Method and device for predicting heat dissipation performance of hydraulic engineering machinery

Abstract

The invention provides a method and a device for predicting the heat dissipation performance of hydraulic engineering machinery, and the method comprises the steps: building a function relation between the working duration and each heat dissipation performance influence factor during heat balance through the historical working condition data (including working conditions, environment temperature and other factors) of the heat balance of the engineering machinery, and calculating the heat dissipation performance of the engineering machinery according to the threshold value and correlation coefficient of each heat dissipation performance influence factor. And predicting the overall working time of the radiator and respectively reminding the working time of each radiating performance influence factor. And considering the influence of each factor on the heat dissipation performance, predicting the dynamic change of the working duration along with the working state of the engineering machinery every day, and outputting a dynamic prediction result. According to the method, the working duration can be predicted, timely machine maintenance or timely fault point finding can be reminded, shutdown or serious machine damage caused by serious faults can be avoided, and the equipment safety can be improved.

Description

Technical Field

[0001] The invention relates to a method and a device for predicting the heat dissipation performance of hydraulic engineering machinery, and belongs to the technical field of heat dissipation of hydraulic engineering machinery. Background Art

[0002] The performance of hydraulic engineering machinery radiators is affected by ambient temperature, working scene, working conditions, and radiator status. The prediction of radiator performance helps remind customers to maintain the machine in time or find fault points in time to avoid serious failures, causing downtime or major machine damage. However, there are many factors that affect radiator performance, and each influencing factor is difficult to quantify, making it difficult to make effective predictions.

[0003] Chinese patent CN 117057275 A is a vehicle radiator performance prediction method that uses a combination of CFD and empirical formulas to predict the performance of the radiator during actual operation. However, this technology is only applicable to the radiator selection and design stage, and fails to consider the radiator performance prediction method when the vehicle radiator performance in the market is affected by multiple factors;

[0004] Chinese Patent CN 117291027 A A method, system, engineering machinery and engineering machinery for early warning of heat dissipation performance based on big data, which calculates the extreme ambient temperature that the engineering machinery can adapt to through data analysis and data fitting of the actual working conditions of the engineering machinery, and then compares it with the preset threshold to determine the performance status of the radiator and provide an early warning. However, this method fails to consider the fan speed during independent heat dissipation, and can only give a warning near the threshold, and secondly, it cannot predict the working time of the radiator. Summary of the invention

[0005] The purpose of the present invention is to overcome the deficiencies in the prior art and provide a method and device for predicting the heat dissipation performance of hydraulic engineering machinery, which can predict the working time, remind timely maintenance of the machine or timely discover the fault point, avoid serious failures, cause shutdown or major machine damage, and improve equipment safety.

[0006] To achieve the above object, the present invention is implemented by adopting the following technical solutions:

[0007] In a first aspect, the present invention provides a method for predicting heat dissipation performance of hydraulic engineering machinery, comprising the following steps:

[0008] Acquire heat dissipation performance data; the heat dissipation performance data includes at least one heat dissipation performance influencing factor and thermal balance history data of corresponding working hours;

[0009] Performing data fitting based on the heat dissipation performance data to obtain a fitting formula for each heat dissipation performance influencing factor and working time when thermal equilibrium is reached;

[0010] Calculate the correlation coefficient of the fitting formula of each heat dissipation performance influencing factor and working time;

[0011] Compare the correlation coefficient of the fitting formula of each heat dissipation performance influencing factor and the working time with a preset minimum threshold of the correlation coefficient, and obtain a fitting formula whose correlation coefficient exceeds the preset minimum threshold of the correlation coefficient;

[0012] According to the fitting formula when the correlation coefficient exceeds the preset minimum threshold of the correlation coefficient, combined with the correlation threshold of each heat dissipation performance influencing factor, the working time of each heat dissipation performance influencing factor is calculated;

[0013] The minimum value of the working time of each heat dissipation performance influencing factor is taken as the predicted overall working time of the heat dissipation performance.

[0014] The technical effect achieved by the above settings: The present invention provides a feasible solution for predicting the working time of the overall hydraulic engineering machinery heat dissipation performance by fitting the thermal balance data and calculating the overall working time of the heat dissipation performance as the minimum value of each influencing factor. It can timely remind customers of the changing trend of the heat dissipation performance and the expected working time, remind customers to maintain the machine in time or find the fault point in time, avoid serious failures, cause shutdowns or major machine damage, and improve equipment safety.

[0015] Furthermore, the factors affecting the heat dissipation performance include water temperature, hydraulic oil temperature, intercooler temperature and fan speed of the independent heat dissipation system.

[0016] Furthermore, the method for obtaining heat dissipation performance data includes:

[0017] Read the stored engine water temperature and intercooler temperature from the engine electronic control unit ECU; the water temperature and intercooler temperature data of the engine ECU are derived from the heat balance history data of the heat dissipation performance influencing factors and the corresponding working hours collected by the engine water temperature sensor and the intake air temperature sensor;

[0018] The hydraulic oil temperature and the fan speed of the independent cooling system are read from the instrument end of the whole machine; the hydraulic oil temperature at the instrument end of the whole machine is collected by the temperature sensor of the hydraulic oil tank, and the fan speed is collected by the speed sensor on the independent fan side;

[0019] The data are screened according to the data collection principle, and the screened data are obtained as the heat dissipation performance data;

[0020] The data collection principles include:

[0021] Only the water temperature, hydraulic oil temperature, intercooler temperature and fan speed of the independent cooling system are retained when the engine reaches thermal equilibrium;

[0022] If the construction machinery is not working or is working but has not reached thermal equilibrium, the data of that working day will be skipped;

[0023] The selection of working day data is set to a monthly cycle. If it crosses a month, the data will be invalid and the working day data will be re-selected for calculation.

[0024] Furthermore, data fitting is performed based on the heat dissipation performance data to obtain fitting formulas for various heat dissipation performance influencing factors and working time when thermal equilibrium is reached, including:

[0025] The heat dissipation performance data of each working day is taken as a data point, and multiple data points collected from multiple consecutive working days are fixedly taken as a group of data for each heat dissipation performance influencing factor. The heat dissipation performance data is iterated on a daily basis as the working time of the construction machinery;

[0026] A function model was established to fit the relationship between each heat dissipation performance influencing factor and the working time in the thermal equilibrium state with a set of data as a unit. The working time was calculated based on working days, and the fitting formulas of each heat dissipation performance influencing factor and the working time were obtained.

[0027] Furthermore, the function model is: ,

[0028] Among them, t i represents the working time of the i-th factor affecting heat dissipation performance, where t1, t2, t3, and t4 represent the working time of water temperature, oil temperature, intercooler temperature, and fan speed, respectively. i represents the i-th factor affecting heat dissipation performance, where Y1, Y2, Y3, and Y4 represent water temperature, oil temperature, intercooler temperature, and fan speed, respectively. i represents the slope of the fitting formula curve of the i-th heat dissipation performance influencing factor, where k1, k2, k3, and k4 represent the slopes of the fitting formula curves of water temperature, oil temperature, intercooler temperature, and fan speed, respectively, calculated through data points, b i Represents the intercept of the fitting formula curve of the i-th heat dissipation performance influencing factor, where b1, b2, b3, and b4 represent the intercepts of the fitting formula curves of water temperature, oil temperature, intercooler temperature, and fan speed, respectively. The values ​​of a and b are continuously iterated and updated as the number of working day data increases.

[0029] Furthermore, the correlation coefficient is .

[0030] Furthermore, the preset minimum threshold of the correlation coefficient is 0.08.

[0031] Furthermore, according to the fitting formula in which the correlation coefficient exceeds the preset minimum threshold of the correlation coefficient, combined with the correlation threshold of each heat dissipation performance influencing factor, the working time of each heat dissipation performance influencing factor is calculated, including:

[0032] Setting the water temperature threshold , Oil temperature threshold , Intercooler temperature threshold , Fan speed threshold , recorded as data set: ,

[0033] Assigning values ​​to the function model , respectively solve the working time of each factor affecting heat dissipation performance value, and analyze and compare the working hours of various heat dissipation performance influencing factors, and output the overall working hours of heat dissipation performance and the working hours of various heat dissipation performance influencing factors;

[0034] The overall working time of the heat dissipation performance is the minimum value of the working time of each heat dissipation performance influencing factor.

[0035] Furthermore, the function model includes any one or more of a linear relationship, an exponent, a power function, and a polynomial.

[0036] Furthermore, the method further comprises:

[0037] The overall working time of the heat dissipation performance and the working time of each heat dissipation performance influencing factor are sent to the instrument end, and a warning signal is output and displayed on the instrument end for the heat dissipation performance influencing factor whose working time is lower than a preset warning value.

[0038] Furthermore, the method further comprises:

[0039] Determine the alarm level of the prediction result of each heat dissipation performance influencing factor according to the correlation coefficient of the fitting formula of each heat dissipation performance influencing factor and the working time, and output and display it;

[0040] When the correlation coefficient of the fitting formula between the heat dissipation performance influencing factor and the working time is greater than the first threshold, determining that the alarm level of the prediction result of the heat dissipation performance influencing factor is level I;

[0041] When the correlation coefficient of the fitting formula of the heat dissipation performance influencing factor and the working time is less than the first threshold value and greater than the second threshold value, it is determined that the alarm level of the prediction result of the heat dissipation performance influencing factor is level II;

[0042] When the correlation coefficient of the fitting formula of the heat dissipation performance influencing factor and the working time is less than the second threshold value and greater than the minimum threshold value of the correlation coefficient, it is determined that the alarm level of the prediction result of the heat dissipation performance influencing factor is level III.

[0043] The technical effect achieved by the above settings is as follows: after determining the linear correlation, the prediction results are sent to the instrument in levels according to the strength of the correlation. If there is a strong correlation, a level I prediction result is sent, indicating that the credibility of the prediction result is very high; if there is a medium correlation, a level II prediction result is sent, indicating that the credibility of the prediction result is medium; if there is a weak correlation, a level III prediction result is sent, indicating that the credibility of the prediction result is not high.

[0044] In a second aspect, the present invention provides a device for predicting heat dissipation performance of hydraulic engineering machinery, comprising:

[0045] Data acquisition unit: used to obtain heat dissipation performance data; the heat dissipation performance data includes at least one heat dissipation performance influencing factor and thermal balance history data of the corresponding working time;

[0046] Data analysis and calculation unit: used for performing data fitting according to the heat dissipation performance data to obtain a fitting formula for each heat dissipation performance influencing factor and working time when thermal equilibrium is reached;

[0047] Formula analysis unit: calculates the correlation coefficient of the fitting formula of each heat dissipation performance influencing factor and working time; compares the correlation coefficient of the fitting formula of each heat dissipation performance influencing factor and working time with a preset minimum threshold of the correlation coefficient, and obtains a fitting formula whose correlation coefficient exceeds the preset minimum threshold of the correlation coefficient;

[0048] Prediction unit: used to calculate the working time of each heat dissipation performance influencing factor according to a fitting formula in which the correlation coefficient exceeds a preset minimum threshold of the correlation coefficient, combined with the relevant threshold of each heat dissipation performance influencing factor; and take the minimum value of the working time of each heat dissipation performance influencing factor as the predicted overall working time of the heat dissipation performance.

[0049] Compared with the prior art, the present invention has the following beneficial effects:

[0050] 1. The present invention uses the historical operating condition data of thermal balance of engineering machinery (including operating conditions, ambient temperature and other factors) to establish a functional relationship between the working time and the water temperature, oil temperature and fan speed when the engineering machinery reaches thermal balance under the current working conditions. By using the preset water temperature, oil temperature and fan speed thresholds and functional relationships, the overall working time of the radiator is predicted and the working time of each factor affecting the heat dissipation performance is reminded respectively.

[0051] 2. Consider the impact of various factors (water radiator, oil radiator, intercooler, etc.) on heat dissipation performance, predict the working time as the working status of the construction machinery changes dynamically every day, and output dynamic prediction results. The prediction results are displayed on the instrument end to remind customers of the changing trend of heat dissipation performance and the expected working time, remind customers to maintain the machine in time or find the fault point in time to avoid serious failures, causing downtime or major machine damage, and improve equipment safety.

[0052] 3. The present invention can dynamically analyze the changing trend of heat dissipation performance at all times and accurately predict the working time of the radiator. BRIEF DESCRIPTION OF THE DRAWINGS

[0053] Figure 1 Functional models established for data analysis and computational units;

[0054] Figure 2 A calculation method model for predicting heat dissipation performance. DETAILED DESCRIPTION

[0055] The present invention will be further described below in conjunction with the accompanying drawings. The following embodiments are only used to more clearly illustrate the technical solution of the present invention, and cannot be used to limit the protection scope of the present invention.

[0056] Embodiment 1:

[0057] The radiator performance of hydraulic engineering machinery (such as excavators, loaders, etc.) is affected by ambient temperature, working scene, working conditions, and radiator status. The prediction of radiator performance helps remind customers to maintain the machine in time or find fault points in time to avoid serious failures, causing downtime or major machine damage. However, there are many factors that affect radiator performance, and each influencing factor is difficult to quantify, making it difficult to make effective predictions.

[0058] In view of the characteristics of heat dissipation performance, this embodiment proposes a method for predicting the heat dissipation performance of hydraulic engineering machinery. Through the historical operating condition data of thermal balance of engineering machinery (including operating conditions, ambient temperature and other factors), a functional relationship between the working time and the water temperature, oil temperature and fan speed when the engineering machinery reaches thermal balance under the current working conditions is established. By using the preset water temperature, oil temperature and fan speed thresholds and functional relationships, the overall working time of the radiator is predicted and the working time of each heat dissipation performance influencing factor (water radiator, oil radiator, intercooler, etc.) is reminded respectively.

[0059] Considering the impact of various factors on heat dissipation performance, the predicted working hours change dynamically with the working status of the construction machinery every day, and the dynamic prediction results are output. The prediction results are displayed on the instrument end to remind customers of the changing trend of heat dissipation performance and the expected working hours.

[0060] The specific method includes the following steps:

[0061] Step 1: Collect data;

[0062] Step 2: Perform data analysis and calculation based on the collected data;

[0063] Step 3: Based on the results of data analysis and calculation, predict the heat dissipation performance of hydraulic engineering machinery.

[0064] Specifically, step 1: data collection includes: reading the engine water temperature A and intercooler temperature C from the engine electronic control unit ECU. The engine ECU water temperature A and intercooler temperature C data are derived from the engine water temperature sensor and the intake air temperature sensor. After collecting data, the signal is transmitted to the ECU, and then the whole machine instrument establishes communication with the ECU to read and display relevant data. Read the hydraulic oil temperature B and independent fan speed n from the whole machine instrument end, where the hydraulic oil temperature B is collected by the temperature sensor of the hydraulic oil tank and transmitted to the instrument end, and the independent fan speed n is collected by the speed sensor on the independent fan side and transmitted to the instrument end.

[0065] Due to the complex working conditions of vehicles and the changing environment, data collection needs to follow certain rules. First, the data collected is the water temperature A, hydraulic oil temperature B, intercooler temperature C and fan speed n of the independent cooling system when the engine reaches the thermal equilibrium state under the current working conditions; secondly, if the construction machinery is not working or is working but the water temperature has not reached the thermal equilibrium state during the data collection process, the working day will be skipped. The selection of working day data can be set to a maximum of one month as a cycle. If it crosses a month, the data will be invalid and re-selected and calculated.

[0066] Specifically, step 2: perform data analysis and calculation based on the collected data, including: a unit for data analysis and calculation integrated in the instrument end, whose main function is to select and arrange the data according to certain rules based on the collected data, and calculate the working time of the heat dissipation performance through the built-in function model combined with relevant thresholds .

[0067] Take each working day as a point to take a group of counts, and take N (N is a natural number greater than or equal to 3) consecutive working days to collect N data points as a group of data and transmit them to the data analysis and calculation unit. The selection of working day data is iterated daily with the working time of construction machinery. The selection of working day data should follow the above-mentioned data collection principles.

[0068] A function model was established to fit the relationship between water temperature, oil temperature, intercooler temperature and fan speed and working time in thermal equilibrium state.

[0069] Working hours are calculated based on working days. The unit of working hours is day, recorded as t.

[0070] Function model: ,

[0071] Among them, t i represents the working time of the i-th factor affecting heat dissipation performance, where t1, t2, t3, and t4 represent the working time of water temperature, oil temperature, intercooler temperature, and fan speed, respectively. irepresents the i-th factor affecting heat dissipation performance, where Y1, Y2, Y3, and Y4 represent water temperature, oil temperature, intercooler temperature, and fan speed, respectively. i represents the slope of the fitting formula curve of the i-th heat dissipation performance influencing factor, where k1, k2, k3, and k4 represent the slopes of the fitting formula curves of water temperature, oil temperature, intercooler temperature, and fan speed, respectively, calculated through data points, b i Represents the intercept of the fitting formula curve of the i-th heat dissipation performance influencing factor, where b1, b2, b3, and b4 represent the intercepts of the fitting formula curves of water temperature, oil temperature, intercooler temperature, and fan speed, respectively. b represents the intercept, which indicates the corresponding Y value at time t=0. The first point collected in the data group can be used as the t=0 data point. The values ​​of and b change continuously with the increase of working days t. The calculation is performed iteratively, and the correlation of the fitted linear curve is judged.

[0072] Setting the water temperature threshold , Oil temperature threshold , Intercooler temperature threshold , Fan speed threshold , recorded as data set: , based on historical test data, the preferred values ​​of this data set are {105℃, 90℃, 93℃, 1500RPM}.

[0073] Calculated from historical data and After that, the function model is established and the correlation coefficient of the function model is calculated. , using the correlation coefficient Determine the relevance of the fitting function curve at each point.

[0074] Then, assign , respectively solve the corresponding value, and calculate the result Analyze and compare, output the overall working time of heat dissipation performance and the working time of each heat dissipation performance influencing factor . The overall working time of heat dissipation performance is The minimum value of .

[0075] Specifically, step 3: predicting the heat dissipation performance of hydraulic engineering machinery based on the results of data analysis and calculation, including: an early warning unit integrated in the instrument end for judging and warning the calculated data according to certain rules and sending the early warning to the instrument display.

[0076] Preferably, the heat dissipation performance prediction data is displayed on the instrument side, early warning is given for the overall heat dissipation performance and each unit, and the failure risk of each heat dissipation performance influencing factor is identified.

[0077] The specific early warning process is as follows: First, according to the data analysis and calculation output results include: function model , and function parameters and , and the function model correlation coefficient And the working time of various factors affecting heat dissipation performance .

[0078] When a data set function model ≤0, the curve has no intersection with the threshold, which means that according to the current working condition prediction, the remaining time of the radiator performance is infinite. When the curve intersects the threshold value, it means that according to the current working condition prediction, there is a remaining time for the radiator performance to continue. Due to the discreteness of the data, the fitted functions are not always related, and the correlation coefficient is needed Determine the correlation of the fitting function curve at each point, A large value means that the collected data points have a strong linear correlation and the collected data have a certain development trend. At this time, the results can be predicted with high credibility. The opposite is not true.

[0079] The overall working time of the heat dissipation performance and the working time of each heat dissipation performance influencing factor are sent to the instrument end, and a warning signal is output and displayed on the instrument end for the heat dissipation performance influencing factor whose working time is lower than a preset warning value.

[0080] The preset warning value is set based on historical data, usually for 7 days.

[0081] According to the correlation coefficient The value of the alarm level is set, and the correlation coefficient A large value indicates a strong correlation, and a level I prediction result can be sent, indicating that the prediction result is highly reliable, and so on;

[0082] Using the correlation coefficient The correlation of the fitted linear curves at each point was determined, as shown in Table 1.

[0083] When the fitting curve shows a linear correlation, the intersection of the linear curve and the threshold is used to predict the working time of the components related to the heat dissipation performance.

[0084] Different levels of early warning information are sent to the instrument end according to the strength of the correlation; when the correlation coefficient of the fitting formula of the heat dissipation performance influencing factors and the working time is less than the minimum threshold of the correlation coefficient, that is, when the fitting curve shows linear irrelevance, the relevant data is discarded and the current heat dissipation performance working time prediction analysis of each component is not performed:

[0085] Determine the alarm level of the prediction result of each heat dissipation performance influencing factor according to the correlation coefficient of the fitting formula of each heat dissipation performance influencing factor and the working time, and output and display it;

[0086] When the correlation coefficient of the fitting formula between the heat dissipation performance influencing factor and the working time is greater than the first threshold, determining that the alarm level of the prediction result of the heat dissipation performance influencing factor is level I;

[0087] When the correlation coefficient of the fitting formula of the heat dissipation performance influencing factor and the working time is less than the first threshold value and greater than the second threshold value, it is determined that the alarm level of the prediction result of the heat dissipation performance influencing factor is level II;

[0088] When the correlation coefficient of the fitting formula of the heat dissipation performance influencing factor and the working time is less than the second threshold value and greater than the minimum threshold value of the correlation coefficient, it is determined that the alarm level of the prediction result of the heat dissipation performance influencing factor is level III.

[0089] The specific first threshold, the second threshold and the lowest threshold of the correlation coefficient are preferably: 0.81, 0.49 and 0.08. The specific relationship between the calculated correlation coefficient and the current correlation is shown in Table 1:

[0090] Table 1 Relationship with linear correlation

[0091]

[0092] The heat dissipation performance prediction calculation method model of this method is as follows Figure 2 shown.

[0093] The data acquisition and analysis device collects some data of the whole machine water temperature A, oil temperature B, intercooler temperature C, and independent fan speed n, which are recorded as: ,

[0094] The data is fitted using a linear equation, and the linear correlation coefficient is calculated, the linear correlation is determined and whether to predict the heat dissipation performance, and the predicted working time is calculated using the threshold and the fitting equation.

[0095] After using Table 1 to determine the linear correlation, the prediction results are sent to the instrument in different levels according to the strength of the correlation. If there is a strong correlation, a level I prediction result is sent, indicating that the credibility of the prediction result is very high; if there is a medium correlation, a level II prediction result is sent, indicating that the credibility of the prediction result is medium; if there is a weak correlation, a level III prediction result is sent, indicating that the credibility of the prediction result is not high.

[0096] If not relevant, no warning is sent. The warning unit sends the warning result to the instrument terminal for display, which is used to identify the failure risk of each heat dissipation performance influencing factor.

[0097] The functional fitting relationship between the heat dissipation performance and the working time of the present invention includes but is not limited to a linear relationship, including an exponential, a power function, a polynomial, etc., and an existing neural network structure may also be used.

[0098] Embodiment 2:

[0099] The present embodiment provides a device for predicting the heat dissipation performance of hydraulic engineering machinery, which mainly includes: a data acquisition unit, a data analysis and calculation unit, a formula analysis unit, a prediction unit, an instrument display and warning unit, and a data storage and calling unit.

[0100] Data acquisition unit: used to obtain heat dissipation performance data; the heat dissipation performance data includes at least one heat dissipation performance influencing factor and the corresponding thermal balance historical data of the working time; specifically used to collect the water temperature, hydraulic oil temperature, intercooler temperature and fan speed of the independent cooling system when the engine reaches a thermal equilibrium state under the current working conditions, and analyze and process the data, output water temperature A, oil temperature B, intercooler C, fan speed n, take a group of counts for each working day as a point, and take N data points collected from N consecutive working days as a group of data to be transmitted to the data analysis and calculation unit. The selection of working day data is iterated daily with the working time of the construction machinery. Taking N=7 as an example, assuming that the construction machinery works for several hours a day in July 2024, the data collection record format is: such as { } represents the first set of data recorded from July 1, 2024 to July 7, 2024; { } represents the second set of data recorded from July 2, 2024 to July 8, 2024, and so on. If the construction machinery is not working, the working day will be skipped.

[0101] Data analysis and calculation unit: used for performing data fitting according to the heat dissipation performance data to obtain a fitting formula for each heat dissipation performance influencing factor and working time when thermal equilibrium is reached;

[0102] Formula analysis unit: calculates the correlation coefficient of the fitting formula of each heat dissipation performance influencing factor and working time; compares the correlation coefficient of the fitting formula of each heat dissipation performance influencing factor and working time with a preset minimum threshold of the correlation coefficient, and obtains a fitting formula whose correlation coefficient exceeds the preset minimum threshold of the correlation coefficient;

[0103] The data analysis and calculation unit is used to establish a function model to fit the relationship between the water temperature, oil temperature, intercooler temperature and fan speed and the working hours in the thermal equilibrium state. The working hours are calculated based on working days. The unit of working hours is day, recorded as t. The function model is: , Y can represent water temperature, oil temperature, intercooler temperature, fan speed, represents the slope of the curve, which can be calculated from the data points. b represents the intercept, which indicates the time t=0. The corresponding Y value can be obtained by using the first point collected in the data set as the t=0 data point. The values ​​of and b change continuously with the increase of working days t. The calculation is performed iteratively, and the correlation of the fitted linear curve is judged.

[0104] Prediction unit: used to calculate the working time of each heat dissipation performance influencing factor according to a fitting formula in which the correlation coefficient exceeds a preset minimum threshold of the correlation coefficient, combined with the relevant threshold of each heat dissipation performance influencing factor; and take the minimum value of the working time of each heat dissipation performance influencing factor as the predicted overall working time of the heat dissipation performance.

[0105] Setting the water temperature threshold , Oil temperature threshold , Intercooler temperature threshold , Fan speed threshold , recorded as data set: , calculated from historical data and After that, assign , respectively solve the corresponding value, and calculate the result Analyze and compare, output the overall working time of heat dissipation performance and the working time of each heat dissipation performance influencing factor. The minimum value of .

[0106] The instrument display warning unit is used to display the heat dissipation performance prediction data on the instrument end, issue warnings for the overall heat dissipation performance and each unit, and identify the failure risks of each heat dissipation performance influencing factor.

[0107] Taking the water temperature threshold of 105℃ and the thermostat fully open temperature of 85℃ as an example, the data analysis of the thermal equilibrium state achieved in 12 working days is statistically analyzed. The function model established by the data analysis and calculation unit is shown in the attached figure. Figure 1 The intersection of the fitted curve and the water temperature threshold is the remaining time that the radiator can continue to work under the current working conditions. When ≤0, the curve has no intersection with the threshold, which means that according to the current working condition prediction, the remaining time of the radiator performance is infinite.

[0108] Using the correlation coefficient The correlation of the fitted linear curves at each point was determined, as shown in Table 1.

[0109] When the fitting curve shows a linear correlation, the intersection of the linear curve and the threshold is used to predict the working time of the components related to the heat dissipation performance.

[0110] Different levels of early warning information are sent to the instrument end according to the strength of the correlation; when the correlation coefficient of the fitting formula of the heat dissipation performance influencing factors and the working time is less than the minimum threshold of the correlation coefficient, that is, when the fitting curve shows linear irrelevance, the relevant data is discarded and the current heat dissipation performance working time prediction analysis of each component is not performed:

[0111] Determine the alarm level of the prediction result of each heat dissipation performance influencing factor according to the correlation coefficient of the fitting formula of each heat dissipation performance influencing factor and the working time, and output and display it;

[0112] When the correlation coefficient of the fitting formula between the heat dissipation performance influencing factor and the working time is greater than the first threshold, determining that the alarm level of the prediction result of the heat dissipation performance influencing factor is level I;

[0113] When the correlation coefficient of the fitting formula of the heat dissipation performance influencing factor and the working time is less than the first threshold value and greater than the second threshold value, it is determined that the alarm level of the prediction result of the heat dissipation performance influencing factor is level II;

[0114] When the correlation coefficient of the fitting formula of the heat dissipation performance influencing factor and the working time is less than the second threshold value and greater than the minimum threshold value of the correlation coefficient, it is determined that the alarm level of the prediction result of the heat dissipation performance influencing factor is level III.

[0115] The specific first threshold, the second threshold and the lowest threshold of the correlation coefficient are preferably: 0.81, 0.49 and 0.08. The specific relationship between the calculated correlation coefficient and the current correlation is shown in Table 1.

[0116] The heat dissipation performance prediction calculation method model of this method is as follows Figure 2 shown.

[0117] The data acquisition and analysis device collects some data of the whole machine water temperature A, oil temperature B, intercooler temperature C, and independent fan speed n, which are recorded as: ,

[0118] The data is fitted using a linear equation, and the linear correlation coefficient is calculated, the linear correlation is determined and whether to predict the heat dissipation performance, and the predicted working time is calculated using the threshold and the fitting equation.

[0119] After using Table 1 to determine the linear correlation, the prediction results are sent to the instrument in different levels according to the strength of the correlation. If there is a strong correlation, a level I prediction result is sent, indicating that the credibility of the prediction result is very high; if there is a medium correlation, a level II prediction result is sent, indicating that the credibility of the prediction result is medium; if there is a weak correlation, a level III prediction result is sent, indicating that the credibility of the prediction result is not high.

[0120] If not relevant, no warning is sent. The warning unit sends the warning result to the instrument terminal for display, which is used to identify the failure risk of each heat dissipation performance influencing factor.

[0121] Heat dissipation performance prediction calculation method model Figure 2 shown.

[0122] The data acquisition and analysis device collects some data of the whole machine water temperature A, oil temperature B, intercooler temperature C, and independent fan speed n, which are recorded as: ,

[0123] The data is transmitted to the data analysis and calculation unit, and the data is fitted using a linear equation, and the linear correlation coefficient is calculated, the linear correlation and whether to predict the heat dissipation performance are determined, and the predicted working time is calculated using a threshold and a fitting equation.

[0124] After using Table 1 to determine the linear correlation, the prediction results are sent to the instrument in different levels according to the strength of the correlation. If there is a strong correlation, a level I prediction result is sent, indicating that the credibility of the prediction result is very high; if there is a medium correlation, a level II prediction result is sent, indicating that the credibility of the prediction result is medium; if there is a weak correlation, a level III prediction result is sent, indicating that the credibility of the prediction result is not high.

[0125] Embodiment 3:

[0126] This embodiment provides a device for predicting heat dissipation performance of hydraulic engineering machinery, including a processor and a storage medium;

[0127] The storage medium is used to store instructions;

[0128] The processor is used to operate according to the instructions to execute the steps of the method according to embodiment 1.

[0129] Embodiment 4:

[0130] This embodiment provides an engineering machine, including a controller and an instrument terminal;

[0131] The instrument terminal is used to display warning information; the controller is used to execute the steps of the method described in Example 1 to predict and warn the heat dissipation performance.

[0132] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems, or computer program products. Therefore, the present application may adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the present application may adopt the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program codes.

[0133] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0134] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0135] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process in the computer or other programmable device. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0136] The above is only a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the technical principles of the present invention. These improvements and modifications should also be regarded as the scope of protection of the present invention.

Claims

1. A method for predicting heat dissipation performance of hydraulic engineering machinery, characterized in that: The following steps are involved: Acquire heat dissipation performance data; the heat dissipation performance data includes at least one heat dissipation performance influencing factor and thermal balance history data of corresponding working hours; Performing data fitting based on the heat dissipation performance data to obtain a fitting formula for each heat dissipation performance influencing factor and working time when thermal equilibrium is reached; Calculate the correlation coefficient of the fitting formula of each heat dissipation performance influencing factor and working time; Compare the correlation coefficient of the fitting formula of each heat dissipation performance influencing factor and the working time with a preset minimum threshold of the correlation coefficient, and obtain a fitting formula whose correlation coefficient exceeds the preset minimum threshold of the correlation coefficient; According to the fitting formula when the correlation coefficient exceeds the preset minimum threshold of the correlation coefficient, combined with the correlation threshold of each heat dissipation performance influencing factor, the working time of each heat dissipation performance influencing factor is calculated; The minimum value of the working time of each heat dissipation performance influencing factor is taken as the predicted overall working time of the heat dissipation performance.

2. The method for predicting heat dissipation performance of hydraulic engineering machinery according to claim 1, characterized in that: The factors affecting the heat dissipation performance include water temperature, hydraulic oil temperature, intercooler temperature and fan speed of the independent heat dissipation system.

3. The method for predicting heat dissipation performance of hydraulic engineering machinery according to claim 2, characterized in that: Methods for obtaining thermal performance data include: Read the stored engine water temperature and intercooler temperature from the engine electronic control unit ECU; the water temperature and intercooler temperature data of the engine ECU are derived from the heat balance history data of the heat dissipation performance influencing factors and the corresponding working hours collected by the engine water temperature sensor and the intake air temperature sensor; The hydraulic oil temperature and the fan speed of the independent cooling system are read from the instrument end of the whole machine; the hydraulic oil temperature at the instrument end of the whole machine is collected by the temperature sensor of the hydraulic oil tank, and the fan speed is collected by the speed sensor on the independent fan side; The data are screened according to the data collection principle, and the screened data are obtained as the heat dissipation performance data.

4. The method for predicting heat dissipation performance of hydraulic engineering machinery according to claim 2, characterized in that: Data fitting is performed according to the heat dissipation performance data to obtain fitting formulas for various heat dissipation performance influencing factors and working time when thermal equilibrium is reached, including: The heat dissipation performance data of each working day is taken as a data point, and multiple data points collected from multiple consecutive working days are fixedly taken as a group of data for each heat dissipation performance influencing factor. The heat dissipation performance data is iterated on a daily basis as the working time of the construction machinery; A function model was established to fit the relationship between each heat dissipation performance influencing factor and the working time in the thermal equilibrium state with a set of data as a unit. The working time was calculated based on working days, and the fitting formulas of each heat dissipation performance influencing factor and the working time were obtained.

5. The method for predicting heat dissipation performance of hydraulic engineering machinery according to claim 4, characterized in that: The function model is: , Among them, t i represents the working time of the i-th factor affecting heat dissipation performance, where t1, t2, t3, and t4 represent the working time of water temperature, oil temperature, intercooler temperature, and fan speed, respectively. i represents the i-th factor affecting heat dissipation performance, where Y1, Y2, Y3, and Y4 represent water temperature, oil temperature, intercooler temperature, and fan speed, respectively. i represents the slope of the fitting formula curve of the i-th heat dissipation performance influencing factor, where k1, k2, k3, and k4 represent the slopes of the fitting formula curves of water temperature, oil temperature, intercooler temperature, and fan speed, respectively, which are obtained by fitting the data points. i Represents the intercept of the fitting formula curve of the i-th heat dissipation performance influencing factor, where b1, b2, b3, and b4 represent the intercepts of the fitting formula curves of water temperature, oil temperature, intercooler temperature, and fan speed, respectively. The values ​​of and b are iteratively calculated and updated as the number of working days t increases.

6. The method for predicting heat dissipation performance of hydraulic engineering machinery according to claim 1, characterized in that: The correlation coefficient is .

7. The method for predicting heat dissipation performance of hydraulic engineering machinery according to claim 5, characterized in that: According to the fitting formula when the correlation coefficient exceeds the preset minimum threshold of the correlation coefficient, combined with the correlation threshold of each heat dissipation performance influencing factor, the working time of each heat dissipation performance influencing factor is calculated, including: Setting the water temperature threshold , Oil temperature threshold , Intercooler temperature threshold , Fan speed threshold , recorded as data set: , Assigning values ​​to the function model , respectively solve the working time value of each heat dissipation performance influencing factor, analyze and compare the working time of each heat dissipation performance influencing factor, and output the overall working time of heat dissipation performance and the working time of each heat dissipation performance influencing factor; The overall working time of the heat dissipation performance is the minimum value of the working time of each heat dissipation performance influencing factor.

8. The method for predicting heat dissipation performance of hydraulic engineering machinery according to claim 1, characterized in that: The method further comprises: The overall working time of the heat dissipation performance and the working time of each heat dissipation performance influencing factor are sent to the instrument end, and a warning signal is output and displayed on the instrument end for the heat dissipation performance influencing factor whose working time is lower than a preset warning value.

9. The method for predicting heat dissipation performance of hydraulic engineering machinery according to claim 8, characterized in that: The method further comprises: Determine the alarm level of the prediction result of each heat dissipation performance influencing factor according to the correlation coefficient of the fitting formula of each heat dissipation performance influencing factor and the working time, and output and display it; When the correlation coefficient of the fitting formula between the heat dissipation performance influencing factor and the working time is greater than the first threshold, determining that the alarm level of the prediction result of the heat dissipation performance influencing factor is level I; When the correlation coefficient of the fitting formula between the heat dissipation performance influencing factor and the working time is less than the first threshold value and greater than the second threshold value, it is determined that the alarm level of the prediction result of the heat dissipation performance influencing factor is level II; When the correlation coefficient of the fitting formula of the heat dissipation performance influencing factor and the working time is less than the second threshold value and greater than the minimum threshold value of the correlation coefficient, it is determined that the alarm level of the prediction result of the heat dissipation performance influencing factor is level III.

10. A device for predicting heat dissipation performance of hydraulic engineering machinery, characterized in that: include: Data acquisition unit: used to obtain heat dissipation performance data; the heat dissipation performance data includes at least one heat dissipation performance influencing factor and thermal balance history data of the corresponding working time; Data analysis and calculation unit: used for performing data fitting according to the heat dissipation performance data to obtain a fitting formula for each heat dissipation performance influencing factor and working time when thermal equilibrium is reached; Formula analysis unit: calculates the correlation coefficient of the fitting formula of each heat dissipation performance influencing factor and working time; Compare the correlation coefficient of the fitting formula of each heat dissipation performance influencing factor and the working time with a preset minimum threshold of the correlation coefficient, and obtain a fitting formula whose correlation coefficient exceeds the preset minimum threshold of the correlation coefficient; Prediction unit: used to calculate the working time of each heat dissipation performance influencing factor according to a fitting formula in which the correlation coefficient exceeds a preset minimum threshold of the correlation coefficient, combined with the relevant threshold of each heat dissipation performance influencing factor; and take the minimum value of the working time of each heat dissipation performance influencing factor as the predicted overall working time of the heat dissipation performance.

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