Life evaluation and life extension calculation method for main steam pipeline in overdue service

Abstract

The invention relates to the technical field of pipeline monitoring, and discloses a life evaluation and life extension calculation method for an overdue service main steam pipeline, comprising the following steps: acquiring data from a plurality of data sources, the data sources comprising a temperature sensor, a pressure sensor, a pipeline stress sensor and historical operation data; preprocessing the acquired data of the plurality of data sources, wherein the preprocessing comprises denoising, data standardization and missing value filling; fusing the data of the preprocessed data source based on a data fusion algorithm to generate a unified high-precision data set, and carrying out unified coordination on the data according to time and space distribution to form measurement data under a global coordinate system; and performing error correction on the fused measurement data. Through linkage application of the error correction model and the service life evaluation algorithm, precise judgment of the pipeline state and intelligent output of the service life prolonging strategy are achieved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of pipeline monitoring, in particular to a life evaluation and life extension calculation method for a main steam pipeline in over-service period. BACKGROUND

[0002] In the fields of long-distance pipelines, urban gas pipeline networks, petrochemical facilities, etc., pipelines, as the key transportation channels, their operation safety is directly related to the social public safety and the stable operation of the energy system. However, pipelines are easily affected by corrosion, fatigue and external force interference during long-term service, and there is a risk of leakage, burst and other accidents. Therefore, it is necessary to timely grasp the health status of the pipeline, scientifically predict its remaining life, and develop a reasonable life extension strategy, which has become the core problem in the current industry.

[0003] In the prior art, a sensor array is usually used to monitor the key areas of the pipeline in real time, and parameters such as temperature, pressure and stress are collected to assist in judging the operation state of the pipeline. This method has certain visualization capability and can realize early warning, and has good accuracy in a low interference environment. Some studies also introduce basic data-driven algorithms to identify abnormal points, which improves the sensitivity of fault identification and provides effective support for ensuring the basic safety of the pipeline.

[0004] However, there are still some deficiencies in the prior art. First, most of the schemes lack an error correction mechanism after data fusion, resulting in a large deviation of the data source relied on by the evaluation model, and the subsequent prediction deviation is uncontrollable. Second, the performance of the sensor degrades over time, and the existing system generally uses static calibration, which is difficult to adapt to dynamic environmental changes, resulting in poor data stability. Third, the historical data is almost not fully utilized, and the life is estimated only by the current state, so the prediction accuracy is limited and the overall degradation trend of the pipeline cannot be shown. In addition, the current life evaluation results are mostly qualitative conclusions or brief reports, and the engineering personnel need to rely on experience to develop maintenance strategies, which is highly subjective and has low deployment efficiency. SUMMARY

[0005] In view of the deficiencies of the prior art, the present application provides a life evaluation and life extension calculation method for a main steam pipeline in over-service period, which solves the problems of dynamic correction of data error, effective utilization of historical data, insufficient life prediction accuracy and non-intuitive output of life extension strategy in the prior art.

[0006] To achieve the above purpose, the present application realizes the following technical scheme: a life evaluation and life extension calculation method for a main steam pipeline in over-service period, comprising the following steps:

[0007] acquiring data from multiple data sources, the data sources including temperature sensors, pressure sensors, pipeline stress sensors and historical operation data;

[0008] The data of the plurality of data sources obtained is preprocessed, the preprocessing including denoising, data standardization and missing value filling;

[0009] The data of the preprocessed data sources is fused based on a data fusion algorithm, a unified high-precision data set is generated, and the data is uniformly coordinated according to time and space distribution to form measurement data in a global coordinate system;

[0010] The fused measurement data is error corrected, the error correction including considering sensor accuracy, environmental influence factors and deviation of historical data;

[0011] The error-corrected data is input into a pipeline life assessment model for calculating the remaining life and life extension strategy of the pipeline, and finally outputting life extension suggestions for engineers to make decisions.

[0012] Preferably, the step of obtaining data from a plurality of data sources comprises:

[0013] Real-time temperature data collected by temperature sensors arranged on the surface of the pipeline is obtained;

[0014] Internal pressure data collected by pressure sensors installed at key nodes of the pipeline is obtained;

[0015] Stress change data collected by stress sensors arranged at the outer wall or weld position of the pipeline is obtained;

[0016] Historical operation records in the pipeline operation control system are obtained, the historical operation records including start-stop cycle, load change and overload event data.

[0017] Preferably, the step of preprocessing the data of the plurality of data sources comprises:

[0018] The temperature data is denoised by using a moving average filter to eliminate short-term fluctuations and measurement errors;

[0019] The pressure data is standardized for subsequent data fusion;

[0020] The missing values in the stress data are filled by using an interpolation method for prediction and completion;

[0021] All data is subjected to outlier detection and rejection by using a Z-score method.

[0022] Preferably, the step of detecting by using a Z-score method comprises:

[0023] A sample data set is constructed for each type of preprocessed data, and the mean μ and standard deviation σ of the data set are calculated;

[0024] For each data point x, its Z value is calculated according to the following formula:

[0025]

[0026] Wherein, x i is the i th data point; μ is the mean of the data set; σ is the standard deviation; Z is the Z-score value;

[0027] It is judged whether the data point is an outlier, and when the absolute value of the Z value is greater than a set threshold T, the data point is determined to be an outlier and is rejected.

[0028] Preferably, the step of fusing the data of the preprocessed data source based on the data fusion algorithm comprises:

[0029] A unified time axis is constructed, and different source data are aligned according to collection time stamps;

[0030] The data at the same moment is fused based on a weighted fusion algorithm, and the calculation formula of the fused data is:

[0031]

[0032] Wherein, x f is the fused data value; x i is the preprocessed data of the i th data source; w i is the corresponding weight; n is the number of data sources, satisfying:

[0033]

[0034] Wherein, w i is the corresponding weight; n is the number of data sources;

[0035] The weight w i is dynamically adjusted according to the historical stability and error distribution of the data source.

[0036] Preferably, the weighted fusion algorithm specifically comprises:

[0037] The static fusion is divided into different levels by a preset data source classification system, and the error mean, variance range and fluctuation trend of different types of sensors in the past running period are considered;

[0038] The dynamic fusion scores the current collected data according to the real-time data quality evaluation results, and comprehensively considers the sensor health state, data stability index and instantaneous mutation value amplitude.

[0039] Preferably, the step of uniformly coordinating the data according to the time and space distribution comprises:

[0040] According to the specific operation environment and the monitoring area of the pipeline, the spatial distribution coordinate system of the data is defined, and the collected data is mapped to a unified spatial coordinate system.

[0041] The data at different time nodes are time-sequentially aligned, and the data at each time point are synchronized.

[0042] Spatial interpolation and time interpolation are performed, so that the data can be consistent in space and time.

[0043] Preferably, the step of performing spatial interpolation and time interpolation comprises:

[0044] The spatial interpolation comprises estimating the data of the region not covered by the sensor, and generating a continuous measurement data field in the spatial coordinate system according to the spatial position and the numerical distribution relationship of the peripheral effective measurement points;

[0045] The time interpolation comprises constructing a time sequence model between adjacent sampling time points, and completing the data of the missing time points;

[0046] The selection of the interpolation method is determined according to the variation characteristics of the data, and the multi-point local interpolation method is adopted in the structure continuous region.

[0047] Preferably, the error correction of the fused measurement data comprises:

[0048] The system error of various sensor data is calibrated and corrected, and the linear regression model is adopted to correct the long-term deviation;

[0049] An environmental parameter influence model is established to correct the measurement drift caused by temperature, humidity and working load fluctuation;

[0050] The deviation compensation is performed by using historical measurement data and a statistical error model, and the model is constructed based on a Bayesian estimation framework.

[0051] Preferably, the deviation compensation adopts the following estimation formula:

[0052]

[0053] Wherein, x1 and x2 are data of two estimation sources respectively; And is the corresponding variance; is the weighted compensated estimation value.

[0054] The application provides a life evaluation and life extension calculation method of an over-service main steam pipeline.

[0055] 1、The application adopts the technical scheme of combining error correction of fused measurement data with multi-dimensional parameter residual life prediction, realizes dynamic and accurate evaluation of the health state of the pipeline. Compared with the life estimation mode in the prior art which relies on single sensor data, the problems of data distortion and high misjudgment rate are overcome, and the accuracy of the evaluation is effectively improved.

[0056] 2、The application introduces a multi-element correction model considering sensor precision degradation and environmental factor influence, realizes consistency guarantee of data under different operating conditions. Compared with the traditional error compensation technology path based on static calibration constant, the limitation of calibration failure in long-term monitoring is avoided, and the monitoring result is more stable and reliable.

[0057] 3、The application introduces a mechanism based on historical data deviation dynamic correction in the life evaluation model, and combines the Bayesian statistical method to optimize the prediction accuracy, ensuring the scientificity of the life extension strategy. Compared with the technical mode in the prior art which only estimates life by using real-time data and cannot refer to historical degradation trend, the practical feasibility of the life extension suggestion is significantly improved.

[0058] 4、The application outputs the life extension strategy and residual life suggestion through visualization, so that engineers can quickly master the pipeline state and risk level, and the decision-making is more intuitive and efficient. Compared with the manual judgment of repair plan in the prior art which needs to rely on expert experience, the problem of complicated operation and strong professional dependence is solved, and the method is convenient for rapid deployment and application in engineering field. BRIEF DESCRIPTION OF DRAWINGS

[0059] Figure 1 The method flowchart of the application. DETAILED DESCRIPTION

[0060] The technical solutions in the embodiments of the application will be clearly and completely described below with reference to the drawings in the specification of the application. Obviously, the described embodiments are only part of the embodiments of the application, not all the embodiments. Based on the embodiments in the application, all other embodiments obtained by those skilled in the art without creative labor are within the protection scope of the application.

[0061] Please refer to the drawings in the specification of the application Figure 1 The embodiment of the application provides a life evaluation and life extension calculation method for a super-service main steam pipeline, including the following steps:

[0062] S1, data from multiple data sources is obtained, the data sources including temperature sensors, pressure sensors, pipeline stress sensors and historical operation data;

[0063] In the step of acquiring data from multiple data sources, we rely on multiple sensors and historical data sources for data collection to obtain the real-time running state of the pipeline. Through the combination of various sensors and historical operation records, we can fully understand the running situation of the pipeline under various different working conditions, providing sufficient information for subsequent life assessment and life extension strategies.

[0064] In conjunction with the previous steps, the main purpose of this step is to provide complete and accurate raw data for the data preprocessing stage. These data will be processed through denoising, standardization, missing value filling, etc., and then enter the data fusion and error correction stage. In this way, the obtained data not only has timeliness, but also has accuracy, providing a solid foundation for subsequent calculation and decision-making.

[0065] Temperature sensor: used to obtain real-time temperature data of the pipeline surface. Usually, temperature sensors are placed at multiple locations of the pipeline, which can continuously monitor the temperature changes of the pipeline in different areas. Temperature is an important factor affecting the aging and fatigue of pipeline materials, so its monitoring is crucial for pipeline life assessment.

[0066] Pressure sensor: installed at key nodes of the pipeline, used to collect internal pressure data of the pipeline. The internal pressure fluctuation of the pipeline is often directly related to the working load of the pipeline. Long-term high and low pressure alternation may cause irreversible damage to the pipeline over time, and the pressure sensor can provide real-time data for this change, providing an important reference for the health assessment of the pipeline.

[0067] Stress sensor: usually placed on the outer wall or weld position of the pipeline, which can detect the stress change of the pipeline. The data of the stress sensor is crucial for assessing the structural integrity of the pipeline. Especially in the case of over-service, stress changes in the pipeline may be a precursor to cracks or other damage, so real-time monitoring of stress changes helps to detect potential problems in advance and prevent major failures.

[0068] Historical operation data: historical operation data usually comes from the pipeline operation control system, including start-stop cycle, load change and overload event, etc. These data reflect the working history of the pipeline under different working conditions, and by analyzing the historical operation data, we can judge whether the pipeline has experienced overload work or whether there are improper operation conditions, which provides important background information for subsequent life assessment.

[0069] In some embodiments, when acquiring these data, all sensors transmit the collected measurement data to the data processing center in real time. The data processing center receives these data through a unified communication protocol, ensuring that the data can be accurately and timely aggregated to the designated location.

[0070] Alternatively, in this embodiment, all data collected by the data sources are time-stamped so that they can be processed and analyzed in the order of collection. With the time-stamps, the data from different sources can be precisely aligned, which is crucial for data fusion and coordination. In the processing of time-series data, these time-stamps ensure the timeliness of the data and prevent computational bias caused by time errors.

[0071] Specifically, all sensors and historical data sources in the data collection process will be integrated through standardized interfaces, which ensures that the diversity of data sources does not affect the smoothness and consistency of data processing. For example, when collecting pressure data, different types of pressure sensors may have different measurement ranges and accuracies, so their data needs to be standardized for subsequent fusion processing.

[0072] Before processing the data, all data from different sensors will be pre-classified according to their measurement characteristics. Temperature, pressure, and stress data will be entered into the corresponding preprocessing modules. During processing, a moving average filter algorithm is used to denoise temperature data, a Z-score method is used to detect outliers, and interpolation is used to fill in missing values.

[0073] In one possible implementation, after all these raw data are standardized and preprocessed, they will enter a unified global coordinate system in the data fusion stage. It is important to note that at different positions in the pipeline, the measurement error and sensitivity of the sensors may be different, and the use of a unified coordinate system helps to reduce the impact of such measurement errors. The standardized data will be processed in a weighted fusion manner, ensuring that the relative weights of different data sources can be dynamically adjusted according to their accuracy and stability.

[0074] Alternatively, the weight determination of the weighted fusion algorithm can be dynamically adjusted according to the historical stability and real-time measurement error distribution of each data source. Specifically, the weight of the temperature sensor may be proportional to the change amplitude of the ambient temperature, while the weight of the pressure sensor can be adjusted according to the operating state and load of the pipeline. In this way, the fused data will be more accurate, effectively improving the credibility of the subsequent analysis results.

[0075] In some embodiments, data source fusion not only involves the fusion of single data points, but also involves the alignment of time. The measurement time points of different data sources may differ, so the previously mentioned unified time axis is a key step to ensure smooth data fusion.

[0076] In another possible implementation, to ensure the stability and accuracy of data fusion, a dynamic weighting mechanism can also be adopted. Specifically, by evaluating the health status of each sensor in real time, combined with the stability and reliability of each data source, the weight can be dynamically adjusted. This dynamic adjustment mechanism can correct the errors of each data source in real time during the long-term operation of the pipeline, and maximize the accuracy of the data fusion results.

[0077] S2, pre-processing the acquired data of multiple data sources, the pre-processing including denoising, data standardization and missing value filling;

[0078] In the data acquisition stage, multiple sensors and historical data sources will provide raw data, including temperature, pressure, stress and other measurement values, as well as historical data related to pipeline operation. These data provide key basic information for subsequent life assessment and life extension calculation. In order to ensure that these raw data can effectively support subsequent analysis and decision-making, they must be pre-processed. The purpose of pre-processing is to eliminate external interference factors and convert data into a standardized format suitable for subsequent calculation through methods such as denoising, data standardization and missing value filling. Data preprocessing is closely linked with data collection and subsequent data fusion, error correction and other steps to ensure smooth operation of the entire technical process.

[0079] In the data of temperature sensors and pressure sensors, they are often affected by environmental noise, sensor errors or external interference, resulting in short-term fluctuations. In order to eliminate these meaningless fluctuations, we use the moving average filtering method to denoise the data. Moving average filtering can smooth the data and reduce the impact of transient fluctuations, thus better reflecting the true operating state of the pipeline.

[0080] Generally, the window size of the moving average filter is adjusted according to the actual situation, and selecting an appropriate window length can effectively remove high-frequency noise without losing the trend information of the data. For example, the window size can be adjusted according to the frequency of temperature and pressure changes, and in general, the window length can be set to a certain time period (such as data points per hour or per minute).

[0081] Data standardization is to eliminate the dimensional differences between different sensor data, so that various types of data can be unified to the same scale. Specifically, for pressure data, we use the standardization method to convert it into a standard normal distribution form for subsequent data fusion. The commonly used method of standardization is to use Z-score standardization, the formula is as follows:

[0082]

[0083] where μ is the mean of the dataset; σ is the standard deviation; x is a single data point; and Z is the Z-score value. Through this formula, we can convert each data point into a standardized value, so that the data is not affected by the original unit and dimension when processing.

[0084] As an alternative, in practical applications, Z-score standardization is also applicable to the data of stress sensors. By converting stress data into a standard normal distribution, it can ensure that the stress data of different regions is consistent and comparable when performing weighted fusion.

[0085] Missing values may occur during data collection, especially during long-term operation, some data points may not be collected due to sensor failure or communication interruption. To avoid the impact of missing data on subsequent analysis, we use interpolation method to complete the missing values. Common interpolation methods include linear interpolation and polynomial interpolation, the specific choice of interpolation method depends on the variation characteristics of the data.

[0086] Specifically, if the data changes are relatively smooth, the linear interpolation method can effectively fill in the missing data. Linear interpolation connects adjacent known data points and estimates the missing data value based on its trend. If the data changes are more complex, the interpolation method may use higher-order polynomial interpolation, or use Fourier interpolation method according to the periodic characteristics of the data.

[0087] In one possible implementation, for stress change data of the pipeline, when using the interpolation method, spatial interpolation can also be combined with the spatial distribution of surrounding measuring points to further improve the prediction accuracy of missing data. By generating a continuous measurement data field in the spatial coordinate system, missing values in uncovered areas can be effectively filled in.

[0088] For data points that do not conform to the expected regularity under normal operating conditions, we need to detect and remove outliers. In general, sensor errors, environmental mutations or equipment failures may cause some data points to be abnormal. In order to identify these outliers, the embodiment uses the Z-score method for outlier detection. The specific process is to construct the mean and standard deviation of each type of data, and calculate the Z-score value of each data point according to the following formula:

[0089]

[0090] where x i is the i-th data point; μ is the mean of the dataset; σ is the standard deviation; and Z is the Z-score value. If the absolute value of the Z-score value of the data point is greater than the set threshold T, the data point is determined to be an outlier and is removed.

[0091] In some embodiments, to improve the accuracy of outlier detection, the threshold T of Z-score can be adjusted according to actual needs. For example, for high volatility data, a higher threshold value may be set to avoid removing valid data. Conversely, for more stable data, a lower threshold value can effectively detect more outliers.

[0092] As an option, in practical applications, the outlier detection of data can be combined with the denoising process, that is, while denoising, possible outliers are removed. This can reduce the adverse effects of abnormal data points and make subsequent processing more accurate.

[0093] S3, based on a data fusion algorithm, fusing the data of the preprocessed data sources to generate a unified high-precision data set, and uniformly coordinating the data according to time and space distribution to form measurement data in a global coordinate system;

[0094] After denoising, standardization, missing value filling and outlier detection in the data preprocessing stage, the next step is to fuse the processed data. This step combines data from different data sources into a unified high-precision data set through a data fusion algorithm. These data include temperature, pressure, stress and historical operation data from multiple data sources. Because the time and spatial position of various sensors and historical data are different when collecting, it is necessary to coordinate and fuse them so that all data are processed under the same standard to ensure the accuracy and consistency of the data. Finally, by uniformly coordinating the time and space distribution of the data, a measurement data in a global coordinate system is formed, providing reliable input data for subsequent pipeline life assessment and life extension strategy calculation.

[0095] In this embodiment, based on a data fusion algorithm, the data of the preprocessed data sources are fused to generate a unified high-precision data set, and the data are uniformly coordinated according to time and space distribution to form measurement data in a global coordinate system. Specifically, this step includes the following aspects:

[0096] In the data collection process, the collection time points of various sensor data may be different. Therefore, to ensure the comparability between different data sources, a unified time axis needs to be constructed according to the timestamp information of the data.

[0097] Generally, the data collection frequency and time interval of different data sources may be different, and the establishment of a unified time axis helps to align these data in chronological order and ensure that the data of different sensors can be synchronized at consistent time points. This operation is the basis for subsequent data fusion.

[0098] After the data alignment is completed, the next step is to perform weighted fusion on the data at the same time point. A unified time axis is constructed, and the data from different sources are aligned according to the collection timestamps;

[0099] The data at the same time point is fused based on the weighted fusion algorithm, and the calculation formula of the fused data is:

[0100]

[0101] where x f is the fused data value; x i is the preprocessed data of the i-th data source; w i is the corresponding weight; and n is the number of data sources. It satisfies:

[0102]

[0103] where w i is the corresponding weight; and n is the number of data sources.

[0104] As the data is acquired, the sensors are located at different positions, and the difference in spatial distribution makes the coordination of data more complex. Therefore, in this embodiment, the spatial distribution of data is coordinated by establishing a unified spatial coordinate system.

[0105] Specifically, each data point is mapped into a unified spatial coordinate system according to the actual position of the sensor. In this way, data from different spatial regions can be effectively compared and fused.

[0106] In one possible implementation, the establishment of the spatial coordinate system can be based on the actual layout of the pipeline. For example, different positions of the pipeline can be affected by different temperature, pressure or stress environments, so the spatial coordinate system not only considers the geographical position, but also needs to consider the structure and fluid dynamics characteristics of the pipeline.

[0107] Interpolation in space and time is another key step to ensure data consistency. In the monitoring process of the pipeline, some positions may not be covered by sensors, so spatial interpolation needs to be performed based on the spatial position relationship of the surrounding effective measurement points.

[0108] In some embodiments, the spatial interpolation method can use a multi-point local interpolation method to estimate the data of the missing area by considering the distribution relationship of the surrounding data points. In addition, for the data missing in time, time interpolation is performed through a time series model to fill the data gap between adjacent sampling time points.

[0109] As an alternative, for cases where the measurement points within the pipeline are relatively sparse, interpolation methods can be used in combination with the data's changing characteristics. For example, when the fluid flow in the pipeline is stable, a simpler linear interpolation can be used. However, in areas where the fluid flow is complex, more complex interpolation methods such as polynomial interpolation or local weighted interpolation can be employed to better capture the data's changing trends.

[0110] After the aforementioned time alignment, weighted fusion, and interpolation processing, the final data set is formed and unified into a global coordinate system.

[0111] Specifically, in the global coordinate system, each data point has clear spatial and temporal information, and the relationship between data is clearly defined. The processed data not only enables effective subsequent analysis, but also provides high-precision data input for pipeline life assessment models.

[0112] S4, error correction of the fused measurement data, including consideration of sensor accuracy, environmental factors, and historical data bias;

[0113] After the data fusion and space-time coordination steps are completed, we obtain a high-precision unified data set based on multiple data sources. However, in practical applications, due to issues such as sensor accuracy, environmental factors, and historical data bias, there may still be some errors in the fused measurement data. To ensure the accuracy and reliability of the data, further error correction of the fused measurement data must be performed. Error correction not only effectively improves the accuracy of the data, but also provides more reliable basis for pipeline life assessment and life extension calculation.

[0114] In this embodiment, the error correction process mainly includes the following aspects: considering sensor accuracy, environmental factors, and historical data bias, and further improving data quality and reliability through correction and compensation.

[0115] During long-term use, sensors may experience a decrease in measurement accuracy due to aging, damage, or changes in the working environment. Therefore, when correcting the fused measurement data, the accuracy of the sensors must be considered first.

[0116] Generally, to eliminate the bias caused by sensor aging or environmental changes, we use a linear regression model to calibrate and correct the data. The linear regression model can fit a linear relationship between known historical data and current data, and correct the long-term drift of the measurement data.

[0117] Specifically, if the sensor has a long-term systematic error, it can be corrected by the following formula:

[0118] Dcalibrated = D measured + α · t + β;

[0119] where D calibrated is the corrected data value; D measured is the original measurement data; α and β are correction coefficients obtained through regression analysis; t is the time variable. This formula can be adjusted according to the aging trend and long-term use of the sensor, ensuring that the corrected data is closer to the true value.

[0120] In addition to the accuracy of the sensor itself, environmental factors such as temperature, humidity, etc. also have an impact on the measurement results. Changes in environmental factors may cause the sensor readings to drift, especially fluctuations in temperature and humidity, which will directly affect the sensor readings.

[0121] As an option, we have established an environmental parameter influence model, considering factors such as temperature, humidity, etc. to correct the measurement data. By collecting the relationship between environmental parameters and sensor data, we can use statistical analysis methods (such as multiple regression analysis) to correct the influence of the environment on the data.

[0122] Specifically, for temperature and humidity correction, the following formula can be used for compensation:

[0123] D corrected = D measured - γ · T + δ · H;

[0124] where D corrected is the corrected data; D measured is the original measurement data; γ and δ are correction coefficients obtained through experiments; T is the temperature; H is the humidity. In this way, the interference of environmental fluctuations on data results can be reduced.

[0125] In some cases, historical data may have some bias, especially in long-term monitoring processes, the measurement range, error rate, etc. of the sensor may change. In order to improve the accuracy of the data, we combine historical data and real-time data for bias compensation.

[0126] In some embodiments, bias compensation uses a framework based on Bayesian estimation. Bayesian estimation method can dynamically adjust the weight of data according to the distribution of historical data and current measurement value, so as to effectively reduce the bias brought by historical data.

[0127] Specifically, the calculation of bias compensation can be realized through the following formula:

[0128]

[0129] where x1 and x2 are the data of two estimation sources respectively; and is the corresponding variance thereof; is the weighted compensated estimate.

[0130] As an alternative, in some cases, bias compensation can be achieved through other statistical methods such as weighted average, weighted regression, etc., and the specific method is selected according to the actual application scenario and data characteristics.

[0131] S5, input the error-corrected data into the pipeline life assessment model for calculating the remaining life of the pipeline and the life extension strategy, and finally output the life extension suggestion for the engineer to make a decision;

[0132] After error correction is completed, all measurement data has been corrected and adjusted to a more accurate state. At this time, we need to input these error-corrected data into the pipeline life assessment model to calculate the remaining life of the pipeline and develop a corresponding life extension strategy. Through this process, we can predict the health status of the pipeline in the future period of time and give reasonable life extension suggestions according to the prediction results. Ultimately, these suggestions will help engineers make scientific decisions, thereby effectively extending the service life of the pipeline and ensuring its efficient and safe operation in the long run.

[0133] In this embodiment, the error-corrected data is input into the pipeline life assessment model as input. The main task of this model is to predict the remaining life of the pipeline according to its actual operating state and environmental conditions, and to develop a corresponding life extension strategy based on the prediction results. Specifically, the model first evaluates the current health status of the pipeline based on various parameters in the input data such as temperature, pressure, stress, and historical operating data, and then calculates the remaining life of the pipeline.

[0134] Specifically, in the life assessment process, the remaining life of the pipeline can be predicted through the comprehensive influence of stress, temperature, load, etc. The model will calculate the current remaining service life of the pipeline based on its actual working conditions and the fatigue life theory of materials. At this time, the model will assess the risks that the pipeline may face in the future operation according to different working conditions (such as load changes, temperature fluctuations, etc.) and different sensor data, and further predict its possible damage development.

[0135] In one possible implementation, the process of calculating the remaining life of the pipeline can be achieved through the following formula:

[0136]

[0137] where L remaining is the remaining life of the pipeline; L initial is the initial life of the pipeline; R ithe impact value of each influencing factor (such as temperature, pressure, stress, etc.) on the pipeline life in each time period; N is the number of influencing factors.

[0138] As an option, if different parts or regions of the pipeline have different workloads, the life calculation can also be performed at the regional level, ensuring that the damage level and service life of different regions can be evaluated separately. For example, regions with higher pressure may cause local life to be shortened, so regional life assessment is needed.

[0139] After the remaining life calculation is completed, the next step is to develop a life extension strategy. Based on the current health status and remaining life of the pipeline, the model will propose appropriate life extension measures. These life extension measures may include partial replacement of the pipeline, regular maintenance to strengthen the pipeline, or adjustment of pipeline operating parameters to reduce damage accumulation. For example, reducing operating temperature or pressure can effectively slow down the aging rate of the pipeline and delay its failure.

[0140] Specifically, in the process of developing the life extension strategy, the model combines historical operation records, real-time data, and the current assessment of the pipeline status to propose targeted maintenance solutions. The implementation details of the maintenance solutions will be adjusted according to the actual situation of the pipeline. For example, for pipeline regions with obvious cracks, the model may recommend local replacement; for regions without obvious damage, only routine maintenance and monitoring may be required.

[0141] In one possible implementation, the output format of the life extension recommendations can take the form of a report, detailing the specific content of the life extension measures, implementation time, and required resources. The report can also include a risk assessment to help engineers fully understand the potential risks of implementing the life extension strategy when making decisions.

[0142] As an option, life extension recommendations can also be displayed through visualization tools, allowing engineers to more intuitively understand the health status of the pipeline and the recommended life extension measures. Through a graphical interface, engineers can quickly assess the effectiveness of different life extension strategies by displaying the trends of various sensor data and the predicted values of pipeline life.

[0143] Finally, after a series of calculations and analyses, the generated life extension recommendations will become an important basis for engineers' decision-making, helping them take appropriate measures to ensure the safety and stability of the pipeline during long-term operation. Through reasonable life extension strategies, the service life of the pipeline can be effectively extended, reducing maintenance and replacement costs, and improving the efficiency and safety of the pipeline system.

[0144] While embodiments of the application have been shown and described, it is to be understood that the embodiments described are merely exemplary of the principles and application of the present application. Numerous modifications and adaptions can be effected without departing from the spirit and scope of the present application, which is not limited to the exact construction and arrangement described. It is intended, therefore, to cover all modifications and adaptions that fall within the scope of the claims and their equivalents.

Claims

1. A method for life assessment and life extension calculation of an over-service main steam pipeline, characterized in that: The following steps are involved: Acquiring data from multiple data sources, including temperature sensors, pressure sensors, pipeline stress sensors, and historical operating data; Preprocessing the data obtained from multiple data sources, including denoising, data standardization, and missing value filling; Based on the data fusion algorithm, the data from the pre-processed data sources are fused to generate a unified high-precision data set, and the data are unified and coordinated according to the time and space distribution to form measurement data in a global coordinate system; Performing error correction on the fused measurement data, wherein the error correction includes taking into account sensor accuracy, environmental factors, and deviations from historical data; The error-corrected data is input into the pipeline life assessment model to calculate the remaining life of the pipeline and the life extension strategy, and finally outputs the life extension recommendations for engineers to make decisions.

2. The method for life assessment and life extension calculation of an over-service main steam pipeline according to claim 1 is characterized in that: The step of obtaining data from multiple data sources includes: Obtain real-time temperature data collected by temperature sensors installed on the pipeline surface; Obtain internal pressure data collected by pressure sensors installed at key nodes of the pipeline; Obtain stress change data collected by stress sensors installed on the outer wall of the pipeline or at the weld position; Obtain historical operation records from the pipeline operation control system, including start-stop cycles, load changes, and overload event data.

3. The method for life assessment and life extension calculation of an over-service main steam pipeline according to claim 1 is characterized in that: The step of preprocessing the data from the multiple data sources includes: De-noising the temperature data using a sliding average filter to eliminate short-term fluctuations and measurement errors; Standardizing the pressure data for subsequent data fusion; Filling missing values ​​in the stress data, and using interpolation to predict and complete them; All data were tested and eliminated for outliers using the Z-score method.

4. The method for life assessment and life extension calculation of an over-service main steam pipeline according to claim 3 is characterized in that: The steps of detecting by the Z-score method include: Construct a sample data set for each type of preprocessed data and calculate the mean μ and standard deviation σ of the data set; For each data point x, calculate its Z value according to the following formula: Among them, x i is the i-th data point; μ is the mean of the data set; σ is the standard deviation; Z is the Z-score value; Determine whether the data point is an outlier. When the absolute value of the Z value is greater than the set threshold T, the data point is determined to be an outlier and is removed.

5. The method for life assessment and life extension calculation of an over-service main steam pipeline according to claim 1 is characterized in that: The step of fusing the pre-processed data from the data source based on the data fusion algorithm includes: Build a unified timeline to align data from different sources based on collection timestamps; The data at the same time are fused based on the weighted fusion algorithm. The calculation formula of the fused data is: Among them, x f is the fused data value; x i is the preprocessed data of the i-th data source; w i is its corresponding weight; n is the number of data sources, satisfying: Among them, w i is its corresponding weight; n is the number of data sources; The weight w i Dynamically adjust based on the historical stability and error distribution of the data source.

6. The method for life assessment and life extension calculation of an over-service main steam pipeline according to claim 5 is characterized in that: The weighted fusion algorithm specifically includes: The static fusion is based on the error mean, variance range and fluctuation trend of different types of sensors in past operation cycles through a preset data source classification system; The dynamic fusion scores the currently collected data based on the real-time data quality assessment results, taking into account the sensor health status, data stability indicators and instantaneous mutation value amplitude.

7. The method for life assessment and life extension calculation of an over-service main steam pipeline according to claim 1 is characterized in that: The step of uniformly coordinating data according to time and space distribution includes: According to the specific operating environment and monitoring area of ​​the pipeline, define the spatial distribution coordinate system of the data and map the collected data to a unified spatial coordinate system; Align the data at different time nodes and synchronize the data at each time point; Perform spatial interpolation and temporal interpolation to make the data consistent in both space and time.

8. The method for life assessment and life extension calculation of an over-service main steam pipeline according to claim 7 is characterized in that: The steps of performing spatial interpolation and temporal interpolation include: Spatial interpolation involves estimating the data in the area not covered by the sensor, and generating a continuous measurement data field in the spatial coordinate system based on the spatial position and value distribution relationship of the surrounding effective measurement points; Time interpolation involves constructing a time series model between adjacent sampling time points to complete the data at missing time points; The selection of interpolation method is determined according to the variation characteristics of the data. Spatial interpolation adopts multi-point local interpolation in structural continuous areas.

9. The method for life assessment and life extension calculation of an over-service main steam pipeline according to claim 1 is characterized in that: The error correction of the fused measurement data includes: Calibrate and correct the systematic errors of various sensor data, and use linear regression models to correct long-term offsets; Establish an environmental parameter impact model to correct measurement drift caused by temperature, humidity, and workload fluctuations; Bias compensation is performed using historical measurement data and a statistical error model built based on a Bayesian estimation framework.

10. The method for life assessment and life extension calculation of an over-service main steam pipeline according to claim 9, characterized in that: The deviation compensation adopts the following estimation formula: Among them, x1 and x2 are the data of the two estimation sources respectively; and is its corresponding variance; is the estimated value after weighted compensation.

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