Method and system for dividing tightening process data
By performing differential operations and multidimensional time-series data analysis on the tightening process data, the problem of accurately dividing tightening steps in existing technologies has been solved, achieving precise monitoring of the tightening process and accurate data analysis.
Patent Information
- Application Number
- CN202410403047.6
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2024-04-03
- Publication Date
- 2025-10-17
AI Technical Summary
In the bolt tightening process, existing technologies are unable to accurately divide the various steps based on process data, resulting in data analysis errors and failure to improve production quality.
By performing differential operations on the tightening process data, a multi-dimensional time series data curve is generated, and the division factor is determined using the change rate characteristics to achieve accurate division of the tightening process data.
It enables precise monitoring of the tightening process, ensures the accuracy and consistency of data analysis, and reduces the risk of manual configuration errors.
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Figure CN120804546A_ABST
Abstract
Description
TECHNICAL FIELD
[0001] The present application relates to the technical field of tightening process data processing, in particular to a method for dividing tightening process data and a system thereof. BACKGROUND
[0002] With the diversification of market demand, multi-variety flexible production becomes increasingly important in engine manufacturing processes. In order to meet this demand, enterprises need to flexibly configure different equipment, procedures and parameters on the same production line to achieve mixed production of multiple product models, reduce potential costs due to the addition of new production lines, and improve the market competitiveness of the entire factory. For example, the bolt tightening process in engine manufacturing processes.
[0003] In the bolt tightening process, in order to ensure the accuracy and efficiency of bolt tightening, high-precision tightening tools usually adopt a multi-step tightening process, such as cap recognition, fast pre-tightening, pre-tightening and final tightening, etc. These steps can be monitored and adjusted by obtaining process data (torque, angle time-based sequence data).
[0004] However, since the process curve is mainly used for post-event manual query and audit, most high-precision tightening tools in the industry do not have special point information in the process data to distinguish the relationship between each step. Therefore, when dividing each step based on data, it cannot guarantee 100% accuracy, which leads to errors in subsequent data analysis for each step, and thus cannot improve production quality. SUMMARY
[0005] The purpose of the embodiments of the present application is to provide a method for dividing tightening process data and a system thereof, which can reverse divide tightening steps based on tightening process data, and then accurately locate the data segment corresponding to each tightening step based on the division result, establish an accurate area for data analysis, and thus realize accurate monitoring of the tightening process.
[0006] In order to achieve the above-mentioned purpose, in a first aspect, the embodiments of the present application provide a method for dividing tightening process data, which comprises: obtaining tightening process data; performing differential operation on the tightening process data to obtain corresponding differential time sequence data; based on the tightening process data and the differential time sequence data, drawing a multi-dimensional time sequence data curve; determining a division factor of the tightening process data based on the change rate characteristics of the multi-dimensional time sequence data curve at different time nodes; and dividing the tightening process data based on the division factor.
[0007] In some embodiments, the tightening process data includes torque time sequence data and angle time sequence data; the differential time sequence data includes at least one of first-order differential torque time sequence data, first-order differential angle time sequence data, second-order differential torque time sequence data, and second-order differential angle time sequence data.
[0008] In some embodiments, the differential operation is performed on the tightening process data to obtain corresponding differential time series data, including: performing first-order differential operation on the torque time series data to obtain first-order differential torque time series data; and / or performing first-order differential operation on the angle time series data to obtain first-order differential angle time series data.
[0009] In some embodiments, the differential operation is performed on the tightening process data to obtain corresponding differential time series data, including: performing second-order differential operation on the torque time series data to obtain second-order differential torque time series data; and / or performing second-order differential operation on the angle time series data to obtain second-order differential angle time series data.
[0010] In some embodiments, the multi-dimensional time series data curve includes at least the torque time series data curve and the angle time series data curve, and at least one of the first-order differential torque time series data curve, the first-order differential angle time series data curve, the second-order differential torque time series data curve, and the second-order differential angle time series data curve.
[0011] In some embodiments, the division factor of the tightening process data is determined based on the rate of change of the multi-dimensional time series data curve at different time nodes, including: based on the time sequence, when the rate of change of at least one of the time series data curves in the multi-dimensional time series data curve is greater than a preset threshold, the division factor of the tightening process data is sequentially determined.
[0012] In some embodiments, when the rate of change of at least one of the time series data curves in the multi-dimensional time series data curve is greater than a preset threshold, the division factor of the tightening process data is sequentially determined, including: when the rate of change of the torque time series data curve and / or the angle time series data curve is greater than a first preset threshold, the data point corresponding to the rate of change is determined as the division factor; and / or, when the rate of change of the first-order differential torque time series data curve and / or the first-order differential angle time series data curve is greater than a second preset threshold, the data point corresponding to the rate of change is determined as the division factor; and / or, when the rate of change of the second-order differential torque time series data curve and / or the second-order differential angle time series data curve is greater than a third preset threshold, the data point corresponding to the rate of change is determined as the division factor.
[0013] In a second aspect, the present application provides a monitoring method for a tightening process, including: dividing the data in a monitoring interval by using the division method in the first aspect or any of the embodiments of the first aspect, and locating the division mark in the monitoring interval based on the division result; obtaining the tightening process data corresponding to the located division mark; analyzing the obtained tightening process data to obtain an analysis result; and generating a monitoring report and / or issuing a warning information based on the analysis result.
[0014] In a third aspect, the present application provides a division system of tightening process data, comprising: a data acquisition module configured to acquire the tightening process data; a differential operation module configured to perform differential operation on the tightening process data to obtain corresponding differential time series data; a data visualization module configured to plot a multi-dimensional time series data curve based on the tightening process data and the differential time series data thereof; a data analysis module configured to determine a division factor of the tightening process data based on a variation rate feature of the multi-dimensional time series data curve at different time nodes; and a data division module configured to divide the tightening process data based on the division factor.
[0015] In a fourth aspect, the present application provides a monitoring system of a tightening process, comprising: a data division module configured to divide data in a monitoring interval by using the division method in the first aspect or any one of the embodiments of the first aspect, and locate a division mark in the monitoring interval based on the division result; a data acquisition module configured to acquire tightening process data corresponding to the located division mark; a data analysis module configured to analyze the acquired tightening process data to obtain an analysis result; and a monitoring and early warning module configured to generate a monitoring report or send an early warning information based on the analysis result.
[0016] Through the above technical solutions, the present application can reversely divide the tightening steps based on the tightening process data, and then accurately locate the data segments corresponding to each tightening step based on the division result, to establish an accurate area for data analysis, thereby realizing accurate monitoring of the tightening process.
[0017] Other features and advantages of the embodiments of the present application will be described in detail in the following specific implementation part. BRIEF DESCRIPTION OF DRAWINGS
[0018] The accompanying drawings are included to provide a further understanding of the embodiments of the present application, and constitute a part of the specification, and are used together with the following specific implementation part to explain the embodiments of the present application, but do not constitute a limitation on the embodiments of the present application. In the drawings:
[0019] Figure 1 is a flowchart of the division method of the tightening process data provided by an embodiment of the present application.
[0020] Figure 2 is a schematic diagram of the torque time series data curve provided by an embodiment of the present application.
[0021] Figure 3 is a schematic diagram of the multi-dimensional time series data curve provided by an embodiment of the present application.
[0022] Figure 4 is a flowchart of the monitoring method of the tightening process provided by an embodiment of the present application.
[0023] Figure 5is a structural schematic diagram of a screwing process data division system provided by an embodiment of the present application.
[0024] Figure 6 is a structural schematic diagram of a screwing process monitoring system provided by an embodiment of the present application. DETAILED DESCRIPTION
[0025] The specific embodiments of the embodiments of the present application are described in detail below with reference to the accompanying drawings. It should be understood that the specific embodiments described herein are only used to illustrate and explain the embodiments of the present application, and are not used to limit the embodiments of the present application.
[0026] The present application provides a screwing process data division method, as shown in the figure, the division method can include steps S101-S105. Figure 1
[0027] Step S101, acquiring screwing process data.
[0028] In some embodiments, the screwing process data can include torque time series data and angle time series data.
[0029] Taking a bolt screwing process as an example, in the screwing process, the system will record torque data, angle data and time data, and store these data in a database. At the same time, the system can generate a torque time series data curve according to the torque data and time data, and generate an angle time series data curve according to the angle data and time data.
[0030] For example, Figure 2 is a torque time series data curve generated in the screwing process, as shown in the figure, according to the torque time series data curve, the torque size corresponding to different time nodes can be seen, but the specific corresponding relationship between these torque values and each screwing step in the screwing program cannot be directly reflected. Therefore, when searching for a specific torque range (such as 300Nm-400Nm), multiple corresponding intervals may be obtained, increasing the difficulty of accurate analysis and monitoring of the screwing process. Figure 2
[0031] The present application obtains torque time series data and angle time series data by analyzing torque time series data curve and angle time series data curve respectively. These original time series data can facilitate subsequent data processing to achieve division of screwing process data, and thus provide accurate areas for data analysis.
[0032] Step S102, performing differential operation on the screwing process data to obtain corresponding differential time series data.
[0033] The differential operation can include first-order differential operation and / or second-order differential operation.
[0034] In some embodiments, step S102 can include: performing first-order differentiation operation on the torque time series data to obtain first-order differentiated torque time series data; and / or performing first-order differentiation operation on the angle time series data to obtain first-order differentiated angle time series data.
[0035] In some embodiments, step S102 can further include: performing second-order differentiation operation on the torque time series data to obtain second-order differentiated torque time series data; and / or performing second-order differentiation operation on the angle time series data to obtain second-order differentiated angle time series data.
[0036] In the embodiments of the present application, the differentiated time series data can include at least one of the first-order differentiated torque time series data, the first-order differentiated angle time series data, the second-order differentiated torque time series data, and the second-order differentiated angle time series data. For example, the torque time series data can be subjected to first-order differentiation operation and second-order differentiation operation to obtain the first-order differentiated torque time series data and the second-order differentiated torque time series data, i.e., the corresponding differentiated time series data includes the first-order differentiated torque time series data and the second-order differentiated torque time series data. For another example, the angle time series data can be subjected to first-order differentiation operation and second-order differentiation operation to obtain the first-order differentiated angle time series data and the second-order differentiated angle time series data, i.e., the corresponding differentiated time series data includes the first-order differentiated angle time series data and the second-order differentiated angle time series data. For yet another example, the torque time series data and the angle time series data can be subjected to first-order differentiation operation, respectively, to obtain the first-order differentiated torque time series data and the first-order differentiated angle time series data, i.e., the corresponding differentiated time series data includes the first-order differentiated torque time series data and the first-order differentiated angle time series data. The specific time series data contained in the differentiated time series data is determined according to the complexity of the original time series data of the tightening process data and the variation rate characteristics, and different differentiated time series data can determine different division accuracies.
[0037] It should be noted that the differentiation operation can reveal the rate of change of data over time, i.e., the change trend of the data. The first-order differentiation can represent the rate of change of the data, and the second-order differentiation can further reflect the change of the rate of change, i.e., the acceleration. Therefore, by introducing the differentiated time series data, the original torque time series data and the angle time series data can be analyzed and processed more deeply, so as to realize accurate monitoring of the tightening process.
[0038] The present application proposes torque and angle time series data, and increases first-order and second-order differentiated data of torque and angle, divides the process data, realizes reverse division of tightening steps of tightening data, and establishes accurate area for data analysis.
[0039] Step S103: based on the tightening process data and the differentiated time series data thereof, a multi-dimensional time series data curve is drawn.
[0040] In some embodiments, the multi-dimensional time-series data curve includes at least a torque time-series data curve and an angle time-series data curve, and at least one of a first-order differential torque time-series data curve, a first-order differential angle time-series data curve, a second-order differential torque time-series data curve, and a second-order differential angle time-series data curve. That is, the multi-dimensional time-series data curve includes original time-series data of the tightening process data, and corresponding differential time-series data, and the specific differential time-series data can be determined according to step S102.
[0041] For example, the multi-dimensional time-series data curve can include a torque time-series data curve, an angle time-series data curve, a first-order differential angle time-series data curve, and a second-order differential angle time-series data curve, as shown in FIG. 1, where curve 1 corresponds to the angle time-series data curve, curve 2 corresponds to the torque time-series data curve, curve 3 corresponds to the first-order differential angle time-series data curve, and curve 4 corresponds to the second-order differential angle time-series data curve. Figure 3
[0042] In some embodiments, a multi-curve clustering algorithm can also be added to improve the compatibility of the division method for different data.
[0043] Step S104, determining a division factor of the tightening process data based on the rate of change of the multi-dimensional time-series data curve at different time nodes.
[0044] In some embodiments, the determination of the division factor of the tightening process data can be based on time series, and when the rate of change of at least one of the multi-dimensional time-series data curves is greater than a preset threshold, the division factor of the tightening process data is sequentially determined.
[0045] In some embodiments, when the rate of change of the torque time-series data curve and / or the angle time-series data curve is greater than a first preset threshold, the data point corresponding to the rate of change is determined as the division factor; and / or, when the rate of change of the first-order differential torque time-series data curve and / or the first-order differential angle time-series data curve is greater than a second preset threshold, the data point corresponding to the rate of change is determined as the division factor; and / or, when the rate of change of the second-order differential torque time-series data curve and / or the second-order differential angle time-series data curve is greater than a third preset threshold, the data point corresponding to the rate of change is determined as the division factor. The judgment process can be arbitrarily combined according to actual division conditions, and the embodiments of the present disclosure are not limited thereto.
[0046] Step S105, dividing the tightening process data based on the division factor.
[0047] Reference Figure 3 The determined division factor corresponding to the identification can be v1, v2, v3, v4, and the like in turn, and assuming that the tightening procedure in the tightening process includes 15 tightening steps, v1-v2 can correspond to step 1, v2-v3 can correspond to step 2, v3-v4 can correspond to step 3, and the like.
[0048] The present application has the following advantages and outstanding effects:
[0049] 1. The region is completely divided based on the curve process data, without 100% relying on the tightening equipment step signal point, ensuring the adaptability of different tightening procedures.
[0050] 2. The accuracy and consistency of the batch accurate division of the process curve can establish a stable data analysis region, and realize the scene application of rules and algorithms.
[0051] The following is explained and described in whole in combination with the specific embodiments.
[0052] As shown in the tightening, Figure 3 In order to improve the stability of the friction coefficient of different bolts during tightening, the process design tightening procedure can be completed by 15 steps, including 2 times of reverse tightening, so that the torque and angle will be repeated in some regions, such as 3 regions of 0Nm-30Nm. When the query region coincides with it, the interval range cannot be accurately determined, causing identification abnormalities. The tightening procedure in the manufacturing site has randomness, and the numbering of the region cannot be fixed, so an adaptive compatibility algorithm needs to be developed.
[0053] The present application is based on the analysis of the tightening curve based on the tightening database, to obtain the same length sequence array of torque and angle, and to generate two-dimensional time series data through sampling time. In order to improve the accuracy, the torque time series data and the angle time series data are respectively subjected to first-order differentiation and second-order differentiation, to obtain the torque time series data, the angle time series data, the first-order differentiated torque time series data, the second-order differentiated torque time series data, the first-order differentiated angle time series data, and the second-order differentiated angle time series data, a total of 6 groups of time series data, and then based on the combination of multiple groups of data, the division of each tightening step is realized.
[0054] The specific division method can include the following steps:
[0055] 1) Select 2 groups of tightening procedures to divide into two groups, select one group as a training set, and the other group as a test set. Each group of tightening procedures can include multiple tightening steps.
[0056] 2) Obtain single tightening data through a data management system or a tightening controller, including two-dimensional time series data of torque time and angle time with the same length.
[0057] 3) Perform first-order and second-order differentiation operations on the two-dimensional data, and expand the original two-dimensional data into 6-dimensional time series data;
[0058] 4) Based on the sequence, 6-dimensional data is plotted into the same canvas, the corresponding relationship with the training set tightening program is found through the rate of change of each dimension data, and a set of step-by-step judgment factors is determined;
[0059] 5) The step-by-step judgment factor set obtained in step 4 is verified in the test set, and when the accuracy rate reaches 100%, the iteration is stopped, otherwise the number of programs is increased from step 1 and the iteration optimization is re-performed;
[0060] 6) When a stable set of step-by-step judgment factors is obtained, the accurate step-by-step scheme is obtained by comparing one by one according to the traversal scheme in the new program step.
[0061] The above division method can be widely applied to the secondary development of the output process curve of the high-precision tightening tool. Through the mechanism of establishing the regional division method, the algorithm can be accurately deployed, the error-proof guarantee of the tightening process output can be realized, the batch assembly problems caused by manual configuration errors or operation abnormalities can be solved, and the quality detection capability of the process abnormality can be improved.
[0062] The application also provides a tightening process monitoring method, as shown in Figure 4 The method can include steps S201-S204.
[0063] Step S201, the data in the monitoring interval is divided, and based on the division result, the division mark in the monitoring interval is located.
[0064] Step S202, the tightening process data corresponding to the located division mark is obtained.
[0065] Step S203, the obtained tightening process data is analyzed to obtain an analysis result.
[0066] Step S204, based on the analysis result, a monitoring report is generated and / or a warning information is sent.
[0067] In some embodiments, the monitoring interval can be an interval covered by a group of tightening programs. The data in the monitoring interval is divided using the division method of the tightening process data provided by the application, i.e. the tightening process data used by a group of tightening programs is divided, and after division, the division mark of each division interval can be obtained. Then, when data analysis is performed subsequently, the corresponding tightening process data can be found based on the division mark and specific analysis can be performed to obtain an analysis result, and finally a monitoring report is generated. If the analysis finds that the tightening process data is abnormal, a warning information can be sent.
[0068] When manual query is performed, the parameter output of the tightening step can also be set as needed to improve the sampling frequency of the curve to ensure the output of high-precision process data.
[0069] The present invention also provides a system 100 for dividing tightening process data, such as Figure 5 As shown, the system 100 may include: a data acquisition module 110, a differential operation module 120, a data visualization module 130, a data analysis module 140, and a data partitioning module 150. The data acquisition module 110 is configured to acquire tightening process data; the differential operation module 120 is configured to perform a differential operation on the tightening process data to obtain corresponding differential time series data; the data visualization module 130 is configured to plot a multi-dimensional time series data curve based on the tightening process data and its differential time series data; the data analysis module 140 is configured to determine a partitioning factor for the tightening process data based on the rate of change characteristics of the multi-dimensional time series data curve at different time nodes; and the data partitioning module 150 is configured to partition the tightening process data based on the partitioning factor.
[0070] The benefits of the tightening process data partitioning system 100 can be found in the above description of the tightening process data partitioning method, which will not be repeated here.
[0071] The present invention also provides a monitoring system 200 for a tightening process, such as Figure 6 As shown, the system 200 may include: a data partitioning module 210, a data acquisition module 220, a data analysis module 230, and a monitoring and early warning module 240. The data partitioning module 210 is configured to partition the data of the monitoring interval using the partitioning method according to any one of claims 1 to 7, and locate the partitioning mark within the monitoring interval based on the partitioning result; the data acquisition module 220 is configured to acquire the tightening process data corresponding to the located partitioning mark; the data analysis module 230 is configured to analyze the acquired tightening process data to obtain an analysis result; and the monitoring and early warning module 240 is configured to generate a monitoring report or issue an early warning message based on the analysis result.
[0072] The benefits of the tightening process monitoring system 200 can be found in the above description of the tightening process monitoring method, which will not be repeated here.
[0073] It is understood that the tightening process data partitioning method and system provided by the present invention are not limited to bolt tightening processes and can also be applied to other tightening procedures, such as rivet tightening processes. The acquisition, transmission, storage, use, and processing of data in the technical solution of the present invention comply with relevant national laws and regulations.
[0074] It should be noted that in the embodiments of the present application, some software, components, models and other prior art solutions can be mentioned, which should be considered as exemplary, and the purpose is only to illustrate the feasibility of the implementation of the technical solutions of the present application, but does not mean that the applicant has or will necessarily use the solutions.
[0075] It should also be noted that the terms "comprising", "including", or any other variant thereof are intended to cover non-exclusive inclusions, so that processes, methods, articles or devices that include a series of elements not only include those elements, but also include other elements not explicitly listed, or include elements inherent to such processes, methods, articles or devices. Without more limitations, the element defined by the statement "comprising a" does not exclude the presence of other identical elements in the process, method, article or device that includes the element.
[0076] The above is only an embodiment of the present application and is not intended to limit the present application. The present application can have various modifications and changes for those skilled in the art. Any modification, equivalent replacement, improvement, etc. within the spirit and principles of the present application shall be included in the scope of claims of the present application.
Claims
1. A method for dividing tightening process data, characterized in that: The division method includes: Acquire tightening process data; Performing differential operation on the tightening process data to obtain corresponding differential time series data; Drawing a multi-dimensional time series data curve based on the tightening process data and its differential time series data; Determining a division factor of the tightening process data based on a change rate characteristic of the multidimensional time series data curve at different time nodes; The tightening process data is divided based on the division factor.
2. The division method according to claim 1, characterized in that: The tightening process data includes torque time series data and angle time series data; the differential time series data includes at least one of first-order differential torque time series data, first-order differential angle time series data, second-order differential torque time series data, and second-order differential angle time series data.
3. The division method according to claim 2, characterized in that: The differential operation is performed on the tightening process data to obtain corresponding differential time series data, including: Performing a first-order differential operation on the torque time series data to obtain first-order differential torque time series data; And / or, performing a first-order differential operation on the angle time series data to obtain first-order differential angle time series data.
4. The division method according to claim 2, characterized in that: The differential operation is performed on the tightening process data to obtain corresponding differential time series data, including: Performing a second-order differential operation on the torque time series data to obtain second-order differential torque time series data; And / or, performing a second-order differential operation on the angle time series data to obtain second-order differential angle time series data.
5. The division method according to claim 1, characterized in that: The multidimensional time series data curve includes at least a torque time series data curve and an angle time series data curve, and includes at least one of a first-order differential torque time series data curve, a first-order differential angle time series data curve, a second-order differential torque time series data curve, and a second-order differential angle time series data curve.
6. The division method according to claim 5, characterized in that: Determining the division factors of the tightening process data based on the change rate characteristics of the multidimensional time series data curve at different time nodes includes: Based on the time series, when the change rate of at least one time series data curve in the multi-dimensional time series data curve is greater than a preset threshold, the division factors of the tightening process data are determined in sequence.
7. The division method according to claim 6, characterized in that: When the rate of change of at least one of the multi-dimensional time series data curves is greater than a preset threshold, sequentially determining the division factors of the tightening process data includes: When the change rate of the torque time series data curve and / or the angle time series data curve is greater than a first preset threshold, determining the data point corresponding to the change rate as a division factor; and / or, when a change rate of the first-order differential torque time series data curve and / or the first-order differential angle time series data curve is greater than a second preset threshold, determining a data point corresponding to the change rate as a division factor; And / or, when the change rate of the second-order differential torque time series data curve and / or the second-order differential angle time series data curve is greater than a third preset threshold, the data point corresponding to the change rate is determined as a division factor.
8. A method for monitoring a tightening process, characterized in that: The method includes: Using the division method according to any one of claims 1 to 7, the data of the monitoring interval is divided, and based on the division result, the division mark within the monitoring interval is located; Acquire tightening process data corresponding to the located division mark; Analyze the acquired tightening process data to obtain analysis results; Based on the analysis results, a monitoring report is generated and / or an early warning message is issued.
9. A system for dividing tightening process data, characterized in that: The partitioning system comprises: A data acquisition module is used to acquire tightening process data; A differential operation module, used for performing differential operation on the tightening process data to obtain corresponding differential time series data; A data visualization module, configured to draw a multi-dimensional time series data curve based on the tightening process data and its differential time series data; A data analysis module, configured to determine a partitioning factor of the tightening process data based on a change rate characteristic of the multi-dimensional time series data curve at different time nodes; A data partitioning module is used to partition the tightening process data based on the partitioning factor.
10. A tightening process monitoring system, characterized in that: The method includes: a data partitioning module, which partitions the data of the monitoring interval using the partitioning method according to any one of claims 1 to 7, and locates the partitioning marker within the monitoring interval based on the partitioning result; A data acquisition module, configured to acquire tightening process data corresponding to the located division identifier; A data analysis module is used to analyze the acquired tightening process data and obtain analysis results; The monitoring and early warning module is used to generate a monitoring report or issue an early warning message based on the analysis results.