Lubricating oil flow detection method and system for filling production line

Abstract

The invention relates to the field of flow detection, in particular to a lubricating oil flow detection method and system for a filling production line. The method comprises the steps that the flow value of lubricating oil and the rotation time and the blade reset time after turbine blade pretreatment are collected; constructing a round window, and calculating the first adhesion degree of the turbine blade in any round; calculating a level value and a trend value of the reset time sequence by utilizing a Holt linear trend method so as to calculate a second adhesion degree of any round; the average value of the first adhesion degree and the second adhesion degree serves as the total adhesion degree of any round, the total adhesion degree of each blade of the same turbine in the same round is obtained through traversal and used for correcting the flow value, the actual flow value is obtained, and flow detection is completed. By means of the technical scheme, the precision of the lubricating oil flow detection result can be improved, and it is ensured that a production line obtains sufficient lubrication in the running process.

Description

Technical Field

[0001] The present invention relates to the field of flow detection. More specifically, the present invention relates to a method and system for detecting the flow rate of lubricating oil in a filling production line. Background Art

[0002] In the process of modern industrial production, the filling production line is a crucial part of manufacturing, especially in industries such as petroleum, chemical, food, and beverage, where the requirement for automation is extremely high. The filling equipment needs to accurately, quickly, and stably fill the products into the packaging containers. As the key fluid for the operation of mechanical equipment, the lubricating oil must maintain an appropriate flow rate and stable supply to reduce the friction between mechanical components, improve equipment efficiency and service life.

[0003] However, with the expansion of production scale and the increase in production line speed, traditional methods for detecting the flow rate of lubricating oil face great challenges. Mechanical or electrical flow meters often cannot meet the requirements of high precision and high stability, and there are problems such as difficult maintenance, easy damage, and susceptibility to environmental influence. To solve this problem, using a turbine flow meter to measure the flow rate of lubricating oil has become an effective solution. When the fluid flows through the turbine, the rotation speed of the turbine is proportional to the fluid flow rate. By monitoring the lubricating oil flow rate in real time, it can ensure that the equipment obtains sufficient lubrication during operation, prevent wear, faults, and shutdowns caused by insufficient lubrication, thereby improving production efficiency, extending equipment life, and reducing maintenance costs.

[0004] However, even if the turbine flow meter has been calibrated and calibrated at the factory, the change in the viscosity of the lubricating oil will still affect the measurement accuracy. The viscosity of the lubricating oil changes at different temperatures. When the viscosity increases, the turbine blades will encounter greater resistance when rotating in the fluid, resulting in a slowdown in the blade rotation speed. The decrease in rotation speed means that the measurement signal of the flow meter will also decrease accordingly. Therefore, the reading of the flow meter will be lower than the actual flow rate, resulting in inaccurate detection results of the lubricating oil flow rate. Summary of the Invention

[0005] To solve the problem of inaccurate detection results of the lubricating oil flow rate, the present invention provides solutions in the following aspects.

[0006] First aspect, the present invention discloses a lubricating oil flow rate detection method for a filling production line, including: collecting the flow rate value of the lubricating oil, as well as the rotation time and blade reset time after preprocessing of the turbine blade; constructing a round window for any round, and calculating the first adhesion degree of the turbine blade in any round according to the rotation time within the round window; constructing the blade reset time of each round in the round window into a reset time sequence, and using the Holt linear trend method to calculate the level value and trend value of the reset time sequence, so as to calculate the second adhesion degree of any round; taking the average value of the first adhesion degree and the second adhesion degree as the total adhesion degree of any round, traversing to obtain the total adhesion degree of each blade of the same turbine in the same round, for correcting the flow rate value to obtain the true flow rate value, and completing the flow rate detection.

[0007] By detecting the adhesion degree of the blade in real time, it can reflect the influence of the lubricating oil flow on the blade movement performance, thereby providing more accurate data support for the correction of the flow rate. Through the corrected true flow rate value, the accuracy of the flow rate detection can be effectively improved, avoiding errors caused by adhesion problems, and ensuring the operation efficiency and stability of the turbine system under different working conditions. In addition, this method can capture the changes in the blade state at different time periods and provide long-term reliable operation data.

[0008] Preferably, the preprocessing includes: using a filtering algorithm to remove the noise of the rotation time and the blade reset time, and using polynomial interpolation to fill in the rotation time and the blade reset time.

[0009] Preferably, the round window includes: taking any round as the target round, and constructing a round window with a continuous preset number of rounds in the order of the round size, where the target round is the largest round within the round window.

[0010] Preferably, the first adhesion degree satisfies the relational expression: , represents the first adhesion degree of round , represents the rotation time of round , represents the average value of the rotation time in the round window, represents the rotation time of round , represents the round window length, represents the normalization function.

[0011] The first adhesion can reflect the amplitude of the fluctuation of the rotation time of the turbine blade. Specifically, it measures the time fluctuation caused by the lubricating oil flow and the change of friction force during the rotation of the blade. Through normalization, this measurement can eliminate the interference of external factors such as temperature fluctuation or system load change on the result, ensuring the calculation of adhesion is more accurate. Thus, the first adhesion not only reveals the influence of the lubricating oil on the blade rotation efficiency, but also can reflect the friction condition of the blade under different working conditions.

[0012] Preferably, the first adhesion satisfies the relational expression: , represents the round of the first adhesion, represents the round of the rotation time, represents the average value of the rotation time in the round window, represents the round of the rotation time, represents the length of the round window, represents the normalization function.

[0013] Through normalization, the influence of external interference factors can be effectively reduced, ensuring the calculation of adhesion is more accurate. Specifically, the first adhesion can reflect the mutual relationship between the lubricating oil flow and the blade rotation efficiency, revealing the influence of the change of friction force during the operation of the blade. This method not only provides a more accurate index for evaluating the working state of the blade, but also can identify the change of the lubricating oil flow in advance by monitoring the change of adhesion.

[0014] Preferably, the second adhesion satisfies the relational expression: , represents the round of the second adhesion, represents the level value of the round , represents the average value of the level values in the round window, represents the round of the trend value, represents the average value of the trend values in the round window, represents the normalization function.

[0015] Preferably, the second adhesion also satisfies the relational expression: , represents the round of the second adhesion, represents the round of the level value, represents the round The blade reset time represents the mean value of the horizontal values in the round window represents the round trend value represents the mean value of the trend values in the round window represents the normalization function

[0016] It can sensitively capture the abnormal fluctuations of the blade reset time, whether the blade is affected by external load changes, accumulated resistance or impurities, or the overall performance decline trend of the turbine system. In this way, it can timely identify whether the blade adhesion phenomenon occurs and the degree of adhesion, providing strong data support for the preventive maintenance of the turbine equipment, helping to improve the stability of the turbine operation and the accuracy of the lubricating oil flow detection result

[0017] Preferably, the total adhesion degree further includes: obtaining the influence weights of the first adhesion degree and the second adhesion degree respectively through the particle swarm optimization algorithm, and taking the result of summing the products of the first adhesion degree and the second adhesion degree and the influence weights respectively as the total adhesion degree

[0018] Preferably, the obtaining of the true flow value includes: calculating the average cumulative value of the total adhesion degree of each blade of the same turbine in the same round, and calculating the product of the normalized average cumulative value and the preset weight, and taking the sum of 1 and the product as the correction factor of the flow value for correcting the flow value to obtain the true flow value

[0019] By introducing the correction factor and applying it to the correction of the flow value, not only can the accuracy of the flow measurement be improved, but also the flow state of the turbine in actual operation can be more truly reflected, and this process ensures that the performance evaluation of the turbine system under different working conditions is more accurate

[0020] In a second aspect, the present invention discloses a lubricating oil flow detection system for a filling production line, including: a processor and a memory, where the memory stores computer program instructions, and when the computer program instructions are executed by the processor, any one of the lubricating oil flow detection methods for a filling production line is implemented

[0021] Advantages of the present invention 1. By combining the dynamic changes of the rotation time and reset time of the turbine blade, the present invention accurately detects the lubricating oil flow by using the concept of adhesion degree, and can effectively correct the deviation caused by the flow measurement error, so as to obtain a more accurate true flow value

[0022] 2. The trend change of the reset time series is predicted by the Holt linear trend method, enabling the present invention to still perform flow detection stably and accurately in a working environment with rapid dynamic changes. The particle swarm optimization algorithm is used to dynamically adjust the influence weights of the first adhesion and the second adhesion, further ensuring high-precision detection under different working conditions.

[0023] 3. The present invention has good robustness and adaptability in actual production, can greatly improve the accuracy of flow measurement, reduce the influence of manual errors on the production process, and thus ensure the smooth operation of the filling production line under different load conditions. BRIEF DESCRIPTION OF THE DRAWINGS

[0024] Figure 1 is a flowchart of a lubricating oil flow detection method for a filling production line according to an embodiment of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0025] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, rather than all of the embodiments.

[0026] The following will describe in detail the specific embodiments of the present invention in conjunction with the accompanying drawings.

[0027] Referring to Figure 1 , a lubricating oil flow detection method for a filling production line includes steps S1 - S4, which are specifically described below.

[0028] S1: Collect the flow value of the lubricating oil, the rotation time of the turbine blade after pretreatment, and the blade reset time.

[0029] In one embodiment, selecting a suitable installation position is the key to ensuring accurate flow measurement by the turbine flowmeter. The turbine flowmeter should be installed on a straight pipe section and avoid installing it in places such as elbows and valves that may cause fluid disturbance, because the fluid flow in these areas is unstable and easily affects the accuracy of the flowmeter. After installation, it is necessary to calibrate the turbine flowmeter using a standard device with a known flow rate to ensure the accuracy and reliability of its measurement results.

[0030] In a turbine flowmeter, to accurately measure the flow rate of lubricating oil, a magnetic sensor at a fixed position can be installed near the turbine blades, and magnetic materials can be installed on each blade of the turbine to facilitate the identification and tracking of each blade. When the turbine blades rotate and the magnetic materials pass by the sensor, it will cause a change in the magnetic field, triggering the sensor to generate a voltage pulse signal. The Hall effect sensor is installed on the turbine housing to ensure that the signal of the magnetic field change on the blade can be detected. By measuring the time interval between these pulse signals, the rotation time of each turbine blade and the blade reset time can be accurately obtained. The rotation time and the blade reset time are synchronously collected in real - time to ensure that the flowmeter can accurately reflect the change in fluid flow rate.

[0031] The rotation time of each turbine blade represents the time consumed when the blade passes by the magnetic sensor, that is, each time the blade passes by the magnetic sensor, the pulse interval time recorded by the magnetic sensor.

[0032] The blade reset time represents the time elapsed from when a certain blade passes by the sensor until the blade returns to its original position again. Since the position of the turbine blades is fixed and the relative position between the blades does not change, the reset time data of any one blade can be used as a representative of the overall blade reset time in the current rotation process.

[0033] To improve the accuracy and reliability of the rotation time and the blade reset time, first, a filtering algorithm needs to be used to remove noise from the data of the rotation time and the blade reset time. Since the data collected by the magnetic sensor may be affected by environmental interference or system errors, adopting a suitable filtering algorithm can effectively remove high - frequency noise, retain useful signals, ensure that the data is smoother and more stable. Filtering algorithms such as low - pass filtering, Kalman filtering, etc.

[0034] Use the polynomial interpolation method to fill in the missing values or incomplete data in the rotation time and the blade reset time. Polynomial interpolation can, through known sample data points, use a mathematical model to calculate the values of unknown points, and then smooth the data, fill in the gaps, and ensure the continuity and integrity of the data.

[0035] Pre - processing can effectively eliminate the influence of noise, fill in the missing data, and finally obtain more accurate and reliable rotation time and reset time data, thereby improving the accuracy of flow measurement and the stability of the system.

[0036] S2: Construct a round window for any round, and calculate the first adhesion of the turbine blade in any round according to the rotation time within the round window.

[0037] It should be noted that the rotation time data of each turbine blade represents the time consumed when the blade passes by the magnetic sensor. If all the blades are of the same size and there is no adhesion during the lubricating oil flow, then the rotation time of each blade when passing by the sensor should be the same, because they are subject to the same lubricating oil flow resistance and have the same rotation speed. However, if adhesion occurs to some blades, due to the increased friction between the adhered blades and the fluid or the uneven distribution of the flow resistance, the adhered blades will be subject to a greater flow resistance. Therefore, when these adhered blades pass by the sensor, they will take more time, manifested as a longer rotation time.

[0038] In one embodiment, a round window of any round is constructed. The method for constructing the round window is as follows: taking any round as the target round, and constructing a continuous preset number of rounds as the round window in the order of round size, where the target round is the largest round within the round window.

[0039] Calculate the first adhesion degree, and the first adhesion degree satisfies the relational expression: , represents the first adhesion degree of round , represents the rotation time of round , represents the average value of the rotation times in the round window, represents the rotation time of round , represents the length of the round window, represents the normalization function.

[0040] Among them, reflects the deviation degree of the rotation time of round , that is, the difference between the rotation time of this round and the average rotation time. If the deviation is large, it indicates that there is an abnormality in the blades of this round, that is, there is adhesion; while can reflect the volatility of the rotation time of round . If the volatility is large, it means that there is a large volatility in the rotation times in the overall system, which may indicate that some blades are affected by uneven resistance or adhesion.

[0041] In one embodiment, the first adhesion degree satisfies the relational expression: , represents the first adhesion degree of round , represents the rotation time of round , represents the average value of the rotation times in the round window, represents the round the rotation time of represents the round window length, represents the normalization function.

[0042] Using to measure the difference between the rotation time of the round and the mean value, thereby revealing the deviation of the rotation time. The denominator part balances the volatility between different rounds by performing a weighted sum of the fluctuations of all rotation times within the window, ensuring higher stability and accuracy of the solution when dealing with the fluctuations of the rotation time.

[0043] S3: Construct the blade reset time of each round in the round window into a reset time series, and use the Holt linear trend method to calculate the level value and trend value of the reset time series to calculate the second adhesion of any round.

[0044] It should be noted that the reset time data of each turbine blade reflects the time required for the blade to return to its original position from the moment it passes the sensor. Under normal circumstances, due to the consistent size of all blades and the influence of the same lubricating oil flow resistance, the reset time is relatively constant. However, when the turbine starts to be subject to resistance, impurities or dirt gradually accumulate on the blades, increasing the frictional resistance of the blades and making it more difficult for the blades to rotate. This increase in resistance causes the reset time of the turbine blades to gradually extend, showing an approximately linear upward trend. At the same time, due to the influence of resistance on the fluid flow, the flow rate usually decreases with the increase in resistance, showing an approximately linear downward trend. This phenomenon indicates that as the turbine blades are affected by more and more resistance, the blade reset time increases and the flow rate decreases, reflecting the decline in the efficiency of the turbine system.

[0045] In one embodiment, the blade reset time of each round in the round window is constructed into a reset time series, and the Holt linear trend method is used to calculate the level value and trend value of the reset time series. The Holt linear trend method is a method for time series prediction, which is a prior art and is applicable to data with a linear trend. It calculates the level value and trend value through two smoothing equations. The level value represents the current level or average value of the data; the trend value represents the trend of data change, that is, the rise and fall of the data.

[0046] Calculate the second adhesion, and the second adhesion satisfies the relationship: , represents the round of the second adhesion, represents the round of the level value, represents the mean value of the level values in the round window, represents the round The trend value represents the mean of the trend values in the round window represents the normalization function

[0047] The Holt linear trend method obtains the level value and trend value of the reset time through smoothing. The level value reflects the average state of the reset time, while the trend value reveals the direction and amplitude of the change of the reset time over time. When calculating the second adhesion, the deviation between the level value and the trend value is combined, which can further quantify the abnormal fluctuation degree of the reset time. When the turbine blade is affected by gradually increasing resistance or impurities, the level value and trend value of the reset time will shift significantly. The calculation of the second adhesion can accurately reflect this change, helping to detect potential blade adhesion problems in a timely manner. Through this method, the system can accurately monitor the blade state during the operation of the turbine, effectively improve the accuracy of fault prediction, provide reliable data support for the maintenance and optimization of the turbine, reduce the occurrence of faults, and improve the overall efficiency and stability of the system

[0048] In one embodiment, the second adhesion also satisfies the relational expression , represents the round the second adhesion of represents the round the level value of represents the round the blade reset time of represents the mean of the level values in the round window represents the round the trend value of represents the mean of the trend values in the round window represents the normalization function

[0049] The second adhesion reflects the deviation degree of the reset time from its smoothed expected level and trend. In particular, the deviation is smoothed through the cube root operation, effectively avoiding the influence of a single large deviation on the result

[0050] S4: Use the mean of the first adhesion and the second adhesion as the total adhesion of any round, traverse to obtain the total adhesion of each blade of the same turbine in the same round, use it to correct the flow value, obtain the true flow value, and complete the flow detection

[0051] It should be noted that when the blade rotates, it is affected by the flow of lubricating oil, and different first adhesion degrees may be generated, that is, the change in the friction force between the blade and the lubricating oil during the movement process. This change directly affects the movement performance of the blade. The reset time data reflects the overall adhesion condition of the blade when it returns to the starting position after completing a cycle, that is, the second adhesion degree, which includes the adhesion change of the blade under the action of external factors (such as air flow, resistance, etc.). When the first adhesion degree and the second adhesion degree are combined, the adhesion situation of the blade during rotation can be comprehensively evaluated, and by quantifying the influence of the two, the overall working state of the blade can be more accurately understood.

[0052] In one embodiment, the average value of the first adhesion degree and the second adhesion degree is used as the total adhesion degree of any round. Traverse to obtain the total adhesion degree of each blade of the same turbine in the same round, calculate the average cumulative value of the total adhesion degree of each blade of the same turbine in the same round, and calculate the product of the normalized average cumulative value and the preset weight. The sum of 1 and the product is used as the correction factor of the flow value to correct the flow value to obtain the true flow value, thereby completing the flow detection. Exemplarily, the preset weight is set to 0.5.

[0053] In one embodiment, the total adhesion degree further includes: obtaining the influence weights of the first adhesion degree and the second adhesion degree respectively through the particle swarm optimization algorithm, and taking the result of summing the products of the first adhesion degree and the second adhesion degree and the influence weights as the total adhesion degree.

[0054] The system includes a processor and a memory. The memory stores computer program instructions, and when the computer program instructions are executed by the processor, a steam leakage detection method for a waste heat recovery system according to the first aspect of the present invention is implemented.

[0055] The system further includes a communication bus and a communication interface and other components well known to those skilled in the art. Their settings and functions are known in the art, so they will not be described in detail here.

[0056] It should be noted that for those of ordinary skill in the art, without departing from the concept of the present application, several deformations and improvements can be made, and these all belong to the protection scope of the present application. Therefore, the protection scope of the patent of the present application shall be subject to the appended claims.

Claims

1. A lubricating oil flow detection method for a filling production line, characterized in that, Including: Collecting the flow value of the lubricating oil, the rotation time after the pre-treatment of the turbine blade, and the blade reset time; Constructing a round window for any round, and calculating the first adhesion of the turbine blade in any round according to the rotation time within the round window; Constructing the blade reset time of each round in the round window into a reset time series, and using the Holt linear trend method to calculate the level value and trend value of the reset time series to calculate the second adhesion of any round; Taking the average value of the first adhesion and the second adhesion as the total adhesion of any round, traversing to obtain the total adhesion of each blade of the same turbine in the same round, and using it to correct the flow value to obtain the true flow value, thereby completing the flow detection.

2. The lubricating oil flow rate detection method for a filling production line according to claim 1, wherein, The pre-treatment includes: Using a filtering algorithm to remove the noise of the rotation time and the blade reset time, and using polynomial interpolation to fill in the rotation time and the blade reset time.

3. A lubricating oil flow detection method for a filling production line according to claim 1, characterized in that, The round window includes: Taking any round as the target round, and constructing a round window with a continuous preset number of rounds in the order of the round size, where the target round is the largest round within the round window.

4. A lubricating oil flow rate detection method for a filling production line according to claim 1, characterized in that, The first adhesion satisfies the relationship: , represents the round of the first adhesion represents the round of the rotation time represents the mean value of the rotation time in the round window represents the round of the rotation time represents the round window length represents the normalization function 5. A lubricating oil flow rate detection method for a filling production line according to claim 1, characterized in that, The first adhesion satisfies the relationship: , represents the round of the first adhesion degree, represents the round of the rotation time, represents the average value of the rotation time in the round window, represents the round of the rotation time, represents the length of the round window, represents the normalization function.

6. A lubricating oil flow rate detection method for a filling production line according to claim 1, characterized in that, The second adhesion satisfies the relationship: , represents the round of the second adhesion degree, represents the round of the horizontal value, represents the average value of the horizontal values in the round window, represents the round of the trend value, represents the average value of the trend values in the round window, represents the normalization function.

7. A lubricating oil flow rate detection method for a filling production line according to claim 6, characterized in that, The second adhesion also satisfies the relationship: , indicates the round of the second adhesion degree, indicates the round of the horizontal value, indicates the round of the blade reset time, indicates the average of the horizontal values in the round window, indicates the round of the trend value, indicates the average of the trend values in the round window, indicates the normalization function.

8. A lubricating oil flow rate detection method for a filling production line according to claim 1, characterized in that, The total adhesion also includes: Obtaining the influence weights of the first adhesion and the second adhesion respectively through the particle swarm optimization algorithm, and taking the result of summing the products of the first adhesion and the second adhesion and the influence weights respectively as the total adhesion.

9. A lubricating oil flow detection method for a filling production line according to claim 1, characterized in that The obtaining of the true flow value includes: Calculating the average cumulative value of the total adhesion of each blade of the same turbine in the same round, calculating the product of the normalized average cumulative value and the preset weight, and taking the sum of 1 and the product as the correction factor of the flow value to correct the flow value to obtain the true flow value.

10. A lubricating oil flow detection system for a filling production line, characterized in that, Including: A processor and a memory, the memory stores computer program instructions, and when the computer program instructions are executed by the processor, it realizes a method for detecting the lubricating oil flow rate of a filling production line according to any one of claims 1-9.

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