A thermal power plant equipment early deterioration identification and early warning method based on big data analysis

Abstract

This invention provides a method for early identification and early warning of equipment degradation in thermal power plants based on big data analysis. The method includes the following steps: S1: Multi-source data acquisition and preprocessing. Through a multi-source data acquisition module, long-term historical operating data of the target equipment and its associated systems are collected from the DCS, SIS, and vibration monitoring system. This data includes equipment parameters and operating condition parameters. The data is then cleaned, denoised, and standardized to form a high-quality data analysis foundation. This invention frees maintenance personnel from tedious data monitoring work, allowing them to focus on decision analysis. It significantly improves the overall operation and maintenance efficiency and intelligence level of thermal power plants, promoting a strategic transformation of equipment maintenance from preventative to predictive maintenance, and possesses significant engineering application value and economic benefits.

Description

Technical Field

[0001] This invention relates to the field of industrial big data analysis and predictive maintenance technology, and in particular to a method for early identification and warning of early deterioration of thermal power plant equipment based on big data analysis. Background Technology

[0002] Thermal power plants are enterprises that use fossil fuels such as coal, oil, and natural gas as energy sources. They generate high-temperature and high-pressure steam by burning the heat released from the combustion process. This steam drives a turbine to rotate and then drives a generator to convert mechanical energy into electrical energy. They are an important part of the power system and are equipped with systems for fuel transportation, boiler combustion, turbine power generation, and flue gas treatment. They can stably and continuously supply electricity to the power grid to meet the electricity needs of production and daily life.

[0003] Early identification and warning of equipment degradation in thermal power plants is a key technical means in the operation and maintenance management of thermal power plant equipment. For core and auxiliary equipment such as boilers, steam turbines, and generators, various monitoring methods are used to capture subtle abnormal changes in the performance and status of the equipment during operation. This allows for the accurate identification of early degradation trends and potential hazards before obvious failures are observed. Warning levels are set based on the degree of degradation and the rate of development, and warning signals are issued in a timely manner. This enables early prediction of equipment failure risks, guides operation and maintenance personnel to take targeted prevention and maintenance measures, prevents degradation from developing into sudden failures, ensures stable equipment operation, reduces downtime losses, and improves the overall operation and maintenance efficiency and safety level of thermal power plants.

[0004] Currently, equipment status monitoring in thermal power plants mainly relies on fixed-value alarms from distributed control systems and the experience analysis of operators. This model has significant drawbacks: it has low sensitivity to gradual equipment degradation, and the gradual trend of slow performance decline is difficult to identify through manual observation or short-term curve retrieval, which can easily lead to unplanned shutdowns. Equipment status parameters are greatly affected by various external factors, and existing distributed control systems cannot compare current parameters with historical data under similar or identical operating conditions and environments in real time, making it difficult to accurately determine the cause of parameter changes and prone to misjudgments or omissions. The alarm mechanism is relatively rigid, and abnormal parameters that do not reach preset fixed alarm values ​​cannot be automatically identified by the system. Equipment sub-health conditions rely on accidental discovery by personnel, resulting in delayed early warnings, missed early intervention windows, increased maintenance costs, and shortened equipment lifespan.

[0005] Therefore, it is necessary to provide a method for early identification and warning of equipment degradation in thermal power plants based on big data analysis to solve the above-mentioned technical problems. Summary of the Invention

[0006] This invention provides a method for early identification and warning of equipment degradation in thermal power plants based on big data analysis. It solves the problems that rely on fixed alarm values ​​of distributed control systems and human experience for equipment status monitoring in thermal power plants, which are insensitive to gradual degradation, prone to misjudgment due to lack of operating condition benchmarks, and have rigid alarms and delayed warnings, which can easily lead to downtime and increase costs.

[0007] To address the aforementioned technical problems, the present invention provides a method for early identification and warning of equipment degradation in thermal power plants based on big data analysis, comprising the following steps: S1: Multi-source data acquisition and preprocessing. Through the multi-source data acquisition module, long-term historical operating data of the target equipment and its related systems are collected from DCS, SIS and vibration monitoring system, including equipment body parameters and operating condition parameters. The data is cleaned, denoised and standardized to form a high-quality data analysis foundation. S2: Operating condition identification and clustering. Through the operating condition identification and clustering module, unsupervised machine learning algorithms are used to analyze the operating condition parameters in historical operating data and automatically divide massive historical data into several typical operating conditions. S3: Benchmark Model Construction: In the benchmark model library, for each typical working condition, historical data of the equipment under healthy conditions are extracted to establish an independent benchmark model. This model defines the normal range, expected value and reasonable fluctuation range of various key parameters of the equipment under this working condition. S4: Real-time operating condition matching and degradation analysis: The real-time data input module collects the current operating data of the equipment, the real-time operating condition matching and model calling module matches the current operating condition and calls the corresponding benchmark model, and the degradation deviation calculation and trend analysis module calculates the deviation of key parameters. The time series analysis technology is used to fit the trend of the deviation, quantify the real-time health status and degradation rate of the equipment, and generate a health index. S5: Intelligent early warning and output. The intelligent early warning judgment module combines the historical trend database and the dynamic early warning rule base to make early warning judgments. When the preset early warning conditions are met, the early warning output module outputs an early warning signal. S1. Multi-source data acquisition must cover all dimensions of the target equipment's operational data, ensuring sufficient historical data span. Data cleaning must remove abnormal and missing data. Noise reduction and standardization must follow unified industry standards to ensure consistent data format and units. S2. Operating condition identification and clustering must select appropriate unsupervised machine learning algorithms based on the operating characteristics of thermal power plant equipment. During clustering, the operating condition classification results must be repeatedly verified to ensure the stability and independence of each operating condition and avoid overlapping operating conditions. S3. When building the baseline model, historical data under equipment health status must be strictly screened, removing abnormal data related to equipment failures or maintenance periods. The baseline models for each operating condition need to clearly define parameter boundaries and reasonably set fluctuation ranges. The models must also support future updates and optimizations based on equipment operating data. S4 real-time operating condition matching must ensure the real-time nature of data acquisition, and the matching results must be verified twice. Deterioration deviation calculations must focus on key parameters, and trend fitting must ensure the continuity of time series data. S5 early warning judgments must strictly adhere to the dynamic early warning rule base, verify early warning triggering conditions from multiple dimensions, and ensure the timeliness and comprehensiveness of signal transmission in early warning outputs. The signals must be pushed synchronously to each operation and maintenance terminal, and the operating data of each module must be stored in the historical trend database in real time. Data retention and backup must be properly implemented.

[0008] Preferably, the equipment body parameters in S1 are at least one or more combinations of temperature, pressure, flow rate, vibration, current, and speed; the operating condition parameters include one or more of unit load, ambient temperature, main steam pressure, inlet water temperature, and furnace negative pressure; and the data preprocessing includes one or more of missing value completion, outlier removal, dimension unification, normalization, and smoothing. The equipment body is a steam turbine generator set, circulating water pump set, boiler blower, or auxiliary equipment in a thermal power plant.

[0009] Preferably, the unsupervised machine learning algorithm in S2 is any one of the K-means algorithm, hierarchical clustering algorithm, and DBSCAN density clustering algorithm, and can be flexibly selected according to the operating condition fluctuation characteristics of the target equipment.

[0010] Preferably, the equipment health status in S3 is an operating status where the equipment is fault-free, has no maintenance records, and meets the operating performance standards.

[0011] Preferably, the time series analysis technique in S4 uses the ARIMA model to obtain the equipment degradation rate through trend fitting, thereby achieving a quantitative assessment of the equipment's health status.

[0012] Preferably, the preset warning conditions in S5 include one or more of the following: the real-time deviation of a parameter continuously exceeds a certain set proportion of the working condition reference range; the downward trend of the health index exceeds a preset threshold; and the absolute value of a single parameter is normal but the trend of change shows a continuous directional deviation.

[0013] Preferably, the warning signal in S5 is output through one or more of the following methods: sound, light, text message, and pop-up window in the central control room system.

[0014] Preferably, the multi-level early warning mechanism in S5 is divided into Level 1, Level 2, and Level 3 early warnings based on the degree of equipment degradation and the rate of parameter deviation, with different levels of early warnings adapting to different operation and maintenance intervention needs.

[0015] Preferably, the degradation deviation calculation and trend analysis module used in S4 includes: core parameter stratification and parameter weight assignment, weighted deviation calculation based on parameter weight assignment, single parameter trend fitting and degradation rate classification, multi-parameter coupled degradation analysis, health index calculation and status classification, and degradation source tracing and multi-dimensional result output. The core parameters are stratified and weighted, with core parameters accounting for 60%-80%, important parameters for 20%-30%, and auxiliary parameters for 0%-10%.

[0016] Preferably, the degradation deviation calculation and trend analysis module further includes the following steps during use: S6: Core parameter hierarchical and parameter weight assignment. Based on the equipment operating characteristics, real-time parameters are divided into three levels: core, important, and auxiliary deterioration parameters. Weight assignment is completed through training with historical data and expert experience. The results are stored in the equipment parameter weight library and can be retrieved by equipment type. S7: Calculation of weighted deviation based on parameter weight assignment. On the basis of single parameter deviation calculation, the comprehensive weighted deviation is calculated by combining the weight assignment results. Threshold filtering is set for single parameter deviation. Auxiliary parameter deviation <5% is not included to avoid secondary parameter fluctuations from interfering with the evaluation. S8: Single-parameter trend fitting and degradation rate classification. ARIMA time series analysis technology is used to fit the trends of core and important parameters, calculate the single-parameter degradation rate, and classify them into four levels: micro, slow, medium and fast according to the degradation law of thermal power plant equipment. The quantification coefficient of each level is determined to achieve grading and quantification. S9: Multi-parameter coupled degradation analysis. Based on the built-in parameter coupling rule library, it makes a linkage judgment on the degradation trend of single parameters after classification. If the rule is met, it is judged as composite degradation. The comprehensive weighted deviation is corrected by a coefficient of 1.2-1.3 to increase the evaluation weight of composite degradation. S10: Health Index Calculation and Status Grading. The health index adopts a 100-point system, with the comprehensive weighted deviation as the core, combined with the degradation rate grading coefficient and the coupled degradation correction coefficient for calculation. The HI index is divided into five levels: healthy, sub-healthy, mild, moderate and severe degradation, which can intuitively quantify the health status of the equipment. S11: Degradation source tracing and multi-dimensional result output. Degradation source tracing is carried out based on the preceding calculation data. The parameter with the highest weighted deviation contribution value is defined as the core degradation parameter. Source tracing suggestions are generated in combination with relevant analysis results. Multi-dimensional analysis results are simultaneously output to the intelligent early warning judgment module and the historical trend database.

[0017] Compared with related technologies, the method for early identification and warning of equipment degradation in thermal power plants based on big data analysis provided by this invention has the following beneficial effects: This invention provides a method for early identification and warning of equipment degradation in thermal power plants based on big data analysis. It utilizes a multi-source data acquisition module to complete multi-dimensional and comprehensive data collection and preprocessing, overcoming the limitations of traditional single-parameter monitoring. This ensures the accuracy of degradation identification from the source. Simultaneously, relying on trend analysis and cross-time data comparison, it captures weak degradation signals that are undetectable by the human eye or fixed thresholds, significantly advancing the fault detection time. Through operating condition identification and clustering modules, it achieves intelligent segmentation of operating conditions, solving the industry problem of incomparable equipment parameters under different operating conditions. By establishing an adaptive benchmark model based on operating conditions using a benchmark model library, it replaces the traditional fixed threshold standard, achieving adaptive assessment of equipment status under operating conditions. This effectively eliminates the interference of operating condition fluctuations on equipment status assessment, significantly reducing misjudgments and omissions, and improving the authenticity and reliability of equipment status assessment. The degradation deviation calculation and trend analysis module quantifies equipment degradation trends, abandoning the rigid early warning mechanism of traditional alarms that only sound when thresholds are exceeded. This shifts from fault alarms to health warnings, providing ample time windows for planned maintenance and effectively preventing equipment failures caused by accelerated degradation. The intelligent early warning judgment module and dynamic early warning rule base enable multi-level intelligent early warnings, supported by historical data from a historical trend database, making early warning judgments more accurate and comprehensive. This effectively prevents unplanned downtime and equipment damage in thermal power plants, reduces maintenance costs, and extends equipment lifespan. Simultaneously, it frees maintenance personnel from tedious data monitoring work, allowing them to focus on decision analysis. This significantly improves the overall operation and maintenance efficiency and intelligence level of thermal power plants, driving a strategic transformation in equipment maintenance from preventative to predictive maintenance, and possesses significant engineering application value and economic benefits. Attached Figure Description

[0018] Figure 1 A schematic diagram illustrating the method for early identification and warning of equipment degradation in thermal power plants based on big data analysis provided by this invention; Figure 2 A schematic diagram of a multi-source data acquisition module is provided for this invention; Figure 3 A schematic diagram of the real-time working condition matching and model calling module provided for this invention; Figure 4 A schematic diagram illustrating the dynamic early warning rule base for this invention; Figure 5 This is a schematic diagram of the fourth embodiment of the method for early deterioration identification and early warning of thermal power plant equipment based on big data analysis provided by the present invention. Detailed Implementation

[0019] The present invention will be further described below with reference to the accompanying drawings and embodiments. Example

[0020] This paper presents a method for early identification and warning of equipment degradation in thermal power plants based on big data analysis. Targeting the core equipment of thermal power plants, the steam turbine generator unit, a K-means unsupervised machine learning algorithm is used to achieve operating condition clustering. Bearing temperature, unit vibration, inlet steam pressure, and motor speed are selected as core monitoring parameters. The method for early identification and warning of equipment degradation based on big data analysis is implemented, as detailed below: Multi-source data acquisition and preprocessing: Through the multi-source data acquisition module, historical operating data of the steam turbine generator set for nearly 6 years are collected from the DCS and SIS systems. The equipment body parameters include the temperature of each measuring point of the bearing, the radial and axial vibration values ​​of the unit, the steam pressure, the exhaust temperature, the motor speed, etc. The operating condition parameters include the unit load, the ambient temperature, the main steam flow, etc. The collected data is standardized by filling in missing values, removing outliers, and unifying the units to form a standardized dataset to ensure data quality. Operating condition identification and clustering: Through the operating condition identification and clustering module, the unit load, main steam flow, and ambient temperature are selected as core operating condition parameters. The K-means algorithm is used to perform cluster analysis on the standardized operating condition data. Based on the data distribution characteristics, the historical operating data is automatically divided into four stable typical operating condition clusters: low load stable operation 0-300MW, medium load stable operation 300-600MW, high load stable operation 600-1000MW, and variable load peak shaving, which are adapted to the operating condition characteristics of different operating loads of steam turbine generator units. Benchmark Model Construction: Historical operating data corresponding to four typical operating condition clusters of steam turbine generator sets under healthy conditions of no faults, no maintenance records, and performance compliance were extracted from the benchmark model library. An independent benchmark model was established for each operating condition cluster to clarify the normal range, expected value, and reasonable fluctuation range of the core parameters under each operating condition. Among them, under the high load stable operation condition, the normal range of bearing temperature is 50-65℃, and the reasonable fluctuation range is ±2℃; the normal range of inlet steam pressure is 16-17MPa, and the reasonable fluctuation range is ±0.1MPa.

[0021] Real-time operating condition matching and degradation analysis: The real-time data input module collects the current operating data of the steam turbine generator set in real time, and extracts core parameters such as bearing temperature and unit vibration after preprocessing; The real-time operating condition matching and model calling module matches the real-time operating condition data to a cluster of high-load stable operation and typical operating conditions, and calls the benchmark model for that operating condition from the benchmark model library. The degradation deviation calculation and trend analysis module compares the real-time bearing temperature and unit vibration value with the benchmark model, and calculates that the bearing temperature deviation is 4.6%. Using the ARIMA time series analysis model to fit the trend of the deviation, it is found that the bearing temperature deviation has been rising from 1% to 4.6% in the past 48 hours, with a degradation rate of 0.075% / h. At the same time, the current equipment health index is generated as 89 points.

[0022] Intelligent Early Warning and Output: The intelligent early warning judgment module combines the device's HI historical data for the past 30 days stored in the historical trend database with the secondary early warning rules in the dynamic early warning rule library. If the parameter deviation continuously exceeds the baseline range of the operating condition by 5% or the HI value drops by more than 10 points for 72 consecutive hours, the module will make a logical judgment and determine that the current conditions for a secondary early warning are met. The module will then trigger the secondary early warning and simultaneously output the warning signal through on-site audible and visual alarms, SMS messages to maintenance personnel's mobile phones, and pop-up windows in the central control room system, reminding maintenance personnel to intervene in a timely manner.

[0023] Compared with related technologies, the method for early identification and warning of equipment degradation in thermal power plants based on big data analysis provided by this invention has the following beneficial effects: By utilizing the K-means algorithm to achieve accurate clustering of turbine generator unit operating conditions, early progressive degradation signals of bearing temperature can be quickly captured. Compared with traditional fixed threshold alarms, the alarm for bearing temperature ≥75℃ can detect potential equipment problems about 18 days in advance, avoiding bearing failure and unit shutdown caused by continuous rise in bearing temperature. This allows sufficient time for planned maintenance of turbine generator units, effectively reducing equipment maintenance costs and the risk of unplanned unit shutdowns. Example

[0024] This paper presents a method for early identification and warning of equipment degradation in thermal power plants based on big data analysis. Targeting the circulating water pump set, a core auxiliary equipment in thermal power plants, a hierarchical clustering unsupervised machine learning algorithm is employed to achieve operating condition clustering. Pump body vibration, bearing temperature, outlet pressure, and motor current are selected as core monitoring parameters. The method for early identification and warning of equipment degradation based on big data analysis is implemented, as detailed below: Multi-source data acquisition and preprocessing: Through the multi-source data acquisition module, historical operating data of the circulating water pump set for the past 4 years are collected from the DCS and SIS systems. The equipment body parameters include pump body horizontal and vertical vibration, front and rear bearing temperatures, water pump outlet pressure, motor operating current, etc. The operating condition parameters include unit load, circulating water inlet temperature, ambient humidity, etc. The collected data is denoised and smoothed to filter invalid data generated by instantaneous fluctuations of instruments, thus completing data preprocessing and eliminating the influence of interfering data on subsequent analysis. Operating condition identification and clustering: Through the operating condition identification and clustering module, the unit load, circulating water inlet temperature, and ambient humidity are selected as core operating condition parameters. The hierarchical clustering algorithm is used to perform cluster analysis on the preprocessed operating condition data, and the historical operating data is automatically divided into three stable typical operating condition clusters: low load in winter, high load in summer, and normal load in spring and autumn. This solves the problem of incomparable circulating water pump unit parameters under different seasons and loads. Benchmark Model Construction: Historical operating data corresponding to three typical operating condition clusters of circulating water pump sets under healthy conditions are extracted from the benchmark model library. An independent benchmark model is constructed for each operating condition cluster. The normal range, expected value and reasonable fluctuation range of the core parameters under each operating condition are defined. Among them, under the high load condition in summer, the normal range of water pump bearing temperature is 45-55℃, and the reasonable fluctuation range is ±1.5℃. The normal range of motor current is 180-200A, and the reasonable fluctuation range is ±5A. Real-time operating condition matching and degradation analysis: The real-time data input module collects the current operating data of the circulating water pump set in real time, and extracts core parameters such as pump body vibration and bearing temperature after preprocessing. The real-time operating condition matching and model calling module matches the typical summer high-load operating condition cluster and calls the corresponding benchmark model. The degradation deviation calculation and trend analysis module showed that the real-time value of the water pump bearing temperature was 54℃. However, by fitting the bearing temperature data trend over the past 72 hours using the ARIMA model, it was found that the temperature slowly rose from 50℃ to 54℃, showing a continuous and directional deviation trend. The degradation rate was calculated to be 0.056℃ / h. At the same time, the motor current deviation increased from 0.5% to 2%, and the equipment health index decreased from 95 points to 90 points.

[0025] Intelligent Early Warning and Output: The intelligent early warning judgment module combines historical data from the historical trend database and first-level early warning rules from the dynamic early warning rule library. If the absolute value of a single parameter is normal but there is a continuous directional deviation for 72 hours or the deviation rate exceeds 0.05℃ / h, it is judged that the first-level early warning conditions are met. The first-level early warning is triggered through the early warning output module, and the early warning information is pushed to the operation and maintenance personnel via pop-up window in the central control room system and SMS, prompting them to check the lubrication system and bearing wear of the circulating water pump group.

[0026] Compared with related technologies, the method for early identification and warning of equipment degradation in thermal power plants based on big data analysis provided by this invention has the following beneficial effects: By using hierarchical clustering algorithms, the complex operating conditions of circulating water pump sets can be accurately classified, and the gradual deterioration trend of bearing temperature that cannot be detected by manual observation can be identified. Potential bearing wear hazards can be warned about 22 days in advance, avoiding unplanned shutdowns of circulating water pump sets caused by accelerated bearing deterioration, ensuring the normal operation of the unit's cooling system, improving the stability of auxiliary equipment in thermal power plants, and reducing the operation and maintenance costs of auxiliary equipment systems. Example

[0027] This paper presents a method for early identification and warning of equipment degradation in thermal power plants based on big data analysis. Targeting the blower, a core component of boiler equipment in thermal power plants, the method employs the DBSCAN density clustering unsupervised machine learning algorithm to achieve operating condition clustering. The core monitoring parameters are selected as blower pressure, impeller vibration, motor temperature, and outlet flow rate. The specific steps of this method are as follows: Multi-source data acquisition and preprocessing: Through the multi-source data acquisition module, historical operating data of the boiler blower for the past 5 years are collected from the DCS and SIS systems. The equipment parameters include blower inlet and outlet air pressure, impeller vibration value, motor stator temperature, blower outlet flow rate, etc., and the operating condition parameters include boiler load, furnace negative pressure, ambient temperature, etc. The collected data is normalized, data evaluation standards are unified, and data cleaning and preprocessing are completed to ensure data consistency and comparability. Operating condition identification and clustering: Through the operating condition identification and clustering module, boiler load, furnace negative pressure, and ambient temperature are selected as core operating condition parameters. The DBSCAN density clustering algorithm is used to perform cluster analysis on the preprocessed operating condition data. Considering the operating condition characteristics of frequent fluctuations in boiler load, the historical operating data is automatically divided into three stable typical operating condition clusters: boiler start-up condition, boiler stable combustion full load condition, and boiler low load oil injection condition, which are adapted to the operating condition fluctuation characteristics of the boiler blower. Benchmark Model Construction: In the benchmark model library, historical operating data corresponding to three typical operating condition clusters of the blower under healthy conditions with no faults and no maintenance records are extracted. An independent benchmark model is established for each operating condition cluster to clarify the normal range, expected value and reasonable fluctuation range of the core parameters under each operating condition. Among them, under the stable combustion and full load operating condition of the boiler, the normal range of blower impeller vibration is 0.04-0.09mm and the reasonable fluctuation range is ±0.01mm. The normal range of motor stator temperature is 65-75℃ and the reasonable fluctuation range is ±2℃. Real-time operating condition matching and degradation analysis: The real-time data input module collects the current operating data of the boiler blower in real time, and extracts core parameters such as impeller vibration, motor temperature, and blower air pressure after preprocessing. The real-time operating condition matching and model calling module matches the real-time operating condition data to the typical operating condition cluster of boiler stable combustion at full load, and calls the benchmark model under this operating condition from the benchmark model library. The degradation deviation calculation and trend analysis module compared real-time parameters with the benchmark model and found that the impeller vibration was 0.095 mm with a deviation of 5.6%, the motor temperature was 72℃ with a deviation of 3.8%, and the outlet air pressure deviation was 2.3%. The deviation of each single parameter did not exceed the set threshold. However, through multi-parameter fusion trend analysis and fitting with the ARIMA model, it was found that the deviation of the three parameters showed a synchronous upward trend in the past 60 hours. The equipment health index dropped from 99 points to 86 points, and the overall degradation rate was 0.22 points / h. Intelligent Early Warning and Output: The intelligent early warning judgment module combines historical data from the historical trend database and the three-level early warning rules in the dynamic early warning rule library. It performs logical judgment based on the synchronous and continuous deterioration of multiple parameters and the rate of decline of the health index exceeding 0.2 minutes / hour. If the condition of the current condition of the three-level early warning is met, the three-level early warning is triggered through the early warning output module. The early warning signal is output through on-site audible and visual alarms, pop-up windows in the central control room system, and SMS messages to maintenance personnel, reminding maintenance personnel to check the dust accumulation on the blower impeller, bearing lubrication, and motor heat dissipation.

[0028] Compared with related technologies, the method for early identification and warning of equipment degradation in thermal power plants based on big data analysis provided by this invention has the following beneficial effects: By utilizing the DBSCAN density clustering algorithm to adapt to the complex operating conditions of boiler load fluctuations, the limitations of single-parameter monitoring are overcome. Through multi-parameter fusion trend analysis, early deterioration problems caused by ash accumulation on the blower impeller are identified. This allows for the detection of potential hazards approximately 15 days earlier than traditional fixed-value alarms, preventing problems such as reduced blower efficiency and motor overload caused by aggravated impeller ash accumulation. This extends the service life of the blower equipment, reduces boiler system operation and maintenance costs, and ensures the stable operation of the boiler combustion system. Example

[0029] Please refer to the following: Figure 5 , Figure 5 This is a schematic diagram of the fourth embodiment of the method for early deterioration identification and early warning of thermal power plant equipment based on big data analysis provided by the present invention. The difference of the method for early deterioration identification and early warning of thermal power plant equipment based on big data analysis is that the deterioration deviation calculation and trend analysis module used in S4 includes: core parameter layering and parameter weight assignment, weighted deviation calculation based on parameter weight assignment, single parameter trend fitting and deterioration rate classification, multi-parameter coupled deterioration analysis, health index calculation and status classification, and deterioration source tracing and multi-dimensional result output.

[0030] The degradation deviation calculation and trend analysis module also includes the following steps during use: S6: Core parameter hierarchical and parameter weight assignment. Based on the equipment operating characteristics, real-time parameters are divided into three levels: core, important, and auxiliary deterioration parameters. Weight assignment is completed through training with historical data and expert experience. The results are stored in the equipment parameter weight library and can be retrieved by equipment type. S7: Calculation of weighted deviation based on parameter weight assignment. On the basis of single parameter deviation calculation, the comprehensive weighted deviation is calculated by combining the weight assignment results. Threshold filtering is set for single parameter deviation. Auxiliary parameter deviation <5% is not included to avoid secondary parameter fluctuations from interfering with the evaluation. S8: Single-parameter trend fitting and degradation rate classification. ARIMA time series analysis technology is used to fit the trends of core and important parameters, calculate the single-parameter degradation rate, and classify them into four levels: micro, slow, medium and fast according to the degradation law of thermal power plant equipment. The quantification coefficient of each level is determined to achieve grading and quantification. S9: Multi-parameter coupled degradation analysis. Based on the built-in parameter coupling rule library, it makes a linkage judgment on the degradation trend of single parameters after classification. If the rule is met, it is judged as composite degradation. The comprehensive weighted deviation is corrected by a coefficient of 1.2-1.3 to increase the evaluation weight of composite degradation. S10: Health Index Calculation and Status Grading. The health index adopts a 100-point system, with the comprehensive weighted deviation as the core, combined with the degradation rate grading coefficient and the coupled degradation correction coefficient for calculation. The HI index is divided into five levels: healthy, sub-healthy, mild, moderate and severe degradation, which can intuitively quantify the health status of the equipment. S11: Degradation source tracing and multi-dimensional result output. Degradation source tracing is carried out based on the preceding calculation data. The parameter with the highest weighted deviation contribution value is defined as the core degradation parameter. Source tracing suggestions are generated in combination with relevant analysis results. Multi-dimensional analysis results are simultaneously output to the intelligent early warning judgment module and the historical trend database.

[0031] Compared with related technologies, the method for early identification and warning of equipment degradation in thermal power plants based on big data analysis provided by this invention has the following beneficial effects: After optimizing the degradation deviation calculation and trend analysis module, four sub-functions, including parameter weight assignment and coupled degradation analysis, have been integrated into the original module. First, parameters are assigned values ​​according to equipment characteristics and weighted deviation is calculated. Then, the trend is fitted through the ARIMA model and the degradation rate is classified. Combined with the coupling rule base, compound degradation is judged and the deviation is corrected. Finally, multi-dimensional coefficients are integrated to calculate the health index. At the same time, degradation source tracing is completed and full data is output, making the quantification of equipment health more in line with actual operating characteristics, accurately identifying compound degradation with multiple parameters, making up for the shortcomings of single parameter analysis, providing more quantitative basis for early warning judgment, and directly locating core degradation parameters, providing clear direction for operation and maintenance intervention. The module's built-in database supports flexible adaptation to various thermal power plant equipment, and the output multi-dimensional data also provides refined data support for equipment life cycle management and maintenance strategy optimization.

[0032] The above description is merely an embodiment of the present invention and does not limit the patent scope of the present invention. Any equivalent structural or procedural transformations made based on the content of the present invention specification and drawings, or direct or indirect applications in other related technical fields, are similarly included within the patent protection scope of the present invention.

Claims

1. A method for early identification and warning of equipment degradation in thermal power plants based on big data analysis, characterized in that, Includes the following steps: S1: Multi-source data acquisition and preprocessing. Through the multi-source data acquisition module, long-term historical operating data of the target equipment and its related systems are collected from DCS, SIS and vibration monitoring system, including equipment body parameters and operating condition parameters. The data is cleaned, denoised and standardized to form a high-quality data analysis foundation. S2: Operating condition identification and clustering. Through the operating condition identification and clustering module, unsupervised machine learning algorithms are used to analyze the operating condition parameters in historical operating data and automatically divide massive historical data into several typical operating conditions. S3: Benchmark Model Construction: In the benchmark model library, for each typical working condition, historical data of the equipment under healthy conditions are extracted to establish an independent benchmark model. This model defines the normal range, expected value and reasonable fluctuation range of various key parameters of the equipment under this working condition. S4: Real-time operating condition matching and degradation analysis: The real-time data input module collects the current operating data of the equipment, the real-time operating condition matching and model calling module matches the current operating condition and calls the corresponding benchmark model, and the degradation deviation calculation and trend analysis module calculates the deviation of key parameters. The time series analysis technology is used to fit the trend of the deviation, quantify the real-time health status and degradation rate of the equipment, and generate a health index. S5: Intelligent early warning and output. The intelligent early warning judgment module combines historical trend database and dynamic early warning rule base to make early warning judgment. When the preset early warning conditions are met, the early warning output module outputs an early warning signal.

2. The method for early deterioration identification and early warning of thermal power plant equipment based on big data analysis according to claim 1, characterized in that, The equipment body parameters in S1 are at least one or more combinations of temperature, pressure, flow rate, vibration, current, and speed. The operating condition parameters include one or more of unit load, ambient temperature, main steam pressure, inlet water temperature, and furnace negative pressure. Data preprocessing includes one or more of missing value completion, outlier removal, dimension unification, normalization, and smoothing. The equipment body is a steam turbine generator set, circulating water pump set, boiler blower, or auxiliary equipment in a thermal power plant.

3. The method for early deterioration identification and early warning of thermal power plant equipment based on big data analysis according to claim 1, characterized in that, The unsupervised machine learning algorithm in S2 can be any one of the K-means algorithm, hierarchical clustering algorithm, or DBSCAN density clustering algorithm, and can be flexibly selected according to the operating condition fluctuation characteristics of the target equipment.

4. The method for early deterioration identification and early warning of thermal power plant equipment based on big data analysis according to claim 1, characterized in that, The equipment health status in S3 refers to an operating state where the equipment is fault-free, has no maintenance records, and meets the operating performance standards.

5. The method for early deterioration identification and early warning of thermal power plant equipment based on big data analysis according to claim 1, characterized in that, The time series analysis technique in S4 uses the ARIMA model to obtain the equipment degradation rate through trend fitting, thereby achieving a quantitative assessment of the equipment's health status.

6. The method for early deterioration identification and early warning of thermal power plant equipment based on big data analysis according to claim 1, characterized in that, The preset warning conditions in S5 include one or more of the following: the real-time deviation of a parameter continuously exceeds a certain set proportion of the benchmark range of the working condition; the downward trend of the health index exceeds a preset threshold; and the absolute value of a single parameter is normal but the trend of change shows a continuous directional deviation.

7. The method for early deterioration identification and early warning of thermal power plant equipment based on big data analysis according to claim 1, characterized in that, The warning signal in S5 is output through one or more of the following methods: sound, light, text message, and pop-up window in the central control room system.

8. The method for early deterioration identification and early warning of thermal power plant equipment based on big data analysis according to claim 1, characterized in that, The multi-level early warning mechanism in S5 is divided into Level 1, Level 2, and Level 3 early warnings based on the degree of equipment degradation and the rate of parameter deviation. Different levels of early warning are adapted to different operation and maintenance intervention needs.

9. The method for early deterioration identification and early warning of thermal power plant equipment based on big data analysis according to claim 1, characterized in that, The degradation deviation calculation and trend analysis module used in S4 includes: core parameter stratification and parameter weight assignment, weighted deviation calculation based on parameter weight assignment, single parameter trend fitting and degradation rate classification, multi-parameter coupled degradation analysis, health index calculation and status classification, and degradation source tracing and multi-dimensional result output.

10. The method for early deterioration identification and early warning of thermal power plant equipment based on big data analysis according to claim 9, characterized in that, The degradation deviation calculation and trend analysis module also includes the following steps during use: S6: Core parameter hierarchical and parameter weight assignment. Based on the equipment operating characteristics, real-time parameters are divided into three levels: core, important, and auxiliary deterioration parameters. Weight assignment is completed through training with historical data and expert experience. The results are stored in the equipment parameter weight library and can be retrieved by equipment type. S7: Calculation of weighted deviation based on parameter weight assignment. On the basis of single parameter deviation calculation, the comprehensive weighted deviation is calculated by combining the weight assignment results. Threshold filtering is set for single parameter deviation. Auxiliary parameter deviation <5% is not included to avoid secondary parameter fluctuations from interfering with the evaluation. S8: Single-parameter trend fitting and degradation rate classification. ARIMA time series analysis technology is used to fit the trends of core and important parameters, calculate the single-parameter degradation rate, and classify them into four levels: micro, slow, medium and fast according to the degradation law of thermal power plant equipment. The quantification coefficient of each level is determined to achieve grading and quantification. S9: Multi-parameter coupled degradation analysis. Based on the built-in parameter coupling rule library, it makes a linkage judgment on the degradation trend of single parameters after classification. If the rule is met, it is judged as composite degradation. The comprehensive weighted deviation is corrected by a coefficient of 1.2-1.3 to increase the evaluation weight of composite degradation. S10: Health Index Calculation and Status Grading. The health index adopts a 100-point system, with the comprehensive weighted deviation as the core, combined with the degradation rate grading coefficient and the coupled degradation correction coefficient for calculation. The HI index is divided into five levels: healthy, sub-healthy, mild, moderate and severe degradation, which can intuitively quantify the health status of the equipment. S11: Degradation source tracing and multi-dimensional result output. Degradation source tracing is carried out based on the preceding calculation data. The parameter with the highest weighted deviation contribution value is defined as the core degradation parameter. Source tracing suggestions are generated in combination with relevant analysis results. Multi-dimensional analysis results are simultaneously output to the intelligent early warning judgment module and the historical trend database.

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