Turbine oil seal sealing performance evaluation method and system based on active maintenance

Abstract

The present application belongs to the field of steam turbine management, and specifically relates to a steam turbine oil seal sealing performance evaluation method and system based on active maintenance. The method and system are characterized by the following: real-time acquisition of multi-dimensional working condition data such as the dynamic gap of the steam turbine oil seal, the gas supply pressure, the oil temperature, and the water content of the lubricating oil; accurate calculation of the deviation between the sealing dynamic gap and the theoretical optimal gap after anti-interference processing; adaptive adjustment of the gas supply pressure based on the deviation to ensure the sealing adaptability; fusion of multi-parameter grading to predict the carbon deposition deterioration risk and trigger active maintenance; comprehensive evaluation and closed-loop optimization of the sealing efficiency through weighted scoring; and formation of a whole-process active maintenance mechanism. The method and system solve the problems of large measurement error, passive maintenance lag, and easy sealing failure of the oil seal in the traditional manual measurement, significantly improve the sealing stability of the oil seal and the operation safety of the unit, realize predictive active maintenance of the steam turbine, and reduce the maintenance cost during shutdown.

Description

Technical Field

[0001] This invention relates to the field of steam turbine management, specifically to a method and system for evaluating the sealing performance of steam turbine oil seals based on proactive maintenance. Background Technology

[0002] Steam turbine units are core power equipment in industries such as power generation and petrochemicals. Safe, continuous, and efficient operation is the key foundation for ensuring stable industrial production and reliable energy supply. As the core component of the unit's shaft end seal, the steam turbine oil seal plays an important role in preventing lubricating oil leakage, isolating external steam from penetrating the lubricating oil system, and protecting the rotor journal from wear and corrosion. The quality of the seal and the level of intelligence in operation and maintenance management directly determine the steam turbine's operational safety, the service life of the lubricating oil, and the overall operation and maintenance costs.

[0003] With the rapid development of industrial equipment operation and maintenance towards intelligence, initiative, and predictive methods, the traditional passive oil seal maintenance mode, which relies on manual inspection and periodic maintenance, can no longer meet the high reliability and long-cycle operation control requirements of modern large steam turbine units. The current passive oil seal maintenance mode for steam turbines has the following technical limitations: The passive oil seal maintenance mode can no longer achieve dynamic and accurate assessment of the sealing gap and adaptive and active control of the air supply pressure based on real-time operating conditions; it can only carry out remedial treatment after the seal fails. The lack of a mechanism for predicting and optimizing the risk of seal deterioration and the closed-loop verification of seal performance makes it difficult for passive maintenance mode to suppress the decline in seal performance at its source. It also makes it impossible to avoid unit failures caused by seal failure in advance, increasing the risk of unplanned shutdowns and maintenance costs, and seriously restricting the improvement of the level of intelligent operation and maintenance of steam turbines. Therefore, developing a turbine oil seal performance evaluation system and method that can achieve dynamic evaluation, active control, deterioration prediction and verification optimization of sealing performance has become a core technical problem that urgently needs to be solved in the field of intelligent operation and maintenance of turbines. Summary of the Invention

[0004] The purpose of this invention is to provide a method and system for evaluating the sealing performance of turbine oil seals based on active maintenance, so as to solve the above-mentioned technical defects.

[0005] To achieve the above objectives, the present invention provides the following technical solution: a turbine oil seal sealing performance evaluation system based on active maintenance, including an oil seal operating condition sensing module, a sealing gap calculation module, a gas seal adaptation and control module, a carbon deposit deterioration early warning module, and a sealing performance closed-loop verification module; The oil seal condition sensing module is used to collect full-dimensional condition data of the turbine oil seal and complete interference removal and data normalization. The sealing gap calculation module is used to calculate the deviation between the actual dynamic gap of the oil seal and the theoretical optimal gap. The gas seal adaptation and control module is used to adaptively adjust the gas supply pressure based on the gap deviation value. The carbon deposit deterioration early warning module is used to integrate multiple parameters to assess the risk of carbon deposit deterioration and trigger early warnings in stages. The sealing performance closed-loop verification module is used to establish weighted evaluation logic to complete the comprehensive judgment and closed-loop optimization of sealing performance, realize the whole process of active maintenance and accurate evaluation of turbine oil seal sealing performance, and improve the operational stability of the sealing system.

[0006] Furthermore, the sealing gap calculation module uses 0.2mm as the static standard gap reference value, and combines it with the dynamic correction of the theoretical optimal gap by the pressure of the gas supply branch pipe. For every 0.01MPa increase or decrease in pressure, the gap increases or decreases by 0.005mm. It calculates the deviation between the actual and theoretical gap, records the deviation every 10 minutes and generates a 72-hour gap change trend curve, accurately quantifies the degree of gap anomaly, replaces manual measurement, and eliminates the problems of lag and high error.

[0007] Furthermore, the gas seal adaptation and control module adjusts the pressure according to a preset strategy based on the gap deviation value. For example, if the gap deviation value is 0-0.05mm, the pressure remains constant; if it is 0.05-0.1mm, the pressure increases by 0.01MPa; if it is >0.1mm, the pressure increases by 0.02MPa, etc. The pressure adjustment rate is 0.001MPa / s, which realizes precise and active adjustment of the gas seal pressure, ensures seal compatibility, and avoids seal failure caused by abnormal pressure.

[0008] Furthermore, the gas seal adaptation and control module constrains the gas supply pressure within the range of 0.1 to 0.2 MPa. If the pressure exceeds this range, the boundary value is used, and the pressure is adjusted and calibrated in real time through a stainless steel regulating valve. When the difference between the actual gas supply pressure and the target pressure is greater than 0.005 MPa, the opening is finely adjusted to strictly constrain the gas supply pressure within the technical range, prevent sudden pressure changes from impacting the sealing system, and improve the accuracy of regulation.

[0009] Furthermore, the carbon deposit deterioration early warning module uses oil temperature, lubricating oil water content, and clearance deviation as evaluation parameters, presets corresponding thresholds and classifies them into low, medium, and high risk levels, and determines the comprehensive carbon deposit deterioration risk level according to the principle of choosing the highest level, so as to accurately predict the carbon deposit deterioration risk and achieve a scientific risk assessment based on the fusion of multiple parameters.

[0010] Furthermore, the carbon buildup deterioration early warning module only records data for low-risk cases, issues maintenance reminders for medium-risk cases, and triggers audible and visual warnings and proactive maintenance commands for high-risk cases. It also transmits risk data to the sealing performance closed-loop verification module, replacing the traditional passive periodic cleaning mode, realizing proactive maintenance early warning for oil seals, and reducing unit downtime costs.

[0011] Furthermore, the sealing performance closed-loop verification module adopts a 100-point system with negative weighted deductions. The weights for gap deviation, carbon deposit risk, and air supply pressure deviation are 40%, 30%, and 30%, respectively. The module judges the sealing performance level as excellent, qualified, or unqualified based on the score, objectively quantifying the sealing performance level and providing a standardized judgment basis for sealing effect verification.

[0012] Furthermore, the sealing performance closed-loop verification module generates reverse optimization instructions for unqualified results, optimizes the air supply pressure and warning level, automatically generates a sealing performance evaluation report and permanently archives the data, forming a complete closed loop of perception-calculation-control-early warning-verification, ensuring that the sealing performance continuously meets the standards.

[0013] Among them, the turbine oil seal performance evaluation method based on proactive maintenance realizes real-time monitoring, dynamic control and early warning of sealing performance throughout the process, effectively ensuring the long-term stable operation of turbine oil seals. The specific operation steps are as follows: Step 1: System startup and initialization; Step 2: Real-time sensing and data normalization of oil seal conditions; Step 3: Dynamic calculation of sealing gap; Step 4: Adaptive control of gas seal pressure; Step 5: Graded early warning of carbon deposit deterioration; Step 6: Closed-loop verification of sealing performance; Step 7: Execution of reverse optimization instructions; Step 8: Data archiving and maintenance reporting; Step 9: Cyclic operation.

[0014] Compared with the prior art, the beneficial effects of the present invention are: In this invention, multi-dimensional, full-parameter real-time acquisition of oil seal operating condition data and completion of anti-interference regularization are used to accurately calculate the dynamic gap deviation of the seal, completely replacing the lag and high error problems of traditional manual measurement. Furthermore, the air supply pressure is adaptively and smoothly adjusted according to the gap deviation to strictly stabilize the air pressure within the standard range, effectively avoiding oil leakage and rotor journal damage caused by abnormal gaps, as well as oil mist leakage and shaft seal steam leakage caused by abnormal air pressure, thus significantly improving the adaptability and operational stability of the oil seal. In this invention, the risk of carbon buildup deterioration is predicted in stages by integrating three core parameters: oil temperature, lubricating oil water content, and clearance deviation. This proactive maintenance and early warning system replaces the traditional periodic passive cleaning mode, avoiding seal failures caused by carbon buildup and water content in the oil at the source. Furthermore, a weighted scoring system is used to complete a comprehensive evaluation and closed-loop optimization of seal performance, forming a proactive maintenance mechanism throughout the entire process. This reduces the cost of unit downtime maintenance, improves the long-term reliability of the sealing system, and helps ensure the safe and efficient operation of the steam turbine. Attached Figure Description

[0015] To facilitate understanding by those skilled in the art, the present invention will be further described below with reference to the accompanying drawings; Figure 1 This is a schematic diagram of the overall system structure of the present invention; Figure 2This is a schematic diagram of the operation method of the present invention. Detailed Implementation

[0016] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. 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 are within the scope of protection of the present invention.

[0017] Example 1: Refer to Figure 1 As shown, the turbine oil seal sealing performance evaluation system based on active maintenance proposed in this embodiment includes an oil seal operating condition sensing module, a sealing gap calculation module, a gas seal adaptation and control module, a carbon deposit deterioration early warning module, and a sealing performance closed-loop verification module. As the data source of the system, the oil seal condition sensing module collects real-time raw condition data of the turbine oil seal in all dimensions, completes interference removal and data normalization, and realizes real-time acquisition of all parameters of the gas seal oil seal condition without blind spots. It covers the three core dimensions of the modified oil seal sealing, gas path and oil quality, avoiding the one-sidedness and lag of traditional single-point acquisition, and provides a distortion-free and highly synchronized basic data source for subsequent modules, comprehensively covering the core monitoring indicators of gas seal oil seal operation.

[0018] Specifically, the oil seal condition sensing module is equipped with non-contact eddy current displacement sensors, air pressure transmitters, contact oil temperature sensors, online detection probes for lubricating oil moisture content, and compressed air cleanliness detection units at the corresponding positions of the oil baffles in the front and rear bearing housings of the turbine. It synchronously collects parameters such as the dynamic clearance between the oil seal teeth and the rotor journal, the air supply pressure, the ambient temperature of the oil seal area, the real-time moisture content of the lubricating oil, and the dust content and moisture content after the compressed air is filtered. The acquisition frequency of all parameters is completely synchronized with the sampling frequency of the unit's SCADA system. Furthermore, after the data acquisition is completed, the interference signals generated by the vibration of the on-site unit and electrical equipment are eliminated through hardware anti-electromagnetic interference filtering and software digital normalization processing, and only the real and valid original operating condition data are retained. Then, all the normalized data is transmitted to the sealing gap calculation module in the form of a continuous data stream without loss.

[0019] The sealing gap calculation module, based on the raw data transmitted by the oil seal condition sensing module and combined with the unit's fixed design standards and real-time air supply pressure, accurately calculates the deviation between the actual dynamic gap and the theoretical optimal gap of the oil seal. It quantifies the degree of gap anomaly, replacing the lag and high error of traditional manual measurement. It provides the only core calculation basis for air seal pressure regulation and carbon deposit deterioration early warning, strictly controlling the gap within the technical requirements and fundamentally avoiding the problems of oil leakage caused by excessive gap and damage to the rotor journal caused by insufficient gap.

[0020] Specifically, the sealing gap calculation module continuously receives real-time data on the dynamic gap of the oil seal and the pressure of the air supply branch pipe transmitted by the oil seal condition sensing module. First, it retrieves the fixed design parameters of the gas seal oil baffle of the N50-6.1 / 475 steam turbine and uses the static standard gap of 0.2mm as the basic reference value. Subsequently, dynamic corrections are made based on the real-time collected gas supply branch pressure. When the gas supply pressure is within the technical range of 0.1 to 0.2 MPa, 0.15 MPa is used as the intermediate reference value. For every 0.01 MPa increase in pressure, the theoretical optimal gap is increased by 0.005 mm based on the reference value. For every 0.01 MPa decrease in pressure, the theoretical optimal gap is decreased by 0.005 mm based on the reference value. The theoretical optimal gap is always controlled within the design allowable range of 0.2 to 0.3 mm. Next, the absolute difference between the actual dynamic gap and the theoretical optimal gap is calculated to obtain the gap deviation value. A positive gap deviation value indicates that the actual gap is too large, and a negative gap deviation value indicates that the actual gap is too small. At the same time, the gap deviation value is recorded every 10 minutes and continuously stored to form a gap change trend curve, which clearly reflects the fluctuation and gradual change pattern of the gap within 72 hours. Furthermore, after the calculation and storage are completed, three data items are output: the gap deviation value, the real-time theoretical optimal gap, and the gap change trend curve, providing a fixed and clear analytical basis for subsequent analysis.

[0021] Example 2: The difference between this example and Example 1 is that, referring to... Figure 1 As shown, this embodiment adds a gas seal adaptation and control module and a carbon buildup deterioration early warning module; Based on the fixed gap deviation value output by the sealing gap calculation module, the gas seal adaptation and control module actively and adaptively adjusts the gas supply pressure according to the preset adjustment strategy, keeping the branch pipe gas pressure stable within the technical range of 0.1 to 0.2 MPa throughout the process. This achieves active adaptive adjustment of the gas seal pressure, strictly conforming to the technical requirements of the branch pipe gas pressure range. This helps to avoid problems such as oil mist leakage due to excessive pressure or leakage of shaft seal vapor into the bearing housing due to insufficient pressure, ensuring the sealing adaptability of the gas seal structure and significantly improving the sealing stability of the gas seal structure.

[0022] Specifically, the gas seal adaptation and control module receives the gap deviation value transmitted by the sealing gap calculation module in real time, and uses the gap deviation value as the sole basis for adjustment, with a pre-defined adjustment correspondence, such as: When the gap deviation is between 0 and 0.05 mm, keep the current gas supply pressure unchanged; when the gap deviation is between 0.05 and 0.1 mm, increase the gas supply pressure by 0.01 MPa; when the gap deviation is greater than 0.1 mm, increase the gas supply pressure by 0.02 MPa; when the gap deviation is between -0.05 and 0 mm, decrease the gas supply pressure by 0.01 MPa; when the gap deviation is less than -0.05 mm, decrease the gas supply pressure by 0.02 MPa.

[0023] Furthermore, the adjustment process is smoothly and gradually changed at a rate of 0.001 MPa / s, preventing sudden pressure changes from impacting the sealing system; the adjustment target is always strictly constrained within the branch gas pressure range of 0.1 to 0.2 MPa. If the calculated adjustment pressure exceeds this range, the boundary value of the range is automatically taken as the final target pressure. The target pressure command is then precisely sent to the stainless steel regulating valve actuator of the compressed air filter assembly. After receiving the command, the regulating valve adjusts its opening in real time. At the same time, the air seal adaptation control module continuously collects the actual air supply pressure fed back by the regulating valve and compares the difference between the actual air supply pressure and the target pressure. When the difference is greater than 0.005MPa, the opening of the regulating valve is immediately finely adjusted until the actual pressure is completely consistent with the target pressure, forming a complete closed-loop regulation system of command issuance, pressure regulation, real-time feedback, and precise calibration. Once the pressure regulation and calibration are completed, four data points are output: target pressure command, regulating valve opening, actual operating pressure, and regulation time, providing a complete control basis for subsequent evaluation and analysis.

[0024] The carbon deposit deterioration early warning module integrates three types of fixed core data: oil temperature, lubricating oil water content, and clearance deviation. It assesses the risk of carbon deposit formation in oil seals according to preset thresholds, triggers proactive maintenance warnings in stages, and completely replaces the traditional periodic passive cleaning mode. It can accurately predict the risk of carbon deposit deterioration, replace the traditional passive periodic cleaning mode, realize proactive maintenance of oil seals, and avoid seal failure caused by carbon deposits and water content in the oil from the root, thereby reducing unit downtime maintenance costs.

[0025] Specifically, the carbon deposit deterioration early warning module simultaneously receives real-time oil temperature and real-time water content data of the oil seal area from the oil seal condition sensing module, as well as gap deviation value from the sealing gap calculation module. It establishes a clear logic for assessing carbon deposit deterioration risk by taking oil temperature and water content as the core causes of carbon deposit formation and gap deviation as the cause of accelerated carbon deposit formation. The preset oil temperature threshold is 200℃, water content threshold is 0.5%, and gap deviation threshold is 0.05mm. Oil temperature below 200℃, water content below 0.5%, and gap deviation below 0.05mm are considered low-risk; oil temperature between 200 and 250℃, water content between 0.5% and 1%, and gap deviation between 0.05 and 0.1mm are considered medium-risk; oil temperature above 250℃, water content above 1%, and gap deviation above 0.1mm are considered high-risk.

[0026] Furthermore, the carbon deposit deterioration early warning module compares the collected data with preset thresholds in real time to determine the corresponding risk level. It then applies a comprehensive judgment rule that selects the highest risk level among the three parameters as the final carbon deposit deterioration risk level. For example, if the oil temperature is low, the water content is medium, and the gap deviation is high, the final judgment is high risk; if the oil temperature is medium, the water content is low, and the gap deviation is low, the final judgment is medium risk; and if all three parameters are low, the final judgment is low risk. Low-risk scenarios only record data and track trends without triggering any alerts; medium-risk scenarios issue visual maintenance alerts and record the duration of the risk; high-risk scenarios immediately trigger audible and visual warnings and proactive maintenance commands, which are directly pushed to the maintenance terminal without waiting for the periodic cleanup rules of "accumulated operation exceeding 100 days or downtime exceeding 6 days". Once the early warning judgment and instruction are completed, four data items are output: risk level, risk trigger parameters, early warning trigger time, and proactive maintenance instruction, providing clear risk basis for subsequent assessment and analysis.

[0027] Example 3: The difference between this example and Examples 1 and 2 is that, referring to... Figure 1 As shown, this embodiment adds a sealing performance closed-loop verification module; the sealing performance closed-loop verification module establishes a comprehensive evaluation logic for sealing performance according to preset weighting rules, verifies the sealing effect after the gas seal oil seal is modified, forms a complete active maintenance closed loop, and ensures that the modified oil seal has no oil leakage, no carbon deposits, and no water in the oil, fully meets the technical modification acceptance standards, and greatly improves the long-term operational stability of the sealing system.

[0028] Specifically, the sealing performance closed-loop verification module synchronously and completely receives raw operating condition data from the oil seal operating condition sensing module, clearance deviation data from the sealing clearance calculation module, pressure regulation data from the gas seal adaptation and control module, and risk level data from the carbon deposit deterioration early warning module. A comprehensive evaluation is then conducted according to a fixed rule of negative weighted deduction out of 100 points, as shown below: The base score is set at 100 points, and three negative indicators are deducted points according to fixed weights: gap deviation accounts for 40% of the deduction, carbon deposit risk level accounts for 30% of the deduction, and gas supply pressure deviation accounts for 30% of the deduction. Among them, the deduction for gap deviation is as follows: 0-0.05mm deviation deducts 0 points, 0.05-0.1mm deducts 5 points, and greater than 0.1mm deducts 10 points, which are then multiplied by 40% and included in the total deduction; the deduction for carbon deposit risk is as follows: low risk deducts 0 points, medium risk deducts 5 points, and high risk deducts 10 points, which are then multiplied by 30% and included in the total deduction; the deduction for gas supply pressure deviation is as follows: 0-0.005MPa deviation deducts 0 points, 0.005-0.01MPa deducts 5 points, and greater than 0.01MPa deducts 10 points, which are then multiplied by 30% and included in the total deduction. The total performance score equals 100 points minus the sum of the three weighted deductions. The higher the score, the better the sealing performance, and the lower the score, the worse the sealing performance. Grade determination: a total performance score of 90 points or above is excellent, 70-89 points is qualified, and below 70 points is unqualified.

[0029] If the assessment is deemed unqualified, a reverse optimization command is immediately generated and sent to both the gas seal adaptation and control module and the carbon buildup deterioration early warning module. The optimization command requires the gas seal adaptation and control module to re-optimize the pressure parameters with the maximum adjustment range, and requires the carbon buildup deterioration early warning module to directly upgrade to a high-risk warning. If the assessment is deemed qualified, data changes are continuously tracked, and a simple operation and maintenance suggestion is generated every 24 hours. If the assessment is deemed excellent, only data archiving is performed, and no additional operations are triggered. Furthermore, once the evaluation is completed, a complete sealing performance evaluation report containing all operational data, evaluation results, optimization instructions, and maintenance suggestions is automatically generated. The report and all evaluation data are permanently archived, completing the entire process of proactive maintenance loop from perception to calculation, control, early warning, and verification.

[0030] Example 4: Refer to Figure 2 As shown, the difference between this embodiment and Embodiments 1, 2, and 3 is that this invention proposes a method for evaluating the sealing performance of turbine oil seals based on active maintenance. The specific method flow can be referred to as follows: Step 1: System Startup and Initialization Phase 1: After the system is powered on, the fixed design parameters of the gas seal oil baffle of the N50-6.1 / 475 steam turbine are automatically loaded (e.g., static reference clearance 0.2mm, standard range of gas supply pressure 0.1~0.2MPa, various warning thresholds, deduction weights, etc.). Phase 2: Perform zero-point calibration and communication self-test on the displacement sensor, pressure transmitter, and oil temperature / moisture content detection probe of the oil seal condition sensing module to confirm that all acquisition units are working properly; Phase 3: Establish data transmission channels between modules to ensure data exchange without delay or loss.

[0031] Step 2: Real-time sensing and data processing of oil seal operating conditions: Phase 1: According to the sampling frequency of the unit's SCADA system, synchronously collect data on oil seal dynamic clearance, main / branch air supply pressure, oil temperature in the oil seal area, lubricating oil moisture content, and compressed air cleanliness. The stage uses hardware filtering and software normalization to eliminate invalid signals such as vibration and electromagnetic interference, while retaining the true original working condition data. Phase 3: The normalized full-dimensional data is continuously transmitted to the sealing gap calculation module in real time.

[0032] Step 3: Dynamic calculation of sealing gap: Phase 1: Set the static reference gap to 0.2mm, and adjust the theoretical optimal gap based on the real-time air supply pressure. For every 0.01MPa increase or decrease in pressure, the optimal gap increases or decreases by 0.005mm, and is always controlled within the range of 0.2 to 0.3mm. Phase 2: Calculate the absolute difference between the actual dynamic clearance and the theoretical optimal clearance to obtain the clearance deviation value; Phase 3: Record the gap deviation every 10 minutes to generate a 72-hour gap change trend curve; Phase 4: The gap deviation value, theoretical optimal gap, and trend curve are synchronously transmitted to the gas seal adaptation and control module and the carbon deposition deterioration early warning module.

[0033] Step 4: Adaptive adjustment of gas seal pressure: Phase 1: Receive the gap deviation value and perform voltage adjustment according to the preset strategy; Phase 2: The pressure regulation result is constrained to be within the range of 0.1 to 0.2 MPa; if it exceeds this range, the boundary value is taken as the target pressure. Phase 3: Issue instructions to the stainless steel regulating valve, collect the actual pressure in real time, and immediately fine-tune and calibrate when the deviation from the target pressure is >0.005MPa; Phase 4: Transmit the control data (target pressure, regulating valve opening, actual pressure) to the sealing performance closed-loop verification module.

[0034] Step 5: Graded Early Warning of Carbon Deterioration: Phase 1: Independently determine the risk level for three parameters: oil temperature, water content, and clearance deviation; Phase 2: Following the principle of choosing the highest level over the lowest, the highest level among the three parameters will be taken as the comprehensive carbon deposit risk level. Phase 3: Low risk only records data; medium risk issues maintenance prompts; high risk triggers audible and visual warnings and proactive maintenance commands. Phase 4: Transmit the early warning data (overall level, single parameter level, trigger time) to the sealing performance closed-loop verification module.

[0035] Step Six: Closed-Loop Verification of Sealing Performance Phase 1: Based on a base score of 100, calculate the sealing performance score by weighting deductions according to negative indicators; Phase 2, Grading: ≥90 points is excellent, 70-89 points is qualified, <70 points is unqualified; Phase 3: Excellent data is archived only; qualified data generates 24-hour operation and maintenance suggestions; unqualified data triggers reverse optimization instructions.

[0036] Step 7: Execute reverse optimization instructions: Phase 1: When the sealing performance is unqualified, a command is sent to the gas seal adaptation and control module to re-optimize the gas supply pressure according to the maximum adjustment range; Phase 2: Simultaneously issue instructions to the carbon buildup deterioration early warning module to directly upgrade the overall risk level to high risk, and strengthen early warning and maintenance prompts; Phase 3: After optimization, repeat steps 4 to 6 until the sealing performance meets the standard.

[0037] Step 8: Data Archiving and Maintenance Reporting Phase 1: The system automatically generates a complete evaluation report that includes collected data, calculation results, control records, early warning information, performance scores, and optimization records; Phase 2: Permanently archive all operational data and evaluation reports to support historical traceability; Phase 3: Push real-time status, early warning information and maintenance suggestions to the operation and maintenance terminal to complete the closed loop of a single proactive maintenance.

[0038] Step 9: Run in a loop: The system continuously executes steps two through eight in a loop to achieve real-time monitoring, dynamic calculation, proactive control, early warning, and closed-loop verification of the turbine oil seal performance, enabling proactive maintenance throughout the entire process.

[0039] The working principle of this invention is as follows: During use, multi-dimensional operating condition data such as the dynamic clearance of the turbine oil seal, air supply pressure, oil temperature, and lubricating oil water content are collected in real time and anti-interference processing is completed. Combined with the unit design parameters, the deviation between the dynamic clearance and the theoretical optimal clearance is calculated. Based on the deviation, the air supply pressure is adaptively and smoothly adjusted. The risk of carbon deposit deterioration is judged by classifying oil temperature, water content, and clearance deviation and active maintenance is triggered. The sealing performance is comprehensively evaluated through weighted scoring. If it is unqualified, reverse optimization is performed to form a closed loop of active maintenance throughout the entire process. This solves the problems of large measurement errors, delayed passive maintenance, and easy sealing failure and carbon deposit damage to the unit in traditional turbine oil seals. It can accurately control the sealing clearance and air supply pressure, predict the risk of carbon deposit deterioration in advance, improve the sealing stability and long-term operational reliability of the oil seal, significantly reduce downtime maintenance costs, and help ensure the safe and efficient operation of the turbine.

[0040] The preferred embodiments of the present invention disclosed above are merely illustrative of the invention. These preferred embodiments do not exhaustively describe all details, nor do they limit the invention to any specific implementation. Clearly, many modifications and variations can be made based on the content of this specification. This specification selects and specifically describes these embodiments to better explain the principles and practical applications of the invention, enabling those skilled in the art to better understand and utilize it. The invention is limited only by the claims and their full scope and equivalents.

Claims

1. A system for evaluating the sealing performance of a steam turbine oil seal based on active maintenance, characterized in that, It includes an oil seal condition sensing module, a seal gap calculation module, a gas seal adaptation and control module, a carbon buildup deterioration early warning module, and a seal performance closed-loop verification module; The oil seal condition sensing module is used to collect full-dimensional condition data of the turbine oil seal and complete interference removal and data normalization. The sealing gap calculation module is used to calculate the deviation between the actual dynamic gap of the oil seal and the theoretical optimal gap. The gas seal adaptation and control module is used to adaptively adjust the gas supply pressure based on the gap deviation value. The carbon deposit deterioration early warning module is used to integrate multiple parameters to assess the risk of carbon deposit deterioration and trigger early warnings in stages. The sealing performance closed-loop verification module is used to establish weighted evaluation logic to complete the comprehensive judgment and closed-loop optimization of sealing performance.

2. The turbine oil seal performance evaluation system based on active maintenance according to claim 1, characterized in that, The sealing gap calculation module uses 0.2mm as the static standard gap reference value, and combines it with the dynamic correction of the theoretical optimal gap by the pressure of the gas supply branch pipe. For every 0.01MPa increase or decrease in pressure, the gap increases or decreases by 0.005mm, and the deviation between the actual and theoretical gap is calculated.

3. The turbine oil seal performance evaluation system based on active maintenance according to claim 1, characterized in that, The gas seal adaptation and control module adjusts the pressure according to the gap deviation value and a preset strategy, with a pressure adjustment rate of 0.001 MPa / s.

4. The turbine oil seal performance evaluation system based on active maintenance according to claim 3, characterized in that, The gas seal adaptation and control module constrains the gas supply pressure within the range of 0.1 to 0.2 MPa. If the pressure exceeds this range, the boundary value is taken, and the pressure is adjusted and calibrated in real time through the regulating valve. When the difference between the actual gas supply pressure and the target pressure is greater than 0.005 MPa, the opening degree is finely adjusted.

5. The turbine oil seal performance evaluation system based on active maintenance according to claim 1, characterized in that, The carbon deposit deterioration early warning module uses oil temperature, lubricating oil water content, and clearance deviation as evaluation parameters, presets corresponding thresholds, and classifies them into low, medium, and high risk levels. It determines the comprehensive carbon deposit deterioration risk level according to the principle of choosing the highest risk level.

6. The turbine oil seal performance evaluation system based on active maintenance according to claim 5, characterized in that, The carbon buildup deterioration early warning module only records data for low-risk cases, issues maintenance prompts for medium-risk cases, and triggers audible and visual warnings and proactive maintenance commands for high-risk cases. It also transmits risk data to the sealing performance closed-loop verification module.

7. The turbine oil seal performance evaluation system based on active maintenance according to claim 6, characterized in that, The sealing performance closed-loop verification module uses a 100-point system with negative weighted deductions. The weights for gap deviation, carbon deposit risk, and air supply pressure deviation are 40%, 30%, and 30%, respectively. The scores are used to determine three levels: excellent, qualified, and unqualified.

8. The turbine oil seal performance evaluation system based on active maintenance according to claim 7, characterized in that, The sealing performance closed-loop verification module generates reverse optimization instructions for unqualified results, optimizes the air supply pressure and warning level, automatically generates a sealing performance evaluation report and permanently archives the data.

9. A method for evaluating the sealing performance of turbine oil seals based on active maintenance, employing the active maintenance-based turbine oil seal sealing performance evaluation system as described in any one of claims 1-8, characterized in that, Includes the following steps: Step 1: System startup and initialization; Step 2: Real-time sensing and data normalization of oil seal conditions; Step 3: Dynamic calculation of sealing gap; Step 4: Adaptive control of gas seal pressure; Step 5: Graded early warning of carbon deposit deterioration; Step 6: Closed-loop verification of sealing performance; Step 7: Execution of reverse optimization instructions; Step 8: Data archiving and maintenance reporting; Step 9: Cyclic operation.

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