A method, device, equipment and storage medium for predicting hydraulic system state

Abstract

The present application provides a method, device, equipment and storage medium for predicting the state of a hydraulic system, and relates to the technical field of state monitoring. The method includes: calculating the first characteristic parameter of the corresponding power element according to the operating data sent by multiple sensors installed on the power element; judging whether the first characteristic parameter is within a preset range, if so, obtaining the real-time state data of the hydraulic linkage element associated with the power element, and determining the first influence rate of the hydraulic linkage element on the first characteristic parameter according to the real-time state data; correcting the first characteristic parameter by the first influence rate to obtain the second characteristic parameter of the power element; judging whether the second characteristic parameter is within a preset range, if not, determining that the power element has a fault trend, and formulating a maintenance plan according to the fault trend. The technical effect of the present application is: predicting the state of the power element, predicting possible faults in advance, and preventing faults from occurring.

Description

Technical Field

[0001] The present application relates to the technical field of condition monitoring, and in particular to a method, device, equipment and storage medium for predicting the condition of a hydraulic system. Background Art

[0002] Hydraulic systems are the core components of many heavy machinery and automation equipment. These systems contain multiple power components, such as hydraulic pumps and hydraulic motors, which are responsible for generating and transmitting power. During the operation of the hydraulic system, the performance and status of the power components play a decisive role in the overall performance and stability of the system. However, since the power components operate in an environment of high pressure, high temperature and high load, they often suffer from faults such as wear, overheating and leakage. These problems may lead to a decline in the performance of the power components and even the complete failure of the hydraulic system. Therefore, real-time monitoring and prediction of the status of the power components is extremely important to ensure the normal operation of the hydraulic system and extend the service life of the equipment.

[0003] However, there is currently no effective method to predict the status of power components. The traditional method usually diagnoses and repairs the fault only after it occurs. This passive maintenance method cannot detect the fault in time and prevent the occurrence of the fault. When a fault occurs, it may require a long period of downtime for maintenance, which may eventually cause significant losses to production. Summary of the invention

[0004] The present application provides a hydraulic system state prediction method, device, equipment and storage medium for predicting the state of a power element, predicting possible failures in advance, and preventing the failures from occurring.

[0005] In a first aspect, the present application provides a method for predicting the state of a hydraulic system, the method comprising: calculating a first characteristic parameter corresponding to the power element based on operating data sent by multiple sensors installed on the power element; judging whether the first characteristic parameter is within a preset range, and if so, obtaining real-time state data of a hydraulic linkage element associated with the power element, and determining a first influence rate of the hydraulic linkage element on the first characteristic parameter based on the real-time state data; correcting the first characteristic parameter by the first influence rate to obtain a second characteristic parameter of the power element; judging whether the second characteristic parameter is within the preset range, and if not, determining that the power element has a fault trend, and formulating a maintenance plan based on the fault trend.

[0006] By adopting the above technical solution, the operating data sent by multiple sensors on the power element are collected, and the first characteristic parameter is established and calculated, which can more comprehensively reflect the working state of the power element. In view of the linkage effect of the components of the hydraulic system, the method also introduces the real-time state parameters of the linkage components to determine its influence rate on the first characteristic parameter, so that the first characteristic parameter can be corrected to obtain a second characteristic parameter that more accurately expresses the state of the power element itself. After obtaining the second characteristic parameter, the method uses a threshold judgment method to predict the fault state of the power element, and formulates a maintenance plan based on this, so as to predict the state of the power element, predict possible faults in advance, and prevent faults from occurring.

[0007] Optionally, the operating data includes pressure data, flow data and temperature data, and the first characteristic parameter corresponding to the power element is calculated based on the operating data sent by multiple sensors installed on the power element, including: standardizing the pressure data, flow data and temperature data to obtain standard data; extracting characteristic variables in the standard data; and determining the first characteristic parameter of the power element based on the characteristic variables.

[0008] By adopting the above technical solution, these multi-dimensional data are standardized, which can eliminate the impact of different dimensions between data and reduce the interference of abnormal data on subsequent analysis. Feature extraction can reduce the dimension, remove redundant variables, and extract several main characteristic parameters that best reflect the status of the equipment. Therefore, determining the first characteristic parameter of the power element based on the extracted characteristic variables can effectively reduce the amount of calculation, reduce the complexity of state judgment, and make subsequent state prediction simpler and more effective.

[0009] Optionally, determining a first influence rate of the hydraulic linkage element on the first characteristic parameter based on the real-time status data includes: acquiring the real-time status data of the hydraulic linkage element; determining an operating status of the hydraulic linkage element based on the real-time status data; determining an influence relationship of the hydraulic linkage element on the first characteristic parameter based on the operating status of the hydraulic linkage element; and calculating the first influence rate based on the influence relationship.

[0010] By adopting the above technical scheme, the real-time working parameters of the hydraulic linkage element are obtained, its operating state is judged, and its influence relationship on the first characteristic parameter is determined, and finally the influence rate is calculated. Obtaining the real-time working parameters can fully understand the actual operation of the linkage element. Determining the operating state is the basis for evaluating whether it works normally. The influence relationship reflects the dynamic connection between the two hydraulic elements and is the basis for determining the degree of influence. Calculating the influence rate can obtain a quantitative indicator to represent the influence of the linkage element on the characteristic parameter. This scheme fully considers the linkage mechanism inside the hydraulic system and can improve the grasp of the dynamic characteristics of the hydraulic system. Compared with simply judging the abnormality of the characteristic parameter itself, introducing the influence rate of the hydraulic linkage element can eliminate its interference with the judgment result and improve the accuracy of state prediction.

[0011] Optionally, determining that the power element has a failure trend and formulating a maintenance plan based on the failure trend includes: calculating a deviation value between the second characteristic parameter and the preset range; if the deviation value is greater than a set threshold, predicting the failure trend of the power element based on the size of the deviation value; determining the method and time of failure maintenance based on the failure trend, and generating a maintenance plan.

[0012] By adopting the above technical solution, by calculating the deviation value between the second characteristic parameter and the preset range, it is determined whether it is abnormal and the fault trend is predicted. The calculated deviation value can quantify the degree of abnormality of the characteristic parameter, and by comparing it with the preset threshold, it can accurately determine whether there is an abnormality. The larger the deviation value, the more serious the degree of fault. The fault trend of the power component is accurately predicted according to the size of the deviation value, and the qualitative and quantitative prediction of the fault is achieved. Maintenance measures and time are formulated according to the predicted fault trend, and a maintenance plan is generated. This solution realizes the accurate judgment of the state of the power component, can locate the fault trend, and propose a targeted maintenance plan. Compared with simple threshold judgment, it can realize quantitative analysis of the degree of fault, optimize the maintenance strategy, and make maintenance more accurate and effective.

[0013] Optionally, after formulating the maintenance plan according to the failure trend, it also includes: collecting historical maintenance data of the power element and operation and maintenance data of the hydraulic system in which the power element is located; obtaining a corresponding relationship between the number of applications of each maintenance measure and the failure rate after maintenance based on the number of applications of each maintenance measure in the historical maintenance data; determining an optimal time window for performing maintenance based on the operation and maintenance data of the hydraulic system; and optimizing the maintenance measures and maintenance time of the maintenance plan in combination with the corresponding relationship and the optimal time window to generate an optimized maintenance plan.

[0014] By adopting the above technical solution, we can avoid the increase of system failure rate caused by too many or too few maintenance times, and reduce unnecessary system downtime. At the same time, the optimized solution can also be used as new historical data accumulation for subsequent solution optimization. This solution can continuously improve maintenance strategies, reduce maintenance costs, reduce system downtime, and improve system reliability and availability.

[0015] Optionally, after formulating the maintenance plan according to the fault trend, it also includes: after performing maintenance on the power element, reacquiring the operating data of the power element; judging whether there is any abnormality in the operating data of the power element after maintenance, and if there is any abnormality, judging whether the abnormality is consistent with the abnormality caused by the fault trend; if the abnormality is inconsistent with the abnormality caused by the fault trend, re-formulating the maintenance plan; if the abnormality is consistent with the abnormality caused by the fault trend, retaining the maintenance plan.

[0016] By adopting the above technical solution, the operation data is re-acquired after maintenance, and it is determined whether there are statistical anomalies to verify the maintenance effect. If an abnormality occurs, it is necessary to further analyze the cause of the abnormality and compare it with the original fault trend. If the two are inconsistent, it means that the maintenance has not completely solved the fault and a new plan needs to be formulated; if they are consistent, it means that the maintenance is effective and the current plan is maintained. This solution realizes the verification and evaluation of the implemented maintenance. Compared with the simple execution of the maintenance plan, the subsequent effect verification link is added, which can check the maintenance quality and provide a basis for subsequent optimization. When an unresolved fault occurs, the plan can be updated in time instead of continuing to perform invalid maintenance.

[0017] Optionally, the method also includes: collecting large sample operating parameters of multiple hydraulic systems of the same type under different working conditions, and fault sample parameters corresponding to the large sample operating parameters; matching the operating parameters of the current power element with the large sample operating parameters, and determining whether the real-time state parameters of the current power element are within the parameter range of the fault sample parameters; if the real-time state parameters of the current power element are within the parameter range of the fault sample parameters, determining that the power element is tending towards a corresponding fault state; if the real-time state parameters of the current power element are not within the parameter range of the fault sample parameters, determining that the power element is in a normal state.

[0018] By adopting the above technical solution, a large sample of operating data of the same type of hydraulic system under different working conditions and the corresponding fault sample data are collected. When predicting the state of the power element, its real-time state parameters are matched and compared with the collected sample data to determine whether they are close to or in the parameter range of the fault sample. If the real-time parameters are close to or in the fault sample range, it can be judged that the power element tends to the corresponding fault state; if the real-time parameters are not in the fault sample range, the power element state is judged to be normal. This state judgment based on instance matching can make full use of the collected operation data and fault data to improve the accuracy and flexibility of the judgment. At the same time, the continuous accumulation of sample data can continuously optimize the effect of state judgment and fault prediction.

[0019] In a second aspect, the present application provides a hydraulic system state prediction device, which includes: a calculation module, a judgment module, a correction module and an adjustment module; wherein the calculation module is used to calculate a first characteristic parameter corresponding to the power element based on operating data sent by multiple sensors installed on the power element; the judgment module is used to determine whether the first characteristic parameter is within a preset range, and if so, obtain the real-time status data of the hydraulic linkage element associated with the power element, and determine the first influence rate of the hydraulic linkage element on the first characteristic parameter based on the real-time status data; the correction module is used to correct the first characteristic parameter by the first influence rate to obtain the second characteristic parameter of the power element; the adjustment module is used to determine whether the second characteristic parameter is within the preset range, and if not, determine that the power element has a fault trend, and formulate a maintenance plan based on the fault trend.

[0020] By adopting the above technical solution, the operating data sent by multiple sensors on the power element are collected, and the first characteristic parameter is established and calculated, which can more comprehensively reflect the working state of the power element. In view of the linkage effect of the components of the hydraulic system, the method also introduces the real-time state parameters of the linkage components to determine its influence rate on the first characteristic parameter, so that the first characteristic parameter can be corrected to obtain a second characteristic parameter that more accurately expresses the state of the power element itself. After obtaining the second characteristic parameter, the method uses a threshold judgment method to predict the fault state of the power element, and formulates a maintenance plan based on this, so as to predict the state of the power element, predict possible faults in advance, and prevent faults from occurring.

[0021] In a third aspect, the present application provides an electronic device that adopts the following technical solution: it includes a processor, a memory, a user interface and a network interface, the memory is used to store instructions, the user interface and the network interface are used to communicate with other devices, and the processor is used to execute the instructions stored in the memory so that the electronic device executes a computer program such as any of the above-mentioned hydraulic system state prediction methods.

[0022] In a fourth aspect, the present application provides a computer-readable storage medium, which adopts the following technical solution: storing a computer program that can be loaded by a processor and execute any of the above-mentioned hydraulic system state prediction methods.

[0023] In summary, the present application includes at least one of the following beneficial technical effects:

[0024] 1. It can predict the status of power components, predict possible failures in advance, and prevent them from happening;

[0025] 2. It realizes accurate judgment of the state of power components, can locate fault trends, and propose targeted maintenance plans. Compared with simple threshold judgment, it can realize quantitative analysis of the degree of fault, optimize maintenance strategies, and make maintenance more accurate and effective. BRIEF DESCRIPTION OF THE DRAWINGS

[0026] Figure 1 It is a flow chart of a hydraulic system state prediction method provided in an embodiment of the present application;

[0027] Figure 2 It is a structural schematic diagram of a hydraulic system state prediction device provided in an embodiment of the present application;

[0028] Figure 3 It is a structural schematic diagram of an electronic device provided in an embodiment of the present application.

[0029] Description of reference numerals: 1000, electronic device; 1001, processor; 1002, communication bus; 1003, user interface; 1004, network interface; 1005, memory. DETAILED DESCRIPTION

[0030] In order to enable those skilled in the art to better understand the technical solutions in this specification, the technical solutions in the embodiments of this specification will be clearly and completely described below in conjunction with the drawings in the embodiments of this specification. Obviously, the described embodiments are only part of the embodiments of this application, not all of the embodiments.

[0031] In the description of the embodiments of the present application, words such as "illustrative", "for example" or "for example" are used to indicate examples, illustrations or descriptions. Any embodiment or design described as "illustrative", "for example" or "for example" in the embodiments of the present application should not be interpreted as being more preferred or more advantageous than other embodiments or designs. Specifically, the use of words such as "illustrative", "for example" or "for example" is intended to present related concepts in a concrete way.

[0032] Hydraulic systems are widely used in engineering machinery and other fields, and their operating status directly affects the reliability and safety of the equipment. Traditional hydraulic system status monitoring mainly relies on manual experience and judgment, but with the improvement of the degree of equipment automation, more accurate and intelligent status prediction and fault warning technologies are needed. Existing related technologies mainly focus on fault diagnosis methods of hydraulic systems, but rarely consider the dynamic linkage effects between multiple hydraulic components. Therefore, how to combine the mutual influence of multiple components in the hydraulic system to achieve dynamic system status prediction is a current technical challenge.

[0033] The present invention provides a method for predicting the state of a hydraulic system, which can realize dynamic prediction of the fault trend of the hydraulic system based on the operating data of a power element and taking into account the influence of a hydraulic linkage element.

[0034] Figure 1 is a flow chart of a method for predicting a hydraulic system state provided in an embodiment of the present application. It should be understood that although Figure 1 The steps in the flowchart are shown in sequence as indicated by the arrows, but the steps are not necessarily executed in the order indicated by the arrows; unless otherwise specified in this document, there is no strict order restriction for the execution of the steps, and the steps may be executed in other orders; and Figure 1 At least part of the steps may include multiple sub-steps or multiple stages. These sub-steps or stages are not necessarily executed at the same time, but can be executed at different times. The execution order of these sub-steps or stages is not necessarily sequential, but can be executed in turn or alternately with other steps or at least part of the sub-steps or stages of other steps.

[0035] The present application discloses a method for predicting the state of a hydraulic system. Figure 1 As shown, the method includes S101-S104.

[0036] S101, calculating a first characteristic parameter of a corresponding power element according to operation data sent by a plurality of sensors installed on the power element.

[0037] In one example, the power element includes a hydraulic pump, a hydraulic motor, etc., which are the core actuators of the hydraulic system. In order to accurately monitor the operating state of the power element, its operating characteristic parameters need to be calculated. In this embodiment, multiple sensors installed on the power element are used to collect the operating data of the power element such as pressure, flow, temperature, etc. in real time. These operating data reflect the changes in the working conditions of the power element, and these data are used to calculate the characteristic parameters that can fully reflect the operating state of the power element.

[0038] In order to monitor the operating status of the power components of the hydraulic system in real time, it is necessary to collect its operating data and calculate the characteristic parameters representing the equipment status. Taking the hydraulic pump as an example, it is equipped with pressure sensors, flow sensors and temperature sensors, which can obtain real-time operating data of pressure, flow and temperature. These data can reflect the working condition of the pump. For example, pressure data can indicate the pressure condition of the pump and the size of the internal resistance of the system; flow data can reflect the delivery condition of the pump; temperature data can monitor the thermal deformation and sealing condition of the pump. Based on these sensor data, characteristic parameters such as flow regulation degree and pressure stability of the hydraulic pump can be calculated. Flow regulation degree refers to the sensitivity of the pump to the change of system demand pressure, and pressure stability reflects the pressure fluctuation range of the pump. These characteristic parameters can comprehensively reflect the operating status of the hydraulic pump. Therefore, calculating characteristic parameters can realize quantitative monitoring of the operating status of the power components, and also lay the foundation for subsequent status judgment and fault prediction, and realize intelligent predictive maintenance.

[0039] The first characteristic parameter is a comprehensive parameter used to represent the operating state of the power element. The first characteristic parameter refers to one or a group of characteristic quantities used to represent the operating state of the power element of the hydraulic system. By extracting and calculating the sensor operating data of the power element, these key parameters that can represent the operating conditions of the equipment can be obtained. These parameters, as the first characteristic parameters, can comprehensively reflect the working conditions of the power element.

[0040] Based on the above embodiment, as an optional embodiment, the operating data includes pressure data, flow data and temperature data. In S101: according to the operating data sent by multiple sensors installed on the power element, calculating the first characteristic parameter of the corresponding power element specifically includes:

[0041] The pressure data, flow data and temperature data are standardized to obtain standard data; characteristic variables in the standard data are extracted; and the first characteristic parameter of the power element is determined according to the characteristic variables.

[0042] In a feasible example, in order to obtain characteristic parameters that can accurately represent the state of the hydraulic pump, the original monitoring data needs to be preprocessed. When calculating the first characteristic parameter corresponding to the power element, it is necessary to collect the operating data output by multiple sensors on the power element. These operating data include pressure data, flow data and temperature data. Since the measurement range and unit of different sensors may be different, directly using the original data for feature analysis will cause errors due to inconsistent data. Therefore, it is necessary to first standardize the collected pressure data, flow data and temperature data so that they are mapped to the numerical interval [0, 1]. This can eliminate the dimensional influence between the data and facilitate subsequent processing.

[0043] After standardization, the processed pressure, flow and temperature standardized data are analyzed to extract the key variables related to the state characteristics of the power element. These key variables are characteristic variables, which reflect the main working parameters of the power element under the current working conditions. For example, the pressure change rate, flow extreme value, temperature rise curve, etc. can all be used as characteristic variables. Then, based on the extracted characteristic variables, the first characteristic parameter that comprehensively reflects the overall state of the power element can be calculated. The first characteristic parameter can be a linear combination of characteristic variables, or it can be a state indicator determined by a certain mapping function. In this way, the working state of the power element under the joint detection of multiple sensors can be objectively reflected.

[0044] S102, determining whether the first characteristic parameter is within a preset range. If the first characteristic parameter is within the preset range, obtaining real-time status data of a hydraulic linkage element associated with the power element, and determining a first influence rate of the hydraulic linkage element on the first characteristic parameter based on the real-time status data.

[0045] In one example, in order to determine whether the operating state of the power element is normal, it is necessary to determine whether its first characteristic parameter is within the normal operating range. The first characteristic parameter is calculated from the collected data of multiple sensors and can more comprehensively reflect the state of the power element itself. However, due to the interconnection and interaction of the components in the hydraulic system, the state of the power element will also be affected by other linkage components. Therefore, it is not possible to make an accurate judgment based on the first characteristic parameter of the power element itself.

[0046] In order to improve the accuracy of judgment, judging whether the first characteristic parameter is normal is the first step in evaluating the state of the power element. If the parameter is normal, the dynamic linkage effect inside the hydraulic system needs to be considered. For example, the first characteristic parameters of the hydraulic pump are flow regulation and pressure stability. If both are within the normal range, the linkage effect between the pump and the hydraulic motor needs to be considered. The real-time current, speed and other operating data of the motor are obtained. According to the data, it is judged that the motor is in a light-load state, then the state of the motor will affect the pressure parameters of the pump. According to the hydraulic transmission relationship between the two, it can be obtained that the operation of the motor will increase the pressure stability of the pump by 5%. In this way, the influence rate of the motor as a hydraulic linkage element on the pump pressure parameter is determined. The introduction of the influence rate of the linkage element can eliminate its interference with the judgment of the pump state, making the judgment more accurate and reliable. Otherwise, if the linkage effect of the motor is not considered, the increase in the pressure stability of the pump may also be mistakenly identified as an abnormality of the pump's own state. Therefore, this step fully considers the inherent dynamic characteristics of the hydraulic system and can improve the accuracy of state prediction.

[0047] Based on the above embodiment, as an optional embodiment, in S102: determining the first influence rate of the hydraulic linkage element on the first characteristic parameter according to the real-time state data specifically includes:

[0048] Acquire real-time status data of the hydraulic linkage element; determine the operating status of the hydraulic linkage element according to the real-time status data; determine the influence relationship of the hydraulic linkage element on the first characteristic parameter according to the operating status of the hydraulic linkage element; and calculate the first influence rate according to the influence relationship.

[0049] In one example, in order to accurately evaluate the impact of the hydraulic linkage element on the state of the power element, it is necessary to obtain the real-time working parameters of the linkage element, such as obtaining the real-time current and speed data of the hydraulic motor. According to its load condition, it can be judged that the motor is in a light-load state. This operating state will have an impact on the pressure parameters of the hydraulic pump. According to the fluid transfer theory, it is determined that the light load of the motor will affect the pressure stability of the pump through the dynamic response of the hydraulic system. Then, it can be obtained that the influence relationship that the motor will increase the pressure stability of the pump by 5% when it is lightly loaded can be obtained. Based on this influence relationship, the first influence rate of the motor as a hydraulic linkage element on the pump pressure parameter can be quantitatively calculated to be 5%. In this way, obtaining real-time working parameters can determine the actual state of the linkage element, and finally obtain a quantitative calculation of the influence rate. This process fully considers the dynamic linkage characteristics inside the hydraulic system. The calculated influence rate can be directly used for parameter correction, thereby eliminating the linkage effect and improving the accuracy of state prediction.

[0050] By determining the first influence rate of the linkage element, it is possible to eliminate the linkage interference from the state of the power element, make the judgment of the state of the power element itself more accurate, achieve the purpose of state monitoring and fault warning, and improve the level of system health management.

[0051] S103, correcting the first characteristic parameter by using the first influence rate to obtain a second characteristic parameter of the power element.

[0052] In one example, after determining the influence rate of the hydraulic linkage element, it is necessary to use the influence rate to correct the first characteristic parameter of the power element to eliminate the influence of the linkage interference. For example, the pressure static stability of the hydraulic pump is determined to be an abnormal value when it is judged as the first characteristic parameter. However, the hydraulic motor linked to it is in a light load state, which will cause the pump pressure static stability to increase by 5%. Then the 5% influence rate of the motor can be used to correct the pressure static stability parameter of the pump to reduce it by 5%. The corrected pressure parameter is the second characteristic parameter of the hydraulic pump. In this way, by correcting the first influence rate, the interference of the hydraulic linkage element on the parameter judgment can be eliminated, so that the second characteristic parameter can more truly reflect the state of the hydraulic pump itself, and improve the accuracy of subsequent judgments. Otherwise, not considering the linkage effect may lead to misjudgment of the hydraulic pump state. Therefore, this step is very necessary to correct the first characteristic parameter, which can effectively reduce the state prediction deviation caused by the linkage effect of the hydraulic system and improve the reliability of the judgment process.

[0053] S104, determining whether the second characteristic parameter is within a preset range. If the second characteristic parameter is not within the preset range, determining that the power component has a fault trend, and formulating a maintenance plan based on the fault trend.

[0054] In one example, after obtaining the second characteristic parameter that eliminates the linkage effect, it is necessary to determine whether it is normal to predict the state of the power element. Taking the hydraulic pump as an example, if its corrected pressure static stability parameter has exceeded the normal range, it can be determined that the hydraulic pump has a fault trend. In order to predict the fault, the difference between the pressure parameter and the normal range can be calculated. The larger the difference, the more serious the fault. According to the difference in the pressure parameter, it can be predicted that the hydraulic pump has an internal leakage fault caused by fatigue of the hydraulic components. After obtaining the prediction of the fault trend, it can be determined that the hydraulic seal needs to be replaced, and according to the installation location and importance of the hydraulic system, it is determined to perform preventive maintenance 10 days in advance in the system maintenance cycle, and formulate a corresponding maintenance plan. In this way, the abnormal judgment of the second characteristic parameter can predict the fault of the power element, put forward maintenance measures in a targeted manner, realize the predictive maintenance of the hydraulic system, and avoid system accidents caused by the inability to judge the fault trend. Therefore, this step produces a deterministic prediction of the state of the power element, which is conducive to improving the reliability of the system in a targeted manner.

[0055] Based on the above embodiment, as an optional embodiment, in S104: determining that the power component has a fault trend, and formulating a maintenance plan according to the fault trend specifically includes:

[0056] Calculate the deviation value between the second characteristic parameter and the preset range; if the deviation value is greater than the set threshold, predict the fault trend of the power element according to the size of the deviation value; determine the method and time of fault maintenance according to the fault trend, and generate a maintenance plan.

[0057] In one example, in order to achieve quantitative analysis of the fault state of the power element, it is necessary to calculate the difference between the second characteristic parameter and the normal range. For example, if the pressure static stability of the hydraulic pump is 20% lower than normal, the difference can be obtained as 20%. Compare the difference with the preset threshold value of 15%. If the difference is greater than the threshold, it is determined that the pump has a fault trend. Then, according to the size of the 20% difference, it is judged that the internal hydraulic components of the pump are severely fatigued, which will cause internal leakage of the hydraulic oil. According to this trend, it can be predicted that the hydraulic pump will fail due to a decrease in the amount of hydraulic oil. Therefore, a maintenance plan is formulated to replace the hydraulic seals of the hydraulic pump during the system shutdown maintenance cycle. In this way, through the quantitative analysis of the degree of parameter abnormality, not only can the fault trend of the power element be judged, but also the degree of fault can be quantitatively evaluated, and targeted maintenance measures can be proposed to generate the best maintenance plan. This realizes the full process planning from state judgment to targeted maintenance, effectively improving the economy and rationality of maintenance.

[0058] After formulating a maintenance plan based on the failure trend, it also includes: collecting historical maintenance data of power components and operation and maintenance data of the hydraulic system in which the power components are located; obtaining the corresponding relationship between the number of applications of each maintenance measure and the failure rate after maintenance based on the number of applications of each maintenance measure in the historical maintenance data; determining the optimal time window for performing maintenance based on the operation and maintenance data of the hydraulic system; optimizing the maintenance measures and maintenance time of the maintenance plan based on the corresponding relationship and the optimal time window to generate an optimized maintenance plan.

[0059] In one example, in order to achieve economical and efficient maintenance, after formulating the maintenance plan, the plan needs to be optimized. For example, by collecting historical data and counting the number of applications and failure rates of hydraulic seal replacement on different hydraulic pumps, it can be found that the maintenance effect on high-speed pumps is the best. At the same time, according to the historical operation records of the system, it is determined that the time window with the lowest operating demand of the hydraulic system is January of each year. Taking the two parts of data into consideration, the original maintenance plan can be optimized and adjusted, and the replacement maintenance of hydraulic seals for high-speed pumps can be selected in January, so that maintenance can be achieved during the idle period of the system, and the subsequent failure rate can be reduced by selecting the most appropriate maintenance measures. Compared with the original plan, the optimized maintenance plan can make full use of historical data to improve the targeted maintenance, reasonably arrange the maintenance time window, and reduce the impact of system downtime. This realizes the precision of maintenance, which can reduce unnecessary system downtime, while improving maintenance effects and reducing the risk of subsequent failures.

[0060] After formulating the maintenance plan based on the fault trend, it also includes: after implementing maintenance on the power component, reacquiring the operating data of the power component; judging whether the operating data of the power component after maintenance is abnormal, and if abnormal, judging whether the abnormality is consistent with the abnormality caused by the fault trend; if the abnormality is inconsistent with the abnormality caused by the fault trend, re-formulating the maintenance plan; if the abnormality is consistent with the abnormality caused by the fault trend, retaining the maintenance plan.

[0061] In one example, to verify the maintenance effect, it is necessary to re-acquire its operating data after the maintenance of the power element and determine whether it is normal. Assume that after the hydraulic pump has undergone maintenance by replacing the hydraulic seal, its pressure parameter data is re-acquired and it is found that it is still abnormal. This statistical anomaly indicates that the maintenance has not completely eliminated the fault. It is necessary to compare the abnormal cause of the pressure parameter with the original fault trend, and it is found that the abnormal pressure parameter is caused by the decline in oil quality, which is inconsistent with the original trend of fatigue damage of internal components. This shows that the maintenance of replacing the seal has not eliminated the root cause of the fault, and a new maintenance plan for repairing the internal hydraulic components needs to be re-formulated. In this way, by analyzing and evaluating the data after maintenance, the maintenance quality can be checked. If it is found that the maintenance has not completely solved the fault, the optimization plan can be adjusted in time. Compared with simple execution, the addition of closed-loop evaluation can continuously improve the maintenance plan, avoid waste of resources caused by maintenance that does not solve the problem, and improve system reliability.

[0062] The method also includes: collecting large sample operating parameters of multiple hydraulic systems of the same type under different working conditions, and fault sample parameters corresponding to the large sample operating parameters; matching the operating parameters of the current power element with the large sample operating parameters, and judging whether the real-time state parameters of the current power element are within the parameter range of the fault sample parameters; if the real-time state parameters of the current power element are within the parameter range of the fault sample parameters, determining that the power element is tending to a corresponding fault state; if the real-time state parameters of the current power element are not within the parameter range of the fault sample parameters, determining that the power element is in a normal state.

[0063] In one example, in order to achieve more accurate and reliable state prediction, a large number of operating data samples of the same type of hydraulic system can be collected, and the normal or faulty state corresponding to the samples can be marked. When judging the state of a certain power component, its real-time operating parameters are matched and compared with the collected sample data. For example, the flow regulation parameter of the hydraulic pump is close to the range of the internal leakage fault condition in the sample data, but is significantly different from the normal sample, which indicates that the hydraulic pump tends to the internal leakage fault state. On the contrary, if the real-time flow regulation parameter is completely within the normal sample range, it can be judged that the current state of the hydraulic pump is normal. This state judgment based on instance matching makes full use of large sample data, judges the current state by finding the most similar sample, and realizes simple and effective state prediction.

[0064] Based on the above method, the present application also discloses a hydraulic system state prediction device, such as Figure 2 As shown, Figure 2 It is a structural schematic diagram of a hydraulic system state prediction device provided in an embodiment of the present application.

[0065] A hydraulic system state prediction device comprises: a calculation module, a judgment module, a correction module and an adjustment module; wherein the calculation module is used to calculate a first characteristic parameter corresponding to the power element according to operation data sent by a plurality of sensors installed on the power element;

[0066] A judgment module is used to judge whether the first characteristic parameter is within a preset range. If the first characteristic parameter is within the preset range, the real-time status data of the hydraulic linkage element associated with the power element is obtained, and the first influence rate of the hydraulic linkage element on the first characteristic parameter is determined based on the real-time status data; a correction module is used to correct the first characteristic parameter by the first influence rate to obtain the second characteristic parameter of the power element; an adjustment module is used to judge whether the second characteristic parameter is within the preset range. If it is not within the preset range, it is determined that the power element has a fault trend, and a maintenance plan is formulated based on the fault trend.

[0067] It should be noted that: when the device provided in the above embodiment realizes its function, only the division of the above functional modules is used as an example. In actual application, the above functions can be assigned to different functional modules as needed, that is, the internal structure of the device is divided into different functional modules to complete all or part of the functions described above. In addition, the device and method embodiments provided in the above embodiment belong to the same concept, and the specific implementation process is detailed in the method embodiment, which will not be repeated here.

[0068] See also Figure 3 , is a schematic diagram of the structure of an electronic device provided in an embodiment of the present application. Figure 3 As shown, the electronic device 1000 may include: at least one processor 1001 , at least one network interface 1004 , a user interface 1003 , a memory 1005 , and at least one communication bus 1002 .

[0069] The communication bus 1002 is used to realize the connection and communication between these components.

[0070] The user interface 1003 may include a display screen (Display) and a camera (Camera). Optionally, the user interface 1003 may also include a standard wired interface and a wireless interface.

[0071] The network interface 1004 may optionally include a standard wired interface or a wireless interface (such as a WI-FI interface).

[0072] Among them, the processor 1001 may include one or more processing cores. The processor 1001 uses various interfaces and lines to connect various parts in the entire server, and executes various functions of the server and processes data by running or executing instructions, programs, code sets or instruction sets stored in the memory 1005, and calling data stored in the memory 1005. Optionally, the processor 1001 can be implemented in at least one hardware form of digital signal processing (Digital Signal Processing, DSP), field programmable gate array (Field-Programmable Gate Array, FPGA), and programmable logic array (Programmable Logic Array, PLA). The processor 1001 can integrate one or a combination of a central processing unit (Central Processing Unit, CPU), a graphics processing unit (Graphics Processing Unit, GPU) and a modem. Among them, the CPU mainly processes the operating system, user interface and application programs; the GPU is responsible for rendering and drawing the content to be displayed on the display screen; the modem is used to process wireless communications. It can be understood that the above-mentioned modem may not be integrated into the processor 1001, and it can be implemented separately through a chip.

[0073] Among them, the memory 1005 may include a random access memory (Random Access Memory, RAM) and may also include a read-only memory (Read-Only Memory). Optionally, the memory 1005 includes a non-transitory computer-readable storage medium. The memory 1005 can be used to store instructions, programs, codes, code sets or instruction sets. The memory 1005 may include a program storage area and a data storage area, wherein the program storage area may store instructions for implementing an operating system, instructions for at least one function (such as a touch function, a sound playback function, an image playback function, etc.), instructions for implementing the above-mentioned various method embodiments, etc.; the data storage area may store data involved in the above-mentioned various method embodiments, etc. The memory 1005 may optionally also be at least one storage device located away from the aforementioned processor 1001. As Figure 3 As shown, the memory 1005 as a computer storage medium may include an operating system, a network communication module, a user interface module, and an application program of a hydraulic system state prediction method.

[0074] exist Figure 3In the electronic device 1000 shown, the user interface 1003 is mainly used to provide an input interface for the user and obtain data input by the user; and the processor 1001 can be used to call an application program for a hydraulic system state prediction method stored in the memory 1005. When executed by one or more processors, the electronic device executes one or more methods described in the above embodiments.

[0075] An electronic device readable storage medium stores instructions, which, when executed by one or more processors, enable the electronic device to execute one or more of the methods described in the above embodiments.

[0076] It should be noted that, for the aforementioned method embodiments, for the sake of simplicity, they are all described as a series of action combinations, but those skilled in the art should be aware that the present application is not limited by the order of the actions described, because according to the present application, certain steps can be performed in other orders or simultaneously. Secondly, those skilled in the art should also be aware that the embodiments described in the specification are all preferred embodiments, and the actions and modules involved are not necessarily required for the present application.

[0077] In the above embodiments, the description of each embodiment has its own emphasis. For parts that are not described in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0078] In the several embodiments provided in the present application, it should be understood that the disclosed devices can be implemented in other ways. For example, the device embodiments described above are only schematic, such as the division of the units, which is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some service interfaces, and the indirect coupling or communication connection of devices or units can be electrical or other forms.

[0079] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0080] In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0081] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable memory. Based on this understanding, the technical solution of the present application, or the part that contributes to the prior art, or all or part of the technical solution can be embodied in the form of a software product, which is stored in a memory and includes several instructions for a computer device (which can be a personal computer, server or network device, etc.) to execute all or part of the steps of the method described in each embodiment of the present application. The aforementioned memory includes: various media that can store program codes, such as USB flash drives, mobile hard drives, magnetic disks or optical disks.

[0082] The above is only an exemplary embodiment of the present disclosure, and the scope of the present disclosure cannot be limited thereto. That is, any equivalent changes and modifications made according to the teachings of the present disclosure are still within the scope of the present disclosure. After considering the specification and practicing the disclosure here, those skilled in the art will easily think of other embodiments of the present disclosure. This application is intended to cover any modification, use or adaptation of the present disclosure, which follows the general principles of the present disclosure and includes common knowledge or customary technical means in the technical field not recorded in the present disclosure. The description and examples are only regarded as exemplary, and the scope and spirit of the present disclosure are defined by the claims.

Claims

1. A method for predicting a hydraulic system state, characterized in that: The method comprises: Calculating a first characteristic parameter corresponding to the power element according to operation data sent by multiple sensors installed on the power element; the operation data includes pressure data, flow data and temperature data, and calculating the first characteristic parameter corresponding to the power element according to the operation data sent by multiple sensors installed on the power element includes: standardizing the pressure data, flow data and temperature data to obtain standard data; extracting characteristic variables from the standard data; and determining the first characteristic parameter of the power element according to the characteristic variables; Determine whether the first characteristic parameter is within a preset range. If the first characteristic parameter is within the preset range, obtain real-time status data of a hydraulic linkage element associated with the power element, and determine a first influence rate of the hydraulic linkage element on the first characteristic parameter according to the real-time status data; determining the first influence rate of the hydraulic linkage element on the first characteristic parameter according to the real-time status data includes: obtaining the real-time status data of the hydraulic linkage element; determining the operating state of the hydraulic linkage element according to the real-time status data; determining the influence relationship of the hydraulic linkage element on the first characteristic parameter according to the operating state of the hydraulic linkage element; and calculating the first influence rate according to the influence relationship; Correcting the first characteristic parameter by using the first influence rate to obtain a second characteristic parameter of the power element; It is determined whether the second characteristic parameter is within the preset range. If the second characteristic parameter is not within the preset range, it is determined that the power element has a failure trend, and a maintenance plan is formulated based on the failure trend.

2. The method for predicting the state of a hydraulic system according to claim 1, characterized in that: Determining that the power element has a failure trend and formulating a maintenance plan based on the failure trend includes: Calculating a deviation value between the second characteristic parameter and the preset range; If the deviation value is greater than a set threshold, predicting the fault trend of the power element according to the size of the deviation value; According to the fault trend, the method and time of fault maintenance are determined, and a maintenance plan is generated.

3. The hydraulic system state prediction method according to claim 1, characterized in that: After formulating a maintenance plan according to the failure trend, the following steps are also included: Collecting historical maintenance data of the power element and operation and maintenance data of the hydraulic system in which the power element is located; According to the application times of each maintenance measure in the historical maintenance data, a corresponding relationship between the application times of each maintenance measure and the failure rate after maintenance is obtained; Determining an optimal time window for performing maintenance based on the operation and maintenance data of the hydraulic system; In combination with the corresponding relationship and the optimal time window, the maintenance measures and maintenance time of the maintenance plan are optimized to generate an optimized maintenance plan.

4. The method for predicting the state of a hydraulic system according to claim 1, characterized in that: After formulating a maintenance plan according to the failure trend, the following steps are also included: After performing maintenance on the power element, reacquiring the operating data of the power element; Determine whether the operation data of the power element after maintenance is abnormal, and if the abnormality is present, determine whether the abnormality is consistent with the abnormality caused by the fault trend; If the anomaly is inconsistent with the anomaly caused by the fault trend, a maintenance plan is re-formulated; If the anomaly is consistent with the anomaly caused by the fault trend, the maintenance plan is retained.

5. The method for predicting the state of a hydraulic system according to claim 1, characterized in that: The method further comprises: Collecting large sample operating parameters of multiple hydraulic systems of the same type under different working conditions, and fault sample parameters corresponding to the large sample operating parameters; Matching the operating parameters of the current power element with the large sample operating parameters to determine whether the real-time state parameters of the current power element are within the parameter range of the fault sample parameters; If the real-time state parameter of the current power element is within the parameter range of the fault sample parameter, it is determined that the power element is tending towards a corresponding fault state; If the real-time state parameter of the current power element is not within the parameter range of the fault sample parameter, it is determined that the state of the power element is normal.

6. A hydraulic system state prediction device, characterized in that: The device comprises: a calculation module, a judgment module, a correction module and an adjustment module; wherein, The calculation module is used to calculate the first characteristic parameter corresponding to the power element according to the operation data sent by multiple sensors installed on the power element; the operation data includes pressure data, flow data and temperature data, and the calculation of the first characteristic parameter corresponding to the power element according to the operation data sent by the multiple sensors installed on the power element includes: standardizing the pressure data, flow data and temperature data to obtain standard data; extracting characteristic variables from the standard data; and determining the first characteristic parameter of the power element according to the characteristic variables; The judgment module is used to judge whether the first characteristic parameter is within a preset range. If the first characteristic parameter is within the preset range, real-time status data of a hydraulic linkage element associated with the power element is obtained, and a first influence rate of the hydraulic linkage element on the first characteristic parameter is determined according to the real-time status data; the determination of the first influence rate of the hydraulic linkage element on the first characteristic parameter according to the real-time status data includes: obtaining the real-time status data of the hydraulic linkage element; determining the operating state of the hydraulic linkage element according to the real-time status data; determining the influence relationship of the hydraulic linkage element on the first characteristic parameter according to the operating state of the hydraulic linkage element; and calculating the first influence rate according to the influence relationship; The correction module is used to correct the first characteristic parameter by using the first influence rate to obtain a second characteristic parameter of the power element; The adjustment module is used to determine whether the second characteristic parameter is within the preset range. If it is not within the preset range, it is determined that the power component has a failure trend, and a maintenance plan is formulated according to the failure trend.

7. An electronic device, characterized in that: It includes a processor, a memory, a user interface and a network interface, the memory is used to store instructions, the user interface and the network interface are used to communicate with other devices, and the processor is used to execute the instructions stored in the memory so that the electronic device executes the method as described in any one of claims 1-5.

8. A computer-readable storage medium, characterized in that: A computer program is stored which can be loaded by a processor and execute the method according to any one of claims 1 to 5.

Images