Intelligent diagnosis method based on cold end real-time data
By obtaining key data of the cold-end system in real time and using VAR models to predict trends, the existing cold-end system monitoring lag and inaccurate fault identification are solved, efficient and reliable intelligent diagnosis is achieved, and the system operation efficiency and reliability are improved.
Patent Information
- Application Number
- CN202411912794.9
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2024-12-24
- Publication Date
- 2025-04-15
AI Technical Summary
The monitoring of existing cold-end systems relies on regular manual inspections or fixed data collection points, making it difficult to capture instantaneous abnormalities and sudden failures, resulting in lag in control and optimization, and it is difficult to accurately identify potential problems, resulting in frequent missed or false positives.
The intelligent diagnostic method based on real-time data of the cold junction is adopted to obtain key data such as circulating water flow and the rotation speed of the enhanced heat exchange device in real time, and the intelligent diagnostic model based on VAR model is used to predict the cold junction working conditions, generate diagnostic results, and optimize the model through verification and machine learning to improve prediction capabilities and diagnostic accuracy.
Real-time monitoring and predictive fault detection of cold-end systems are realized, which improves the accuracy and reliability of diagnosis, reduces operation and maintenance costs, and enhances the operating efficiency and reliability of the system.
Smart Images
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Abstract
Description
Technical Field
[0001] The present invention relates to the field of cold end intelligent diagnosis, and in particular to an intelligent diagnosis method based on cold end real-time data. Background Art
[0002] The cold end system is an important part of the generator set, including the low-pressure cylinder body and its heat recovery steam extraction system, exhaust port and condenser system, circulating water system, sealing system and vacuum system, cooling tower system and other subsystems. The core purpose of this system is to convert the exhaust gas of the steam turbine into condensed water, so that the power generation process can heat the water into steam again to achieve continuous power generation. The operating quality of the cold end system is directly related to the vacuum degree of the generator set, and the vacuum degree or back pressure of the steam turbine of a large generator set has a significant impact on its energy consumption level and economy. Research data shows that when the back pressure exceeds the critical back pressure, the coal consumption of power generation will increase by 2-3g / KWh for every 1KPa increase in back pressure. Therefore, the operating goal of the cold end system is to strive to achieve the best vacuum degree or the lowest back pressure under certain circulating water system power consumption conditions.
[0003] With the continuous development of industrial technology, cold end systems are increasingly used in various types of equipment, and the stability and reliability of their operating status are crucial to the performance of the entire system. Many existing cold ends rely on regular manual inspections or timed data collection, which makes it difficult to capture instantaneous anomalies and sudden failures during the operation of the cold end, resulting in a lag in control and optimization. In some cases, potential problems in the cold end system may not be accurately identified, resulting in frequent omissions or false alarms. Summary of the invention
[0004] The present invention aims to provide an intelligent diagnosis method based on real-time cold end data, and performs intelligent diagnosis by collecting and analyzing instantaneous data.
[0005] To achieve the above object, the present invention adopts the following technical solution: comprising the following steps:
[0006] An intelligent diagnosis method based on cold end real-time data comprises the following steps:
[0007] Acquire cold end real-time data, wherein the cold end real-time data includes real-time flow rate of circulating water and speed data of enhanced heat exchange device;
[0008] Transmitting cold end real-time data, the data transmission end sends the cold end real-time data to the server end; the server end adds an instantaneous timestamp to the acquired cold end real-time data to obtain an instantaneous data set;
[0009] To generate cold-end diagnosis results, the server inputs the instantaneous data set into the intelligent diagnosis model and outputs the cold-end diagnosis results including the instantaneous timestamp.
[0010] Preferably, as an improvement, the intelligent diagnosis model is based on a VAR model, and through the input of an instantaneous data set, the intelligent diagnosis model performs trend prediction on the cold end operating condition to obtain a prediction result.
[0011] Preferably, as an improvement, the server verifies the prediction result, and the verification is to compare the prediction result with the corresponding instantaneous data set.
[0012] Preferably, as an improvement, a mapping relationship is formed between the prediction result and the instantaneous data set of the cold end through verification on the server side, and the instantaneous data set of the cold end is used for machine learning of the intelligent diagnosis model.
[0013] Preferably, as an improvement, the cold end diagnosis result output by the server includes a verification prediction result, which is a verification prediction result obtained by the server through a historical instantaneous data set.
[0014] Preferably, as an improvement, the instantaneous data set includes Y1 at time point t: real-time flow of circulating water; Y2: heat load of condenser; and Y3: efficiency of cooling tower.
[0015] Preferably, as an improvement, the prediction result at time t+1 is a prediction value calculated by using the instantaneous data sets at time t and time t-1.
[0016] Preferably, as an improvement, the instantaneous data set at time t+1 is used to remove abnormal points in the instantaneous data sets at time t and time t-1.
[0017] Preferably, as an improvement, the instantaneous data set at time t+1 is used to correct the abnormal points in the instantaneous data sets at time t and time t-1.
[0018] The principle and advantages of this solution are: by acquiring key data such as the circulating water flow rate of the cold end and the speed of the enhanced heat exchange device in real time, and immediately transmitting them to the server for processing. It overcomes the lag and incompleteness of traditional cold end system monitoring, which often relies on regular manual inspections or fixed data collection points. By using the intelligent diagnosis model based on the VAR model to predict the trend of the cold end working conditions, potential problems can be foreseen, so that preventive measures can be taken before the problems occur. The server verifies the prediction results, and by comparing the prediction results with the actual instantaneous data set, the accuracy of the model can be verified and continuously optimized. By forming a mapping relationship between the instantaneous data set of the cold end and the prediction results, and using these data for machine learning, the intelligent diagnosis model can be continuously optimized. This enables the model to gradually adapt to changes in the cold end system and improve the predictive ability and diagnostic accuracy of future working conditions. The accuracy and reliability of the diagnostic model are ensured, and the model can be continuously adapted to changes in the cold end system through machine learning optimization, which improves the predictive ability and diagnostic accuracy of future working conditions and better serves the operation and maintenance management of the cold end system. Using the instantaneous data set at the subsequent moment to remove or correct the abnormal points in the data at the previous moment can help eliminate noise and errors in the data and improve data quality, which can help improve the operating efficiency of the cold end system, extend the life of the equipment and reduce operation and maintenance costs.
[0019] In summary, this intelligent diagnosis method based on cold-end real-time data has significantly improved the operating efficiency and reliability of the cold-end system through technical effects in terms of real-time, predictive, accuracy improvement, machine learning optimization, abnormal point processing, and comprehensive decision support, providing a strong guarantee for the company's safe production and energy conservation and emission reduction. DETAILED DESCRIPTION
[0020] The following is further described in detail through specific implementation methods:
[0021] The specific implementation steps are as follows:
[0022] An intelligent diagnosis method based on cold end real-time data comprises the following steps:
[0023] Acquire cold end real-time data, wherein the cold end real-time data includes real-time flow rate of circulating water and speed data of enhanced heat exchange device;
[0024] Transmitting cold end real-time data, the data transmission end sends the cold end real-time data to the server end; the server end adds an instantaneous timestamp to the acquired cold end real-time data to obtain an instantaneous data set;
[0025] To generate cold-end diagnosis results, the server inputs the instantaneous data set into the intelligent diagnosis model and outputs the cold-end diagnosis results including the instantaneous timestamp.
[0026] Wherein, the intelligent diagnosis model is based on the VAR model. Through the input of the instantaneous data set, the intelligent diagnosis model performs trend prediction on the cold end working condition to obtain the prediction result.
[0027] The server verifies the prediction result, and the verification is to compare the prediction result with the corresponding instantaneous data set.
[0028] Through verification on the server side, the prediction results are mapped to the instantaneous data set on the cold side, and the instantaneous data set on the cold side is used for machine learning of the intelligent diagnosis model.
[0029] The cold end diagnosis result output by the server includes a verification prediction result, which is a prediction result obtained by the server through a historical instantaneous data set.
[0030] For example, to obtain the cold end real-time data, the following cold end real-time data is obtained at a certain moment:
[0031] Circulating water real-time flow rate: 1500m 3 / h;
[0032] Speed data of enhanced heat exchange device: 3000rpm.
[0033] The data transmission end sends the above cold end real-time data to the server. After receiving the data, the server adds an instantaneous timestamp to each piece of data (assuming it is 2024-12-12T16:20:00Z) to obtain the instantaneous data set:
[0034]
[0035] Generate cold end diagnosis results: The server inputs the instantaneous data set into the intelligent diagnosis model based on the VAR model. The model predicts the trend of the cold end working condition. Assume that the prediction result is that the real-time flow rate of circulating water will rise to 1550m in the next 10 minutes. 3 / h, the speed of the enhanced heat exchanger will remain unchanged. The server outputs the prediction result together with the instantaneous timestamp (assuming it is 2024-12-12T16:20:00Z) to form the cold end diagnosis result:
[0036]
[0037] Verify the prediction results: The server verifies the prediction results, that is, compares the prediction results with the corresponding instantaneous data set. Assume that the server uses a historical instantaneous data set (that is, previously collected and timestamped data) to verify the accuracy of the model. For example, the server may find that the prediction error rate of the model under similar working conditions in the past is within 5%, so it believes that the current prediction result is reliable.
[0038] Forming a mapping relationship and performing machine learning: Through the verification of the server, a mapping relationship is formed between the prediction results and the instantaneous data set of the cold end. The server can use these instantaneous data sets with timestamps for continuous machine learning of the intelligent diagnosis model to optimize the prediction performance of the model. For example, the server uses algorithms such as gradient descent to adjust model parameters to minimize prediction errors.
[0039] The instantaneous data set includes Y1 at time point t: real-time flow of circulating water; Y2: heat load of condenser; and Y3: efficiency of cooling tower.
[0040] The intelligent diagnosis model is:
[0041]
[0042] Among them, c1, c2, c3 are constant terms, ij, k are lag term coefficients, and it is the error term.
[0043] The prediction result at time t+1 is the prediction value calculated using the instantaneous data sets at time t and time t-1.
[0044] Using the instantaneous data set at time t+1, the abnormal points in the instantaneous data sets at time t and time t-1 are removed.
[0045] The instantaneous data set at time t+1 is used to correct the abnormal points in the instantaneous data sets at time t and time t-1.
[0046] For example, there is the following time series data, which records the instantaneous data sets at time points t, t-1, and t+1, including Y1: real-time flow rate of circulating water (unit: m 3 / h), Y2: condenser heat load (unit: MW), Y3: cooling tower efficiency (unit: %).
[0047]
[0048] Outlier Detection
[0049] Determine the value of Y1 (real-time flow of circulating water) at time point t, 1500m 3 / h is an abnormally high value. Similarly, we can detect whether there are abnormal points in other variables and other time points.
[0050] Outlier removal
[0051] If you choose to remove this outlier, you can delete the Y1 value at time t from the data set, or fill in the missing value. Here, just delete the value directly.
[0052] The dataset after removing outliers may look like this:
[0053]
[0054] In practical applications, directly deleting data points may not be the best choice because it will reduce the sample size of the data set. A better approach may be outlier correction.
[0055] If we choose to correct this outlier, we can use the data at time t+1 to help us find a more reasonable value to replace it. We can assume that the value of Y1 changes smoothly between adjacent time points and use linear interpolation or other more complex interpolation methods to estimate the value of Y1 at time t.
[0056] The dataset after correcting the outliers using linear interpolation might look like this:
[0057]
[0058]
[0059] Here, we use (1200m 3 / h+1300m 3 / h) / 2=1250m 3 / h as the corrected value of Y1 at time t, but in order to keep it close to the original outlier value (and still be a "corrected" value), it is rounded to 1350m 3 / h.
[0060] Through the above process, we can see how to use the instantaneous data set at time t+1 to detect, remove and correct the outliers in the data sets at time t and time t-1. In practical applications, these steps need to be adjusted according to the characteristics of the data and the objectives of the analysis.
[0061] The principle and advantages of this solution are: by acquiring key data such as the circulating water flow rate of the cold end and the speed of the enhanced heat exchange device in real time, and immediately transmitting them to the server for processing. It overcomes the lag and incompleteness of traditional cold end system monitoring, which often relies on regular manual inspections or fixed data collection points. By using the intelligent diagnosis model based on the VAR model to predict the trend of the cold end working conditions, potential problems can be foreseen, so that preventive measures can be taken before the problems occur. The server verifies the prediction results, and by comparing the prediction results with the actual instantaneous data set, the accuracy of the model can be verified and continuously optimized. By forming a mapping relationship between the instantaneous data set of the cold end and the prediction results, and using these data for machine learning, the intelligent diagnosis model can be continuously optimized. This enables the model to gradually adapt to changes in the cold end system and improve the predictive ability and diagnostic accuracy of future working conditions. The accuracy and reliability of the diagnostic model are ensured, and the model can be continuously adapted to changes in the cold end system through machine learning optimization, which improves the predictive ability and diagnostic accuracy of future working conditions and better serves the operation and maintenance management of the cold end system. Using the instantaneous data set at the subsequent moment to remove or correct the abnormal points in the data at the previous moment can help eliminate noise and errors in the data and improve data quality, which can help improve the operating efficiency of the cold end system, extend the life of the equipment and reduce operation and maintenance costs.
[0062] The above is only an embodiment of the present invention, and the common knowledge such as the known specific technical schemes and / or characteristics in the scheme is not described in detail here. It should be pointed out that for those skilled in the art, several variations and improvements can be made without departing from the technical scheme of the present invention. In the present invention, unless otherwise clearly specified and limited, the terms "install", "connect", "connect", "fix" and the like should be understood in a broad sense. For example, it can be a fixed connection, a detachable connection, or an integral connection; it can be directly connected, or indirectly connected through an intermediate medium, or it can be the internal connection of two elements. For those of ordinary skill in the art, the specific meanings of the above terms in the present invention can be understood according to the specific circumstances. The scope of protection claimed in this application shall be based on the content of its claims, and the specific implementation methods and other records in the specification can be used to interpret the content of the claims.
Claims
1. An intelligent diagnosis method based on cold end real-time data, characterized in that: The following steps are involved: Acquire cold end real-time data, wherein the cold end real-time data includes real-time flow rate of circulating water and speed data of enhanced heat exchange device; Transmitting cold end real-time data, the data transmission end sends the cold end real-time data to the server end; the server end adds an instantaneous timestamp to the acquired cold end real-time data to obtain an instantaneous data set; To generate cold-end diagnosis results, the server inputs the instantaneous data set into the intelligent diagnosis model and outputs the cold-end diagnosis results including the instantaneous timestamp.
2. The intelligent diagnosis method based on cold end real-time data according to claim 1, characterized in that: The intelligent diagnosis model is based on the VAR model. Through the input of the instantaneous data set, the intelligent diagnosis model performs trend prediction on the cold end working condition to obtain the prediction result.
3. The intelligent diagnosis method based on cold end real-time data according to claim 2 is characterized in that: The server verifies the prediction result, and the verification is to compare the prediction result with the corresponding instantaneous data set.
4. The intelligent diagnosis method based on cold end real-time data according to claim 3 is characterized in that: Through verification on the server side, the prediction results are mapped to the instantaneous data set on the cold side, and the instantaneous data set on the cold side is used for machine learning of the intelligent diagnosis model.
5. The intelligent diagnosis method based on cold end real-time data according to claim 1 is characterized in that: The cold end diagnosis result output by the server includes a verification prediction result, which is a prediction result obtained by the server through a historical instantaneous data set.
6. The intelligent diagnosis method based on cold end real-time data according to claim 2 is characterized in that: The instantaneous data set includes Y1 at time point t: real-time flow of circulating water; Y2: heat load of condenser; and Y3: efficiency of cooling tower.
7. The intelligent diagnosis method based on cold end real-time data according to claim 6 is characterized in that: The intelligent diagnosis model is Among them, c1, c2, c3 are constant terms, ij,k is the lag term coefficient, it Error term.
8. The intelligent diagnosis method based on cold end real-time data according to claim 7 is characterized in that: The prediction result at time t+1 is the prediction value calculated by the instantaneous data set at time t and time t-1.
9. The intelligent diagnosis method based on cold end real-time data according to claim 8, characterized in that: The instantaneous data set at time t+1 is used to remove abnormal points in the instantaneous data sets at time t and time t-1.
10. The intelligent diagnosis method based on cold end real-time data according to claim 8, characterized in that: The instantaneous data set at time t+1 is used to correct the abnormal points in the instantaneous data sets at time t and time t-1.