Intelligent diagnosis method and device for operation state of air preheater

Abstract

According to the intelligent diagnosis method and device for the operation state of the air preheater, by constructing a multi-mode state recognition and trend pre-judgment system of the air preheater, three operation states of'normal ', 'slight anomaly' and'serious anomaly 'can be quantitatively distinguished, historical trend visual comparison is supported, and scientific making of a maintenance plan is assisted; a modeling and multi-parameter combined judgment mechanism is adopted, manual experience is effectively replaced, low-probability and high-risk fault symptoms are accurately captured, false alarm and missing alarm are remarkably reduced, and dependence on manual inspection is reduced; each module of the system supports independent modeling and dynamic updating, has good adaptability and expandability, and is suitable for different units, capacities and manufacturer equipment; through early warning of hidden dangers such as blockage, leakage and aging in advance, non-planned furnace shutdown is avoided, meanwhile, the operation and maintenance rhythm is optimized, the operation resistance and energy consumption are reduced, the heat exchange efficiency is improved, and therefore the reliability and economical efficiency of equipment operation are enhanced.

Description

Technical Field

[0001] This invention belongs to the field of power plant boiler auxiliary equipment monitoring technology, specifically relating to an intelligent diagnostic method and device for the operating status of an air preheater. Background Technology

[0002] As a core waste heat recovery device in coal-fired power plant boiler systems, the air preheater's operating status directly affects the unit's thermal efficiency (3-5%), combustion stability (NOx emission control), and equipment reliability (annual unplanned downtime losses of approximately 2 million RMB per unit). In related technologies, a monitoring system for the air preheater's operating status has been constructed through the collaborative operation of differential pressure monitoring, leakage rate calculation, and sector plate gap analysis. Specifically, this system covers the entire process from data acquisition and parameter modeling to fault diagnosis, including key aspects such as DCS system integration, OIF platform data processing, and expert knowledge base construction. With the development of smart power plant technology, existing monitoring systems have evolved from traditional manual inspections to digital systems based on single parameter threshold judgments; however, their technical systems still suffer from inherent defects such as a lack of multi-dimensional correlation analysis.

[0003] Existing air preheater monitoring methods rely on isolated parameters such as differential pressure over-limit alarms and oxygen changes for judgment, without establishing a multivariate coupled dynamic prediction model. Consequently, traditional systems only trigger alarms when heat exchange efficiency drops below 15% in blockage prediction, and in leakage monitoring, they wait for abnormal oxygen levels (leaking to 20%) before responding, by which time a 0.8-1.2% increase in coal consumption has already occurred. Furthermore, changes in the gap between the fan-shaped plates require a 7-15 day manual measurement cycle, making it impossible to capture structural aging trends in real time. Current technologies do not consider the impact of load changes on parameter distribution, resulting in a false alarm rate as high as 35%. This single-parameter alarm mechanism and the limitations of static threshold settings lead to significant delays in equipment maintenance, resulting in an average annual downtime rate of 12%, causing serious economic losses and safety hazards. Summary of the Invention

[0004] The present invention aims to at least partially solve one of the technical problems in the related art.

[0005] Therefore, the first objective of this invention is to propose an intelligent diagnostic method for the operating status of an air preheater.

[0006] The second objective of this invention is to provide an intelligent diagnostic device for the operating status of an air preheater.

[0007] To achieve the above objectives, a first aspect of the present invention provides an intelligent diagnostic method for the operating status of an air preheater, comprising: S1, collect multi-source real-time parameter data during the operation of the air preheater, including inlet and outlet differential pressure, air leakage rate, sector plate gap value, load signal, air volume and oxygen content; S2, a multivariate statistical model is constructed based on historical stable operating condition data. The model establishes a standard predictive relationship of operating parameters through multivariate regression analysis and multivariate state estimation methods. S3, calculate the residual between the real-time parameters and the model prediction values ​​and make anomaly judgments. When the residual exceeds the dynamic statistical threshold range, an anomaly alarm is triggered. The dynamic threshold is adaptively adjusted according to the statistical distribution characteristics of the operating conditions. S4 combines fluctuation value analysis, benchmark value comparison and trend prediction function to conduct multi-dimensional correlation analysis on abnormal parameters to identify fault development trends; S5 calls the expert rule base to match the fault modes corresponding to abnormal parameter combinations and generates intelligent diagnostic results that include fault type classification and handling suggestions.

[0008] In one embodiment of the present invention, S2 includes: S21 uses multiple regression analysis to establish a linear relationship model between operating parameters, covering the entire operating range from 30% load to 100% load; S22, a multivariate distribution model of parameters is constructed through a multivariate state estimation method, forming a 7×7 parameter correlation matrix to characterize the multivariate coupling relationship.

[0009] In one embodiment of the present invention, S3 includes: S31, Calculate the residual between the actual value and the model prediction value. The residual distribution approximately follows a normal distribution, and the outlier threshold is the mean value. Make a judgment; S32, dynamically adjusts the threshold range based on the current operating conditions; when the residual exceeds... An abnormal alarm is triggered at any time.

[0010] In one embodiment of the present invention, S4 further includes: S41, calculate the standard deviation of the parameter within a set time window using the fluctuation value function, and determine that there is abnormal fluctuation when the standard deviation exceeds the preset fluctuation threshold; S42 uses a time-weighted approach to construct a prediction function, which predicts parameter values ​​for a future period based on current values ​​and trends, enabling early detection of potential problems.

[0011] In one embodiment of the present invention, S5 includes: S51 transforms historical failure cases into an expert rule base, and automatically identifies failure modes through a matching algorithm between abnormal parameter combinations and the rule base. S52, the generated intelligent diagnostic results include fault type classification and handling suggestions, including specific measures such as increasing the smoke temperature and intensifying soot blowing, checking the sealing system, or adjusting the sealing device.

[0012] To achieve the above objectives, a second aspect of the present invention provides an intelligent diagnostic device for the operating status of an air preheater, comprising: The multi-source parameter acquisition module is used to collect multi-source real-time parameter data during the operation of the air preheater. The parameters include inlet and outlet differential pressure, air leakage rate, sector plate gap value, load signal, air volume and oxygen content. The multivariate statistical model construction module is used to construct a multivariate statistical model based on historical stable operating condition data. The model establishes a standard predictive relationship of operating parameters through multivariate regression analysis and multivariate state estimation methods. The residual calculation and anomaly detection module is used to calculate the residual between real-time parameters and model prediction values ​​and to detect anomalies. When the residual exceeds the dynamic statistical threshold range, an anomaly alarm is triggered. The dynamic threshold is adaptively adjusted according to the statistical distribution characteristics of the operating conditions. The multi-dimensional correlation analysis module is used to combine fluctuation value analysis, benchmark value comparison and trend prediction function to perform multi-dimensional correlation analysis on abnormal parameters in order to identify the fault development trend. The expert rule base invocation module is used to invoke the expert rule base to match the fault modes corresponding to abnormal parameter combinations and generate intelligent diagnostic results that include fault type classification and handling suggestions.

[0013] This invention discloses an intelligent diagnostic method and device for the operating status of an air preheater. By constructing a multi-mode status identification and trend prediction system for air preheaters, it can quantitatively distinguish between three operating states: "normal," "minor abnormality," and "serious abnormality," and supports historical trend visualization comparison to assist in the scientific formulation of maintenance plans. Employing a model-based, multi-parameter joint judgment mechanism, it effectively replaces manual experience, accurately captures low-probability, high-risk fault symptoms, significantly reduces false alarms and missed alarms, and decreases reliance on manual inspections. Each module of the system supports independent modeling and dynamic updates, possessing good adaptability and scalability, and is applicable to equipment from different units, capacities, and manufacturers. By providing early warnings of potential hazards such as blockages, leaks, and aging, it avoids unplanned shutdowns, optimizes operation and maintenance rhythm, reduces operational resistance and energy consumption, and improves heat exchange efficiency, thereby enhancing the reliability and economy of equipment operation.

[0014] Additional aspects and advantages of the invention will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention. Attached Figure Description

[0015] The above and / or additional aspects and advantages of the present invention will become apparent and readily understood from the following description of the embodiments taken in conjunction with the accompanying drawings, wherein: Figure 1 This is a flowchart of an intelligent diagnostic method for the operating status of an air preheater according to an embodiment of the present invention; Figure 2 This is an analysis diagram of the operating status of an air preheater according to an embodiment of the present invention; Figure 3 This is a structural diagram of an intelligent diagnostic device for the operating status of an air preheater according to an embodiment of the present invention. Detailed Implementation

[0016] It should be noted that, unless otherwise specified, the embodiments and features described in the present invention can be combined with each other. The present invention will now be described in detail with reference to the accompanying drawings and embodiments.

[0017] To enable those skilled in the art to better understand the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of the present invention.

[0018] The following description, with reference to the accompanying drawings, describes an intelligent diagnostic method and apparatus for the operating status of an air preheater according to an embodiment of the present invention.

[0019] Example 1 Figure 1 This is a flowchart of an intelligent diagnostic method for the operating status of an air preheater according to an embodiment of the present invention, such as... Figure 1 As shown, it includes: S1 collects multi-source real-time parameter data during the operation of the air preheater. The parameters include inlet and outlet differential pressure, air leakage rate, sector plate gap value, load signal, air volume and oxygen content.

[0020] Specifically, in some implementations, this step involves real-time acquisition of six core operating parameters, including inlet and outlet differential pressure, air leakage rate, sector plate gap value, load signal, air volume, and oxygen content, through sensors deployed at key locations in the air preheater. The inlet and outlet differential pressure is measured by a differential pressure transmitter, with a sampling frequency typically set to 1 Hz and a data accuracy of ±0.5%FS. The air leakage rate is estimated using a formula that calculates the difference in oxygen content between the air and flue gas sides, combined with air volume data. The sector plate gap value is monitored in real-time by a displacement sensor, with a sampling period of 0.5 seconds and an accuracy of ±0.1 mm. The load signal, air volume, and oxygen content are provided by the DCS system, with a sampling frequency of 1 Hz and a data format conforming to the IEC 61131-3 standard.

[0021] S2. A multivariate statistical model is constructed based on historical stable operating condition data. The model establishes a standard predictive relationship of operating parameters through multivariate regression analysis and multivariate state estimation methods.

[0022] In some implementations, the system first collects historical operating data of the air preheater under different load conditions from the DCS system, including but not limited to key parameters such as inlet and outlet differential pressure, air leakage rate, sector plate gap value, air volume, and oxygen content. During the data preprocessing stage, the system removes outliers and missing values, and uses a sliding window method to standardize the data, ensuring the stability and representativeness of the modeling data. Subsequently, the system uses multiple regression analysis to construct a linear relationship model between the parameters, which is used to predict the theoretical values ​​of the parameters under specific operating conditions.

[0023] Furthermore, the system introduces the MSET model, which captures the nonlinear correlation and dynamic change characteristics between parameters by establishing a multivariate joint distribution model. Based on the covariance matrix of historical data, the MSET model calculates the confidence interval of each parameter under the current operating conditions, thereby achieving a multidimensional assessment of the operating status.

[0024] Specifically, the system sets the residual threshold to be [value]. This means that when the deviation between the actual value and the model's predicted value exceeds three times the standard deviation, it is considered an anomaly. This threshold setting conforms to the Industrial Statistical Process Control (SPC) standard and has a high confidence level (approximately 99.7%).

[0025] Furthermore, S2 includes: S21 uses multiple regression analysis to establish a linear relationship model between operating parameters, covering the full operating range from 30% load to 100% load.

[0026] In some implementations, the multiple regression analysis method is based on historical operating data, selecting representative operating parameters (such as inlet and outlet differential pressure, air leakage rate, sector plate gap value, load signal, air volume, oxygen content, etc.) as independent and dependent variables to construct a linear regression model. The model construction needs to cover typical operating conditions of the air preheater within the load range of 30% to 100% to ensure that the model has broad applicability and adaptability to operating conditions.

[0027] Specifically, the system collects historical data, filters out variables with significant correlations, and uses the least squares (OLS) method to estimate regression coefficients. The model's goodness of fit should be no less than 0.85 to ensure its explanatory power for actual operating conditions. Meanwhile, residual analysis employs statistical thresholds. Anomaly detection is performed, including The mean of the residuals, The standard deviation of the residuals is widely used in industrial process monitoring to identify outliers that deviate from the normal distribution.

[0028] S22. A multivariate distribution model of the parameters is constructed using the multivariate state estimation (MSET) method, forming a 7×7 parameter correlation matrix to characterize the multivariate coupling relationship.

[0029] In some implementations, the MSET method, based on historical operating data, extracts linear and nonlinear correlations between parameters using multivariate statistical modeling techniques such as Principal Component Analysis (PCA) or Partial Least Squares (PLS). Specifically, the system collects historical datasets of seven key operating parameters of the air preheater under different load conditions: inlet and outlet differential pressure, air leakage rate, sector plate gap value, air volume, and oxygen content, constructing a multidimensional feature space containing these variables. By calculating the covariance matrix of this space, the correlation coefficients between variables are further extracted, forming a 7×7 parameter correlation matrix. Each element R ij Indicates the first The parameter and the first The correlation strength between the parameters is represented by a matrix ranging from -1 to 1. This matrix can intuitively reflect the interdependence of the parameters under normal operating conditions, providing a statistical basis for subsequent anomaly detection.

[0030] Specifically, the system sets a correlation coefficient threshold. When the correlation between a parameter and other parameters deviates significantly from a threshold, it indicates that the system's operating status may have malfunctioned. Simultaneously, the system employs residual analysis to calculate the deviation between the actual value and the model's predicted value. And set the abnormal judgment criteria as ,in For the first The mean of each parameter, Its standard deviation is given. This standard conforms to the 3σ principle commonly used in industrial process monitoring and has high statistical significance.

[0031] S3, calculate the residual between the real-time parameters and the model prediction values ​​and make anomaly judgments. When the residual exceeds the dynamic statistical threshold range, an anomaly alarm is triggered. The dynamic threshold is adaptively adjusted according to the statistical distribution characteristics of the operating conditions.

[0032] In some implementations, the system first establishes a linear relationship model between parameters based on historical stable operating data using multiple regression analysis. Then, it constructs a multivariate joint distribution model using the MSET method to reflect the normal variation range of parameters under different loads, temperatures, air volumes, and other operating conditions. During real-time operation, the system collects parameter values ​​under current operating conditions, calculates their predicted values ​​using the model, and then obtains the residuals. ,in For real-time measurements, These are the model predictions. The residual sequence is used to measure the deviation between the actual operating conditions and the standard model.

[0033] Specifically, the system employs a dynamic statistical threshold mechanism, that is, setting an anomaly detection threshold as follows: ,in Let be the mean of the residuals. The standard deviation is used. This threshold is adaptively adjusted based on the statistical distribution characteristics of the residuals, effectively addressing parameter fluctuations caused by changes in operating conditions and avoiding false alarms or missed alarms caused by static thresholds during load changes. In practical applications, this step involves refactoring the Analogstore and Analogretrieve functions in C language to achieve efficient storage, callback, and analysis processing of real-time data.

[0034] S31, Calculate the residual between the actual value and the model prediction value. The residual distribution approximately follows a normal distribution, and the outlier threshold is the mean value. Make a judgment.

[0035] In some implementations, the system first constructs a standard model using historical stable operating data. This model can predict the theoretical values ​​of key parameters such as the differential pressure between the inlet and outlet of the air preheater, the air leakage rate, and the gap between the fan plates under current load, air volume, and oxygen content. Subsequently, during real-time operation, the system collects the actual parameter values ​​at the current moment and compares them point-by-point with the model's predicted values ​​to calculate the residuals. ,in Indicates the first The measured value at each moment, This represents the model's predicted values. The residual sequence statistically approximates a normal distribution. ,in The mean of the residuals, The standard deviation is denoted as .

[0036] Optionally, the system employs a sliding window mechanism to perform dynamic statistical analysis of the residuals, and the window size can be set according to the equipment operating cycle. Minutes. Furthermore, the anomaly threshold is set to... This is the 3σ criterion, commonly used in statistics to identify low-probability anomalies. The residual at a certain moment... When the value exceeds the threshold range, the system determines that the parameter has abnormal fluctuations and triggers an alarm mechanism.

[0037] S32, dynamically adjusts the threshold range based on the current operating conditions; when the residual exceeds... An abnormal alarm is triggered at any time.

[0038] In some implementations, the system first constructs a multivariate state estimation model using historical stable operating data. This model reflects the normal distribution characteristics of parameters such as the inlet and outlet differential pressure of the air preheater, air leakage rate, and sector plate gap under specific load, air volume, and oxygen content conditions. During real-time operation, the system continuously collects parameter values ​​under the current operating conditions and compares them with the model's predicted values ​​to calculate the residuals. ,in These are actual measured values. These are the model's predicted values. Subsequently, the system performs statistical analysis on the residual sequence and calculates its mean. with standard deviation And set the dynamic threshold range as This threshold range is based on the 3σ principle of normal distribution, meaning that under normal operating conditions, 99.73% of the residuals should fall within this range; anything exceeding this range is considered abnormal.

[0039] Specifically, the system dynamically updates based on actual operational data. and The system adjusts the values ​​to accommodate the impact of external factors such as load changes and seasonal variations on equipment operating status. For example, under high load conditions, the normal fluctuation range of the inlet and outlet differential pressure may increase, thus requiring recalculation of statistical parameters based on the current operating conditions to ensure the accuracy of threshold judgment. Furthermore, the system supports setting independent statistical window lengths (such as 30 minutes, 1 hour, etc.) for different parameters to balance response speed and false alarm rate.

[0040] S4 combines fluctuation value analysis, benchmark value comparison and trend prediction function to perform multi-dimensional correlation analysis on abnormal parameters to identify fault development trends.

[0041] At the technical implementation level, the system first quantifies the short-term fluctuations of parameters using a fluctuation value function. This function, calculated based on the standard deviation within a sliding time window, identifies whether the parameter exhibits abnormal fluctuations over a short period. For example, for the inlet / outlet differential pressure... The system sets the time window length to be [length]. Calculate its standard deviation within minutes. ,like Exceeding the set threshold If this occurs, an abnormal fluctuation signal is triggered. Secondly, the baseline function is used to identify the average parameter values ​​of the equipment under stable operating conditions, serving as a comparison benchmark. The system identifies load stabilization phases (such as...) ), calculate the average value for this stage. And set a deviation threshold. When the real-time value deviates If the threshold is exceeded, it is considered abnormal.

[0042] Furthermore, the predicted value function employs a time-weighted averaging method to predict future values ​​based on current values ​​and their rate of change. The parameter trend over a period of minutes. The prediction model can be represented as:

[0043] in These are weighting coefficients. The time step is in minutes. If the residual between the predicted and actual values ​​exceeds... If the range is considered to be within a certain range, then a trend anomaly is considered to exist.

[0044] In practical applications, this step is deployed in a DCS system based on the OIF platform. It achieves efficient data storage and callback by refactoring the `Analogstore` and `Analogretrieve` functions. Its purpose is to transform isolated parameter anomalies into fault identifications with causal relationships, improving the accuracy and foresight of diagnosis. Through multi-dimensional correlation analysis, the system can effectively identify typical faults such as blockages, air leaks, and abnormal sector plate gaps, providing timely and scientific decision support for maintenance personnel, thereby improving the safety and economy of equipment operation.

[0045] Furthermore, S4 includes: S41 calculates the standard deviation of the parameter within a set time window using the fluctuation value function. When the standard deviation exceeds the preset fluctuation threshold, it is determined that there is abnormal fluctuation.

[0046] In some implementations, the fluctuation value function first defines a sliding time window. , usually set to Minutes, based on the sampling frequency of actual operating data (e.g.) The system dynamically adjusts the parameters (per second / time). Within this time window, the system collects a continuous sequence of operating parameters. ,in This indicates the number of data points within the window. Then, the mean of the sequence is calculated. with standard deviation The system further sets a preset fluctuation threshold, typically a percentage of the standard deviation under historical stable operating conditions. Times. When the standard deviation is calculated in real time. When the threshold is exceeded, the system determines that the parameter has abnormal fluctuations and triggers further abnormal alarms and fault diagnosis processes.

[0047] S42 uses a time-weighted approach to construct a prediction function, which predicts parameter values ​​for a future period based on current values ​​and trends, enabling early detection of potential problems.

[0048] In some implementations, the prediction function is calculated as a weighted average of historical data within a sliding time window. The weighting coefficients decay exponentially with distance from the current time, and can be defined using an exponential decay function. This prediction model incorporates the output of a multivariate state estimation (MSET) model to generate future predictions. The parameter prediction sequence is calculated within each sampling period. The prediction results are compared with the real-time acquired values, and the residuals are calculated. If the residuals exceed... If the statistical threshold is reached, an anomaly warning mechanism will be triggered.

[0049] S5 calls the expert rule base to match the fault modes corresponding to abnormal parameter combinations and generates intelligent diagnostic results that include fault type classification and handling suggestions.

[0050] In some implementations, the system first calculates the residual between real-time parameters and the predicted values ​​of the standard model through a state determination module. When the residual exceeds a set statistical threshold... When this occurs, the system determines that the parameter is abnormal. Subsequently, the parameter function processing module performs combined analysis on multiple abnormal parameters to identify their correlation and trend over time. For example, if the inlet and outlet differential pressure increases significantly, the leakage rate rises continuously, and the outlet oxygen content increases abnormally, the system will trigger the matching process for the "blockage + leakage" composite fault mode.

[0051] Specifically, the expert rule base pre-sets various typical fault judgment conditions, such as blockage requiring differential pressure to rise above a set threshold (e.g., ...). () For abnormal air leakage, the air leakage rate must be met. ,in Historical average Plus The upper limit value. If the gap between the sector plates is abnormal, the deviation between the current gap value and the reference value is compared. If the dust removal effect does not improve, then the gap abnormality diagnosis is triggered.

[0052] Furthermore, S5 includes: S51 transforms historical failure cases into an expert rule base, and automatically identifies failure modes through a matching algorithm between abnormal parameter combinations and the rule base.

[0053] In some implementations, this step first cleans and extracts features from historical operating data, filtering out parameters closely related to the air preheater's operating status, such as inlet and outlet differential pressure, air leakage rate, sector plate gap, soot blowing frequency, and outlet oxygen content. Through analysis of over 200 fault cases, abnormal parameter combination patterns corresponding to each type of fault (such as blockage, air leakage, and abnormal sector plate gap) are extracted and transformed into logical rules. For example, when the inlet and outlet differential pressure rises above a set threshold, and the soot blowing frequency increases significantly but the differential pressure does not decrease significantly, the system will match the "blockage" rule and output the corresponding diagnostic conclusion.

[0054] Optionally, the rule base can be constructed using decision trees, fuzzy logic, or a rule-based reasoning engine. Each rule contains multiple conditional clauses and supports combinations of logical operators such as AND, OR, and NOT. For example, a diagnostic rule for abnormal air leakage can be expressed as: the air leakage rate shows a continuous upward trend and exceeds the historical average. Simultaneously, the oxygen content at the outlet increases, and the airflow in the positive pressure area increases abnormally. The system automatically identifies faults by calculating residuals and statistical indicators in real time to determine whether the rule conditions are met.

[0055] Furthermore, the matching algorithm used in this step needs to be efficient and accurate, typically based on rule priority ranking and weighted matching strategies. Each rule in the rule base can be assigned a weight coefficient. This is used to reflect its importance in fault diagnosis.

[0056] S52, the generated intelligent diagnostic results include fault type classification and handling suggestions, including specific measures such as increasing the smoke temperature and intensifying soot blowing, checking the sealing system or adjusting the sealing device.

[0057] In some implementations, the system detects abnormal parameters (such as increased inlet / outlet differential pressure, excessive air leakage rate, etc.). After a threshold error (e.g., abnormal gap between sector plates), the system will invoke a pre-defined fault diagnosis module. This module contains a rule base built based on historical fault data and expert experience. Each rule in the rule base corresponds to a specific fault mode. For example, when the differential pressure continues to rise and the soot blowing frequency is increased but the effect is not obvious, the system will match the fault type as "blockage" and generate corresponding handling suggestions. The generation of handling suggestions depends on the pre-defined response strategy. For example, "increase flue gas temperature and intensify soot blowing" means increasing the soot blowing frequency and increasing the purging steam temperature on the basis of the original soot blowing cycle to enhance the soot cleaning effect; "check the sealing system" prompts maintenance personnel to check the axial and radial sealing devices of the air preheater to confirm whether there is a problem of air leakage caused by wear or aging; "adjust the sealing device" suggests optimizing the sealing performance by adjusting the gap between the sector plate and the sealing plate.

[0058] Specifically, the system calculates the residuals using a statistical model; if the residuals exceed... If the range is exceeded, the fault diagnosis process is triggered. Simultaneously, the system supports dynamic updates to the baseline value and prediction model to adapt to operating characteristics under different load conditions. For example, in the baseline value function, the system sets a threshold based on the average value under stable operating conditions; deviations exceeding the set value (e.g., ±10%) are considered abnormal.

[0059] An intelligent diagnostic method for the operating status of an air preheater according to an embodiment of the present invention can achieve early identification and trend warning of blockage, air leakage and structural abnormalities in the air preheater, improve the accuracy of fault diagnosis and system adaptability, reduce the risk of unplanned shutdown, and improve the safety, economy and environmental protection of boiler operation.

[0060] Example 2 The following describes in detail an intelligent diagnostic method for the operating status of an air preheater according to an embodiment of the present invention, with reference to the accompanying drawings.

[0061] The system structure and module division of this invention are as follows: Based on the structural characteristics of the air preheater, this system is divided into the following three sub-modules according to the equipment function: Air preheater body module: mainly monitors the inlet and outlet differential pressure and air leakage rate; Fan plate module: monitors the gap between the fan plates, the movement law and the sealing effect; Soot blower module: monitors the soot blowing frequency, action signal and soot cleaning efficiency.

[0062] Each module includes two core functions: operation status judgment and fault diagnosis and analysis.

[0063] Specifically, the multivariate model establishment and parameter anomaly detection are as follows: The system collects historical operating data of the air preheater under different loads, such as... Figure 2As shown, the following methods are used to establish the standard model: Multiple regression model: used to construct the linear relationship between operating parameters; Multiple state estimation (MSET) model: to establish a multivariate distribution model of the normal values ​​of each parameter under typical load conditions.

[0064] The parameters collected in real time are compared with the predicted values, and the residual (actual value - model predicted value) is calculated. If the residual exceeds μ ± 3σ (mean ± 3 times the standard deviation), the data point is considered abnormal, and the abnormal alarm mechanism is triggered.

[0065] Parametric function design and implementation: To further improve diagnostic accuracy, the system designs the following calculation functions and implements them in C language. The Analogstore and Analogretrieve functions are refactored using the OIF platform to enhance data processing capabilities: Fluctuation value function: used to identify the degree of fluctuation of parameters in the short term, and to determine whether there is abnormal fluctuation by analyzing the standard deviation over a period of time; Baseline value function: suitable for stable parameters, the system automatically identifies the stable operating state of the equipment, calculates the average value as the baseline value, and considers the deviation as abnormal if it exceeds the set threshold; Predicted value function: predicts the value for a future period of time based on the current value and the trend of change through time weighting, so as to realize the early detection of potential problems.

[0066] Fault diagnosis mechanism and expert rule base: The system integrates expert knowledge and historical failure cases to build a rule base for the diagnosis and classification of typical failures. Air preheater blockage diagnosis: Conditions: The differential pressure between the inlet and outlet exceeds the threshold; the soot blowing frequency increases significantly but the cleaning effect is not obvious; the differential pressure under historical loads deviates significantly from the model prediction. Judgment result: "Air preheater may be clogged," and suggested actions such as increasing flue gas temperature and intensifying soot blowing, or arranging a shutdown for inspection, are provided.

[0067] Diagnosis of air leakage abnormalities: Conditions: The air leakage rate is showing a continuous upward trend, exceeding the historical average +3σ; the outlet oxygen content is increasing; the air volume in the positive pressure area is abnormally increasing. Judgment Result: "Abnormal air preheater leakage rate" is indicated; it is recommended to check the sealing system and the position of the sector plates.

[0068] Sector-shaped plate seal failure or abnormal gap: Conditions: Under the same load, the gap value of the sector plates increases relatively and exceeds the set threshold; the soot blowing cycle is shortened but the differential pressure does not improve significantly; the change in outlet oxygen content deviates more from the historical load. Judgment result: "The sector plates may have structural aging or excessive gaps," and it is recommended to check, replace, or adjust the sealing device.

[0069] Example 3 To achieve the above embodiments, such as Figure 3 As shown, this embodiment also provides an intelligent diagnostic device 10 for the operating status of an air preheater. The device 10 includes a multi-source parameter acquisition module 100, a multivariate statistical model construction module 200, a residual calculation and anomaly judgment module 300, a multi-dimensional correlation analysis module 400, and an expert rule base calling module 500.

[0070] The multi-source parameter acquisition module 100 is used to acquire multi-source real-time parameter data during the operation of the air preheater. The parameters include inlet and outlet differential pressure, air leakage rate, sector plate gap value, load signal, air volume and oxygen content. The multivariate statistical model construction module 200 is used to construct a multivariate statistical model based on historical stable operating condition data. The model establishes a standard predictive relationship of operating parameters through multivariate regression analysis and multivariate state estimation methods. The residual calculation and anomaly detection module 300 is used to calculate the residual between real-time parameters and model prediction values ​​and to detect anomalies. When the residual exceeds the dynamic statistical threshold range, an anomaly alarm is triggered. The dynamic threshold is adaptively adjusted according to the statistical distribution characteristics of the operating conditions. The multi-dimensional correlation analysis module 400 is used to combine fluctuation value analysis, benchmark value comparison and trend prediction function to perform multi-dimensional correlation analysis on abnormal parameters in order to identify the fault development trend. The expert rule base invocation module 500 is used to call the expert rule base to match the fault modes corresponding to abnormal parameter combinations and generate intelligent diagnostic results that include fault type classification and handling suggestions.

[0071] Furthermore, the aforementioned multivariate statistical model construction module 200 is also used for: A linear relationship model between operating parameters was established using multiple regression analysis, covering the entire operating condition range from 30% load to 100% load; A multivariate distribution model of the parameters is constructed using a multivariate state estimation method, forming a 7×7 parameter correlation matrix to characterize the multivariate coupling relationship.

[0072] Furthermore, the aforementioned residual calculation and anomaly detection module 300 is also used for: Calculate the residuals between the actual values ​​and the model predictions. The residual distribution approximately follows a normal distribution. The outlier threshold is the mean. Make a judgment; The threshold range is dynamically adjusted based on the current operating conditions. When the residual exceeds... An abnormal alarm is triggered at any time.

[0073] Furthermore, the aforementioned multi-dimensional correlation analysis module 400 is also used for: The standard deviation of the parameter within a set time window is calculated using the fluctuation value function. When the standard deviation exceeds the preset fluctuation threshold, it is determined that there is abnormal fluctuation. A time-weighted prediction function is constructed to predict parameter values ​​for a future period based on current values ​​and trends, enabling early detection of potential problems.

[0074] Furthermore, the aforementioned expert rule base invocation module 500 is also used for: Historical failure cases are transformed into an expert rule base, and the failure mode is automatically identified by matching anomaly combinations of parameters with the rule base algorithm. The generated intelligent diagnostic results include fault type classification and handling suggestions, which include specific measures such as increasing the smoke temperature and intensifying soot blowing, checking the sealing system, or adjusting the sealing device.

[0075] An intelligent diagnostic device for the operating status of an air preheater according to an embodiment of the present invention can realize early identification and trend warning of blockage, air leakage and structural abnormalities in the air preheater, improve the accuracy of fault diagnosis and system adaptability, reduce the risk of unplanned shutdown, and improve the safety, economy and environmental protection of boiler operation.

[0076] In the description of this specification, the references to terms such as "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., refer to specific features, structures, materials, or characteristics described in connection with that embodiment or example, which are included in at least one embodiment or example of the present invention. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples. Moreover, without contradiction, those skilled in the art can combine and integrate the different embodiments or examples described in this specification, as well as the features of different embodiments or examples.

[0077] Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one of that feature. In the description of this invention, "a plurality of" means at least two, such as two, three, etc., unless otherwise explicitly specified.

Claims

1. A method for intelligent diagnosis of the operating status of an air preheater, characterized in that, include: S1, collect multi-source real-time parameter data during the operation of the air preheater, including inlet and outlet differential pressure, air leakage rate, sector plate gap value, load signal, air volume and oxygen content; S2, a multivariate statistical model is constructed based on historical stable operating condition data. The model establishes a standard predictive relationship of operating parameters through multivariate regression analysis and multivariate state estimation methods. S3, calculate the residual between the real-time parameters and the model prediction values ​​and make anomaly judgments. When the residual exceeds the dynamic statistical threshold range, an anomaly alarm is triggered. The dynamic threshold is adaptively adjusted according to the statistical distribution characteristics of the operating conditions. S4 combines fluctuation value analysis, benchmark value comparison and trend prediction function to conduct multi-dimensional correlation analysis on abnormal parameters to identify fault development trends; S5 calls the expert rule base to match the fault modes corresponding to abnormal parameter combinations and generates intelligent diagnostic results that include fault type classification and handling suggestions.

2. The method as described in claim 1, characterized in that, The S2 includes: S21 uses multiple regression analysis to establish a linear relationship model between operating parameters, covering the entire operating range from 30% load to 100% load; S22, a multivariate distribution model of parameters is constructed through a multivariate state estimation method, forming a 7×7 parameter correlation matrix to characterize the multivariate coupling relationship.

3. The method as described in claim 1, characterized in that, The S3 includes: S31, Calculate the residual between the actual value and the model prediction value. The residual distribution approximately follows a normal distribution, and the outlier threshold is the mean value. Make a judgment; S32, dynamically adjusts the threshold range based on the current operating conditions; when the residual exceeds... An abnormal alarm is triggered at any time.

4. The method as described in claim 1, characterized in that, The S4 further includes: S41, calculate the standard deviation of the parameter within a set time window using the fluctuation value function, and determine that there is abnormal fluctuation when the standard deviation exceeds the preset fluctuation threshold; S42 uses a time-weighted approach to construct a prediction function, which predicts parameter values ​​for a future period based on current values ​​and trends, enabling early detection of potential problems.

5. The method as described in claim 1, characterized in that, The S5 includes: S51 transforms historical failure cases into an expert rule base, and automatically identifies failure modes through a matching algorithm between abnormal parameter combinations and the rule base. S52, the generated intelligent diagnostic results include fault type classification and handling suggestions, including specific measures such as increasing the smoke temperature and intensifying soot blowing, checking the sealing system, or adjusting the sealing device.

6. An intelligent diagnostic device for the operating status of an air preheater, characterized in that, include: The multi-source parameter acquisition module is used to collect multi-source real-time parameter data during the operation of the air preheater. The parameters include inlet and outlet differential pressure, air leakage rate, sector plate gap value, load signal, air volume and oxygen content. The multivariate statistical model construction module is used to construct a multivariate statistical model based on historical stable operating condition data. The model establishes a standard predictive relationship of operating parameters through multivariate regression analysis and multivariate state estimation methods. The residual calculation and anomaly detection module is used to calculate the residual between real-time parameters and model prediction values ​​and to detect anomalies. When the residual exceeds the dynamic statistical threshold range, an anomaly alarm is triggered. The dynamic threshold is adaptively adjusted according to the statistical distribution characteristics of the operating conditions. The multi-dimensional correlation analysis module is used to combine fluctuation value analysis, benchmark value comparison and trend prediction function to perform multi-dimensional correlation analysis on abnormal parameters in order to identify the fault development trend. The expert rule base invocation module is used to call the expert rule base to match the fault modes corresponding to abnormal parameter combinations and generate intelligent diagnostic results that include fault type classification and handling suggestions.

7. The apparatus as claimed in claim 6, characterized in that, The multivariate statistical model building module is also used for: A linear relationship model between operating parameters was established using multiple regression analysis, covering the entire operating condition range from 30% load to 100% load; A multivariate distribution model of the parameters is constructed using a multivariate state estimation method, forming a 7×7 parameter correlation matrix to characterize the multivariate coupling relationship.

8. The apparatus as claimed in claim 6, characterized in that, The residual calculation and anomaly detection module is also used for: Calculate the residuals between the actual values ​​and the model predictions. The residual distribution approximately follows a normal distribution. The outlier threshold is the mean. Make a judgment; The threshold range is dynamically adjusted based on the current operating conditions. When the residual exceeds... An abnormal alarm is triggered at any time.

9. The apparatus as claimed in claim 6, characterized in that, The multi-dimensional correlation analysis module is also used for: The standard deviation of the parameter within a set time window is calculated using the fluctuation value function. When the standard deviation exceeds the preset fluctuation threshold, it is determined that there is abnormal fluctuation. A time-weighted prediction function is constructed to predict parameter values ​​for a future period based on current values ​​and trends, enabling early detection of potential problems.

10. The apparatus as claimed in claim 6, characterized in that, The expert rule base invocation module is also used for: Historical failure cases are transformed into an expert rule base, and the failure mode is automatically identified by matching anomaly combinations of parameters with the rule base algorithm. The generated intelligent diagnostic results include fault type classification and handling suggestions, which include specific measures such as increasing the smoke temperature and intensifying soot blowing, checking the sealing system, or adjusting the sealing device.

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