High-water-head sand-containing water flow hydropower station water turbine wear shutdown fault prediction method and intelligent reporting system

Abstract

The invention discloses a high-water-head sand-containing water flow hydropower station water turbine wear shutdown fault prediction method and an intelligent reporting system, and belongs to the field of hydroelectric power generation equipment state monitoring and early warning. The method comprises the following steps: collecting multi-source heterogeneous data in real time and fusing the multi-source heterogeneous data; a water turbine wear state comprehensive index is constructed, and the overall health degree of a water turbine flow passage component is quantified; predicting the remaining service life based on a physical mechanism and a data-driven fusion model; and dividing and dynamically adjusting a fault risk level, and generating adaptive early warning information. According to the method, sediment characteristic monitoring, vibration spectrum analysis, efficiency calculation and historical maintenance data are fully fused, risk grade evaluation and self-adaptive early warning of abrasion faults are achieved, the accuracy and the real-time performance of water turbine state monitoring are improved, meanwhile, a decision support report is automatically generated through an intelligent reporting device, and the water turbine state monitoring accuracy and the water turbine state monitoring real-time performance are improved. Power station operation and maintenance are promoted to be converted from regular maintenance to predictive maintenance, and the method has good engineering application value.

Description

Technical Field

[0001] This invention belongs to the field of hydropower equipment condition monitoring and intelligent fault early warning technology, specifically relating to a method and intelligent reporting system for predicting turbine wear shutdown faults in high-head, sediment-laden hydropower stations. Background Technology

[0002] High-head hydropower stations, especially those operating on silty rivers in southwestern my country, subject their turbine components to long-term erosion and wear from high-speed, sediment-laden water flows. Over time, the wear on critical components such as the runner and guide vanes intensifies, leading to decreased unit efficiency, increased vibration and noise, and weakened material strength. Ultimately, this can result in severe performance degradation or even forced emergency shutdown, causing significant power generation losses and safety risks.

[0003] Currently, monitoring the wear condition of hydro turbines mainly relies on periodic disassembly and maintenance, vibration spectrum analysis, or efficiency monitoring. Periodic maintenance cannot provide early warning of faults and is costly; vibration analysis is not sensitive to early wear and is easily confused with other mechanical faults such as dynamic imbalance and misalignment; efficiency monitoring is significantly affected by changes in head and load, making it difficult to accurately quantify the degree of performance degradation caused by wear.

[0004] Therefore, existing technologies lack a comprehensive solution that can directly correlate with sediment conditions, assess wear status in real time, accurately predict the time of failure, and automatically generate decision support reports. Power plant operators often only take passive measures after a failure occurs, and the level of intelligent operation and maintenance needs to be improved. Summary of the Invention

[0005] To address the shortcomings of existing technologies, this invention provides a method and intelligent reporting system for predicting turbine wear-related shutdown faults in high-head, sediment-laden hydropower stations. The method offers high prediction accuracy, good real-time performance, and the ability to provide proactive early warning and intelligent decision support.

[0006] This invention adopts the following technical solution: a method for predicting turbine wear-related shutdown faults in high-head, sediment-laden hydropower stations, comprising the following steps:

[0007] S1. Through the sensor network and data interface deployed in the hydropower station, multi-source heterogeneous data are collected in real time to construct a wear analysis dataset, including hydraulic operating parameters, sediment characteristic parameters, unit condition monitoring parameters and historical maintenance and inspection data;

[0008] S2. Based on the wear analysis dataset, construct the Comprehensive Wear Status Index (MCI) to quantify the overall health of the turbine's flow components, and calculate the MCI value using dynamic weighting coefficients.

[0009] S3. Based on the physical mechanism and data-driven fusion model, establish a wear development and MCI index deterioration model, predict the remaining service life (RUL), and trigger an alarm when the predicted RUL value exceeds the preset safety threshold.

[0010] S4. Based on the predicted RUL value, the current MCI value and its rate of change, classify and dynamically adjust the fault risk level, and generate adaptive early warning information.

[0011] As a preferred embodiment, in step S1, multi-source heterogeneous data is collected in real-time or near real-time through a sensor network and data interface deployed at the hydropower station to construct a wear analysis dataset, including:

[0012] Sediment characteristic parameters were collected using an online laser particle size analyzer and turbidimeter, including: river sediment content, sediment particle size distribution, and sediment mineral hardness;

[0013] Hydraulic operating parameters include: upstream water level, downstream water level, head, flow rate, unit active power, and guide vane opening.

[0014] The unit condition monitoring parameters include: triaxial vibration acceleration of each bearing housing (with special attention to high-frequency components above 500Hz), main shaft runout, key phaser signal, and turbine casing noise sound pressure level spectrum;

[0015] Historical maintenance and inspection data, including: flow component profile measurement data from each major overhaul, crack detection reports, welding repair records, and spare parts replacement records;

[0016] Furthermore, through data processing, hydraulic operating parameters, unit status monitoring parameters, and historical maintenance data are integrated into a time series dataset for real-time analysis and long-term trend mining.

[0017] As a preferred option, in step S2, a comprehensive wear condition index (MCI) is constructed to quantify the overall health of the turbine's flow components. The formula is as follows: ; in, To be based on real-time head and power The actual operating efficiency of the reverse calculation; The reference efficiency curve values ​​for the unit at the time of manufacture or when it is newly put into operation, under the same head and load combination; The high-frequency energy envelope of the spindle / guide bearing vibration signal, after standardization and spectral feature extraction, is sensitive to cavitation and airfoil wear. This is the cumulative total mass of sediment passing through the machine, weighted by particle size, since the last major overhaul; The change in clearance of rotating components such as a maze ring is estimated based on a model or indirect measurement. The dynamic weighting coefficients for each component are determined through principal component analysis or machine learning models based on historical data.

[0018] As a preferred embodiment, in step S3, the method for establishing a wear development and MCI index deterioration model to predict the remaining service life (RUL) is as follows:

[0019] S31. Constructing a physical mechanism model: Based on the theory of micro-cutting and plastic deformation wear of ductile materials, establish the wear depth at key locations per unit time. The relationship between sand particle characteristics and operating conditions;

[0020] S32. Construct a data-driven fusion model: Use an attention-enhanced Long Short-Term Memory (LSTM) network model, with historical MCI sequences, operating condition sequences, and sediment parameter sequences as inputs, and output the future change trend of MCI for training;

[0021] S33. Fusion Prediction: The trend calculated by the physical mechanism model is used as a priori constraint for the prediction of the Long Short-Term Memory network model. Dynamic data fusion and parameter updates are performed through particle filtering or Bayesian inference to output the predicted curve of MCI and its confidence interval for a future period. When the predicted MCI value exceeds the preset safety threshold, the prediction is further refined. critical When the alarm is triggered, the system calculates the predicted time from the current moment to the triggering of the alarm, and obtains the RUL value.

[0022] Furthermore, in step S31, the wear depth at the key location per unit time... The relationship between sand grain characteristics and operating conditions is expressed as follows: ; in, The relative velocity of the water flow. For sand content, Characteristic particle size of sediment, This represents the hardness coefficient of sediment minerals. , For model index, This is the cumulative runtime.

[0023] As a preferred embodiment, in step S4, the fault risk level is divided according to the RUL value and MCI change rate, including: normal monitoring, attention, early warning, alarm and emergency, and a preset time threshold is set for each level;

[0024] When the risk level reaches the warning level or higher, a maintenance action recommendation report is automatically generated and pushed to designated personnel through multiple channels.

[0025] The technical solution of this invention also provides: an intelligent reporting system for turbine wear and shutdown faults in high-head, sediment-laden hydropower stations, which integrates data acquisition, processing, prediction, display and reporting, and is used to implement the aforementioned turbine wear and shutdown fault prediction method. It includes: a multi-source data interface and acquisition module, an edge computing and intelligent prediction module, a human-computer interaction and visualization display module, and an intelligent report generation and multi-channel push module.

[0026] The multi-source data interface and acquisition module is used to connect the power plant monitoring system, online sediment monitor, vibration sensor and condition monitoring system to realize the automatic acquisition and redundancy processing of multi-source heterogeneous data in step S1, and to perform protocol parsing and standardization.

[0027] Furthermore, the multi-source data interface and acquisition module supports multiple communication protocols, including Modbus, IEC61850 and MQTT. It acquires water head, power, guide vane opening, sediment content and vibration spectrum parameters in real time through sensor networks, analyzes data protocols, identifies key features, performs data cleaning and alignment, and standardizes the extracted information to form a time series dataset.

[0028] The edge computing and intelligent prediction module has an embedded high-performance processor and memory, which stores the algorithm program for executing steps S2, S3 and S4. It is used to calculate the MCI index in real time, run the fusion prediction model, LSTM prediction and risk level assessment. It adopts parallel computing technology to optimize the model training and prediction speed and has the ability to learn and update the model.

[0029] The human-computer interaction and visualization module includes a high-resolution touch screen interface that supports user interaction queries and parameter configuration. It is used to dynamically display historical and predictive curves of the MCI index, RUL, risk level, wear heat map of key components, and real-time operating parameters.

[0030] Furthermore, the wear heat map of key components is generated by a CFD model based on wear depth data at key locations.

[0031] The intelligent report generation and multi-channel push module automatically generates structured, multi-level detailed fault prediction and analysis reports based on prediction and evaluation results, and pushes the reports to designated operation, maintenance and management personnel in a targeted and hierarchical manner through the power plant intranet, email, SMS and dedicated mobile application.

[0032] Furthermore, the fault prediction analysis report includes: core prediction conclusions, data support, risk level, and maintenance recommendations.

[0033] Compared with the prior art, the present invention, employing the above technical solution, has the following technical effects:

[0034] 1. Foresight and Precision: The method of this invention achieves early and accurate prediction of turbine wear failures through the deep integration of physical mechanisms and data-driven approaches, transforming the operation and maintenance mode from post-maintenance and periodic maintenance to predictive maintenance.

[0035] 2. Comprehensive assessment: The multi-factor fusion wear status index (MCI) constructed in this invention can more comprehensively and sensitively reflect the health status of the turbine, overcoming the one-sidedness and lag of single parameter monitoring.

[0036] 3. Intelligence and Automation: The integrated intelligent reporting system of this invention realizes full-process automation from data to decision-making, which significantly improves operation and maintenance efficiency and intelligent management level.

[0037] 4. Proactive Safety and Decision Support: The invention’s clearly defined risk level assessment and timely multi-channel early warning provide power plant managers with a scientific basis for decision-making, enabling them to proactively optimize scheduling, plan maintenance, effectively avoid unplanned outages, and ensure the safe and economical operation of the power plant. Attached Figure Description

[0038] Figure 1 This is a flowchart illustrating the steps of the method for predicting turbine wear-induced shutdown faults in high-head, sediment-laden hydropower stations according to the present invention.

[0039] Figure 2 This is a schematic diagram of the display interface of the intelligent reporting system in an embodiment of the present invention;

[0040] Figure 3 This is a schematic diagram of historical MCI data and future prediction curves based on a fusion model in an embodiment of the present invention. Detailed Implementation

[0041] To make the objectives, technical solutions, and advantages of this invention clearer, the technical solutions of the application will be further described in detail below with reference to the accompanying drawings. The described embodiments are only a part of the embodiments involved in this invention. All non-innovative embodiments based on these embodiments by other researchers in the art are within the protection scope of this invention. Furthermore, the step numbers in the embodiments of this invention are only set for ease of explanation and do not limit the order of the steps. The execution order of each step in the embodiments can be adaptively adjusted according to the understanding of those skilled in the art.

[0042] Example 1

[0043] A method for predicting turbine wear-related shutdown faults in high-head, sediment-laden hydropower stations is proposed, such as... Figure 1 As shown, it includes the following steps:

[0044] S1. Through the sensor network and data interface deployed in the hydropower station, multi-source heterogeneous data are collected in real time to construct a wear analysis dataset, including hydraulic operating parameters, sediment characteristic parameters, unit condition monitoring parameters, and historical maintenance and inspection data.

[0045] This step is a key preliminary step in the fault prediction method of this invention. It is implemented through a multi-source data interface and acquisition module. Its core lies in integrating data from the power plant monitoring system, online sediment monitor, vibration sensor and condition monitoring system to provide a comprehensive and accurate data foundation for subsequent wear condition assessment and prediction.

[0046] In this embodiment, various sensors and data sources are first connected via standardized protocols (such as Modbus, IEC61850, MQTT) to collect multi-source heterogeneous data, specifically including:

[0047] Hydraulic operating parameters include upstream water level, downstream water level, head, flow rate, unit active power, and guide vane opening.

[0048] Sediment characteristic parameters include: river sediment content, sediment particle size distribution, and sediment mineral hardness;

[0049] The unit condition monitoring parameters include: triaxial vibration acceleration of each bearing housing, main shaft runout, key phase signal, and turbine casing noise sound pressure level spectrum;

[0050] Historical maintenance and inspection data, including: flow component profile measurement data from each major overhaul, crack detection reports, and welding repair records.

[0051] Then, data cleaning and alignment techniques are employed to synchronize timestamps and handle outliers in the collected multi-source data, ensuring data quality. For example, a sliding window averaging method is used to smooth vibration data, and redundancy checks are implemented to improve data reliability.

[0052] This step, by constructing a complete wear analysis dataset, lays the data foundation for subsequent MCI index calculation and RUL prediction, and can be widely applied to turbine condition monitoring scenarios in high-head, sediment-laden hydropower stations. For example, when sediment concentration increases during the flood season, the system automatically increases the data acquisition frequency, tracks changes in sediment parameters in real time, and provides input for wear accumulation calculation.

[0053] Furthermore, S1 includes:

[0054] S11. Standardized protocol parsing technology is used to collect and fuse multi-source heterogeneous data, including collecting river sediment content, sediment particle size distribution and sediment mineral hardness through online laser particle size analyzer and turbidimeter.

[0055] In some implementations, key parameters such as river sediment content, sediment particle size distribution, and sediment mineral hardness are acquired in real time by integrating specialized sensors such as online laser particle size analyzers and turbidimeters, ensuring the accuracy and consistency of data collection.

[0056] Specifically, the system first connects to an online laser particle size analyzer and a turbidimeter via an RS-485 or Ethernet interface, and uses Modbus or Profibus protocols to parse the sensor output data. The laser particle size analyzer provides sediment particle size distribution curves, including the D50 median particle size and particle size span coefficient; the turbidimeter outputs turbidity values, which are converted into sediment content (unit: kg / m³) through a calibration curve.

[0057] This embodiment simultaneously collects hardness data of sediment minerals, obtaining hardness coefficients using an X-ray diffraction analyzer or a pre-set mineral database. The data acquisition frequency is dynamically adjusted according to operating conditions; for example, during flood season or high-load operation, the acquisition interval can be set to 1-5 minutes to ensure real-time performance.

[0058] Furthermore, data fusion algorithms are used to correlate sediment parameters with hydraulic operating parameters. For example, Kalman filtering is employed to fuse sediment concentration and flow rate data to estimate the total amount of sediment passing through the machine per unit time.

[0059] In addition, it supports data verification mechanisms, such as detecting outliers by comparing historical sediment data and improving reliability through redundant sensor data.

[0060] This step, through parsing and data fusion, achieves efficient acquisition and integration of multi-source heterogeneous data, providing reliable data support for subsequent wear status assessment and prediction. It significantly improves the system's real-time performance and accuracy, and can be widely applied to wear monitoring scenarios in high-head hydropower stations. For example, in hydropower stations on rivers with high sediment loads, the system monitors changes in sediment content and particle size in real time, providing input parameters for wear accumulation models and helping operators adjust unit loads in a timely manner to mitigate wear risks.

[0061] S12. Through data processing, hydraulic operating parameters, unit status monitoring parameters and historical maintenance data are integrated into a time series dataset, supporting real-time analysis and long-term trend mining.

[0062] This step aims to standardize and synchronize data from different sources to build a unified time-series dataset, providing structured input for subsequent wear status index calculation and prediction models.

[0063] Specifically, the process begins by integrating hydraulic operating parameters (such as water level, flow rate, and power), unit condition monitoring parameters (such as vibration, sway, and noise), and historical maintenance data (such as overhaul records and repair reports) using timestamp alignment technology. Data processing includes removing outliers, filling in missing values, and smoothing noise. For example, linear interpolation is used to fill in short-term missing data, and a low-pass filter is used to process high-frequency vibration signals while preserving wear-sensitive characteristic frequency bands.

[0064] The time series dataset constructed in this embodiment supports real-time analysis and long-term trend mining. The dataset is stored in a time series database, such as InfluxDB or TimescaleDB, which supports efficient query and aggregation operations. The data update frequency can be set according to needs, such as real-time data being updated every 5 seconds and long-term data being archived once a day.

[0065] This step, through data processing, achieves seamless integration of multi-source data, improving data quality and usability, and providing a solid foundation for subsequent intelligent prediction and decision support. It can be widely applied to turbine condition monitoring and fault prediction scenarios. For example, when analyzing wear trends, the system can call upon historical datasets to compare operating parameters and wear indicators from different periods, identifying wear acceleration stages and key influencing factors.

[0066] S2. Based on the fusion data collected in S1, a comprehensive wear status index (MCI) is constructed to quantify the overall health of the turbine's flow components, and the MCI value is calculated using dynamic weighting coefficients.

[0067] In some implementations, constructing the Comprehensive Wear Status Index (MCI) is the core evaluation step in the fault prediction method of this invention. This step integrates multi-dimensional parameters such as efficiency loss, high-frequency vibration energy, sediment accumulation, and gap changes to construct a quantitative index that comprehensively reflects the health of the turbine's flow components, providing a unified evaluation standard for fault prediction.

[0068] Specifically, in this embodiment, based on real-time water head and power Inverse calculation of actual operating efficiency and compared with the baseline efficiency curve The efficiency loss component is calculated through comparison. The baseline efficiency curve is obtained by fitting performance data from the unit's factory test or initial commissioning. High-frequency vibration energy index. By extracting the vibration signal from the main shaft or guide bearing through spectral analysis, the focus is on high-frequency components above 500Hz, which are sensitive to cavitation and airfoil wear. The total mass of sediment passing through the machine is also considered. The wear contribution of sediment with different particle sizes is reflected by integrating sediment concentration and flow rate, and considering particle size weighting. Variation in the clearance of rotating components. The degree of mechanical wear is assessed based on model estimation or indirect measurement (such as inference from vibration and sway data).

[0069] The formula for calculating MCI is: ; Among them, the weighting coefficient ω is dynamically determined through principal component analysis or machine learning models based on historical data to adapt to the operating characteristics of different power plants and units. For example, in power plants dominated by sediment abrasion, ω s Possibly higher; in vibration-sensitive units, ω v It may carry more weight.

[0070] This step, through the construction and dynamic calculation of the MCI index, achieves a comprehensive quantitative assessment of the wear status of the turbine, providing a core basis for subsequent prediction and early warning, and can be widely applied in turbine condition assessment and operation and maintenance decision-making scenarios. For example, when the MCI value continues to rise, the system prompts operators to pay attention to the risk of accelerated wear and analyzes the root cause in conjunction with other parameters.

[0071] Furthermore, S2 includes:

[0072] S21. Use principal component analysis or machine learning models to dynamically determine the weighting coefficients in the MCI calculation formula to ensure the adaptability of the index to different operating conditions.

[0073] In some implementations, this invention employs principal component analysis or machine learning models to dynamically determine the weighting coefficients in the MCI calculation formula, serving as a key optimization step in calculating the comprehensive wear status index. This step automatically adjusts the weights of each component by analyzing historical data and operational characteristics, enabling the MCI index to more accurately reflect the actual health status of the turbine.

[0074] Specifically, historical MCI data and its components (efficiency loss, high-frequency vibration energy, sediment accumulation, and gap changes) are first collected and correlated with actual wear records (such as overhaul measurement data) for analysis. Principal component analysis is used to extract the main influencing factors, and the contribution of each component is calculated as the initial weight value.

[0075] Then, a weight prediction model is trained using machine learning models (such as random forests or gradient boosting trees). The input features include operating conditions, sediment parameters, vibration spectrum, etc., and the output is the optimal weight combination. The model is updated regularly to adapt to equipment aging and changes in operating conditions.

[0076] Next, the weight determination process must satisfy the normalization constraint, i.e., ω η +ω v +ω s +ω c= 1, ensuring the MCI value is within a reasonable range and supporting dynamic weight adjustment, such as automatically increasing ω when sediment content increases during the flood season. s The weighting of sediment wear is emphasized, highlighting its impact.

[0077] This step, through dynamic weighting, allows the MCI index to more sensitively capture wear changes, improving the accuracy and reliability of the prediction model. It can be widely applied to adaptive optimization scenarios in turbine condition monitoring systems. For example, after a major overhaul, the system recalibrates the weights to reflect the health baseline of the equipment after repair.

[0078] S3. Based on the physical mechanism and data-driven fusion model, establish a wear development and MCI index deterioration model, predict the remaining service life (RUL), and trigger an alarm when the predicted MCI value exceeds the preset safety threshold.

[0079] In some implementations, this step is the core prediction step in the fault prediction method of this invention. By combining wear physics theory and deep learning technology, it can accurately predict the future trend of MCI and provide a time window for operation and maintenance decisions.

[0080] Specifically, the system first constructs a physical mechanism model, and based on the theory of micro-cutting and plastic deformation wear, establishes the relationship between the wear depth ΔW at key locations (such as the water inlet edge of the blade) per unit time and the characteristics of sand particles and operating conditions.

[0081] Then, through CFD simulation and experimental data calibration, a theoretical trend of wear development is provided. Simultaneously, an attention-enhanced Long Short-Term Memory (LSTM) network model is employed as a data-driven model. Using historical MCI sequences, operating condition sequences, and sediment parameter sequences as inputs, the future trend of MCI changes is output. The LSTM model focuses on key time steps through an attention mechanism, improving its ability to capture abrupt change patterns.

[0082] Finally, the fusion prediction is achieved through particle filtering or Bayesian inference. The output of the physical mechanism model is used as the prior distribution and dynamically fused with the LSTM prediction results to output the MCI prediction curve and its confidence interval. When the predicted MCI value exceeds a preset safety threshold, the MCI prediction is considered complete. critical An alarm is triggered at a certain time, and the predicted time from the current moment to the triggering of the alarm, i.e., RUL, is calculated.

[0083] This step, through fusion model prediction, enables early warning of wear-related failures, providing a scientific basis for planned maintenance. It can be widely applied to scenarios involving prediction and early warning of the remaining service life (RUL) of hydroelectric turbines. For example, when the predicted RUL is less than 60 days, the system generates maintenance recommendations in advance to avoid unplanned downtime.

[0084] Furthermore, S3 includes:

[0085] S31. Based on the theory of micro-cutting and plastic deformation wear, establish the relationship between the wear depth at key locations per unit time and the characteristics of sand particles and operating conditions, as a physical mechanism model.

[0086] In some implementations, this step models the physical processes of sediment erosion through physical mechanism modeling, enhancing the theoretical foundation and applicability of the prediction model, improving the accuracy of RUL predictions, and providing theoretical constraints and interpretability for data-driven predictions. Furthermore, model parameters are dynamically updated using real-time running data. For example, adjustments are made based on current sediment concentration and flow velocity. The calculation results are output as a physical benchmark for the MCI trend and fused with data-driven predictions.

[0087] Specifically, the model first identifies key wear locations, such as the inlet edge of the impeller blades and the lower ring wear plate, which are typically subjected to direct erosion by high-speed, sand-laden water flow.

[0088] Wear depth The calculation is based on the following relation: ; in, The relative velocity of the water flow is estimated through CFD simulation or empirical formulas. Sand content (kg / m³); The characteristic particle size (mm) of sediment is usually taken as the median particle size of D50. is the hardness coefficient of sediment minerals, based on the Mohs hardness scale; , This is the model index, typically ranging from k=2 to 3 and m=0.5 to 1.5, with the specific value determined through regression analysis; This is the cumulative runtime.

[0089] This step can be widely applied to scenarios involving turbine wear prediction and material selection optimization. For example, during the power plant design phase, this model can be used to evaluate the wear resistance of different materials and guide equipment selection.

[0090] S32. A long short-term memory network model enhanced with attention mechanism is adopted, which takes historical MCI sequence, operating condition sequence and sediment parameter sequence as input, and outputs the future change trend of MCI.

[0091] In some implementations, attention-enhanced Long Short-Term Memory (LSTM) network models are used to capture long-term dependencies and key features in time series data, enabling high-precision prediction of future MCI trends.

[0092] Specifically, the model inputs include historical MCI sequences (e.g., daily MCI values ​​over the past 180 days), operating condition sequences (e.g., head, power, guide vane opening), and sediment parameter sequences (e.g., sediment concentration, particle size distribution). The input data is standardized to eliminate the influence of dimensions. The LSTM network contains multiple hidden layers to learn temporal patterns in the sequences. An attention mechanism automatically assigns weights to different time steps, focusing on historical points with the greatest impact on prediction, such as periods of accelerated wear or high sediment concentration.

[0093] The model outputs a sequence of MCI predictions for a future period (e.g., 90 days). During training, mean squared error (MSE) is used as the loss function, and early stopping is employed to prevent overfitting. The model is periodically retrained with the latest data to maintain its predictive ability.

[0094] This step enhances the modeling capability for complex time series using an attention-enhanced LSTM model, resulting in more accurate and stable predictions. This makes it widely applicable to turbine condition prediction and operation and maintenance planning scenarios. For example, the system predicts future MCI trends using an LSTM model and combines this with a physical model to output a fused result, providing a more reliable RUL estimate.

[0095] S33. Dynamically fuse the physical mechanism model and the data-driven model through particle filtering or Bayesian inference, output the MCI prediction curve and its confidence interval, and calculate the RUL.

[0096] In some implementations, physical mechanism models and data-driven models are dynamically fused through particle filtering or Bayesian inference, combining theoretical priors and real-time data to improve prediction accuracy and quantify uncertainty.

[0097] Specifically, the wear trend output by the physical mechanism model is first used as a prior distribution, for example, assuming that the MCI change follows a linear or exponential growth based on wear theory. The output of the data-driven model (LSTM) is used as a likelihood function to reflect the pattern in the actual data.

[0098] Then, the system updates the posterior distribution using particle filtering or Bayesian inference to obtain the fused MCI prediction curve. Particle filtering represents the state distribution using a set of particles and is suitable for nonlinear non-Gaussian systems; Bayesian inference calculates the posterior distribution using analytical or numerical methods and provides confidence intervals.

[0099] Finally, the fused output includes the MCI prediction value and its confidence interval (e.g., 95% confidence band). The RUL is calculated as the time from the current moment to the point where the MCI prediction curve crosses the safety threshold MCI_critical. The system considers the confidence interval width to assess the uncertainty of the RUL.

[0100] This step, through dynamic fusion prediction, achieves a complementary advantage between theoretical models and data-driven approaches, improving the reliability and practicality of the prediction results and enabling its widespread application in high-reliability prediction scenarios. For example, in RUL estimation, the system provides both optimistic and pessimistic predictions to help operations personnel develop risk response strategies.

[0101] S4. Based on the predicted RUL, the current MCI value and its rate of change, classify and dynamically adjust the fault risk level, and generate adaptive early warning information.

[0102] In some implementations, this step uses a rules engine and a state machine to dynamically assess the risk level and generate corresponding early warning information and maintenance suggestions for different levels.

[0103] Specifically, the system first presets risk level thresholds, including time thresholds T1, T2, and T3, and the MCI threshold value MCI. critical The risk levels are divided into five levels, as follows:

[0104] Level 1 (Normal Monitoring): RUL > T1 (e.g., 120 days), MCI and rate of change are within the normal range, continuous monitoring is required.

[0105] Level 2 (Watchlist): T2 < RUL ≤ T1 (e.g., 60 days to 120 days), MCI shows a stable upward trend, a "Watchlist" notification is issued, and operation optimization is recommended.

[0106] Level 3 (Warning): T3 < RUL ≤ T2 (e.g., 30 to 60 days), MCI accelerates upward, a "warning" is issued, prompting the formulation of a preliminary maintenance plan.

[0107] Level 4 (Alert): RUL ≤ T3 (e.g., 30 days), MCI is approaching the critical value, an "alert" is issued, and preparations for shutdown are recommended.

[0108] Level 5 (Emergency): Measured MCI value exceeds MCI critical If the rate of change increases abnormally sharply, an "emergency" alert will be issued, and it is recommended to immediately stop the machine for inspection.

[0109] Then, the risk level is dynamically adjusted based on real-time forecast results, and the level conversion conditions are verified through a rules engine. For example, when the RUL forecast value drops from 65 days to 55 days, the risk level rises from level two to level three. Early warning information includes a level description, forecast basis, and recommended measures, and is automatically pushed through the intelligent report generation module.

[0110] This step, through risk level assessment and adaptive early warning, achieves a closed loop from prediction to decision-making, enhancing the initiative and safety of power plant operation and maintenance. It can be widely applied to power plant operation and maintenance decision support scenarios. For example, when the system issues a level 3 early warning, operators can arrange maintenance resources in advance to avoid unplanned downtime.

[0111] Furthermore, S4 includes:

[0112] S41. The risk level is divided into five levels based on the RUL and MCI change rates, and a preset time threshold and action guidelines are set for each level. This division is based on historical failure data and expert experience to ensure that the level setting is scientific and reasonable.

[0113] Specifically, the current risk level is determined by querying a preset threshold table, which includes the RUL threshold (T1=120 days, T2=60 days, T3=30 days) and the MCI change rate threshold (e.g., an increase of 0.5% per day). Level conversion requires both the RUL and change rate conditions to be met simultaneously. For example, to upgrade from Level 2 to Level 3, the RUL must be ≤60 days and the MCI change rate must be >0.3% / day.

[0114] Each level corresponds to a specific action guideline. For example, a level 3 warning suggests developing a preliminary maintenance plan, while a level 4 alarm suggests preparing for a shutdown. The action guidelines are dynamically updated through a knowledge base to adapt to the operation and maintenance strategies of different power plants.

[0115] This step, through clearly defined risk levels, provides operators with a clear basis for decision-making, improves emergency response efficiency, and can be widely applied in risk management and operation and maintenance scheduling scenarios. For example, the system automatically associates risk levels with operation and maintenance work orders, triggering corresponding processing procedures.

[0116] S42. When the risk level reaches the warning level or higher, a maintenance action recommendation report is automatically generated and pushed to designated personnel through multiple channels.

[0117] In some implementations, this step enables rapid report generation and targeted distribution through template engines and push gateways.

[0118] Specifically, the system first selects a suitable template from the report template library based on the risk level. The template content includes: core prediction conclusions (such as RUL and MCI values), key data support and trend analysis, risk level assessment, and maintenance action recommendations (such as "focus on inspecting the runner blade inlet edge and the inner side of the lower ring").

[0119] Once generated, the report is pushed to pre-defined recipients (such as the deputy plant manager in charge of production, the operations and maintenance manager, and the shift supervisor) via the power plant intranet, email, SMS, and a dedicated mobile application. The push mechanism supports priority settings; for example, emergency alarms can be sent simultaneously via SMS and the app to ensure timeliness.

[0120] This step, through automated report generation and multi-channel push notifications, enables the rapid dissemination of predictive information and the efficient execution of operation and maintenance decisions, and can be widely applied to power plant operation and maintenance communication and decision-making scenarios. For example, when a Level 3 warning is issued, the system automatically generates a report and pushes it, allowing the operation and maintenance team to hold a maintenance preparation meeting based on it.

[0121] In summary, the turbine wear shutdown failure prediction method for high-head sediment-laden hydropower stations in this embodiment can achieve accurate prediction and intelligent early warning of wear status based on multi-source data fusion, improve the initiative and safety of power station operation and maintenance, and effectively reduce the risk of unplanned shutdowns.

[0122] Example 2

[0123] A smart reporting system for turbine wear and shutdown faults in high-head, sediment-laden hydropower stations is proposed. The system mainly comprises four parts: a multi-source data interface and acquisition module, an edge computing and intelligent prediction module, a human-computer interaction and visualization module, and an intelligent report generation and multi-channel push module. By integrating multi-source data acquisition, intelligent prediction, and visualization, real-time monitoring and fault early warning of turbine wear status are achieved, improving the automation level and decision-making efficiency of power station operation and maintenance.

[0124] In this embodiment, the multi-source data interface and acquisition module is responsible for connecting the power plant monitoring system, online sediment monitor, vibration sensor, and condition monitoring system to realize the automatic acquisition, protocol parsing, and standardization of multi-source heterogeneous data. Specifically, it includes the acquisition and fusion of hydraulic operating parameters, sediment characteristic parameters, unit condition monitoring parameters, and historical maintenance data to construct a wear analysis dataset, providing a data foundation for subsequent predictions. This module can effectively improve the completeness and accuracy of data acquisition and ensure the reliability of subsequent analysis.

[0125] During operation, comprehensive data monitoring is performed on the turbine, acquiring parameters such as head, power, guide vane opening, sediment concentration, and vibration spectrum in real time through a sensor network. Data protocols are analyzed, key features are identified, and data cleaning and alignment are performed. The extracted information is standardized to form a time-series dataset, which serves as input for wear condition assessment and prediction.

[0126] In this embodiment, the edge computing and intelligent prediction modules are responsible for calculating the MCI index in real time based on the collected data, running a fusion model of physical mechanism and data-driven approach, predicting the remaining useful life (RUL), and assessing the failure risk level. This includes functions such as modeling wear development trends, LSTM network training and prediction, and execution of the fusion algorithm to ensure the accuracy and real-time performance of the prediction results. This module has self-learning capabilities at the edge, supports dynamic updates of model parameters, and adapts to different operating conditions.

[0127] When in use, the built-in algorithm is first called to calculate the MCI index. The fusion prediction model is then run by combining historical data and real-time input to output the MCI prediction curve, RUL estimate and confidence interval. Then, the risk level is divided according to the prediction results and early warning information is generated.

[0128] In this embodiment, the human-computer interaction and visualization module dynamically displays historical and predicted curves of the MCI index, RUL, risk level, wear heat maps of key components, and real-time operating parameters. It also includes a high-resolution touchscreen interface that supports user interaction, parameter configuration, and trend analysis, helping operators intuitively grasp the health status of the turbine. This module enhances the user experience and improves the transparency and operability of condition monitoring.

[0129] When using, through, as Figure 2 The graphical interface shown displays the MCI trend chart, RUL dashboard, risk level indicator, and wear heat map. Users can zoom in and out of charts, query detailed data, or adjust display parameters via the touchscreen.

[0130] In this embodiment, the intelligent report generation and multi-channel push module is responsible for automatically generating structured fault prediction and analysis reports based on prediction and evaluation results, and pushing them to designated personnel through multiple channels. It also includes functions such as report template management, content generation, and push scheduling, ensuring timely delivery of early warning information and efficient execution of operation and maintenance decisions. This guarantees closed-loop management from prediction to action and constructs an intelligent operation and maintenance reporting system.

[0131] When in use, the system automatically generates multi-level reports of varying detail based on risk level, including forecast conclusions, data support, risk assessment, and maintenance recommendations. Reports are pushed via the power plant intranet, email, SMS, and mobile app, and support receipt confirmation and feedback collection. This embodiment is based on the fusion model's historical MCI data and future prediction curves (including confidence intervals), such as... Figure 3 As shown.

[0132] Through the above technical solution, this invention combines multi-source data fusion, wear index construction, and intelligent prediction model to achieve early warning of wear failures and prediction of remaining life of water turbines.

[0133] This invention employs a wear condition assessment technology based on multi-source heterogeneous data acquisition and fusion. The system integrates hydraulic, sediment, vibration, and maintenance data to construct a comprehensive wear index (MCI), which fully reflects the health status of the turbine. This technology effectively overcomes the limitations of single-parameter monitoring and improves the accuracy and sensitivity of condition assessment.

[0134] This invention introduces a remaining useful life (RUL) prediction method based on a fusion of physical mechanisms and data-driven approaches. By combining a wear theory model and an LSTM neural network, it dynamically predicts the MCI trend and RUL and provides confidence intervals. This technology improves prediction accuracy through a fusion algorithm, enabling early fault warning.

[0135] This invention employs an adaptive risk level assessment and early warning generation mechanism, dynamically adjusting the risk level based on the RUL and MCI change rates, and automatically generating decision support reports. This mechanism ensures timely information delivery through multi-channel push notifications, helping power plant operation and maintenance shift from passive response to proactive prevention.

[0136] In summary, the method of the present invention can monitor the wear status of turbines in real time under high head and sediment-laden water flow conditions, accurately predict the remaining service life, and generate intelligent early warning reports, significantly improving the intelligence level and safety of power plant operation and maintenance, and has good engineering application value.

[0137] The above description is only a preferred embodiment of the present invention. It should be noted that for those skilled in the art, several improvements and modifications can be made without departing from the principle of the present invention, and these improvements and modifications should also be considered within the scope of protection of the present invention.

Claims

1. A method for predicting turbine wear-related shutdown faults in high-head, sediment-laden hydropower stations, characterized in that, Includes the following steps: S1. Through the sensor network and data interface deployed in the hydropower station, multi-source heterogeneous data are collected in real time to construct a wear analysis dataset, including hydraulic operating parameters, sediment characteristic parameters, unit condition monitoring parameters and historical maintenance and inspection data; S2. Based on the wear analysis dataset, construct the Comprehensive Wear Status Index (MCI) to quantify the overall health of the turbine's flow components, and calculate the MCI value using dynamic weighting coefficients. S3. Based on the physical mechanism and data-driven fusion model, establish a wear development and MCI index deterioration model, predict the remaining service life (RUL), and trigger an alarm when the predicted RUL value exceeds the preset safety threshold. S4. Based on the predicted RUL value, the current MCI value and its rate of change, classify and dynamically adjust the fault risk level, and generate adaptive early warning information.

2. The method for predicting turbine wear-related shutdown faults as described in claim 1, characterized in that, In step S1: The sediment characteristic parameters were collected by an online laser particle size analyzer and a turbidimeter, including: river sediment content, sediment particle size distribution, and sediment mineral hardness. The hydraulic operating parameters include: upstream water level, downstream water level, head, flow rate, unit active power, and guide vane opening. The unit status monitoring parameters include: triaxial vibration acceleration of each bearing housing, main shaft runout, key phase signal, and turbine casing noise sound pressure level spectrum; The historical maintenance and inspection data includes: flow component profile measurement data, crack detection reports, welding repair records, and spare parts replacement records from each major overhaul. Through data processing, hydraulic operating parameters, unit status monitoring parameters, and historical maintenance data are integrated into a time series dataset for real-time analysis and long-term trend mining.

3. The method for predicting turbine wear-related shutdown faults as described in claim 1, characterized in that, In step S2, the Comprehensive Wear Condition Index (MCI) is constructed, and the formula is as follows: ； in, For actual operating efficiency, The value is the baseline efficiency curve value. The high-frequency energy index of vibration, To calculate the total mass of sediment passing through the machine, This represents the change in clearance between rotating parts. For dynamically weighted coefficients, satisfying .

4. The method for predicting turbine wear-related shutdown faults as described in claim 3, characterized in that, The actual operating efficiency According to real-time water head and power Inverse calculation and comparison with the baseline efficiency curve Compare and calculate the efficiency loss component; The vibration high-frequency energy index Extracting vibration signals from the main shaft or guide bearing through spectral analysis; The cumulative total mass of sediment passing through the machine The wear contribution of sediment with different particle sizes is reflected by integrating sediment concentration and flow rate, and taking into account particle size weighting. The change in clearance of the rotating component Based on model estimation or by inferring from vibration and sway data, it is used to assess the degree of mechanical wear.

5. The method for predicting turbine wear-related shutdown faults as described in claim 1, characterized in that, In step S3, the method for establishing a wear development and MCI index deterioration model to predict the remaining service life (RUL) is as follows: S31. Constructing a physical mechanism model: Based on the theory of micro-cutting and plastic deformation wear of ductile materials, establish the wear depth at key locations per unit time. The relationship between sand particle characteristics and operating conditions; S32. Construct a data-driven fusion model: Employ a long short-term memory network model enhanced with an attention mechanism, taking historical MCI sequences, operating condition sequences, and sediment parameter sequences as inputs, and outputting the future trend of MCI changes. S33. Fusion Prediction: The trend calculated by the physical mechanism model is used as a priori constraint for the prediction of the Long Short-Term Memory network model. Dynamic data fusion and parameter updates are performed through particle filtering or Bayesian inference to output the predicted curve of MCI and its confidence interval for a future period. When the predicted MCI value exceeds the preset safety threshold, the prediction is further refined. critical When the alarm is triggered, the system calculates the predicted time from the current moment to the triggering of the alarm, and obtains the RUL value.

6. The method for predicting turbine wear-related shutdown faults as described in claim 5, characterized in that, In step S31, the wear depth at the key location per unit time The relationship between sand grain characteristics and operating conditions is expressed as follows: ； in, The relative velocity of the water flow. For sand content, Characteristic particle size of sediment, This represents the hardness coefficient of sediment minerals. , For model index, This is the cumulative runtime.

7. The method for predicting turbine wear-related shutdown faults as described in claim 1, characterized in that, In step S4, the fault risk level is divided according to the RUL value and MCI change rate, including: normal monitoring, attention, early warning, alarm and emergency, and a preset time threshold is set for each level; When the risk level reaches the warning level or higher, a maintenance action recommendation report is automatically generated and pushed to designated personnel through multiple channels.

8. An intelligent reporting system for turbine wear-related shutdown faults in high-head, sediment-laden hydropower stations, used to implement the turbine wear-related shutdown fault prediction method according to any one of claims 1 to 6, characterized in that... include: The multi-source data interface and acquisition module are used to connect the power plant monitoring system, online sediment monitor, vibration sensor and condition monitoring system to realize the automatic acquisition and redundancy processing of multi-source heterogeneous data, and to perform protocol parsing and standardization. The edge computing and intelligent prediction module integrates a high-performance processor and memory for real-time calculation of MCI indicators, running of fusion prediction models, LSTM prediction and risk level assessment. It adopts parallel computing technology to optimize model training and prediction speed and has the ability to learn and update the model. The human-computer interaction and visualization module provides a high-resolution touch screen interface, supports user interaction queries and parameter configuration, and is used to dynamically display historical and predictive curves of MCI indicators, RUL, risk level, wear heat maps of key components, and real-time operating parameters. The intelligent report generation and multi-channel push module automatically generates structured fault prediction and analysis reports based on prediction and evaluation results, and pushes them to designated personnel via the power plant intranet, email, SMS and mobile applications.

9. The method for predicting turbine wear-related shutdown faults as described in claim 8, characterized in that, The multi-source data interface and acquisition module supports multiple communication protocols, including Modbus, IEC61850 and MQTT. It acquires water head, power, guide vane opening, sediment concentration and vibration spectrum parameters in real time through sensor network, analyzes data protocols, identifies key features, cleans and aligns data, and standardizes the extracted information to form a time series dataset.

10. The method for predicting turbine wear-related shutdown faults as described in claim 8, characterized in that, The wear heat map of the key components is generated by a CFD model based on wear depth data at key locations. The fault prediction and analysis report includes: core prediction conclusions, data support, risk level, and maintenance recommendations.

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