Fixed-point resampling method for chain system

Abstract

The invention discloses a fixed-point resampling method for a chain system, belongs to the technical field of signal processing of rotating machinery, and aims to solve the problems that the regularity of a time domain signal of a single chain link is poor, the dynamic characteristics of the whole system are difficult to directly reflect, and frequency domain feature extraction cannot be carried out. The method comprises the following steps: extracting original data of a discrete motion unit from a multi-body dynamics simulation result; searching the nearest discrete motion unit as a monitoring unit in real time by calculating the distance at a preset data monitoring point position of the system; according to the data type, processing the original data of the monitoring unit by adopting one of a direct method, a synthesis method and an average method, and generating characteristic data representing the dynamic state of the fixed position; and finally, writing the feature data into a file according to a time sequence. According to the method, time sequence data of a fixed position is collected through a fixed-point resampling method, and the problems that due to tracking of a single motion unit, data contingency is high, regularity is poor, and frequency domain deepening and statistical analysis are difficult to carry out are solved.

Description

Technical Field

[0001] This invention belongs to the field of rotating machinery signal processing technology, specifically relating to a fixed-point resampling method for chain systems. Background Technology

[0002] Chain drive systems are widely used in mechanical transmission and have practical applications in transportation, material handling, traction and lifting, and other fields.

[0003] Currently, multibody dynamics simulation technology has become an indispensable part of the design, optimization, and verification of chain drive systems. This is especially true for chain drive systems with complex structures, complex boundary conditions, and high-speed, heavy-load operation (such as engine timing chain systems, mining scraper conveyors, and large cranes), where the design and testing require significant time and financial resources, hindering efficient and rapid iteration of solutions. Multibody dynamics simulation abstracts complex mechanical systems into solid models interconnected by kinematic pairs and forces, and uses computer numerical methods to obtain the dynamic time-series signals of each entity. Based on these time-series signals, a deep analysis of the entire system can be performed to determine whether the system can achieve the design and optimization goals.

[0004] In the field of chain system design, dynamic quantities such as tension, displacement, and velocity during chain system operation are parameters that must be considered during design. However, since a chain system is a rotational system, the position of individual chain links changes with the movement of the chain. In complex systems, the time-domain signal of a single chain link has poor regularity, making it difficult to directly reflect the dynamic characteristics of the entire system, and frequency-domain features cannot be extracted. Therefore, those skilled in the art urgently need a fixed-point resampling method for chain systems, capable of extracting data reflecting the dynamic characteristics of the system at a fixed spatial location from multibody dynamics simulation results. This method collects stable signals near a specific point that reflect the dynamic characteristics of a local region to supplement and improve the simulation data analysis methods for chain drive systems, and to establish a multi-dimensional evaluation system for chain systems. Summary of the Invention

[0005] The purpose of this invention is to propose a fixed-point resampling method for chain systems, which solves the problems of poor time-domain signal regularity of individual chain links in the prior art, making it difficult to directly reflect the dynamic characteristics of the entire system and unable to extract frequency domain features. The method reconstructs continuous and stable characteristic time-series data at preset fixed monitoring points from the simulation data of discrete motion units (such as chain links), which facilitates subsequent frequency domain, statistical and multi-dimensional analysis.

[0006] To achieve the above objectives, the present invention provides a chain system fixed-point resampling method, which is applicable to the post-processing of multibody dynamics simulation data of a transmission system containing multiple discrete motion units, wherein the discrete motion units move in an orderly and continuous manner along a predetermined trajectory and do not overlap in the motion plane. The method includes the following steps: Step 1: Extract simulation result data. Extract the dynamic data of the discrete motion unit from the completed multibody dynamics simulation result file and save it to an intermediate file. Step 2: Fixed-point link search. Set at least one fixed data monitoring point in the transmission system, calculate the relative distance of all discrete motion units with respect to the data monitoring point in the plane at each moment, and select the discrete motion unit with the closest distance as the monitoring unit of the corresponding data monitoring point at the current moment. Step 3: Fixed-point link data processing. Based on the original dynamic data type corresponding to each data monitoring point obtained in Step 2, the corresponding processing method is used to obtain the characteristic data representing that data monitoring point. Step 4: Write the fixed-point data result file. Write the characteristic data of each data monitoring point, obtained in chronological order, into the result file.

[0007] Step 1, which involves extracting simulation result data, includes starting the post-processing module, importing the simulation result file, importing the required data path, and exporting the data to a file.

[0008] The simulation results data need to meet the following requirements depending on the different chain systems: When all simulation boundary conditions are in a steady state and do not change over time, the steady-state simulation duration is at least 6 meshing cycles. When the simulation boundary conditions are in an unstable state, the steady-state simulation duration is at least twice the time period of the boundary condition change.

[0009] The dynamic data mentioned in step one includes the position, velocity, acceleration, and interaction force data between discrete motion units.

[0010] The data monitoring points described in step two are set on the running trajectory of the chain links; the location information of the data monitoring points includes the horizontal coordinate, vertical coordinate and phase angle in the chain plane; when multiple data monitoring points are set up, multi-threaded fixed-point chain link data processing is adopted.

[0011] The phase angle included in the data monitoring point location information is the angle between the chain link movement direction and the positive horizontal direction.

[0012] Step two, the search for fixed-point links, includes two methods: global search and local search. The global search involves calculating the relative distance of all links to the data monitoring point in the plane and selecting the data of the link closest to the data monitoring point as the monitoring data. The local search involves determining the search range centered on the nearest link to the corresponding data monitoring point after the initial global search confirms the nearest link. RWithin the chain, only the search range is calculated. R The distance between the inner link and the corresponding data monitoring point is compared, and the data of the closest link is selected as the monitoring data. The fixed-point link search employs a global search when initially selecting a link for each data monitoring point, and a local search in subsequent searches.

[0013] The local search calculates the number of chain links passing through the data monitoring point within each simulation output interval based on the simulation output interval time, the number of sprocket teeth, and the maximum sprocket speed; the search range of the local search method... R The search range is dynamically determined based on the simulation output interval, the number of sprocket teeth, and the maximum sprocket speed. R It should be a positive integer greater than the number of chain links being calculated.

[0014] Step 3, the fixed-point link data processing, includes: Direct method: When the feature data is a physical quantity that can be directly obtained in an absolute coordinate system, the instantaneous value of the nearest monitored link is directly used; Synthesis method: When the feature data is a physical quantity that needs to be represented in a relative coordinate system, the monitored data is transformed according to the phase angle of the chain movement direction; Average method: When the characteristic data is a physical quantity that is indirectly reflected in a discrete motion unit, the average value of the dynamic quantities at both ends of the unit is calculated as the characteristic data.

[0015] When there are multiple data monitoring points, a multi-threaded parallel processing method is used to perform the fixed-point link search and fixed-point link data processing.

[0016] The beneficial effects of this invention are as follows: 1. Traditional data processing methods directly extract the dynamic data of a single link, and the value at a certain moment has a strong randomness. The data obtained by fixed-point resampling is actually statistically meaningful, has strong regularity, and can accurately reflect the dynamic characteristics of a certain local region of the chain system, thus avoiding the random distortion phenomenon in computer simulation calculations to the greatest extent. 2. Because the links of the chain reciprocate periodically along a predetermined trajectory, their positions change constantly. The dynamic data of a single link are affected by multiple factors and can only exhibit limited regularity in the time domain, lacking value for further analysis. Data obtained by the fixed-point resampling method can be further processed and analyzed, including statistical, frequency domain analysis, and phase difference calculation methods. 3. Traditional data processing methods typically require any link in the chain to run for at least two cycles in steady state to ensure the accuracy and repeatability of the obtained data. Fixed-point resampling methods collect data with strong regularity, require less effective data for data analysis, and only need a relatively short simulation time to reflect the dynamic characteristics of a fixed-point region, which can significantly save computation time and resources; 4. Set up multiple monitoring points in key areas of the chain system to collect data, including but not limited to the midpoint of the chain segment, the sprocket engagement point, the sprocket disengagement point, and the unrestrained free chain link. This can eliminate the limitation that data from a single monitoring point cannot reflect the overall dynamic characteristics of the chain system.

[0017] In summary, the fixed-point resampling method for chain systems eliminates the limitations of traditional data processing methods and avoids result bias caused by the randomness of data from a single chain link. Furthermore, the data obtained by this method has value for further analysis and is an important prerequisite for extracting important characteristics of chain systems, including but not limited to system order, meshing order, data phase, and vibration amplitude, providing theoretical support for establishing a multi-parameter, multi-dimensional evaluation system for chain drive systems. Moreover, this method can significantly save simulation computation time and resources, and data resampling using multiple monitoring points can fully reflect the dynamic characteristics of the entire system. Attached Figure Description

[0018] Figure 1 This is a schematic diagram of a chain system fixed-point resampling method according to the present invention; Figure 2 This is a schematic diagram of the transmission system structure in a chain system fixed-point resampling according to the present invention; Figure 3 This is a schematic diagram of a fixed-point resampling method for a chain system according to the present invention; Figure 4 This invention relates to a typical monitoring point setting method for a chain system fixed-point resampling method; Figure 5 The time series curve of the data obtained by using the chain system fixed-point resampling method of the present invention; Figure 6 This is a schematic diagram illustrating statistical analysis of data obtained using a chain system fixed-point resampling method according to the present invention; Figure 7 This is a schematic diagram of a spectrum analysis performed using data obtained from a chain system fixed-point resampling method according to the present invention; Among them: 1. Drive sprocket, 101, drive sprocket engagement point; 102, drive sprocket disengagement point; 2. Driven sprocket, 201, driven sprocket engagement point; 202, driven sprocket disengagement point; 3. Chain, 301, slack side; 302, tight side. Detailed Implementation

[0019] The embodiments of the present invention will be further described below with reference to the accompanying drawings.

[0020] See Figure 1 This invention discloses a chain system fixed-point resampling method, applicable to the post-processing of multibody dynamics simulation data of a transmission system containing multiple discrete motion units. The discrete motion units move continuously and orderly along a predetermined trajectory without overlapping within the motion plane. Constraints or interaction forces exist between the discrete motion units, and they remain within the same plane. The discrete motion units exhibit rigidity and do not undergo significant deformation. The transmission system includes, but is not limited to, chain drive systems, belt drive systems, and rope drive systems, etc., which contain multiple discrete motion units or can be divided into multiple discrete motion units. The method includes the following steps: Step 1: Extract simulation result data. Extract the dynamic data of the discrete motion unit from the completed multibody dynamics simulation result file and save it to an intermediate file. Step 2: Fixed-point link search. Set at least one fixed data monitoring point in the transmission system, calculate the relative distance of all discrete motion units with respect to the data monitoring point in the plane at each moment, and select the discrete motion unit with the closest distance as the monitoring unit of the corresponding data monitoring point at the current moment. Step 3: Fixed-point link data processing. Based on the original dynamic data type corresponding to each data monitoring point obtained in Step 2, the corresponding processing method is used to obtain the characteristic data representing that data monitoring point. Step 4: Write the fixed-point data result file. Write the characteristic data of each data monitoring point, obtained in chronological order, into the result file.

[0021] The simulation result data extraction described in step one includes starting the post-processing module (RecurDyn Post), importing the simulation result file (*.rplt), importing the required data path, and exporting the data to a *.csv file.

[0022] The simulation result data extraction is based on a secondary development of RecurDyn / ProcessNet, used to extract discrete element dynamics data for subsequent processing. The simulation result data extraction method is written in C#, compiled using Visual Studio, and finally generated as a dynamic link library (*.dll), which is then executed using RecurDyn / ProcessNetManager.

[0023] The simulation results data must meet the following requirements depending on the specific chain system to ensure the completeness and validity of the obtained data: When all simulation boundary conditions are in a steady state and do not change over time, the steady-state simulation duration is at least 6 meshing cycles. When the simulation boundary conditions are in an unstable state, such as when the angular velocity and torque of a certain sprocket change with the sprocket angle, the steady-state simulation time is at least twice the time period of the boundary condition change.

[0024] The dynamic data mentioned in step one includes the position, velocity, acceleration, and interaction force data between discrete motion units.

[0025] The discrete unit dynamics data includes the position data of the centroid of all discrete units in the plane and in two directions; The discrete unit dynamics data also includes the velocity or acceleration data of the centroid of all discrete units in the plane and in two directions. The discrete unit dynamics data also includes the rotational pair or bushing force or contact force data between all discrete units.

[0026] The data monitoring points mentioned in step two are set on the running trajectory of the chain links, or as close as possible to the theoretical running trajectory of the chain; the location information of the data monitoring points includes the horizontal coordinate, vertical coordinate and phase angle in the chain plane; when setting up multiple data monitoring points, multi-threaded fixed-point chain link data processing is used to improve computational efficiency.

[0027] The phase angle included in the data monitoring point location information is the angle between the chain link movement direction and the positive horizontal direction, which is used to determine the horizontal and vertical directions of the chain movement and to perform coordinate transformation in the fixed-point chain link data processing method.

[0028] See Figure 2 and Figure 3 In this embodiment, the transmission system includes a driving sprocket 1, a driven sprocket 2, and a chain 3 connecting the driving sprocket 1 and the driven sprocket 2. The chain 3 has a slack side 301 and a tight side 302. Data monitoring points are set at: the engagement point 101 of the driving sprocket, the midpoint of the tight side 302 of the chain 3, the disengagement point 202 of the driven sprocket, the engagement point 201 of the driven sprocket, the midpoint of the slack side 301 of the chain 3, and the disengagement point 102 of the driving sprocket. This method of setting up data monitoring points can comprehensively acquire characteristic information of the chain drive system for subsequent analysis.

[0029] Step two, the search for fixed-point links, includes two methods: global search and local search. The global search involves calculating the relative distance of all links to the data monitoring point in the plane and selecting the data of the link closest to the data monitoring point as the monitoring data. The local search involves determining the search range centered on the nearest link to the corresponding data monitoring point after the initial global search confirms the nearest link. RWithin the chain, only the search range is calculated. R The distance between the inner link and the corresponding data monitoring point is compared, and the data of the closest link is selected as the monitoring data. The fixed-point link search employs a global search when initially selecting a link for each data monitoring point, and a local search in subsequent searches to improve search efficiency.

[0030] The local search calculates the number of chain links passing through the data monitoring point within each simulation output interval based on the simulation output interval time, the number of sprocket teeth, and the maximum sprocket speed; the search range of the local search method... R The search range is dynamically determined based on the simulation output interval, the number of sprocket teeth, and the maximum sprocket speed. R It should be a positive integer greater than the number of chain links being calculated to ensure maximum search efficiency. ;

[0031] in, This is the maximum speed of the sprocket. This is the simulation output interval time. This represents the number of teeth on the sprocket.

[0032] See Figure 4 This embodiment mainly describes the search range. R Local search with a value of 1.

[0033] The local search is performed after the initial global search is completed and the nearest node of the data monitoring point is identified. The search range is then determined centered on the selected nearest node. R Calculate the search range for each link within the chain. R Distance of inner link relative to the corresponding data monitoring point d 1. d 2 and d 3. Compare the data and select the data from the nearest link as the monitoring data. Selected link number. : ; The acquired monitoring data : .

[0034] Step 3, the fixed-point link data processing, includes: Direct method: When the feature data is a physical quantity that can be directly obtained in an absolute coordinate system, the instantaneous value of the nearest monitored link is directly used; the direct method is applicable to physical quantities such as displacement, velocity, and acceleration that can be directly reflected in discrete units in an absolute coordinate system; such as the feature displacement collected by the data monitoring point. x Directly equal to the displacement of the nearest link : ; Synthesis method: When the feature data is a physical quantity that needs to be represented in a relative coordinate system, it is based on the phase angle of the chain's motion direction. The monitored data undergoes coordinate transformation; the synthesis method is applicable to physical quantities such as chain lateral or longitudinal displacement, velocity, and acceleration that can be directly reflected in discrete units in a relative coordinate system. For example, the lateral velocity of the characteristic chain collected by data monitoring points and longitudinal speed : ; ; in, , These are the velocity components of the monitored link in the X and Y directions, respectively.

[0035] Average method: When the characteristic data is a physical quantity indirectly reflected in a discrete motion unit, the average value of the dynamic quantities at both ends of the unit is calculated as the characteristic data; the average method is applicable to physical quantities indirectly reflected in discrete units, such as chain tension. For example, the characteristic chain tension collected by data monitoring points. F Equal to the force on the left side of the nearest link F left Force on the right side F right Half the sum of the forces acting on it: .

[0036] When multiple data monitoring points exist, a multi-threaded parallel processing method is employed to perform fixed-point link search and data processing. This includes: initializing the task list, configuring parallel processing, parallel loop execution, task synchronization waiting, and exception handling mechanisms. This multi-threaded data processing method is suitable for scenarios requiring the processing of a large number of independent computational tasks, with the number of threads executed in a single operation not exceeding the maximum number of CPU threads.

[0037] The fixed-point data result file writing method writes the data collected from each monitoring point into a *.csv file in chronological order, with each monitoring point generating its own data file.

[0038] All data resampled from all monitoring points are plotted in RecurDyn Post for inspection or further analysis.

[0039] The resampled data can be further processed for deeper data analysis of the chain system, including but not limited to data processing methods such as amplitude statistics, Fourier transform, short-time Fourier transform, and phase difference calculation.

[0040] Figure 5-7 Further data processing and analysis are performed after the chain is resampled at fixed points. The analysis and comparison results can be used as one of the evaluation indicators of the chain drive system. See Figure 5 The figure shows a time-series curve of data obtained using a chain system fixed-point resampling method of the present invention; the curve shown is directly drawn using the data in the data result file, which contains complete information on the resampling of the corresponding detection point and can be used for comparison between different systems.

[0041] See Figure 6 This is a schematic diagram of statistical analysis using data obtained from a chain system fixed-point resampling method of the present invention, used to compare the distribution of monitoring data between different systems in order to quantify the advantages and disadvantages of different systems.

[0042] See Figure 7 This is a schematic diagram of spectrum analysis using data obtained by a chain system fixed-point resampling method of the present invention. It is used to compare the spectrum of monitoring data between different systems, so as to compare the response amplitude between different systems and quantify the advantages and disadvantages of different systems.

Claims

1. A method for chain system spot resampling, characterized in that, The application relates to post-processing of multi-body dynamics simulation data of a transmission system comprising a plurality of discrete moving units, which are sequentially moved along a predetermined trajectory without overlapping in a moving plane. The method comprises the following steps: Step 1: simulation result data extraction, extracting the dynamics data of the discrete moving units from the completed multi-body dynamics simulation result file and saving the data to an intermediate file; Step 2: fixed point chain searching, setting at least one fixed data monitoring point in the transmission system, calculating the relative distance of all discrete moving units in the plane relative to the data monitoring point at each time, and selecting the discrete moving unit closest to the data monitoring point as the monitoring unit at the current time of the corresponding data monitoring point; Step 3: fixed point chain data processing, according to the original dynamics data type corresponding to each data monitoring point obtained by monitoring in step 2, using the corresponding processing method to obtain the characteristic data representing the data monitoring point; Step 4: fixed point data result file writing, writing the characteristic data of each type of data of each data monitoring point obtained in time sequence into a result file.

2. The method of claim 1, wherein, The simulation result data extraction of step 1 comprises starting the post-processing module, importing the simulation result file, importing the required data path and exporting the data to the file.

3. A chain system spot resampling method according to claim 1 or 2, characterized in that, The simulation result data meets the following requirements according to the different needs of the chain system: When all the simulation boundary conditions are in a stable state and do not change with time, the stable state simulation time is at least 6 meshing periods; When the simulation boundary conditions are in an unstable state, the stable state simulation time is at least 2 times the time period of the boundary condition change.

4. The method of claim 1, wherein, The dynamics data of step 1 comprises the position, velocity, acceleration of the discrete moving units and the interaction force data between the units.

5. The method of claim 1, wherein, The data monitoring point of step 2 is set on the chain running track; the data monitoring point position information comprises a horizontal coordinate, a vertical coordinate and a phase angle in the chain plane; when a plurality of data monitoring points are set, multi-thread fixed point chain data processing is adopted.

6. The method of claim 5, wherein, The phase angle contained in the data monitoring point position information is the included angle between the chain movement direction and the horizontal positive direction.

7. The method of claim 1, wherein, The fixed point chain searching of step 2 comprises two methods of global searching and local searching: The global searching is to calculate the relative distance of all chain links in the plane relative to the data monitoring point, and select the data of the chain link closest to the data monitoring point as the monitoring data; The local search is to determine the chain links within the search range centered on the chain link after the global search is first completed to confirm the corresponding data monitoring point R The chain links within the search range R The distances of the chain links within the search range to the corresponding data monitoring point are calculated and compared, and the data of the nearest chain link is selected as the monitoring data The fixed point chain searching adopts the global searching when the chain link is selected for the first time, and adopts the local searching in the subsequent search for each data monitoring point.

8. The method of claim 7, wherein, The local search calculates the number of chain links passing through the data monitoring point in each simulation output interval according to the simulation output interval time, the number of sprocket teeth, and the maximum rotation speed of the sprocket; and the search range of the local search method R The search range is dynamically determined according to the simulation output interval time, the number of sprocket teeth, and the maximum rotation speed of the sprocket R The integer number should be greater than the calculated number of chain links.

9. The method of claim 1, wherein, The fixed point chain data processing of step 3 comprises: A direct method: when the characteristic data is a physical quantity that can be directly obtained in an absolute coordinate system, the instantaneous value of the nearest chain link monitored is directly adopted; A synthesis method: when the characteristic data is a physical quantity that needs to be represented in a relative coordinate system, the coordinate transformation of the monitored data is carried out according to the phase angle of the chain movement direction; And an average method: when the characteristic data is a physical quantity indirectly reflected on the discrete moving unit, the average value of the dynamics quantity at both ends of the unit is calculated as the characteristic data.

10. The method of claim 1, wherein, When there are multiple data monitoring points, the multi-thread parallel processing method is used to perform the fixed-point chain searching and fixed-point chain data processing process.

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