address the aforementioned problems in the existing technology, this invention provides a method and system for determining the RCM maintenance strategy of hydropower units based on risk priority.

[0006] To achieve the above objectives, the present invention adopts the following technical solution: This invention provides a method for determining the RCM (Recovery Management and Maintenance) strategy for hydropower units based on risk priority, comprising: constructing an FMEA (Fault Factor Analysis) database based on historical operating data, fault event logs, and equipment status monitoring data of the hydropower unit; quantitatively evaluating each fault mode in the FMEA database from the perspectives of occurrence (O), severity (S), and undetectability (D), and calculating the risk priority number (RPN) for each fault mode; classifying risk levels according to the risk priority number (RPN), generating dynamic maintenance strategies based on the risk levels, and outputting the dynamic maintenance strategies to the operation and maintenance management system.

[0007] Further, the calculation of the Risk Priority Number (RPN) for each failure mode includes:

[0008] Where S represents severity, which is the degree of impact of a failure mode on a user; O represents occurrence, which is the frequency with which a failure mode occurs within a predetermined or scheduled time period; and D represents undetectability, which is the estimated probability of identifying and eliminating the failure before it affects the user.

[0009] Furthermore, the step of classifying risk levels based on the Risk Priority Number (RPN) and generating dynamic maintenance based on these risk levels includes: RPN < 10, indicating a low risk level, where repairs are performed based on the actual condition after a fault occurs; 10 ≤ RPN ≤ 19, indicating a low to medium risk level, where maintenance needs are determined based on real-time equipment operating status data; 20 ≤ RPN ≤ 39, indicating a medium risk level, where periodic maintenance is set according to equipment operating patterns; 40 ≤ RPN ≤ 59, indicating a medium to high risk level, where comprehensive inspections and maintenance are performed on the equipment according to a preset fixed cycle; and RPN > 60, indicating a high risk level, where improved maintenance is performed by optimizing equipment structure and upgrading components to reduce fault risk.

[0010] Furthermore, for each failure mode in the FMEA analysis database, quantitative assessments are performed based on occurrence (O), severity (S), and undetectability (D). Occurrence (O) is classified into levels 1 to 4 based on the impact of seasonal changes in water flow, water quality parameters, and equipment geographical location on failure frequency. Severity (S) is classified into levels 1 to 5 based on safety impact, production loss, environmental risk, and social benefits. Undetectability (D) is classified into levels 1 to 5 based on online monitoring coverage, sensor accuracy, and the effectiveness of manual inspections.

[0011] Furthermore, the occurrence rate O is classified into 1 to 4 levels, including: Level 1: annual average occurrence probability of failure < 0.1%; Level 2: 0.1% ≤ annual average occurrence probability of failure < 1%; Level 3: 1% ≤ annual average occurrence probability of failure < 5%; Level 4: annual average occurrence probability of failure ≥ 5%.

[0012] Furthermore, in the severity S classification of 1 to 5 levels: Level 5 is a failure that leads to unit explosion, casualties, or watershed ecological disaster; Level 4 is a failure that causes unplanned shutdown for more than 72 hours; and Level 1 is a minor performance fluctuation that only triggers a warning and does not require shutdown.

[0013] Furthermore, the FMEA analysis database is constructed based on equipment classification and failure modes. The equipment classification includes Class I, Class II, and Class III equipment. Class I equipment consists of core components whose failures directly threaten unit safety or lead to unplanned shutdowns. These core components include water guide mechanisms, water guide bearings, and main shafts. Class II equipment consists of auxiliary equipment whose failures affect unit economy but can be operated in the short term. These auxiliary equipment include top cover drainage pumps and oil regulator pumps. Class III equipment consists of non-critical equipment whose failures only require planned maintenance. These non-critical equipment include water supply valves and water filters.

[0014] This invention also provides a risk-priority-based RCM maintenance strategy determination system for hydropower units, characterized by comprising: a construction module for constructing an FMEA analysis database based on historical operating data, fault event logs, and equipment status monitoring data of the hydropower unit; an evaluation module for quantitatively evaluating each fault mode in the FMEA analysis database based on occurrence (O), severity (S), and undetectability (D), and calculating the risk priority number (RPN) for each fault mode; and an output module for classifying risk levels according to the risk priority number (RPN), generating dynamic maintenance strategies based on the risk levels, and outputting the dynamic maintenance strategies to the operation and maintenance management system.

[0015] Furthermore, the construction module also includes verification of the integrity of the collected data and removal of outliers.

[0016] Furthermore, the evaluation module also includes dynamically adjusting the weighting coefficients of occurrence (O), severity (S), and undetectability (D), and correcting the deviation of the risk priority number (RPN) based on historical failure data.

[0017] Compared with the prior art, the present invention has the following beneficial technical effects: This invention discloses a risk-priority-based method for determining the RCM (Recovery Management and Maintenance) strategy for hydropower units. This method analyzes and processes historical operating data, fault event logs, and equipment condition monitoring data of the hydropower units to construct an FMEA (Factors-Oriented Analysis and Assessment) database. It then quantifies and classifies fault modes based on risk, generates dynamic maintenance strategies, and outputs these strategies to the operation and maintenance management system for implementation. This approach fully considers the actual operating conditions and fault risks of the equipment, effectively improving the reliability and safety of the hydropower units, reducing maintenance costs, and minimizing unplanned downtime.

[0018] This invention provides a risk-priority-based method for determining the RCM maintenance strategy of hydropower units. By constructing an FMEA analysis database covering historical operating data, fault logs, and condition monitoring data, and combining multi-dimensional factors such as seasonal changes in water flow, water quality parameters, and equipment geographical location, the method quantifies and classifies the occurrence, severity, and undetectability of fault modes, significantly improving the comprehensiveness and accuracy of risk assessment.

[0019] This invention discloses a method for determining the RCM (Responsible Maintenance) strategy for hydropower units based on risk priority. It divides the system into five levels based on Risk Priority Number (RPN) and designs differentiated maintenance strategies for each level, achieving a precise match between "high-risk, high-intervention" and "low-risk, low-intervention". For high-risk faults with an RPN > 60, an improved maintenance strategy is adopted to reduce the failure rate at its root; while for low-risk equipment with an RPN < 10, reactive maintenance is used to avoid redundant maintenance costs. Attached Figure Description

[0020] Figure 1 This is a flowchart of a method for determining the RCM maintenance strategy of hydropower units based on risk priority, according to the present invention. Detailed Implementation

[0021] To enable those skilled in the art to better understand the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of the present invention.

[0022] Example 1 This invention discloses a method for determining the RCM (Recovery Management Principle) maintenance strategy for hydropower units based on risk priority. The method includes: constructing an FMEA (Fault Factor Analysis) database based on historical operating data, fault event logs, and equipment status monitoring data of the hydropower unit; quantitatively evaluating each fault mode in the FMEA database based on occurrence (O), severity (S), and undetectability (D), and calculating the Risk Priority Number (RPN) for each fault mode; classifying risk levels according to the RPN; generating a dynamic maintenance strategy based on the risk levels; and outputting the dynamic maintenance strategy to the operation and maintenance management system. Figure 1 As shown.

[0023] Specifically, historical operating data includes long-term records of parameters such as unit start / stop time, running time, load changes, speed, vibration, temperature, and pressure. Analyzing vibration data of the hydropower unit under different loads helps understand its mechanical stability; observing temperature change trends helps determine if overheating or other abnormalities exist. Regular data backup and verification are also necessary. The collected data undergoes preprocessing to remove noise and outliers to improve data quality.

[0024] Outliers are caused by sensor malfunctions, data transmission errors, and other reasons. Statistical analysis and machine learning-based methods are used to identify and remove outliers.

[0025] The fault event log records detailed information on all past fault events of the hydropower unit, including the time, location, symptoms, cause, maintenance measures, and results. By organizing and analyzing the fault event log, we can understand common fault modes of hydropower units and their impacts.

[0026] When collecting fault event logs, it is essential to ensure the information is detailed and accurate. An information management system should be used to record and manage fault events, facilitating subsequent queries and statistical analysis. For example, when a fault occurs, on-site maintenance personnel should promptly record the specific details, including the equipment status and operating parameters at the time of the fault, which will aid in further in-depth analysis of the cause of the fault.

[0027] Vibration sensors, temperature sensors, pressure sensors, and flow sensors collect real-time operating status information of the hydropower unit. Vibration sensors can monitor the unit's vibration and detect mechanical faults in a timely manner; temperature sensors can monitor temperature changes and prevent overheating damage. The equipment status monitoring data is real-time and continuous, and can reflect the operating status of the hydropower unit promptly.

[0028] Before building the FMEA analysis database, the collected data needs to be verified for integrity. This involves checking for missing, incorrect, or inconsistent data. For example, checking for missing data for a specific period in historical operational data, or omissions of key information in the fault event log. Missing data can be supplemented using methods such as interpolation and regression analysis; erroneous or inconsistent data should be corrected or removed. Data integrity verification ensures the accuracy and reliability of the data in the FMEA analysis database.

[0029] The FMEA analysis database is built based on equipment classification and failure modes. Equipment is classified into three categories: Category I, Category II, and Category III. Category I equipment refers to core components whose failures directly threaten the safety of the hydropower unit or lead to unplanned shutdowns. These include the water guide mechanism, water guide bearings, and main shaft. Failures in the water guide mechanism may cause the hydropower unit to become uncontrollable; failures in the water guide bearings may cause increased vibration; and failures in the main shaft may prevent the hydropower unit from rotating normally. Detailed records of the technical parameters, operating status, and failure history of Category I equipment are essential for the timely detection of potential faults.

[0030] Category II equipment refers to auxiliary equipment whose failure affects the unit's economic efficiency but allows for short-term operation. This includes top cover drain pumps and oil conditioner pumps. While they won't immediately cause unit shutdown, they will impact the unit's operating efficiency and economy. A failure of the top cover drain pump may lead to water accumulation on the top cover, affecting the unit's stability; a failure of the oil conditioner pump may cause the speed control system to malfunction, affecting the unit's load regulation capability. Category II equipment requires regular inspection and maintenance.

[0031] Category III equipment refers to non-critical equipment that requires only planned maintenance in case of malfunction. This includes water supply valves and water filters. Maintenance needs to be performed according to a set schedule to ensure their proper functioning. For example, a malfunctioning water supply valve may lead to insufficient water supply, while a malfunctioning water filter may cause water quality deterioration, affecting the equipment's lifespan.

[0032] Enter the compiled data into the FMEA analysis database to establish the correlation between equipment, failure modes, and impact consequences. During data entry, ensure the accuracy and completeness of the data. Data import tools or a data entry program can be used to batch enter data into the database. Simultaneously, the database should be backed up and securely managed to ensure data integrity and security. Regularly back up the database to prevent data loss; set appropriate user permissions to prevent unauthorized access and data tampering.

[0033] The factors for assessing the occurrence degree O are determined based on the impact of seasonal variations in water flow, water quality parameters, and the geographical location of equipment on failure frequency. Hydrological monitoring stations can obtain data such as flow rate and velocity to understand the seasonal variation patterns of water flow; water quality testing institutions can obtain parameters such as pH, hardness, and sediment content to assess the impact of water quality on equipment. Based on the collected data, the occurrence degree O is classified and assessed according to levels 1 to 4: Level 1: Annual average failure probability < 0.1%; Level 2: 0.1% ≤ Annual average failure probability < 1%; Level 3: 1% ≤ Annual average failure probability < 5%; Level 4: Annual average failure probability ≥ 5%.

[0034] Based on factors such as safety impact, production loss, environmental risk and social benefits, the assessment index of severity S is determined. According to the assessment standard, severity S is classified and assessed on a scale of 1 to 5, where 1 represents "minor impact" and 5 represents "catastrophic impact".

[0035] The assessment criteria for undetectability D are determined based on factors such as online monitoring coverage, sensor accuracy, and the effectiveness of manual inspections. Higher online monitoring coverage, higher sensor accuracy, and more effective manual inspections result in a higher probability of identifying and eliminating faults before they affect users, and thus a lower undetectability.

[0036] Based on the collected information, the undetectability D is classified and assessed according to levels 1-5. Level 1: Faults can be easily identified and eliminated using existing monitoring and inspection methods before users are affected, with an estimated probability >90%. Level 2: Faults can be identified and eliminated relatively easily using existing monitoring and inspection methods, with an estimated probability between 70% and 90%. Level 3: Faults can be identified and eliminated using existing monitoring and inspection methods, but with some difficulty, with an estimated probability between 50% and 70%. Level 4: Faults are difficult to identify and eliminate using existing monitoring and inspection methods, with an estimated probability between 30% and 50%. Level 5: Faults are almost impossible to identify and eliminate using existing monitoring and inspection methods before users are affected, with an estimated probability <30%.

[0037] The Risk Priority Number (RPN) is calculated based on the occurrence (O), severity (S), and undetectability (D) of each failure mode. The RPN reflects the overall risk level of each failure mode; a higher RPN indicates a higher risk. For example, if a failure mode has an occurrence (O) of level 3, a severity (S) of level 4, and an undetectability (D) of level 3, then the RPN for that failure mode is 4 × 3 × 3 = 36.

[0038] Risk levels are categorized based on Risk Priority Number (RPN): RPN < 10 indicates low risk, requiring repair based on the actual condition after a fault occurs; RPN 10 ≤ RPN ≤ 19 indicates low to medium risk, requiring maintenance based on real-time equipment operating data; RPN 20 ≤ RPN ≤ 39 indicates medium risk, requiring periodic maintenance based on equipment operating patterns; RPN 40 ≤ RPN ≤ 59 indicates medium to high risk, requiring comprehensive inspection and maintenance at a predetermined fixed interval; RPN > 60 indicates high risk, requiring improved maintenance by optimizing equipment structure and upgrading components to reduce fault risk. See Table 1.

[0039]

[0040] If the Risk Priority Number (RPN) is less than 10, after a failure occurs, technical personnel should be dispatched to inspect and troubleshoot the equipment, identify the cause of the failure, and repair it. Simultaneously, the operating status of the equipment should be continuously monitored to observe whether similar failures occur again.

[0041] If the risk priority number (RPN) is 10 or less and the risk priority number (RPN) is 19 or less, and a slight increase in temperature is detected in a certain part, the operating parameters of the equipment can be adjusted first to observe whether the temperature returns to normal. If the temperature continues to rise, maintenance personnel need to be arranged to inspect the part, find out the cause of the temperature rise, and deal with it.

[0042] For equipment with a Risk Priority Number (RPN) of 20 ≤ RPN ≤ 39, inspections and replacements should be scheduled at regular intervals based on the equipment's service life and operating time. During the maintenance cycle, the equipment should be regularly inspected, maintained, and repaired, including cleaning, tightening bolts, and changing lubricating oil, to ensure its normal operation. Simultaneously, the equipment's operating status should be monitored in real time to promptly identify potential faults and take preventative measures.

[0043] For a risk priority number (RPN) of 40 ≤ RPN ≤ 59, a comprehensive inspection and maintenance can be performed every six months for the water guiding mechanism. This includes checking and adjusting the opening and sealing of the guide vanes, and replacing any damaged parts promptly.

[0044] With a Risk Priority Number (RPN) greater than 60, more advanced control algorithms and equipment can be used in the control system of hydropower units to improve the stability and reliability of the system.

[0045] An information management system is used to generate a list of maintenance tasks, which are then presented as work orders. Each work order must record detailed information about the maintenance task for easy review and execution by maintenance personnel. Simultaneously, maintenance tasks are categorized and prioritized according to their urgency and importance, with high-risk fault modes being given priority.

[0046] The generated maintenance task list is output to the operation and maintenance management system. The operation and maintenance management system is an integrated software platform used to manage and schedule maintenance work for hydropower units. In the system, maintenance tasks are presented as work orders. Maintenance personnel can access their work orders and understand the detailed information and requirements of the tasks. Simultaneously, the system can track and monitor maintenance tasks, providing real-time updates on their progress. By outputting maintenance tasks to the system, information-based management of maintenance work can be achieved, improving work efficiency and management level.

[0047] With the technological upgrades, equipment modifications, and changes in the operating environment of hydropower units, the data in the FMEA analysis database needs to be updated in a timely manner. Regular inspections and assessments should be conducted to identify new failure modes and their impacts, which should then be incorporated into the FMEA analysis database. Simultaneously, existing failure modes and their impacts should be revised and improved to ensure the accuracy and timeliness of the database.

[0048] In summary, the risk-priority-based RCM maintenance strategy determination method for hydropower units constructs an FMEA analysis database by analyzing and processing historical operating data, fault event logs, and equipment condition monitoring data. This database quantifies and classifies fault modes based on risk, generates dynamic maintenance strategies, and outputs them to the operation and maintenance management system for implementation. This approach fully considers the actual operating conditions and fault risks of the equipment, effectively improving the reliability and safety of hydropower units, reducing maintenance costs, and minimizing unplanned downtime.

[0049] Example 2 The present invention provides a risk-priority-based RCM maintenance strategy determination system for hydropower units, comprising a construction module, an evaluation module, and an output module.

[0050] The construction module is used to build an FMEA analysis database based on historical operating data, fault event logs, and equipment status monitoring data of the hydropower unit; the evaluation module is used to quantitatively evaluate each fault mode in the FMEA analysis database from the perspectives of occurrence (O), severity (S), and undetectability (D), and calculate the risk priority number (RPN) for each fault mode; the output module is used to classify risk levels according to the risk priority number (RPN), generate dynamic maintenance strategies based on the risk levels, and output the dynamic maintenance strategies to the operation and maintenance management system.

[0051] The present invention provides a risk-priority-based RCM maintenance strategy determination system for hydropower units that can implement the same method steps as the above method, so it will not be described again.

[0052] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this invention are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of the invention described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover a non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

Claims

1. A method for determining the RCM maintenance strategy of hydropower units based on risk priority, characterized in that, Includes the following steps: Based on historical operating data, fault event logs, and equipment status monitoring data of hydropower units, an FMEA analysis database is constructed. For each failure mode in the FMEA analysis database, quantitative assessments are performed based on occurrence (O), severity (S), and undetectability (D), and the risk priority number (RPN) for each failure mode is calculated. Risk levels are determined based on Risk Priority Number (RPN), dynamic maintenance strategies are generated based on these risk levels, and the dynamic maintenance strategies are output to the operation and maintenance management system.

2. The method for determining the RCM maintenance strategy of hydropower units based on risk priority according to claim 1, characterized in that, The calculation of the Risk Priority Number (RPN) for each failure mode includes: Where S represents severity, which is the degree of impact of a failure mode on a user; O represents occurrence, which is the frequency with which a failure mode occurs within a predetermined or scheduled time period; and D represents undetectability, which is the estimated probability of identifying and eliminating the failure before it affects the user.

3. The method for determining the RCM maintenance strategy of hydropower units based on risk priority according to claim 1, characterized in that, The process of classifying risk levels based on Risk Priority Number (RPN) and generating dynamic maintenance based on these risk levels includes: If the risk priority number RPN < 10, the risk level is low, and repairs will be carried out based on the actual situation after the failure occurs. If 10 ≤ Risk Priority Number (RPN) ≤ 19, the risk level is medium to low risk, and maintenance needs are determined based on real-time equipment operating status data. If 20 ≤ Risk Priority Number (RPN) ≤ 39, the risk level is medium risk, and periodic maintenance is set according to the equipment's operating pattern. If 40 ≤ Risk Priority Number (RPN) ≤ 59, the risk level is medium to high. The equipment should be fully inspected and maintained according to a preset fixed cycle. If the Risk Priority Number (RPN) is greater than 60, the risk level is high. Improved maintenance is carried out by optimizing the equipment structure and upgrading components to reduce the risk of failure.

4. The method for determining the RCM maintenance strategy of hydropower units based on risk priority according to claim 1, characterized in that: For each failure mode in the FMEA analysis database, a quantitative assessment is performed based on occurrence (O), severity (S), and undetectability (D), including: Occurrence level O is classified into 1 to 4 levels based on the impact of seasonal changes in water flow, water quality parameters, and the geographical location of equipment on failure frequency. Severity S is classified into 1 to 5 levels based on safety impact, production loss, environmental risk, and social benefits; Undetectability D is classified into levels 1 to 5 based on online monitoring coverage, sensor accuracy, and the effectiveness of manual inspections.

5. The method for determining the RCM maintenance strategy of hydropower units based on risk priority according to claim 4, characterized in that, The occurrence degree O is classified into 1 to 4 levels, including: Level 1: Annual average probability of failure < 0.1%; Level 2: 0.1% ≤ Annual average probability of failure < 1%; Level 3: 1% ≤ Annual average probability of failure < 5%; Level 4: Annual average probability of failure ≥ 5%.

6. The method for determining the RCM maintenance strategy of hydropower units based on risk priority according to claim 4, characterized in that, In the severity S classification of 1 to 5: Level 5 indicates a malfunction that could lead to a unit explosion, casualties, or a watershed ecological disaster. Level 4 is defined as an unplanned downtime caused by a fault exceeding 72 hours; Level 1 indicates a minor performance fluctuation that triggers a warning but does not require system shutdown.

7. The method for determining the RCM maintenance strategy of hydropower units based on risk priority according to claim 1, characterized in that: The FMEA analysis database is constructed based on equipment classification and failure modes; The equipment classification includes Class I equipment, Class II equipment, and Class III equipment; The first type of equipment is a core component whose failure directly threatens the safety of the unit or leads to unplanned shutdown. The core component includes a water guiding mechanism, a water guiding bearing, and a main shaft. The second type of equipment refers to auxiliary equipment whose failure affects the unit's economy but can be operated for a short period of time. The auxiliary equipment includes the top cover drainage pump and the oil regulator pump. The three types of equipment are non-critical equipment that only requires planned maintenance in case of failure. The non-critical equipment includes water supply valves and water filters.

8. A system for determining the RCM maintenance strategy of hydropower units based on risk priority, characterized in that, include: Module: Used to build an FMEA analysis database based on historical operating data, fault event logs, and equipment status monitoring data of hydropower units; Assessment module: Used to quantitatively assess each failure mode in the FMEA analysis database from occurrence (O), severity (S), and undetectability (D), and calculate the risk priority number (RPN) for each failure mode; Output module: Used to classify risk levels according to the Risk Priority Number (RPN), generate dynamic maintenance strategies based on the risk levels, and output the dynamic maintenance strategies to the operation and maintenance management system.

9. The risk-priority-based hydropower unit RCM maintenance strategy determination system according to claim 8, characterized in that: The construction module also includes verification of the integrity of the collected data and removal of outliers.

10. The risk-priority-based hydropower unit RCM maintenance strategy determination system according to claim 8, characterized in that: The evaluation module also includes dynamically adjusting the weighting coefficients of occurrence (O), severity (S), and undetectability (D), and correcting the bias of the risk priority number (RPN) based on historical failure data.

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