Repair scheme determination method and device, storage medium and electronic device
Through iron content data prediction and sequence analysis, the wear degree of fan gearbox is determined, which solves the problem of lag in the implementation of the repair solution, realizes early prediction and repair, and extends the service life of the equipment.
Patent Information
- Application Number
- CN202411428436.0
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2024-10-14
- Publication Date
- 2025-01-07
AI Technical Summary
The prior art cannot effectively predict the wear level of fan gearboxes, resulting in lagging implementation of the repair plan and timely response measures cannot be taken.
By predicting iron content data based on the prediction of iron content data, an iron content data sequence is constructed, the target parameters are determined, the iron content of the target object is predicted, and the corresponding repair plan is determined based on the preset wear degree, including repair measures for mild, moderate and severe wear.
Predict the wear level of the gearbox in advance, determine and implement corresponding repair plans in a timely manner, extend the service life of the equipment, and reduce fault downtime.
Smart Images
Figure CN119269087A_ABST
Abstract
Description
Technical Field
[0001] The present application relates to the field of wind turbine gearbox repair, and in particular, to a method and device for determining a repair solution, a storage medium, and an electronic device. Background Art
[0002] Wind turbines are highly automated energy conversion mechanical systems, and require extremely high precision and stability in the operation of mechanical equipment. The friction parts of the wind turbine's transmission system bear huge loads and transmit huge torques. All moving parts such as bearings and gears of wind turbines are in frequent start-stop and high-load continuous operation conditions. Lubricating oil is used in the mechanical contact of various transmission gears and bearings of wind turbines, forming an oil film on the contact surface to reduce friction and wear, extend the service life of the machinery, and also play the role of cooling, rust and corrosion prevention, cleaning, sealing and buffering.
[0003] Lubricating oil element analysis is to use instruments to detect the content of wear metals, contaminant elements and additive elements in the oil. According to the composition and content trend of wear metals, the wear condition of relevant parts of the equipment can be judged; according to the composition of contaminant elements, the degree and cause of oil contamination can be judged; according to additive elements, the loss of equipment additives in the oil can be judged. In the test, the main elements such as iron, calcium, boron, phosphorus and zinc are generally measured. Among them, the main source of iron is iron filings produced by gear wear in lubricating oil. As time changes, the iron element in lubricating oil tends to increase. Monitoring the content of metal abrasive iron (Fe) in oil is crucial, but on-site operation and maintenance are affected by various factors, and the sample size of sampling and monitoring data is often insufficient, making it difficult to predict the trend of iron element changes, resulting in the failure to take effective countermeasures in time before the gearbox fails.
[0004] There is no effective solution to the problem that the relevant technology cannot predict the degree of wear of the target object, resulting in delayed execution of the repair plan. Summary of the invention
[0005] The embodiments of the present application relate to a method and device for determining a repair plan, a storage medium, and an electronic device. Through the embodiments of the present application, the problem of delayed execution of the repair plan caused by the inability to predict the degree of wear of the target object in the related art can be solved.
[0006] According to an embodiment of the embodiments of the present application, a method for determining a repair plan is provided, including: predicting a first iron content of the target object in a target time period based on iron content data in the target object, wherein the iron content data is iron content data collected based on a preset time interval within the first time period; determining a preset degree of wear of the target object based on the first iron content, wherein the preset degree of wear includes: light wear, moderate wear, and heavy wear; and determining a preset repair plan for the target object based on the preset degree of wear.
[0007] In an exemplary embodiment, predicting a first iron content of the target object in a target time period based on historical iron content data in the target object includes: sorting the historical iron content data based on the preset time interval to construct an iron content data sequence; determining a target parameter based on the iron content data sequence; and predicting the iron content of the target object in the target time period based on the historical iron content data and the target parameter to obtain the first iron content.
[0008] In an exemplary embodiment, determining a target parameter based on the iron content data sequence includes: constructing a first iron content accumulation sequence based on the iron content data sequence; determining an adjacent mean generation sequence corresponding to the first iron content accumulation sequence, and constructing a first data matrix and a first vector corresponding to the adjacent mean generation sequence corresponding to the first iron content accumulation sequence; determining a first parameter based on the first data matrix and the first vector; predicting a second iron content of the target object at a second time based on the first parameter and the iron content prediction formula, and generating the iron content data of the target object at the second time based on the second iron content and the first iron content accumulation sequence; updating the iron content data sequence based on the iron content data of the target object at the second time, and constructing a second iron content accumulation sequence based on the updated iron content data sequence; determining an adjacent mean generation sequence corresponding to the second iron content accumulation sequence, and constructing a second data matrix and a second vector corresponding to the adjacent mean generation sequence corresponding to the second iron content accumulation sequence; determining the target parameter based on the second data matrix and the second vector.
[0009] In an exemplary embodiment, determining the preset degree of wear of the target object based on the first iron content includes: determining the preset degree of wear of the target object as the light wear when it is determined that the first iron content is greater than the first preset iron content and less than or equal to the second preset iron content; determining the preset degree of wear of the target object as the moderate wear when it is determined that the first iron content is greater than the second preset iron content and less than or equal to the third preset iron content, wherein the third preset iron content is greater than the second preset iron content; determining the preset degree of wear of the target object as the heavy wear when it is determined that the first iron content is greater than the third preset iron content.
[0010] In an exemplary embodiment, determining a preset repair solution for the target object based on the preset wear degree includes: when it is determined that the preset wear degree is light wear, determining the preset repair solution as a first repair solution, wherein the first repair solution is to replace a first substance on the surface of the target object.
[0011] In an exemplary embodiment, a preset repair plan for the target object is determined based on the preset wear degree, including: when it is determined that the preset wear degree is moderate wear, determining the preset repair plan as a second repair plan, wherein the second repair plan is to inspect the gearbox of the target object to determine the wear position corresponding to the target object, and perform local repair on the wear position.
[0012] In an exemplary embodiment, a preset repair plan for the target object is determined based on the preset wear degree, including: when it is determined that the preset wear degree is severe wear, determining the preset repair plan as a third repair plan, wherein the third repair plan is to update the working state of the target object to a stopped running state, and detect the target object to determine a plurality of wear parts in the target object; counting the plurality of wear parts to divide the plurality of wear parts into a first wear part set and a second wear part set, wherein each wear part in the first wear part set is a part that is allowed to be repaired, and each wear part in the second wear part set is a part that is not allowed to be repaired; repairing each wear part in the first wear part set, and replacing each repaired part in the second wear part set.
[0013] According to another embodiment of the embodiment of the present application, a device for determining a repair plan is also provided, including: a prediction module, used to predict a first iron content of the target object in a target time period based on iron content data in the target object, wherein the iron content data is iron content data collected based on a preset time interval within the first time period; a first determination module, used to determine a preset degree of wear of the target object based on the first iron content, wherein the preset degree of wear includes: light wear, moderate wear, and heavy wear; a second determination module, used to determine a preset repair plan for the target object based on the preset degree of wear.
[0014] According to another aspect of the embodiments of the present application, a computer-readable storage medium is provided, in which a computer program is stored, wherein the computer program is configured to execute the above method when running.
[0015] According to another aspect of the embodiments of the present application, an electronic device is provided, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the method through the computer program.
[0016] According to another aspect of the embodiments of the present application, a computer program product is provided, including a computer program, wherein the computer program is executed by a processor to perform the method.
[0017] In an embodiment of the present application, a first iron content of a target object in a target time period is predicted based on iron content data collected at preset time intervals during a first time period of the target object; a preset degree of wear of the target object is determined based on the first iron content, and the preset degree of wear includes: light wear, moderate wear and heavy wear; a preset repair plan for the target object is determined based on the preset degree of wear. That is, the embodiment of the present application predicts the first iron content based on the iron content data collected at equal time intervals of the target object, and the possible degree of wear of the target object in the target time period can be predicted based on the first iron content, and a preset repair plan for the target object is determined based on the predicted preset degree of wear. Through the above technical solution, the problem of delayed execution of the repair plan caused by the inability to predict the degree of wear of the target object in the related technology can be solved, and the preset degree of wear of the target object can be predicted in advance, and the preset repair plan corresponding to the preset degree of wear can be determined in advance. BRIEF DESCRIPTION OF THE DRAWINGS
[0018] The drawings described herein are used to provide a further understanding of the present application and constitute a part of the present application. The illustrative embodiments of the present application and their descriptions are used to explain the present application and do not constitute an improper limitation on the present application. In the drawings:
[0019] Figure 1It is a hardware structure block diagram of a computer terminal of a method for determining a repair solution in an embodiment of the present application;
[0020] Figure 2 is a flow chart of a method for determining a repair solution according to an embodiment of the present application;
[0021] Figure 3 It is a structural block diagram of a device for determining a repair solution according to an embodiment of the present application. DETAILED DESCRIPTION
[0022] In order to enable those skilled in the art to better understand the solution of the present application, the technical solution in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of the present application.
[0023] It should be noted that the terms "first", "second", etc. in the specification and claims of the present application and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchanged where appropriate, so that the embodiments of the present application described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions. For example, a process, method, system, product, or device that includes a series of steps or units is not necessarily limited to those steps or units that are clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products, or devices; "plurality" means two or more.
[0024] The method embodiments provided in the embodiments of the present application can be executed in a computer terminal or similar computing device or cloud platform or independent physical server or software platform, wherein the above software platform is run through one or more servers. Taking running on a computer terminal as an example, Figure 1 1 is a hardware structure block diagram of a computer terminal for determining a repair solution according to an embodiment of the present application. Figure 1 As shown, the computer terminal may include one or more ( Figure 1 Only one is shown in the figure) processor 102 and memory 104 for storing data. In an exemplary embodiment, the computer terminal may also include a transmission device 106 for communication functions and an input and output device 108, wherein the processor 102 may include but is not limited to a processing device such as a microprocessor MCU or a programmable logic device FPGA. A person skilled in the art can understand that Figure 1 The structure shown is only for illustration and does not limit the structure of the above-mentioned computer terminal. Figure 1 More or fewer components as shown, or with Figure 1 Equivalent functions or comparisons shown Figure 1 A different configuration with more features is shown.
[0025] The memory 104 can be used to store computer programs, for example, software programs and modules of application software, such as computer programs corresponding to the methods in the embodiments of the present application. The processor 102 executes various functional applications and data processing by running the computer programs stored in the memory 104, that is, the above-mentioned methods are implemented. The memory 104 may include a high-speed random access memory, and may also include a non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid-state memory. In some examples, the memory 104 may further include a memory remotely arranged relative to the processor 102, and these remote memories may be connected to the computer terminal via a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.
[0026] The transmission device 106 is used to receive or send data via a network. The specific example of the above network may include a wireless network provided by a communication provider of a computer terminal. In one example, the transmission device 106 includes a network adapter (Network Interface Controller, referred to as NIC), which can be connected to other network devices through a base station so as to communicate with the Internet. In one example, the transmission device 106 can be a radio frequency (RF) module, which is used to communicate with the Internet wirelessly.
[0027] In this embodiment, a method for determining a repair solution is provided, which is applied to the above-mentioned computer terminal. Figure 2 : is a flowchart of a method for determining a repair solution according to an embodiment of the present application, the process comprising the following steps:
[0028] Step S202, predicting a first iron content of the target object in a target time period based on the iron content data in the target object, wherein the iron content data is the iron content data collected based on a preset time interval in the first time period;
[0029] The target object may be a wind turbine gearbox. The first time period may be a time period before the current time, and the target time period may be a time period after the current time.
[0030] Step S204, determining a preset wear degree of the target object according to the first iron content, wherein the preset wear degree includes: light wear, moderate wear, and heavy wear;
[0031] Step S206: determining a preset repair solution for the target object according to the preset wear degree.
[0032] In an embodiment of the present application, a first iron content of a target object in a target time period is predicted based on iron content data collected at preset time intervals during a first time period of the target object; a preset degree of wear of the target object is determined based on the first iron content, and the preset degree of wear includes: light wear, moderate wear and heavy wear; a preset repair plan for the target object is determined based on the preset degree of wear. That is, the embodiment of the present application predicts the first iron content based on the iron content data collected at equal time intervals of the target object, and the possible degree of wear of the target object in the target time period can be predicted based on the first iron content, and a preset repair plan for the target object is determined based on the predicted preset degree of wear. Through the above technical solution, the problem of delayed execution of the repair plan caused by the inability to predict the degree of wear of the target object in the related technology can be solved, and the preset degree of wear of the target object can be predicted in advance, and the preset repair plan corresponding to the preset degree of wear can be determined in advance.
[0033] Optionally, predicting a first iron content of the target object in a target time period based on the historical iron content data in the target object in the above step S202 includes: sorting the historical iron content data based on the preset time interval to construct an iron content data sequence; determining a target parameter based on the iron content data sequence; and predicting the iron content of the target object in the target time period based on the historical iron content data and the target parameter to obtain the first iron content.
[0034] It is understandable that the technical solution for predicting the first iron content of the target object in the target time period includes:
[0035] 1. Construct an iron content data sequence: First, process the historical iron content data obtained from the target object (wind turbine gearbox). The historical iron content data is collected based on a preset time interval (for example, every other month) to ensure the temporal continuity and consistency of the data. Sort the historical iron content data in chronological order to construct an iron content data sequence. For example, if the iron content data for the past 12 months is collected from the wind turbine gearbox, then the data will be arranged in chronological order to form a sequence.
[0036] 2. Determine the target parameters: After constructing the iron content data sequence, the target parameters need to be determined through the iron content data sequence.
[0037] 3. Iron content prediction: Using the determined target parameters and historical iron content data, the iron content of the target object in the target time period (i.e., a certain time point in the future) is predicted, that is, the first iron content is obtained.
[0038] Wherein, determining the preset wear degree of the target object according to the first iron content includes: constructing a first iron content accumulation sequence according to the iron content data sequence; determining an adjacent mean generation sequence corresponding to the first iron content accumulation sequence, and constructing a first data matrix and a first vector corresponding to the adjacent mean generation sequence corresponding to the first iron content accumulation sequence; determining a first parameter according to the first data matrix and the first vector; predicting a second iron content of the target object at a second time according to the first parameter and the iron content prediction formula, and generating the iron content data of the target object at the second time according to the second iron content and the first iron content accumulation sequence; updating the iron content data sequence according to the iron content data of the target object at the second time, and constructing a second iron content accumulation sequence according to the updated iron content data sequence; determining an adjacent mean generation sequence corresponding to the second iron content accumulation sequence, and constructing a second data matrix and a second vector corresponding to the adjacent mean generation sequence corresponding to the second iron content accumulation sequence; determining the target parameter according to the second data matrix and the second vector.
[0039] It is understood that determining the target parameters specifically includes:
[0040] 1. Construct the first iron content accumulation sequence (1-AGO sequence): Based on the original data sequence of the iron content of the lubricating oil of the wind turbine, samples are taken at specific time intervals (for example, every 3 months), and the oil samples are analyzed for elements to obtain the data on the iron content. Then, these iron content data are constructed into an original sequence, and accumulated (1-AGO), that is, each data in the sequence is combined with the cumulative sum of all the previous data to form a new sequence (i.e., the first iron content accumulation sequence). The above technical solution can smooth the fluctuations of the original data, making it more regular and convenient for modeling and prediction.
[0041] The above 1-AGO sequence is also called a cumulatively generated sequence. Assuming a sequence is {x1, x2, x3, x4}, the cumulatively generated sequence is {x1, x1+x2, x1+x2+x3, x1+x2+x3+x4}.
[0042] 2. Determine the adjacent mean generation sequence and construct the data matrix and vector: Based on the first iron content cumulative sequence, calculate its adjacent mean generation sequence, that is, take the average of every two consecutive data in the cumulative sequence as a new data point to form an adjacent mean sequence.
[0043] 3. Determine the first parameter: Construct the first data matrix B of the cumulative sequence and the adjacent mean sequence and the second vector Y. Estimate the first parameter between the first data matrix B and the first vector Y by the least squares method, which is usually a parameter column.
[0044] 4. Predict the iron content based on the first parameter: Substitute the determined first parameter into the prediction formula of the grey equal-dimensional recursive model (i.e., the iron content prediction formula), predict the iron content of the target wind turbine at a certain time point in the future (i.e., the second time), and obtain the second iron content.
[0045] 5. Generate the iron content data at the second time: Based on the predicted value of the second iron content and the first iron content cumulative sequence, the iron content data at the second time is obtained by reverse accumulation (ie, subtracting the cumulative value from the cumulative sequence).
[0046] 6. Update the iron content data sequence and construct a second iron content cumulative sequence: Add the iron content data of the second time to the original iron content data sequence, and remove the earliest data in the sequence to update the sequence. Then, based on the updated sequence, reconstruct the iron content cumulative sequence.
[0047] 7. Determine the second data matrix and second vector, as well as the target parameters: Repeat the above steps to construct a new data matrix and vector based on the updated iron content data series, and then re-estimate the parameters, which will be used for prediction at subsequent time points.
[0048] The above process is a cycle of continuously updating the data sequence, reconstructing the cumulative sequence and the prediction model parameters, so that the prediction model can be continuously corrected to make it more adaptable to the latest data and trends, thereby improving the accuracy of the prediction. Each time a cycle is completed, a new iron content prediction value is obtained until the requirements of the prediction accuracy and time range are met. The above technical solution is suitable for situations where the amount of data is insufficient. Through model supplementation and parameter correction, limited data can be effectively used for prediction, which is of great significance for the maintenance and fault prevention of wind power equipment.
[0049] Optionally, the above-mentioned step S204 of determining the preset degree of wear of the target object based on the first iron content includes: when it is determined that the first iron content is greater than the first preset iron content, and less than or equal to the second preset iron content, determining that the preset degree of wear of the target object is the light wear; when it is determined that the first iron content is greater than the second preset iron content, and less than or equal to the third preset iron content, determining that the preset degree of wear of the target object is the moderate wear, wherein the third preset iron content is greater than the second preset iron content; when it is determined that the first iron content is greater than the third preset iron content, determining that the preset degree of wear of the target object is the heavy wear.
[0050] It can be understood that the light wear judgment: when the predicted iron content is higher than the first preset iron content and does not exceed the second preset iron content, the gearbox is considered to be in a light wear state. This interval indicates that the wear inside the gearbox has begun, but it is still within the controllable range. At this time, the wear can be prevented from further aggravation by strengthening monitoring and maintenance.
[0051] Moderate wear judgment: If the predicted iron content exceeds the second preset iron content but does not reach the third preset iron content, the gearbox wear is judged to be moderate. This means that the wear is already serious and a more detailed inspection of the gearbox may be required.
[0052] Severe wear judgment: When the predicted iron content is higher than the third preset iron content, it means that the gearbox wear has reached a dangerous level and a serious failure may be imminent or has already occurred. In this case, immediate action must be taken, such as stopping the machine for inspection, replacing the gearbox, or taking emergency maintenance measures to prevent greater losses.
[0053] This graded judgment of the degree of wear can provide a clear guide for the operation and maintenance personnel of the wind farm, helping them to detect and deal with gearbox wear problems at an early stage, thereby improving the operating efficiency and safety of the equipment and reducing downtime and maintenance costs caused by failures.
[0054] Optionally, determining a preset repair solution for the target object according to the preset wear degree includes:
[0055] (1) When it is determined that the preset wear degree is light wear, the preset repair solution is determined to be a first repair solution, wherein the first repair solution is to replace a first material on the surface of the target object.
[0056] (2) When it is determined that the preset wear degree is moderate wear, the preset repair solution is determined to be a second repair solution, wherein the second repair solution is to detect the gearbox of the target object to determine the wear position corresponding to the target object, and perform local repair on the wear position.
[0057] (3) When it is determined that the preset degree of wear is severe wear, the preset repair solution is determined to be a third repair solution, wherein the third repair solution is to update the working state of the target object to a stopped running state, and detect the target object to determine a plurality of wear parts in the target object; count the plurality of wear parts to divide the plurality of wear parts into a first wear part set and a second wear part set, wherein each wear part in the first wear part set is a part that is allowed to be repaired, and each wear part in the second wear part set is a part that is not allowed to be repaired; repair each wear part in the first wear part set, and replace each repaired part in the second wear part.
[0058] According to the above technical solution, in the stage of mild wear, the wear of the gearbox may be limited to certain surfaces or parts, and this degree of wear can usually be controlled by simple maintenance and care. Therefore, the first repair solution may involve replacing the first substance on the surface of the target object, which may be an additive in the lubricating oil or other surface coating material to enhance its anti-wear performance. This repair solution aims to extend the service life of the gearbox by supplementing or replacing the protective layer on the worn surface to avoid deterioration of wear.
[0059] The second repair plan is suitable for moderate wear. When the degree of wear reaches moderate, simply replacing the lubricant or surface coating may not be enough to solve the problem, and a more in-depth inspection of the gearbox is required to determine the specific wear location. This may include the use of non-destructive testing techniques, such as ultrasonic testing, magnetic particle testing, etc., to inspect key components such as gears and bearings inside the gearbox. Once the wear location is determined, the second repair plan will take local repair measures. For example: grinding, welding or partial replacement of worn parts to restore the function and performance of the components while minimizing the impact on the overall operation of the wind turbine.
[0060] The third repair plan is for severe wear. Severe wear means that multiple parts of the gearbox have been seriously damaged, which may affect the safe operation of the wind turbine. In this case, simple repair or partial replacement is no longer effective, and the working state of the wind turbine needs to be updated to a stopped state for comprehensive inspection and evaluation. The third repair plan first involves the inspection and statistics of all worn parts in the gearbox, dividing them into a first set of worn parts and a second set of worn parts. Parts in the first set of worn parts are allowed to be repaired, while parts in the second set of worn parts need to be replaced because their degree of wear is beyond the scope of repair. This repair plan may take a long time and cost a high cost, but it can ensure that the wind turbine can resume operation safely and reliably after repair.
[0061] By implementing these repair solutions, appropriate maintenance and repair measures can be taken based on the predicted results of gearbox lubricant wear, thereby effectively extending the service life of wind turbines and reducing downtime caused by failures.
[0062] In order to better understand the process of determining the above-mentioned repair solution, the above-mentioned repair solution determination method is described below in conjunction with an optional embodiment, but is not used to limit the technical solution of the embodiment of the present application.
[0063] The optional embodiment of the present application defines a method for predicting lubricating oil wear of a wind turbine gearbox based on a grey equal-dimensional recursive model, specifically:
[0064] Step 1: Sampling the wind turbines at certain time intervals (i.e., preset time intervals), and performing elemental analysis on the oil samples using instruments such as an inductively coupled plasma atomic emission spectrometer to obtain iron element data that changes over time.
[0065] Step 2: Construct the original sequence of iron content, namely:
[0066] X (0) =(x (0) (1), x (0) (2), …x (0) (n)) (i.e., iron content data series), this original
[0067] The sequence is a sequence of equal time intervals.
[0068] Step 3: Construct a cumulative sequence, i.e., a 1-AGO sequence. Add the data in the same sequence one by one to generate new data, and the accumulated sequence becomes the cumulative generated sequence. (1) =(x (1) (1), x (1) (2), …x (1) (n)) (i.e., the first iron content accumulation sequence), wherein,
[0069] Step 4: Calculate the adjacent mean sequence, data sequence:
[0070] X (1) =(x (1) (t1), x (1) (t2), …x (1) (t n The adjacent mean generation sequence of )) is:
[0071] M (1) =(m (1) (t1), m (1) (t2),…m (1) (t n ));
[0072] m (1) (t k )=(x (1) (t k-1 )+x (1) (t k )) / 2, where k=2, 3,…, n.
[0073] Step 5: Construct the data matrix B and vector Y, and define:
[0074]
[0075] Step 6: Calculate the least squares estimated parameter column:
[0076] Step 7: Substitute the prediction formula of the iron series:
[0077]
[0078] Step 8: The grey equal-dimensional recursive model is based on the above model, changing the variables, constantly changing, and constantly correcting the model parameters. That is, according to the formulas in steps 3 and 7, the (n+1)th information is predicted to obtain x (0) (n+1).
[0079] Step 9: Place the x (0) (n+1) is added as the latest data to the original sequence X (0) . At the same time, remove the oldest information x in the original sequence (0) (1) Carry out the first metabolic cycle;
[0080] Step 10: Repeat steps 2 to 7 for the sequence of the first metabolic cycle, predict the n+2th information, and get the new x (0) (n+1);
[0081] Step 11: Repeat steps 8 to 11 above, and continue the modeling cycle until the system prediction requirements are met.
[0082] Step 12: Input any time in the future to predict the corresponding iron content, determine the wear state of the gear based on the element content, and formulate corresponding operation and maintenance measures.
[0083] The iron content in the wind turbine gearbox lubricating oil is monitored in real time and future trends are predicted through the grey equal-dimensional recursive model. This prediction is not only based on the current iron content, but also takes into account the changing trend of historical data, which can more accurately reflect the dynamic process of gear wear. Wear state determination: According to the predicted iron content, combined with the baseline iron content during normal operation of the gearbox and the known wear threshold, the wear state of the gear is determined. Generally, the increase in iron content is positively correlated with the degree of gear wear. Set different warning levels, such as: mild wear, moderate wear, severe wear, etc., corresponding to different iron content ranges.
[0084] Among them, for minor wear, the monitoring frequency can be increased: that is, oil sample analysis can be carried out regularly or more frequently to capture any deterioration trends in time; check the quality of the lubricant: that is, confirm whether the lubricant meets the specifications and whether it needs to be replaced or supplemented with additives to improve lubrication performance; slightly adjust the load: that is, adjust the load appropriately according to the operating status of the equipment to avoid additional mechanical stress in the early stages of wear.
[0085] For moderate wear, the lubricant can be replaced: moderate wear indicates that the performance of the lubricant has significantly decreased, and new lubricant needs to be replaced to restore the lubrication effect; clean the filter: remove impurities in the filter to prevent wear debris from recirculating into the gearbox and causing more damage; adjust operating parameters: including temperature, pressure, etc., to ensure that the gearbox operates under optimal conditions to reduce further wear; detailed mechanical inspection: a more in-depth inspection of the gearbox, including gear meshing, bearing status, seal integrity, etc., to find the root cause of wear.
[0086] Worn parts can be replaced for severe wear: identify and replace all severely worn gears, bearings or other key components; comprehensive inspection and repair: conduct a comprehensive inspection of the gearbox and repair any possible damage, such as cracks, deformation, etc.; temporary shutdown: before replacing parts or performing major repairs, a short-term shutdown may be required to prevent further damage and ensure safety; professional assessment: ask professionals to conduct a comprehensive assessment of the gearbox to ensure that all repair measures are correctly implemented to avoid potential problems in the future.
[0087] Through the description of the above implementation methods, those skilled in the art can clearly understand that the method according to the above embodiment can be implemented by means of software plus a necessary general hardware platform, and of course by hardware, but in many cases the former is a better implementation method. Based on such an understanding, the technical solution of the present application, or the part that contributes to the prior art, can be embodied in the form of a software product, which is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk), and includes a number of instructions for a terminal device (which can be a mobile phone, computer, server, or network device, etc.) to execute the methods of each embodiment of the present application.
[0088] The present application also provides a structural block diagram of a device for determining a repair solution. Figure 3 is a structural block diagram of a device for determining a repair solution according to an embodiment of the present application; Figure 3 As shown, including:
[0089] A prediction module 32, configured to predict a first iron content of the target object in a target time period based on the iron content data in the target object, wherein the iron content data is the iron content data collected based on a preset time interval in the first time period;
[0090] A first determination module 34, configured to determine a preset wear degree of the target object according to the first iron content, wherein the preset wear degree includes: light wear, moderate wear, and heavy wear;
[0091] The second determination module 36 is used to determine a preset repair solution for the target object according to the preset wear degree.
[0092] Through the above-mentioned device, the first iron content of the target object in the target time period is predicted based on the iron content data collected at preset time intervals within the first time period of the target object; the preset degree of wear of the target object is determined based on the first iron content, and the preset degree of wear includes: light wear, moderate wear and heavy wear; and the preset repair plan for the target object is determined based on the preset degree of wear. That is to say, the embodiment of the present application predicts the first iron content based on the iron content data collected at equal time intervals of the target object, and the possible degree of wear of the target object in the target time period can be predicted based on the first iron content, and the preset repair plan for the target object is determined based on the predicted preset degree of wear. Through the above-mentioned technical solution, the problem of the inability to predict the degree of wear of the target object in the related technology, resulting in the delay in the execution of the repair plan, can be solved, and then the preset degree of wear of the target object is predicted in advance, and the preset repair plan corresponding to the preset degree of wear can be determined in advance.
[0093] In an exemplary embodiment, the prediction module 32 is further used to sort the historical iron content data based on the preset time interval to construct an iron content data sequence; determine the target parameters according to the iron content data sequence; and predict the iron content of the target object in the target time period based on the historical iron content data and the target parameters to obtain the first iron content.
[0094] In an exemplary embodiment, the first determination module 34 is further used to construct a first iron content accumulation sequence according to the iron content data sequence; determine an adjacent mean generation sequence corresponding to the first iron content accumulation sequence, and construct a first data matrix and a first vector corresponding to the adjacent mean generation sequence corresponding to the first iron content accumulation sequence; determine a first parameter according to the first data matrix and the first vector; predict a second iron content of the target object at the second time according to the first parameter and the iron content prediction formula, and generate the iron content data of the target object at the second time according to the second iron content and the first iron content accumulation sequence; update the iron content data sequence according to the iron content data of the target object at the second time, and construct a second iron content accumulation sequence according to the updated iron content data sequence; determine an adjacent mean generation sequence corresponding to the second iron content accumulation sequence, and construct a second data matrix and a second vector corresponding to the adjacent mean generation sequence corresponding to the second iron content accumulation sequence; determine the target parameter according to the second data matrix and the second vector.
[0095] In an exemplary embodiment, the first determination module 34 is further used to determine that the preset degree of wear of the target object is the light wear when it is determined that the first iron content is greater than the first preset iron content and less than or equal to the second preset iron content; to determine that the preset degree of wear of the target object is the moderate wear when it is determined that the first iron content is greater than the second preset iron content and less than or equal to the third preset iron content, wherein the third preset iron content is greater than the second preset iron content; and to determine that the preset degree of wear of the target object is the heavy wear when it is determined that the first iron content is greater than the third preset iron content.
[0096] In an exemplary embodiment, the second determination module 36 is further used to determine that the preset repair solution is a first repair solution when it is determined that the preset wear degree is light wear, wherein the first repair solution is to replace a first material on the surface of the target object.
[0097] In an exemplary embodiment, the second determination module 36 is further used to determine that the preset repair solution is a second repair solution when it is determined that the preset wear degree is moderate wear, wherein the second repair solution is to inspect the gearbox of the target object to determine the wear position corresponding to the target object and perform local repair on the wear position.
[0098] In an exemplary embodiment, the second determination module 36 is further used to determine that the preset repair solution is a third repair solution when it is determined that the preset wear degree is severe wear, wherein the third repair solution is to update the working state of the target object to a stopped running state, and detect the target object to determine a plurality of wear parts in the target object; count the plurality of wear parts to divide the plurality of wear parts into a first wear part set and a second wear part set, wherein each wear part in the first wear part set is a part that is allowed to be repaired, and each wear part in the second wear part set is a part that is not allowed to be repaired; repair each wear part in the first wear part set, and replace each repaired part in the second wear part set.
[0099] An embodiment of the present application further provides a storage medium, which includes a stored program, wherein the program executes any of the above methods when it is run.
[0100] Optionally, in this embodiment, the storage medium may be configured to store program codes for executing the following steps:
[0101] S1, predicting a first iron content of the target object in a target time period based on iron content data in the target object, wherein the iron content data is iron content data collected based on a preset time interval in the first time period;
[0102] S2, determining a preset wear degree of the target object according to the first iron content, wherein the preset wear degree includes: light wear, moderate wear, and heavy wear;
[0103] S3, determining a preset repair plan for the target object according to the preset wear degree.
[0104] An embodiment of the present application further provides an electronic device, including a memory and a processor, wherein a computer program is stored in the memory, and the processor is configured to run the computer program to execute the steps in any one of the above method embodiments.
[0105] Optionally, the electronic device may further include a transmission device and an input / output device, wherein the transmission device is connected to the processor, and the input / output device is connected to the processor.
[0106] Optionally, in this embodiment, the processor may be configured to perform the following steps through a computer program:
[0107] S1, predicting a first iron content of the target object in a target time period based on iron content data in the target object, wherein the iron content data is iron content data collected based on a preset time interval in the first time period;
[0108] S2, determining a preset wear degree of the target object according to the first iron content, wherein the preset wear degree includes: light wear, moderate wear, and heavy wear;
[0109] S3, determining a preset repair plan for the target object according to the preset wear degree.
[0110] An embodiment of the present application further provides a computer program product, including a computer program, which is used by a processor to execute the steps in any of the above method embodiments.
[0111] Optionally, in this embodiment, the computer program product may be executed by a processor to perform the following steps:
[0112] S1, predicting a first iron content of the target object in a target time period based on iron content data in the target object, wherein the iron content data is iron content data collected based on a preset time interval in the first time period;
[0113] S2, determining a preset wear degree of the target object according to the first iron content, wherein the preset wear degree includes: light wear, moderate wear, and heavy wear;
[0114] S3, determining a preset repair plan for the target object according to the preset wear degree.
[0115] Optionally, in this embodiment, the above-mentioned storage medium may include but is not limited to: a USB flash drive, a read-only memory (ROM), a random access memory (RAM), a mobile hard disk, a magnetic disk or an optical disk, and other media that can store program codes.
[0116] Optionally, the specific examples in this embodiment may refer to the examples described in the above embodiments and optional implementation modes, and this embodiment will not be described in detail here.
[0117] Obviously, those skilled in the art should understand that the above modules or steps of the present application can be implemented by a general computing device, they can be concentrated on a single computing device, or distributed on a network composed of multiple computing devices, and optionally, they can be implemented by a program code executable by a computing device, so that they can be stored in a storage device and executed by the computing device, and in some cases, the steps shown or described can be executed in a different order from that herein, or they can be made into individual integrated circuit modules, or multiple modules or steps therein can be made into a single integrated circuit module for implementation. Thus, the present application is not limited to any specific combination of hardware and software.
[0118] The above description is only the preferred embodiment of the present application and is not intended to limit the present application. For those skilled in the art, the present application may have various modifications and variations. Any modification, equivalent replacement, improvement, etc. made within the principles of the present application shall be included in the protection scope of the present application.
Claims
1. A method for determining a repair solution, characterized in that: include: Predicting a first iron content of the target object in a target time period based on iron content data in the target object, wherein the iron content data is iron content data collected based on a preset time interval in the first time period; Determining a preset wear degree of the target object according to the first iron content, wherein the preset wear degree includes: light wear, moderate wear, and heavy wear; A preset repair solution for the target object is determined according to the preset wear degree.
2. The method for determining a repair solution according to claim 1, characterized in that: Predicting a first iron content of the target object in a target time period based on historical iron content data of the target object comprises: sorting the historical iron content data based on the preset time interval to construct an iron content data sequence; determining a target parameter according to the iron content data sequence; The iron content of the target object in the target time period is predicted according to the historical iron content data and the target parameter to obtain the first iron content.
3. The method for determining a repair solution according to claim 2, characterized in that: Determining target parameters according to the iron content data sequence includes: constructing a first iron content accumulation sequence according to the iron content data sequence; Determine an adjacent mean value generation sequence corresponding to the first iron content accumulation sequence, and construct a first data matrix and a first vector corresponding to the adjacent mean value generation sequence corresponding to the first iron content accumulation sequence; determining a first parameter according to the first data matrix and the first vector; Predicting a second iron content of the target object at a second time according to the first parameter and the iron content prediction formula, and generating iron content data of the target object at the second time according to the second iron content and the first iron content accumulation sequence; updating the iron content data sequence according to the iron content data of the target object at the second time, and constructing a second iron content accumulation sequence according to the updated iron content data sequence; determining an adjacent mean value generation sequence corresponding to the second iron content accumulation sequence, and constructing a second data matrix and a second vector corresponding to the adjacent mean value generation sequence corresponding to the second iron content accumulation sequence; The target parameter is determined based on the second data matrix and the second vector.
4. The method for determining a repair solution according to claim 1, characterized in that: Determining a preset wear degree of the target object according to the first iron content includes: When it is determined that the first iron content is greater than a first preset iron content and less than or equal to a second preset iron content, determining that the preset wear degree of the target object is the light wear; When it is determined that the first iron content is greater than the second preset iron content and less than or equal to a third preset iron content, determining that the preset wear degree of the target object is the moderate wear, wherein the third preset iron content is greater than the second preset iron content; In a case where it is determined that the first iron content is greater than the third preset iron content, the preset wear degree of the target object is determined to be the heavy wear.
5. The method for determining a repair solution according to claim 1, characterized in that: Determining a preset repair solution for the target object according to the preset wear degree includes: In the case where it is determined that the preset wear degree is light wear, the preset repair solution is determined to be a first repair solution, wherein the first repair solution is to replace a first material on the surface of the target object.
6. The method for determining a repair solution according to claim 1, characterized in that: Determining a preset repair solution for the target object according to the preset wear degree includes: When it is determined that the preset wear degree is moderate wear, the preset repair plan is determined to be a second repair plan, wherein the second repair plan is to detect the gearbox of the target object to determine the wear position corresponding to the target object and perform local repair on the wear position.
7. The method for determining a repair solution according to claim 1, characterized in that: Determining a preset repair solution for the target object according to the preset wear degree includes: In the case where it is determined that the preset wear degree is severe wear, the preset repair plan is determined to be a third repair plan, wherein the third repair plan is to update the working state of the target object to a stopped running state, and detect the target object to determine a plurality of wear parts in the target object; count the plurality of wear parts to divide the plurality of wear parts into a first wear part set and a second wear part set, wherein each wear part in the second wear part set is a part that is not allowed to be repaired; repair each wear part in the first wear part set, and replace each repaired part in the second wear part, wherein each wear part in the first wear part set is a part that is allowed to be repaired.
8. A device for determining a repair solution, characterized in that: include: A prediction module, configured to predict a first iron content of the target object in a target time period based on iron content data in the target object, wherein the iron content data is iron content data collected based on a preset time interval in the first time period; A first determination module, configured to determine a preset wear degree of the target object according to the first iron content, wherein the preset wear degree includes: light wear, moderate wear, and heavy wear; The second determination module is used to determine a preset repair plan for the target object according to the preset wear degree.
9. A computer-readable storage medium, characterized in that: The computer-readable storage medium includes a stored program, wherein the program executes the method described in any one of claims 1 to 7 when executed.
10. An electronic device comprising a memory and a processor, characterized in that: A computer program is stored in the memory, and the processor is configured to execute the method according to any one of claims 1 to 7 through the computer program.