Failure prediction method and device for sand pump of fracturing blender truck, electronic equipment and storage medium

Abstract

The invention provides a fracturing blender truck sand pump fault prediction method and device, electronic equipment and a storage medium, and relates to the technical field of fracturing blender trucks. The fracturing blender truck sand pump fault prediction method comprises the steps that working condition data of a to-be-detected fracturing blender truck are acquired; according to the rotating speed of the sand pump and the sand liquid displacement and sand ratio data, the lift of the to-be-detected fracturing blender truck during sand liquid medium treatment is determined; the theoretical discharge pressure of the sand pump of the fracturing blender truck to be detected is determined according to the function relation between the lift and the theoretical discharge pressure of the sand pump and the lift of the fracturing blender truck to be detected when the fracturing blender truck processes the sand liquid medium; according to the difference between the theoretical discharge pressure and the actual discharge pressure of the sand pump, whether the sand pump of the fracturing blender truck to be detected breaks down or not is predicted. According to the method, fault prediction can be efficiently and accurately carried out on the sand pump of the fracturing blender truck.

Description

Technical Field

[0001] The present invention relates to the technical field of sand blending vehicles, and in particular to a sand blending vehicle sand pump fault prediction method, device, electronic equipment and storage medium. Background Art

[0002] Sand mixer sand pump, as an important application of centrifugal pump, plays a key role in oil extraction, construction and other fields. Previous researchers have accumulated certain theoretical results in the performance comparison of sand pumps under fault and non-fault conditions, which provides a preliminary basis for the performance evaluation and fault detection of sand pumps. However, these research results still face many challenges in practical application.

[0003] In order to improve the accuracy of sand pump fault detection in sand mixers, attempts have been made to use neural network models trained with a large amount of data for fault prediction. This method has improved the efficiency and sensitivity of detection to a certain extent, but it often relies too much on the mathematical statistical characteristics of the data and ignores the in-depth analysis of the inherent performance relationship of the sand pump, resulting in the trained neural network model being unable to accurately capture the actual performance changes of the pump body when predicting sand pump faults, thus affecting the accuracy of the prediction.

[0004] Therefore, how to efficiently and accurately predict the failure of the sand pump of the sand mixing truck is a technical problem that needs to be solved urgently. Summary of the invention

[0005] In view of the above problems existing in the prior art, the present invention provides a sand pump fault prediction method, device, electronic equipment and storage medium for a sand mixing truck, so as to efficiently and accurately predict the sand pump fault of the sand mixing truck.

[0006] The invention provides a method for predicting failure of a sand pump of a sand mixing vehicle, comprising the following steps.

[0007] The working condition data of the sand mixing truck to be tested is obtained; wherein the working condition data includes the rotation speed of the sand pump, the sand-liquid displacement and sand ratio data of the sand-liquid medium, and the actual discharge pressure of the sand pump; according to the rotation speed of the sand pump, and the sand-liquid displacement and sand ratio data, the head of the sand mixing truck to be tested when treating the sand-liquid medium is determined; using the functional relationship between the head and the theoretical discharge pressure of the sand pump, according to the head of the sand mixing truck to be tested when treating the sand-liquid medium, the theoretical discharge pressure of the sand pump of the sand mixing truck to be tested is determined; wherein the theoretical discharge pressure refers to the discharge pressure generated under specific rotation speed and sand-liquid displacement conditions when the sand pump is not faulty; according to the difference between the theoretical discharge pressure and the actual discharge pressure of the sand pump, whether the sand pump of the sand mixing truck to be tested is faulty is predicted.

[0008] According to a sand pump fault prediction method for a sand mixing truck provided by the present invention, the head of the sand mixing truck to be tested when treating the sand-liquid medium is determined according to the rotation speed of the sand pump, the sand-liquid displacement and the sand ratio data, including: determining the viscosity of the sand-liquid medium according to the sand ratio data of the sand-liquid medium; determining a corrected Reynolds number according to the rotation speed of the sand pump and the viscosity of the sand-liquid medium; determining the head of the sand mixing truck to be tested when treating the sand-liquid medium according to the corrected Reynolds number, the sand-liquid displacement and the rotation speed of the sand pump.

[0009] According to a sand pump fault prediction method for a sand mixer provided by the present invention, the head of the sand mixer to be detected when processing sand and liquid medium is determined according to the corrected Reynolds number, the sand and liquid displacement, and the rotation speed of the sand pump, including: iteratively solving to obtain a head correction coefficient and a flow correction coefficient according to the corrected Reynolds number, the sand and liquid displacement, and the rotation speed of the sand pump; wherein the flow correction coefficient is used to convert the flow difference between the sand pump when processing sand and liquid and clean water; the head correction coefficient is used to convert the head difference between the sand pump when processing sand and liquid and clean water; using the flow correction coefficient, the sand and liquid displacement is converted into the clean water displacement that the sand pump should discharge when the sand mixer to be detected processes clean water under the working condition corresponding to the working condition data; using the performance data of the sand pump of the sand mixer to be detected, according to the rotation speed of the sand pump and the clean water displacement, the head of the sand mixer to be detected at the clean water displacement is determined; according to the head at the clean water displacement and the head correction coefficient, the head of the sand mixer to be detected when processing sand and liquid medium is determined.

[0010] According to a sand pump fault prediction method for a sand blender provided by the present invention, the head correction coefficient and the flow correction coefficient are obtained by iteratively solving according to the modified Reynolds number, the sand liquid displacement, and the rotation speed of the sand pump, including: estimating the first clean water displacement discharged by the sand pump when the sand blender to be detected processes clean water under the working condition corresponding to the working condition data according to the sand liquid displacement and the first flow correction coefficient; wherein the first flow correction coefficient is a preset initial value or a flow correction coefficient generated by the last iterative calculation; utilizing the performance data of the sand pump of the sand blender to be detected, according to the rotation speed of the sand pump and the first clean water displacement, determining the first head of the sand blender to be detected at the first clean water displacement; according to the first clean water displacement, the first head, and The speed of the sand pump is used to calculate the specific speed; according to the specific speed and the modified Reynolds number, a second flow correction coefficient and a second head correction coefficient are calculated; when the difference between the first flow correction coefficient and the second flow correction coefficient is less than a preset threshold, the second flow correction coefficient is used as the final flow correction coefficient, and the second head correction coefficient is used as the final head correction coefficient; when the difference between the first flow correction coefficient and the second flow correction coefficient is not less than a preset threshold, the step of re-executing the step of estimating the first clean water displacement discharged by the sand pump when the sand mixing truck to be tested processes clean water under the working condition corresponding to the working condition data according to the sand liquid displacement and the first flow correction coefficient and subsequent steps.

[0011] According to a sand pump fault prediction method for a sand blender provided by the present invention, the method predicts whether the sand pump of the sand blender to be detected has a fault according to the difference between the theoretical discharge pressure and the actual discharge pressure of the sand pump, comprising: determining the damage degree of the sand pump according to the difference between the theoretical discharge pressure and the actual discharge pressure of the sand pump; and determining that the sand pump of the sand blender to be detected has a fault when the damage degree is greater than a fault threshold.

[0012] According to a sand pump failure prediction method for a sand mixer provided by the present invention, the damage degree of the sand pump is determined according to the difference between the theoretical discharge pressure and the actual discharge pressure of the sand pump, including: determining the damage degree of the sand pump using the following formula: : in, is the actual discharge pressure of the sand pump, It is the theoretical discharge pressure of the sand pump.

[0013] The present invention also provides a sand pump fault prediction device for a sand mixing vehicle, comprising the following modules: An acquisition module is used to acquire the working condition data of the sand mixing truck to be tested; wherein the working condition data includes the rotation speed of the sand pump, the sand-liquid displacement and sand ratio data of the sand-liquid medium, and the actual discharge pressure of the sand pump; a first determination module is used to determine the head of the sand mixing truck to be tested when treating the sand-liquid medium according to the rotation speed of the sand pump, and the sand-liquid displacement and sand ratio data; a second determination module is used to determine the theoretical discharge pressure of the sand pump of the sand mixing truck to be tested according to the head of the sand mixing truck to be tested when treating the sand-liquid medium by using the functional relationship between the head and the theoretical discharge pressure of the sand pump; wherein the theoretical discharge pressure refers to the discharge pressure generated under specific rotation speed and sand-liquid displacement conditions when the sand pump is not faulty; a prediction module is used to predict whether the sand pump of the sand mixing truck to be tested is faulty according to the difference between the theoretical discharge pressure and the actual discharge pressure of the sand pump.

[0014] The present invention also provides an electronic device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the computer program, the method for predicting a sand pump failure in a sand mixing vehicle as described above is implemented.

[0015] The present invention also provides a non-transitory computer-readable storage medium on which a computer program is stored. When the computer program is executed by a processor, the sand pump failure prediction method for a sand mixing vehicle as described in any one of the above methods is implemented.

[0016] The present invention also provides a computer program product, comprising a computer program, wherein when the computer program is executed by a processor, the computer program implements any of the above-mentioned methods for predicting failure of a sand pump of a sand blender.

[0017] The viscosity of the sand-liquid medium will have a significant impact on the performance of the sand pump. The sand ratio data of the sand-liquid medium (fracturing fluid) varies greatly at different construction sites and different construction times, and the sand ratio data can reflect the viscosity of the sand-liquid medium. The sand pump fault prediction method provided by the present invention accurately calculates the head of the sand mixing vehicle to be tested when processing the sand-liquid medium based on the rotation speed of the sand pump, the sand-liquid displacement and the sand ratio data. The theoretical discharge pressure of the sand pump is accurately determined based on the head of the sand mixing vehicle to be tested when processing the sand-liquid medium by using the functional relationship between the head and the theoretical discharge pressure of the sand pump. By comparing the difference between the theoretical discharge pressure and the actual discharge pressure of the sand pump in a non-fault state, it is finally possible to accurately predict whether the sand pump of the sand mixing vehicle to be tested has a fault. The present invention closely combines the structural characteristics of the sand pump with the mathematical analysis method. Compared with the method of predicting faults that only relies on the mathematical statistical characteristics of the construction process data, the present invention can more efficiently and accurately predict the faults of the sand pump vehicle. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] In order to more clearly illustrate the technical solutions in the present invention or the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0019] Figure 1 The present invention is a schematic flow chart of a method for predicting a sand pump failure in a sand mixing vehicle.

[0020] Figure 2 It is a flow chart of a method for determining the lift of a sand mixing vehicle to be tested when processing a sand-liquid medium provided by the present invention.

[0021] Figure 3 The present invention is a flow chart of a method for determining a head correction coefficient and a flow correction coefficient.

[0022] Figure 4 It is a schematic diagram of the sand pump performance curve.

[0023] Figure 5 is a schematic diagram of the pump efficiency curve.

[0024] Figure 6 This is a schematic diagram of the German KSB correction method.

[0025] Figure 7 The present invention is a schematic structural diagram of a sand pump fault prediction device for a sand mixing vehicle.

[0026] Figure 8 It is a structural schematic diagram of the electronic device provided by the present invention. DETAILED DESCRIPTION

[0027] In order to make the purpose, technical solution and advantages of the present invention clearer, the technical solution of the present invention will be clearly and completely described below in conjunction with the drawings of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0028] Combine the following Figure 1-Figure 6 The present invention describes the sand pump failure prediction method of the sand mixing vehicle.

[0029] Figure 1 FIG. 1 is a flow chart of a method for predicting a sand pump failure in a sand mixing vehicle provided by the present invention. Figure 1 As shown, the method includes the following: Step 101, obtaining the working condition data of the sand blender to be tested; wherein the working condition data includes the rotation speed of the sand pump, the sand-liquid displacement and sand ratio data of the sand-liquid medium, and the actual discharge pressure of the sand pump.

[0030] The sand blending truck to be inspected is a sand blending truck whose sand pump needs to be determined to be faulty.

[0031] In the specific implementation process, the working condition data of the sand blender to be detected can be obtained through a data platform associated with the sand blender to be detected. The data platform records the working condition data collected by the sensor of the sand blender to be detected at intervals (for example, half a minute).

[0032] The sand-liquid displacement is the volume or weight of sand-liquid that the sand pump can discharge per unit time. The sand-liquid displacement reflects the conveying capacity of the sand pump, that is, how much sand-liquid the sand pump can handle per unit time.

[0033] The actual discharge pressure is the actual pressure reached when the sand pump is working and the sand liquid is pumped out. The actual discharge pressure reflects the sand pump's ability to push the sand liquid, that is, how far or how high the sand pump can transport the sand liquid. The actual discharge pressure is affected by many factors such as the sand pump's structure, speed, sand liquid properties, and pipeline resistance.

[0034] Step 102: Determine the lift of the sand mixing vehicle to be tested when processing the sand-liquid medium according to the rotation speed of the sand pump, the sand-liquid displacement and the sand ratio data.

[0035] The head is the energy value gained after the sand liquid per unit weight passes through the sand pump of the sand mixer truck. It is expressed as the height difference of the sand liquid from the inlet to the outlet of the pump, that is, the energy consumed by the sand liquid to overcome resistance such as gravity and friction.

[0036] The lift cannot be obtained by measurement. Therefore, in the present invention, the lift of the sand mixing vehicle to be tested when processing the sand-liquid medium is determined according to the rotation speed of the sand pump, the sand-liquid displacement and the sand ratio data.

[0037] In the specific implementation process, the lift of the sand mixing vehicle to be tested when processing the sand-liquid medium can be determined in the following way.

[0038] Determine the viscosity of the sand-liquid medium based on the sand ratio data of the sand-liquid medium.

[0039] Since the sand-liquid medium is mixed with finely crushed sand and gravel, the present invention assumes that the sand-liquid medium is a rigid spherical particle dispersion system, and thus the ratio of the viscosity of the sand-liquid medium to the viscosity of clean water can be calculated using the volume fraction of dispersed particles in the medium (sand ratio data) and the Krieger-Dougherty formula. The Krieger-Dougherty formula is as follows: (1) in, It is the ratio of the viscosity of the sand liquid medium to the viscosity of clean water. is the volume fraction of dispersed particles in the sand-liquid medium (sand ratio data). is the critical packing volume fraction, is a coefficient related to the shape of dispersed particles and has nothing to do with the size of dispersed particles. This paper assumes that the finely crushed sand and gravel in the sand-liquid medium are spherical rigid particle dispersions. The value is 64%. Therefore, the condition for the present invention to perform fault prediction is that the sand ratio data of the sand-liquid medium is between 0% and 64%. The sand ratio data exceeding 64% is regarded as abnormal data, and the abnormal data is not used for fault prediction. The intrinsic viscosity is the effect of a single dispersion particle on the dispersion viscosity. It can be measured experimentally. For a rigid sphere dispersion, the intrinsic viscosity obtained experimentally is 2.5. The viscosity of clean water is 1 , using the viscosity of clean water and the calculated The product of can get the viscosity of the sand-liquid medium as shown below.

[0040] (2) in, is the viscosity of clean water, is the viscosity of the sand-liquid medium.

[0041] Determine the corrected Reynolds number based on the speed of the sand pump and the viscosity of the sand-liquid medium.

[0042] In the specific implementation process, the following formula can be used to determine the modified Reynolds number based on the speed of the sand pump and the viscosity of the sand liquid medium: : (3) in, To correct the Reynolds number, is the rated flow of the sand pump when the medium is water, is the head under rated working conditions when the medium is water, , The parameters provided by the manufacturer at the time of shipment (such as Figure 5 ), is the viscosity of the sand-liquid medium, is the acceleration due to gravity (taken as 9.8m / s²).

[0043] According to the modified Reynolds number, sand-liquid displacement, and sand pump speed, determine the head of the sand mixer truck to be tested when processing the sand-liquid medium.

[0044] In the specific implementation process, the head correction coefficient and the flow correction coefficient can be iteratively solved according to the modified Reynolds number, the sand-liquid displacement, and the speed of the sand pump to obtain the head correction coefficient and the flow correction coefficient; according to the sand-liquid displacement, the speed of the sand pump, the head correction coefficient and the flow correction coefficient, the head of the sand mixing truck to be tested when processing the sand-liquid medium is calculated.

[0045] For a detailed description of determining the head of the sand mixer to be tested when handling the sand and liquid medium based on the modified Reynolds number, sand and liquid displacement, and sand pump speed, see Figure 2 The relevant content in will not be repeated here.

[0046] Step 103: using the functional relationship between the head and the discharge pressure of the sand pump, according to the head of the sand blender to be tested when processing the sand-liquid medium, determine the theoretical discharge pressure of the sand pump of the sand blender to be tested.

[0047] The theoretical discharge pressure refers to the discharge pressure generated under specific speed and sand and liquid displacement conditions when the sand pump is not faulty.

[0048] The functional relationship between the head and the theoretical discharge pressure of the sand pump is shown in the following formula.

[0049] (4) in, It is the theoretical discharge pressure of the sand mixer truck to be tested when processing the sand-liquid medium. is the density of water (1000kg / m³), is the acceleration due to gravity (taken as 9.8m / s²), H It is the lift of the sand mixer truck to be tested when processing the sand-liquid medium.

[0050] Step 104: predict whether the sand pump of the sand blender to be inspected has a fault based on the difference between the theoretical discharge pressure and the actual discharge pressure of the sand pump.

[0051] In the specific implementation process, the degree of damage of the sand pump can be determined in various ways according to the difference between the theoretical discharge pressure and the actual discharge pressure of the sand pump. For example, the ratio between the theoretical discharge pressure and the actual discharge pressure of the sand pump can be calculated, and the degree of damage of the sand pump can be determined according to the ratio. For another example, the difference between the theoretical discharge pressure and the actual discharge pressure of the sand pump can be calculated, and the degree of damage of the sand pump can be determined according to the ratio between the difference and the theoretical discharge pressure.

[0052] In some embodiments, the damage degree of the sand pump can be determined using the following formula: : (5) in, is the actual discharge pressure of the sand pump, It is the theoretical discharge pressure of the sand pump.

[0053] When the damage degree is greater than the fault threshold, it is determined that the sand pump of the sand blender to be inspected is faulty.

[0054] In the specific implementation process, the damage degree calculated based on the operating data of the sand pump when it is close to a fault state can be used as a fault threshold. When the damage degree of the sand pump of the sand blending truck to be detected exceeds this threshold, it is determined that it has failed, and the sand pump failure of the sand blending truck is displayed on the data platform.

[0055] As an example, we obtain multiple working condition data of a sand mixer truck when its sand pump is close to failure from the data platform, and calculate that the α value corresponding to each working condition data is 0.5; at the same time, we obtain multiple working condition data of the sand mixer truck when it was just 3 months out of the factory, and calculate that the α value corresponding to each working condition data is 0.2. Therefore, 0.5 can be determined as the failure threshold of the sand pump of the sand mixer truck of the same type as the sand mixer truck.

[0056] Figure 2 : is a flow chart of a method for determining the lift of a sand mixing vehicle to be tested when processing a sand-liquid medium provided by the present invention, such as Figure 2 As shown, the method includes the following: Step 201, according to the modified Reynolds number, the sand liquid displacement, and the rotation speed of the sand pump, the head correction coefficient and the flow correction coefficient are obtained by iterative solution.

[0057] The flow correction factor is used to convert the flow difference between the sand pump when processing sand liquid and clean water.

[0058] Since the viscosity, density and particle content of sand liquid media are different from those of clean water, the actual flow rate of the sand pump when processing sand liquid media will be different from the flow rate when processing clean water. The flow correction factor is used to quantify this difference.

[0059] Just as an example, suppose a sand pump has a flow rate of 100 cubic meters per hour when processing clean water. However, when processing a certain sand liquid, due to the high viscosity of the sand liquid, the pump efficiency decreases and the actual flow rate is only 80 cubic meters per hour. Then, the flow correction coefficient of this sand pump for the sand liquid is 0.8 (i.e. 80 / 100).

[0060] The head correction factor is used to convert the difference in head when the sand pump is processing sand liquid and clean water.

[0061] Since the characteristics of sand-liquid medium are different from those of clean water, the actual head of the sand pump when handling sand-liquid medium will be different from the head when handling clean water. The head correction factor is used to quantify this difference.

[0062] Just as an example, suppose a sand pump has a lift of 30 meters when handling clean water. However, when handling a certain sand liquid, due to the high density of the sand liquid and the friction resistance of the particles, the sand pump needs to provide more energy to overcome these resistances, so the actual lift is only 25 meters. Then, the lift correction factor of the sand pump for the sand liquid is 0.833 (i.e. 25 / 30).

[0063] For a detailed description of the process for solving for the head correction factor and the flow correction factor, see Figure 3 The relevant content in will not be repeated here.

[0064] Step 202, using the flow correction coefficient, converting the sand-liquid displacement into the clean water displacement that the sand pump should discharge when the sand mixer truck to be tested processes clean water under the working condition corresponding to the working condition data.

[0065] In the specific implementation process, the following formula can be used to calculate the clean water discharge: .

[0066] (6) Among them, Q is the sand liquid displacement, is the flow correction factor.

[0067] Step 203: using the performance data of the sand pump of the sand blender to be tested, according to the rotation speed and clean water displacement of the sand pump, determine the head of the sand blender to be tested under the clean water displacement.

[0068] When the sand pump leaves the factory, the manufacturer will be equipped with a corresponding sand pump performance curve (such as Figure 4 As shown in the figure, the performance curve of the sand pump usually reflects the relationship between the speed, head and flow rate of the sand pump when the sand pump is intact and the medium is clean water. The horizontal axis in the figure is the flow rate in US gallons, and the vertical axis is the head in feet. The three curves in the figure correspond to the relationship between the head and flow rate of the sand pump at 1000, 1200, and 1400 rpm. It can be seen from the figure that the three curves are all quadratic functions between the head and the flow rate, thus obtaining a one-to-one correspondence between the flow rate and the head at three speeds. Since the head and the speed are in a quadratic function relationship at the same flow rate, the present invention calculates the function corresponding to the head and the speed at each flow rate within the range of 0-8000 US gallons. The specific method is: using the three curves in the figure to calculate the heads corresponding to the sand pump at 1000, 1200, and 1400 rpm at each flow rate, three pairs of coordinate points reflecting the relationship between the head and the speed are obtained, and a quadratic function relationship is constructed with the speed as the independent variable and the head as the dependent variable, and the values ​​of all coefficients in the quadratic function relationship are calculated using the three pairs of coordinate points. Thus, the functional relationship between the head and the speed at each flow rate is obtained as shown below.

[0069] (7) in, is the clean water discharge, is the head of the sand mixer under clean water displacement, and n is the rotation speed of the sand pump.

[0070] In the specific implementation process, the rotation speed of the sand pump can be substituted into the relationship corresponding to the clean water displacement to calculate the head of the sand mixing truck to be tested under the clean water displacement.

[0071] Step 204: Determine the lift of the sand mixer to be tested when processing the sand-liquid medium according to the lift under the clean water displacement and the lift correction coefficient.

[0072] In the specific implementation process, the lift under the clean water discharge and the lift correction coefficient can be substituted into the following formula to calculate the lift of the sand mixer to be tested when treating the sand and liquid medium: H .

[0073] (8) in, is the head under clean water discharge, is the lift correction factor.

[0074] Figure 3 : is a flow chart of a method for determining a head correction coefficient and a flow correction coefficient provided by the present invention, such as Figure 3 As shown, the method includes the following: Step 301: according to the sand-liquid displacement and the first flow correction coefficient, estimate the first clean water displacement discharged by the sand pump when the sand blender to be tested processes clean water under the working condition corresponding to the working condition data.

[0075] In this step, the clean water displacement is calculated using the flow correction coefficient. The sand liquid displacement is a known quantity obtained from the operating condition data, while the flow correction coefficient is an unknown quantity in the initial calculation. Therefore, in the initial calculation, a preset initial value (for example, 1) is assigned to the first flow correction coefficient. In the subsequent iterative calculation process, the first flow correction coefficient is taken as the flow correction coefficient generated by the last iterative calculation. For a detailed description of the second flow correction coefficient, refer to the relevant content in step 304, which will not be repeated here.

[0076] In the specific implementation process, the sand-liquid displacement and the first flow correction coefficient can be substituted into formula (6) to estimate the first clean water displacement discharged by the sand pump when the sand blender to be tested processes clean water under the working conditions corresponding to the working condition data.

[0077] Step 302: using the performance data of the sand pump of the sand blending vehicle to be tested, according to the rotation speed of the sand pump and the first clean water displacement, determine the first lift of the sand blending vehicle to be tested at the first clean water displacement.

[0078] In a specific implementation process, the rotation speed of the sand pump and the first clean water displacement can be substituted into formula (7) to determine the first lift of the sand blending vehicle to be tested at the first clean water displacement.

[0079] Step 303: Calculate the specific speed according to the first clean water displacement, the first head, and the speed of the sand pump.

[0080] In the specific implementation process, the specific speed can be calculated using the following formula: : (9) in, is the first clean water discharge, in cubic meters per second, is the first head, in meters, n is the speed of the sand pump.

[0081] Step 304: Calculate a second flow correction coefficient and a second head correction coefficient according to the specific speed and the modified Reynolds number.

[0082] like Figure 6 The German KSB (KSB) correction method shown in the figure shows that when the specific speed is 6.5, 10, 20, 30, and 45, the corrected Reynolds number and the flow correction coefficient are and head correction factor The quadratic function relationship of is shown below.

[0083] (10) (11) In other specific speed cases, the correction coefficients of head and flow can be solved by linear interpolation method, and the formula is shown below.

[0084] (12) (13) (14) (15) (16) (17) (18) (19) in, The specific speed is The flow correction factor when The specific speed is The head correction factor at is the flow correction coefficient when the specific speed is 6.5, is the flow correction coefficient when the specific speed is 10, is the flow correction coefficient when the specific speed is 20, is the flow correction coefficient when the specific speed is 30, is the flow correction coefficient when the specific speed is 45, is the head correction coefficient when the specific speed is 6.5, is the head correction coefficient when the specific speed is 10, is the head correction coefficient when the specific speed is 20, is the head correction coefficient when the specific speed is 30, It is the head correction coefficient when the specific speed is 45.

[0085] In a specific implementation process, the corrected Reynolds number can be substituted into the corresponding formulas in formula (10) to formula (19) according to the value of the specific speed to calculate the second flow correction coefficient and the second head correction coefficient.

[0086] Step 305: When the difference between the first flow correction coefficient and the second flow correction coefficient is less than a preset threshold, the second flow correction coefficient is used as the final flow correction coefficient, and the second head correction coefficient is used as the final head correction coefficient.

[0087] When the difference between the first flow correction coefficient and the second flow correction coefficient is less than a preset threshold (for example, 0.005), the second flow correction coefficient calculated this time is determined as the final flow correction coefficient, and the second head correction coefficient calculated this time is used as the final head correction coefficient.

[0088] When the difference between the first flow correction coefficient and the second flow correction coefficient is not less than the preset threshold, steps 301 to 305 are re-executed until the final flow correction coefficient and lift correction coefficient are obtained.

[0089] If the calculated value is obtained using a set of working condition data <6.5 or >45, or the number of iterations of the solution process exceeds the preset threshold (for example, 200 times), the group of operating condition data is considered invalid, and the group of data is discarded, and other operating condition data are used for calculation.

[0090] The sand mixing vehicle sand pump fault prediction device provided by the present invention is described below. The sand mixing vehicle sand pump fault prediction device described below and the sand mixing vehicle sand pump fault prediction method described above can be referred to each other.

[0091] Figure 7Schematic diagram of the structure of the sand pump fault prediction device for the sand mixing vehicle provided by the present invention. Figure 7 As shown, the device 700 includes the following modules.

[0092] The acquisition module 710 is used to acquire the working condition data of the sand blender to be tested; wherein the working condition data includes the rotation speed of the sand pump, the sand-liquid displacement and sand ratio data of the sand-liquid medium, and the actual discharge pressure of the sand pump.

[0093] The first determination module 720 is used to determine the head of the sand mixing vehicle to be tested when processing the sand and liquid medium according to the rotation speed of the sand pump, and the sand-liquid displacement and sand ratio data.

[0094] The second determination module 730 is used to determine the theoretical discharge pressure of the sand pump of the sand mixing truck to be tested according to the lift of the sand mixing truck to be tested when processing the sand and liquid medium, using the functional relationship between the lift and the theoretical discharge pressure of the sand pump; wherein the theoretical discharge pressure refers to the discharge pressure generated under specific rotation speed and sand and liquid discharge conditions when the sand pump is not faulty.

[0095] The prediction module 740 is used to predict whether the sand pump of the sand blender to be inspected has a fault according to the difference between the theoretical discharge pressure and the actual discharge pressure of the sand pump.

[0096] Figure 8 An example of a physical structure diagram of an electronic device is shown in FIG. Figure 8 As shown, the electronic device may include: a processor (processor) 810, a communication interface (Communications Interface) 820, a memory (memory) 830 and a communication bus 840, wherein the processor 810, the communication interface 820, and the memory 830 communicate with each other through the communication bus 840. The processor 810 can call the logic instructions in the memory 830 to execute the sand pump failure prediction method of the sand mixing truck, which includes: obtaining the working condition data of the sand mixing truck to be detected; wherein the working condition data includes the rotation speed of the sand pump, the sand-liquid displacement and sand ratio data of the sand-liquid medium, and the actual discharge pressure of the sand pump; according to the rotation speed of the sand pump, and the sand-liquid displacement and sand ratio data, determining the head of the sand mixing truck to be detected when treating the sand-liquid medium; using the functional relationship between the head and the theoretical discharge pressure of the sand pump, according to the head of the sand mixing truck to be detected when treating the sand-liquid medium, determining the theoretical discharge pressure of the sand pump of the sand mixing truck to be detected; wherein the theoretical discharge pressure refers to the discharge pressure generated under specific rotation speed and sand-liquid displacement conditions when the sand pump is not faulty; according to the difference between the theoretical discharge pressure and the actual discharge pressure of the sand pump, predicting whether the sand pump of the sand mixing truck to be detected is faulty.

[0097] In addition, the logic instructions in the above-mentioned memory 830 can be implemented in the form of a software functional unit and can be stored in a computer-readable storage medium when it is sold or used as an independent product. Based on this understanding, the technical solution of the present invention is essentially or the part that contributes to the prior art or the part of the technical solution can be embodied in the form of a software product, and the computer software product is stored in a storage medium, including a number of instructions to enable a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk and other media that can store program codes.

[0098] On the other hand, the present invention also provides a computer program product, which includes a computer program. The computer program can be stored on a non-transitory computer-readable storage medium. When the computer program is executed by a processor, the computer can execute the sand pump failure prediction method provided by the above methods, which includes: obtaining the working condition data of the sand mixing truck to be detected; wherein the working condition data includes the rotation speed of the sand pump, the sand-liquid displacement and sand ratio data of the sand-liquid medium, and the actual discharge pressure of the sand pump; according to the rotation speed of the sand pump, and the sand-liquid displacement and sand ratio data, determining the head of the sand mixing truck to be detected when treating the sand-liquid medium; using the functional relationship between the head and the theoretical discharge pressure of the sand pump, according to the head of the sand mixing truck to be detected when treating the sand-liquid medium, determining the theoretical discharge pressure of the sand pump of the sand mixing truck to be detected; wherein the theoretical discharge pressure refers to the discharge pressure generated under specific rotation speed and sand-liquid displacement conditions when the sand pump is not faulty; according to the difference between the theoretical discharge pressure of the sand pump and the actual discharge pressure, predicting whether the sand pump of the sand mixing truck to be detected is faulty.

[0099] On the other hand, the present invention also provides a non-transitory computer-readable storage medium having a computer program stored thereon, which is implemented by a processor to execute the sand pump failure prediction method provided by the above-mentioned methods when the computer program is executed, the method comprising: obtaining the operating condition data of the sand mixing truck to be detected; wherein the operating condition data includes the rotation speed of the sand pump, the sand-liquid displacement and sand ratio data of the sand-liquid medium, and the actual discharge pressure of the sand pump; determining the head of the sand mixing truck to be detected when treating the sand-liquid medium according to the rotation speed of the sand pump, and the sand-liquid displacement and sand ratio data; determining the theoretical discharge pressure of the sand pump of the sand mixing truck to be detected according to the head of the sand mixing truck to be detected when treating the sand-liquid medium by utilizing the functional relationship between the head and the theoretical discharge pressure of the sand pump; wherein the theoretical discharge pressure refers to the discharge pressure generated under the conditions of specific rotation speed and sand-liquid displacement when the sand pump is not faulty; predicting whether the sand pump of the sand mixing truck to be detected is faulty according to the difference between the theoretical discharge pressure and the actual discharge pressure of the sand pump.

[0100] The device embodiments described above are merely illustrative, wherein the units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they may be located in one place, or they may be distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the scheme of this embodiment. Ordinary technicians in this field can understand and implement it without paying creative labor.

[0101] Through the description of the above implementation methods, those skilled in the art can clearly understand that each implementation method can be implemented by means of software plus a necessary general hardware platform, and of course, can also be implemented by hardware. Based on this understanding, the above technical solution is essentially or the part that contributes to the prior art can be embodied in the form of a software product, and the computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, a disk, an optical disk, etc., including a number of instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to execute the methods described in each embodiment or some parts of the embodiments.

[0102] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for predicting failure of a sand pump of a sand mixer, characterized in that: include: Acquire the working condition data of the sand blender to be tested; wherein the working condition data includes the rotation speed of the sand pump, the sand-liquid displacement and sand ratio data of the sand-liquid medium, and the actual discharge pressure of the sand pump; According to the rotation speed of the sand pump, the sand-liquid displacement and the sand ratio data, the head of the sand mixing vehicle to be tested when processing the sand-liquid medium is determined; The theoretical discharge pressure of the sand pump of the sand blender to be tested is determined by utilizing the functional relationship between the head and the theoretical discharge pressure of the sand pump according to the head of the sand blender to be tested when processing the sand and liquid medium; wherein the theoretical discharge pressure refers to the discharge pressure generated under the conditions of a specific rotation speed and sand and liquid discharge volume when the sand pump is not faulty; According to the difference between the theoretical discharge pressure and the actual discharge pressure of the sand pump, it is predicted whether the sand pump of the sand blender to be inspected fails.

2. The sand pump failure prediction method for a sand mixer according to claim 1, characterized in that: The step of determining the head of the sand mixing vehicle to be tested when processing the sand and liquid medium according to the rotation speed of the sand pump, the sand and liquid displacement and the sand ratio data comprises: Determining the viscosity of the sand-liquid medium according to the sand ratio data of the sand-liquid medium; Determining a modified Reynolds number according to the rotation speed of the sand pump and the viscosity of the sand-liquid medium; The lift of the sand mixing vehicle to be tested when processing the sand and liquid medium is determined according to the modified Reynolds number, the sand and liquid displacement, and the rotation speed of the sand pump.

3. The sand pump failure prediction method for a sand mixer according to claim 2 is characterized in that: The step of determining the head of the sand mixing vehicle to be tested when processing the sand and liquid medium according to the modified Reynolds number, the sand and liquid displacement, and the rotation speed of the sand pump comprises: According to the modified Reynolds number, the sand liquid displacement, and the rotation speed of the sand pump, the head correction coefficient and the flow correction coefficient are iteratively solved; wherein the flow correction coefficient is used to convert the flow difference between the sand pump when processing sand liquid and clean water; the head correction coefficient is used to convert the head difference between the sand pump when processing sand liquid and clean water; The flow correction coefficient is used to convert the sand-liquid displacement into the clean water displacement that the sand pump should discharge when the sand mixing vehicle to be tested processes clean water under the working condition corresponding to the working condition data; Using the performance data of the sand pump of the sand blending vehicle to be tested, according to the rotation speed of the sand pump and the clean water displacement, the head of the sand blending vehicle to be tested under the clean water displacement is determined; The lift of the sand blender to be tested when processing the sand-liquid medium is determined according to the lift under the clean water displacement and the lift correction coefficient.

4. The sand pump failure prediction method for a sand mixer according to claim 3 is characterized in that: The head correction coefficient and the flow correction coefficient are obtained by iteratively solving according to the modified Reynolds number, the sand liquid displacement, and the rotation speed of the sand pump, including: According to the sand-liquid displacement and the first flow correction coefficient, the first clean water displacement discharged by the sand pump when the sand blender to be tested processes clean water under the working condition corresponding to the working condition data is estimated; wherein the first flow correction coefficient is a preset initial value or a flow correction coefficient generated by the last iterative calculation; Using the performance data of the sand pump of the sand blending vehicle to be tested, according to the rotation speed of the sand pump and the first clean water displacement, determining the first lift of the sand blending vehicle to be tested at the first clean water displacement; Calculating a specific speed according to the first clean water displacement, the first head, and the speed of the sand pump; Calculating a second flow correction coefficient and a second head correction coefficient according to the specific speed and the modified Reynolds number; When the difference between the first flow correction coefficient and the second flow correction coefficient is less than a preset threshold, the second flow correction coefficient is used as a final flow correction coefficient, and the second head correction coefficient is used as a final head correction coefficient; When the difference between the first flow correction coefficient and the second flow correction coefficient is not less than a preset threshold value, the step of estimating the first clean water displacement discharged by the sand pump when the sand mixing truck to be tested processes clean water under the operating condition corresponding to the operating condition data and subsequent steps are re-executed based on the sand-liquid displacement and the first flow correction coefficient.

5. The method for predicting failure of a sand pump in a sand mixer according to claims 1 to 4, characterized in that: The method of predicting whether the sand pump of the sand blender to be inspected fails according to the difference between the theoretical discharge pressure and the actual discharge pressure of the sand pump comprises: determining the damage degree of the sand pump according to the difference between the theoretical discharge pressure and the actual discharge pressure of the sand pump; When the damage degree is greater than the fault threshold, it is determined that the sand pump of the sand blending vehicle to be inspected is faulty.

6. The method for predicting failure of a sand pump in a sand mixer according to claim 5, characterized in that: Determining the damage degree of the sand pump according to the difference between the theoretical discharge pressure and the actual discharge pressure of the sand pump comprises: The damage degree of the sand pump is determined by the following formula: : in, is the actual discharge pressure of the sand pump, It is the theoretical discharge pressure of the sand pump.

7. A sand pump fault prediction device for a sand mixer, characterized in that: include: An acquisition module is used to acquire the working condition data of the sand blender to be tested; wherein the working condition data includes the rotation speed of the sand pump, the sand-liquid displacement and sand ratio data of the sand-liquid medium, and the actual discharge pressure of the sand pump; A first determination module is used to determine the head of the sand mixing vehicle to be tested when processing the sand and liquid medium according to the rotation speed of the sand pump, the sand and liquid displacement and the sand ratio data; The second determination module is used to determine the theoretical discharge pressure of the sand pump of the sand mixing vehicle to be tested according to the lift of the sand mixing vehicle to be tested when the sand and liquid medium is processed by the sand mixing vehicle to be tested, using the functional relationship between the lift and the theoretical discharge pressure of the sand pump; wherein the theoretical discharge pressure refers to the discharge pressure generated under the conditions of a specific rotation speed and sand and liquid discharge volume when the sand pump is not faulty; The prediction module is used to predict whether the sand pump of the sand blending vehicle to be inspected has a fault according to the difference between the theoretical discharge pressure and the actual discharge pressure of the sand pump.

8. An electronic device comprising a memory, a processor, and a computer program stored in the memory and running on the processor, characterized in that: When the processor executes the computer program, the method for predicting failure of a sand pump in a sand blender according to any one of claims 1 to 6 is implemented.

9. A non-transitory computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the method for predicting failure of a sand pump in a sand blender according to any one of claims 1 to 6 is implemented.

10. A computer program product, comprising a computer program, characterized in that When the computer program is executed by a processor, the method for predicting failure of a sand pump in a sand blender according to any one of claims 1 to 6 is implemented.

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