A tension monitoring method, device and system for a chain transmission system with multiple components coupled

Abstract

The present invention discloses a tension monitoring method, device and system for a multi-component coupled chain transmission system, and the method comprises: performing dynamic modeling and simulation analysis of a multi-component coupled chain transmission system; analyzing and calculating the chain tension of the multi-component coupled chain transmission system based on the analysis of the basic structure and working principle of the multi-component coupled chain transmission system; performing finite element analysis on the multi-component coupled chain transmission system and designing a virtual chain tension sensor; and conducting experimental research on the virtual sensor design method. The present invention realizes real-time monitoring of chain tension through a virtual sensor, reduces hardware requirements and system costs, and improves monitoring accuracy and real-time performance.

Description

Technical Field

[0001] The present invention relates to a tension monitoring technology for a chain transmission system, in particular to a tension monitoring method, device and system for a chain transmission system facing multi-component coupling, belonging to the field of mechanical engineering and sensor technology. Background Art

[0002] Chain drive systems face multiple challenges in high-intensity and complex working environments, including load fluctuations, impact loads, and chain wear. These problems not only lead to unstable chain tension, but also cause safety hazards such as chain breakage and equipment failure. Therefore, real-time monitoring of chain tension is particularly important. Reasonable control of chain tension can not only improve the safety and stability of forklift operation, but also extend the service life of the equipment and reduce maintenance costs.

[0003] At present, the traditional chain tension monitoring method mainly relies on physical sensors for direct measurement, including strain sensors, pressure sensors, etc. Although these sensors can provide measurement data of chain tension, they have many limitations. For example, physical sensors are affected by factors such as installation location, ambient temperature, humidity, etc., and are prone to measurement errors and failures. In addition, the layout and maintenance of sensors require additional costs, and the accuracy and reliability under dynamic conditions are poor. Therefore, the existing physical sensor technology cannot fully meet the needs of chain tension monitoring with high precision and real-time requirements in practical applications.

[0004] In summary, the existing physical sensor technology cannot effectively solve the accuracy, real-time and cost problems in chain tension monitoring. In order to overcome these shortcomings, virtual sensor technology is tried, which uses mathematical modeling, simulation analysis and state estimation methods to calculate and estimate the real-time changes of chain tension, thus avoiding the limitations of physical sensors. Virtual sensor technology not only reduces the hardware requirements of the equipment and reduces the system cost, but also improves the accuracy and real-time performance of monitoring. Therefore, developing a chain tension monitoring system based on virtual sensors has become a technical problem that needs to be solved urgently. Summary of the invention

[0005] In order to solve the above problems existing in the prior art, the present invention provides a chain tension monitoring method, device and system based on a virtual sensor.

[0006] A tension monitoring method for a chain drive system with multiple components coupled comprises the following steps:

[0007] S1: Analyze and calculate the tension of the chain of the multi-component coupled chain drive system, and analyze the polygonal effect of the chain drive system, providing a theoretical basis for the dynamic characteristics analysis of the multi-component coupled chain drive system;

[0008] S2: Modeling and simulation analysis of the dynamics of the chain drive system with multiple components coupled, simulating different working conditions, analyzing the variation law of chain tension under corresponding working conditions, obtaining the dynamic loads of the sprocket and chain, and providing load basis for the finite element analysis of the chain drive system;

[0009] S3: Perform finite element analysis on the multi-component coupled chain drive system and design a virtual chain tension sensor to estimate the chain tension distribution at different locations;

[0010] S4: Build a verification platform for the effectiveness of virtual sensors to verify the effectiveness of the chain tension monitoring solution based on virtual sensors.

[0011] Furthermore, step S1 specifically includes:

[0012] S11: Get basic parameters of chain drive system, including chain type , Number of sprocket teeth , Chain link length , Material properties , and integrate them into data set P. Based on data set P, the chain tension calculation method is used to accurately calculate the pretension of the chain drive system under static conditions. , as the initial condition for subsequent dynamic modeling and simulation analysis.

[0013] S12: Based on the geometric structure and kinematics of the chain drive system, a chain dynamics model is constructed when the chain is running without load. , pre-tension the chain As input parameter, use Simulate the motion trajectory and tension distribution of the chain in an unloaded state. The key points are the point where the chain contacts the sprocket, the middle point of the chain, and the end of the chain, and record the tension data of each key point in detail. , where n represents the number of key points.

[0014] S13: Based on the uniform load requirements of the system design, a uniformly distributed load is applied. , while pre-tensioning the chain As the initial parameters, simulate the movement of the chain under uniform load and accurately record the tension data of each key point .

[0015] S14: Based on this, the polygon effect correction factor is introduced , combined with the tension data under no load and uniform load and , construct the modified kinetic model . use , simulate the movement of the chain under the influence of the polygon effect, and record the tension fluctuation data in detail .

[0016] Polygon Effect Correction Factor Defined as:

[0017]

[0018] in, is the magnitude of the correction factor, indicating the strength of the polygon effect, is the periodic fluctuation of the contact between the chain and the sprocket, is the phase shift.

[0019] Furthermore, step S2 specifically includes:

[0020] S21: Use 3D solid model software to build a 3D solid model based on the actual size and structure of the chain drive system .from Extracting geometric dimensions , including sprocket diameter, chain width, and chain link length, recorded as data set G.

[0021] S22: Based on the dataset G and the revised model obtained in step S1 , combined with multi-body dynamics theory, to build a dynamic model of the chain transmission system According to the system characteristics and operating conditions, the model parameters are set, including the friction coefficient μ, the damping coefficient J, the stiffness coefficient K, and the mass m of the chain.

[0022] S23: Set different working conditions, including speed v, load F and temperature CT, as simulation input. Use simulation software to run the dynamic model , simulate the dynamic behavior of the chain drive system under different working conditions. Output the dynamic load data of the sprocket and chain ,in Represents the ith dynamic load and chain tension change data ,in represents the tension change data of the i-th chain, and q represents the dimension of different working conditions.

[0023] Further, the step S3 specifically includes:

[0024] S31: The kinetic model obtained in step S2 Or import the geometric data of key components into the finite element analysis software. Mesh the key components to ensure mesh quality and calculation accuracy. According to the dynamic load data obtained in step S2 and chain tension change data , run finite element analysis and calculate the stress matrix of key components ,in Represents the stress value of the i-th grid element at the j-th grid element. The strain matrix .in represents the strain value of the i-th grid element at the j-th grid element.

[0025] S32: By constructing a state observer and designing a chain virtual sensor, the chain tension at different positions can be estimated. S32 includes three sub-steps:

[0026] S321: define state variables of the state observer;

[0027] S322: constructing a state prediction equation of a state observer;

[0028] S323: constructing a state update equation of a state observer; S324: designing a virtual sensor based on the state observer;

[0029] Step S321 specifically includes:

[0030] Design state variables: Select the tension T at a certain point on the chain as the state variable, analyze the tension at this point, the tension changes with time t, so record it as ;

[0031] Design the input variables of the state observer: The strain results of the finite element analysis As input ;

[0032] Design the observed variables of the state observer: transform the stress results of finite element analysis into As an observed variable ;

[0033] Set the initial value of the state variable of the state observer to be pretension , covariance The initial value is the identity matrix.

[0034] Step S322 specifically includes:

[0035] Construct the equation of state:

[0036]

[0037] Among them, A is the state transfer matrix, which represents the dynamic evolution relationship of the chain tension; B is the input matrix of the state equation, which represents the influence of strain on the change of tension; the state transfer matrix A: represents the relationship between tension and tension change rate; the tension of the chain and its rate of change The relationship between is consistent with a typical second-order vibration system model, in which the mass m, damping coefficient J and stiffness coefficient K of the chain affect the change of tension; the state transfer matrix A can be expressed as:

[0038]

[0039] Among them, each element of the matrix represents a different meaning:

[0040] ,express right The rate of change itself has no direct effect;

[0041] ,express right direct impact of

[0042] , represents the effect of the chain's stiffness-to-mass ratio on the change in tension;

[0043] , represents the effect of damping coefficient on tension change;

[0044] Input matrix B:

[0045] Indicates strain Effects of chain tension changes; external loads With strain It is linearly related and converted into load through the stiffness coefficient K of the chain:

[0046]

[0047] Therefore, the strain Directly affects the rate of change of tension , and for tension itself has no direct effect; therefore, the input matrix B is designed as:

[0048]

[0049] in, Indicates the strain through the load Effect on rate of change of tension;

[0050] Step S323 specifically includes:

[0051] Construct the observation equation:

[0052]

[0053] Among them, C is the observation matrix, which represents the relationship between chain tension T(t) and stress δ(t); D is the input matrix of the observation equation, which represents the strain The relationship between and stress; the observation matrix C and the input matrix D of the observation equation are constructed respectively;

[0054] Observation matrix C:

[0055] The stress of a chain is usually proportional to the chain tension; assuming that the proportionality constant between the stress and tension of the chain is , it can be expressed as:

[0056]

[0057] in is the proportionality coefficient between chain tension and stress, obtained by finite element analysis;

[0058] Input matrix D:

[0059] strain Directly affects the stress state of the chain, and thus affects the stress of the chain ; There is also a linear relationship between strain and stress, and the proportionality constant is , it can be expressed as:

[0060]

[0061] in, It is the proportionality factor between strain and stress, measured by strain-stress testing or the stress-strain curve of a material;

[0062] Step S324 specifically includes:

[0063] Design virtual sensors, including prediction and update steps;

[0064] Construct the prediction equation:

[0065] According to the tension estimate at the last moment and input ; Predict the chain tension at the current moment :

[0066]

[0067] Wherein, A is the state transfer matrix described in step S322; B is the input matrix of the state equation described in step S322;

[0068] Construct the update equation:

[0069] Update the predicted strain data based on the observed stress data:

[0070]

[0071] Wherein, C is the observation matrix described in step S323; Kalman gain matrix The calculation formula is:

[0072]

[0073] in, is the predicted error covariance matrix, R is the covariance of the observation noise, given by the system dynamics model and initially set to 0.0001, and C is the observation matrix, is the transpose of the observation matrix C;

[0074] State covariance matrix update:

[0075]

[0076] Through real-time updates of the virtual sensor, the virtual sensor can accurately estimate the tension at each location on the chain.

[0077] Further, the step S4 specifically includes:

[0078] S41: Build a verification platform for the effectiveness of virtual sensors, namely the chain tension monitoring experimental platform, install strain gauges and accelerometers at different positions of the chain (the contact point of the sprocket, the midpoint and the end of the chain) to monitor the actual stress and strain of the chain.

[0079] S42: Collect actual strain and stress data in real time and use them as input data for virtual sensor estimation results. Configure data acquisition interfaces to ensure that high-frequency data is obtained from strain gauges and accelerometers in real time and transmitted to the central control system for processing.

[0080] S43: Virtual sensors designed based on Kalman filtering estimate the tension at each point through stress and strain data from finite element analysis. By comparing the actual measured values ​​with the virtual sensor estimates, the error is calculated and the parameters of the Kalman filter are adjusted to make the virtual sensor estimates closer to the actual values. The difference between the sensor and the virtual sensor is calibrated through repeated experiments, and the performance of the Kalman filter is gradually improved.

[0081] S44: Verify the estimation accuracy of the virtual sensor under different loads and working conditions by real-time monitoring and recording the tension at different positions of the chain. Verify the accuracy of the virtual sensor's dynamic response to the chain drive system by simulating different working conditions.

[0082] S45: Integrate the virtual sensor with other chain drive system monitoring and control systems to form a complete tension monitoring solution.

[0083] A second aspect of the present invention relates to a tension monitoring device for a chain transmission system with multiple components coupled, comprising:

[0084] The 3D modeling and simulation module builds a 3D model of a multi-component coupled chain drive system to reflect the meshing relationship between the chain and the sprocket; simulates the dynamic characteristics of the chain under different working conditions to obtain the variation law of the chain tension; simulates different working conditions, analyzes the variation law of the chain tension under the corresponding working conditions, obtains the dynamic load of the sprocket and chain, and provides a load basis for the finite element analysis of the chain drive system;

[0085] The data acquisition and preprocessing module uses a high-precision tension sensor in the experimental environment to record the actual tension value of the chain, and a strain gauge and an acceleration sensor to monitor the actual stress and strain of the chain. The collected data is cleaned to remove noise and outliers;

[0086] The tension calculation and polygon effect analysis module performs tension analysis and calculation on the chain of a multi-component coupled chain drive system, and analyzes the polygon effect of the chain drive system, providing a theoretical basis for the dynamic characteristics analysis of the multi-component coupled chain drive system;

[0087] Finite element analysis and virtual sensor design module: perform finite element analysis on key components of the chain drive system to determine the installation location and parameters of the virtual sensor; based on the finite element analysis results, design a state observer to estimate the tension distribution of the chain at different positions in real time;

[0088] The experimental verification and data storage module builds a virtual sensor verification platform, including strain sensors, data acquisition systems and communication modules, to conduct experimental verification to ensure that the system can accurately monitor chain tension; the experimental data and real-time monitoring data are recorded in the system database for subsequent analysis and monitoring;

[0089] The decision-making rules and early warning module sets the upper and lower thresholds of chain tension according to system design requirements and safety standards; monitors the chain tension in real time. When the tension value exceeds the preset upper threshold, the system automatically triggers a fault warning and prompts the operator to perform inspection and maintenance;

[0090] The remote access and management interface module provides a remote access interface, allowing managers to view and manage recorded data at any time through a browser; encrypts transmitted and stored data, and sets up a multi-layer identity authentication mechanism to ensure data security.

[0091] A third aspect of the present invention relates to a system for monitoring chain tension based on a virtual sensor, comprising:

[0092] The chain drive system status data acquisition equipment consists of multiple sensors, including temperature sensors, vibration sensors, speed sensors and load sensors installed on the chain, gears and bearings; all sensors are connected to the central control unit and can collect and transmit the operating data of each component of the chain drive system in real time;

[0093] Multi-component coupling status data processing and analysis equipment, which can monitor the operating status of each component in real time, including temperature, pressure, and speed parameters. The equipment can integrate data from different components to form a unified data stream;

[0094] 3D modeling and simulation equipment, to establish a 3D model of a multi-component coupled chain drive system, to ensure that the model can accurately reflect the meshing relationship between the chain and the sprocket; to simulate the dynamic characteristics of the chain;

[0095] The tension test equipment of the virtual sensor includes finite element analysis and virtual sensor design modules. The finite element analysis is performed on the key components of the chain transmission system to determine the installation position and parameters of the virtual sensor. Based on the finite element analysis results, a state observer is designed to estimate the tension distribution of the chain at different positions in real time.

[0096] Experimental verification and data storage equipment, build a virtual sensor verification platform, including strain sensors, data acquisition systems and communication modules, conduct experimental verification to ensure that the system can accurately monitor chain tension; record experimental data and real-time monitoring data into the system database for subsequent analysis and monitoring;

[0097] Early warning equipment sets the upper and lower limits of chain tension according to system design requirements and safety standards; monitors chain tension in real time. When the tension value exceeds the preset upper limit, the system automatically triggers a fault warning and prompts the operator to perform inspection and maintenance;

[0098] Remote access and management equipment provides a remote access interface, allowing managers to view and manage recorded data at any time through a browser; encrypt transmitted and stored data, and set up a multi-layer identity authentication mechanism to ensure data security.

[0099] The innovative features of the present invention are:

[0100] Virtual sensor design: Use mathematical modeling and simulation analysis to replace traditional physical sensors to reduce costs and improve accuracy.

[0101] Multi-component coupling analysis: Through dynamic simulation and finite element analysis, the reliability of the chain drive system under complex working conditions is improved.

[0102] The working principle of the present invention is as follows: the present invention constructs a three-dimensional model of a multi-component coupled chain transmission system, simulates the actual operating environment with the help of dynamic simulation software, analyzes the chain tension change law, and forms a set of systematic monitoring methods. Virtual sensors are used to replace traditional physical sensors, and finite element analysis and state space models are used to estimate the tension distribution of the chain at various positions in real time, and its effectiveness is verified through an experimental platform. The system design integrates multiple technologies such as three-dimensional modeling, dynamic simulation, finite element analysis and data processing, and has the significant advantages of high precision, low cost, real-time and reliability. It is suitable for monitoring chain transmission systems under complex working conditions, and provides strong technical support for intelligent management and fault prediction of industrial equipment.

[0103] The advantages of the present invention are: the real-time monitoring of chain tension is realized through the virtual sensor, the hardware requirements and system cost are reduced, and the monitoring accuracy and real-time performance are improved. In addition, this method can provide strong support for the intelligent management and fault prediction of industrial equipment. BRIEF DESCRIPTION OF THE DRAWINGS

[0104] The present invention will be further described below in conjunction with the accompanying drawings and embodiments, in which:

[0105] Figure 1 A schematic diagram of the overall process of a chain tension monitoring method based on a virtual sensor provided in Embodiment 1 of the present invention.

[0106] Figure 2 A schematic diagram of the composition of a chain tension monitoring device based on a virtual sensor provided in Embodiment 2 of the present invention.

[0107] Figure 3 A schematic structural diagram of a chain tension monitoring system based on a virtual sensor provided in Embodiment 3 of the present invention.

[0108] Figure 4 A schematic diagram of the steps of constructing a simulation analysis environment for the present invention.

[0109] Figure 5 A schematic diagram of the steps of constructing a virtual sensor according to the present invention. DETAILED DESCRIPTION

[0110] In order to more clearly describe the technical solution of the present invention, the technical solution provided by the present invention will be described in detail in conjunction with specific embodiments, and the present invention will be further described through the accompanying drawings. It should be noted that these specific embodiments are only partial embodiments of the present invention and are used to explain the present invention, rather than to limit the present invention.

[0111] Embodiment 1

[0112] Reference Figure 1 , Figure 4 , Figure 5 The present embodiment provides a chain tension monitoring method based on a virtual sensor, comprising the following steps:

[0113] S1: Analyze and calculate the tension of the chain of the multi-component coupled chain drive system, and analyze the polygonal effect of the chain drive system, providing a theoretical basis for the dynamic characteristics analysis of the multi-component coupled chain drive system;

[0114] S2: Modeling and simulation analysis of the dynamics of the chain drive system with multiple components coupled, simulating different working conditions, analyzing the variation law of chain tension under corresponding working conditions, obtaining the dynamic loads of the sprocket and chain, and providing load basis for the finite element analysis of the chain drive system;

[0115] S3: Perform finite element analysis on the multi-component coupled chain drive system and design a virtual chain tension sensor to estimate the chain tension distribution at different positions;

[0116] S4: Build a verification platform for the effectiveness of virtual sensors to verify the effectiveness of the chain tension monitoring solution based on virtual sensors.

[0117] Furthermore, step S1 specifically includes:

[0118] S11: Get basic parameters of chain drive system, including chain type , Number of sprocket teeth , Chain link length , Material properties , and integrate them into data set P. Based on data set P, the chain tension calculation method is used to accurately calculate the pretension of the chain drive system under static conditions. , as the initial condition for subsequent dynamic modeling and simulation analysis.

[0119] S12: Based on the geometric structure and kinematics of the chain drive system, a chain dynamics model is constructed when the chain is running without load. , pre-tension the chain As input parameter, use Simulate the motion trajectory and tension distribution of the chain in an unloaded state. The key points are the point where the chain contacts the sprocket, the middle point of the chain, and the end of the chain, and record the tension data of each key point in detail. , where n represents the number of key points.

[0120] S13: Based on the uniform load requirements of the system design, a uniformly distributed load is applied. , while pre-tensioning the chain As the initial parameters, simulate the movement of the chain under uniform load and accurately record the tension data of each key point .

[0121] S14: Based on this, a polygon effect correction factor is introduced , combined with the tension data under no load and uniform load and , construct the modified kinetic model . use , simulate the movement of the chain under the influence of the polygon effect, and record the tension fluctuation data in detail .

[0122] Polygon Effect Correction Factor Defined as:

[0123]

[0124] in, is the magnitude of the correction factor, indicating the strength of the polygon effect, is the periodic fluctuation of the contact between the chain and the sprocket, is the phase shift.

[0125] Furthermore, step S2 specifically includes:

[0126] S21: Use 3D solid model software to build a 3D solid model based on the actual size and structure of the chain drive system .from Extracting geometric dimensions , including sprocket diameter, chain width, and chain link length, recorded as data set G.

[0127] S22: Based on the dataset G and the revised model obtained in step S1 , combined with multi-body dynamics theory, to build a dynamic model of the chain transmission system According to the system characteristics and operating conditions, the model parameters are set, including the friction coefficient μ, the damping coefficient J, the stiffness coefficient K, and the mass m of the chain.

[0128] S23: Set different working conditions, including speed v, load F and temperature CT, as simulation input. Use simulation software to run the dynamic model , simulate the dynamic behavior of the chain drive system under different working conditions. Output the dynamic load data of the sprocket and chain ,in Represents the ith dynamic load and chain tension change data ,in represents the tension change data of the i-th chain, and q represents the dimension of different working conditions.

[0129] Further, the step S3 specifically includes:

[0130] S31: The kinetic model obtained in step S2 Or import the geometric data of key components into the finite element analysis software. Mesh the key components to ensure mesh quality and calculation accuracy. According to the dynamic load data obtained in step S2 and chain tension change data , run finite element analysis and calculate the stress matrix of key components ,in Represents the stress value of the i-th grid element at the j-th grid element. The strain matrix .in represents the strain value of the i-th grid element at the j-th grid element.

[0131] S32: By constructing a state observer and designing a chain virtual sensor, the chain tension at different positions can be estimated. S32 includes three sub-steps:

[0132] S321: define state variables of the state observer;

[0133] S322: constructing a state prediction equation of a state observer;

[0134] S323: constructing a state update equation of a state observer;

[0135] S324: Design virtual sensor based on state observer;

[0136] Step S321 specifically includes:

[0137] Design state variables: Select the tension T at a certain point on the chain as the state variable, analyze the tension at this point, the tension changes with time t, so record it as ;

[0138] Design the input variables of the state observer: The strain results of the finite element analysis As input ;

[0139] Design the observed variables of the state observer: transform the stress results of finite element analysis into As an observed variable ;

[0140] Set the initial value of the state variable of the state observer to be pretension , covariance The initial value is the identity matrix.

[0141] Step S322 specifically includes:

[0142] Construct the equation of state:

[0143]

[0144] Among them, A is the state transfer matrix, which represents the dynamic evolution relationship of the chain tension; B is the input matrix of the state equation, which represents the influence of strain on the change of tension; the state transfer matrix A: represents the relationship between tension and tension change rate; the tension of the chain and its rate of change The relationship between is consistent with a typical second-order vibration system model, in which the mass m, damping coefficient J and stiffness coefficient K of the chain affect the change of tension; the state transfer matrix A can be expressed as:

[0145]

[0146] Among them, each element of the matrix represents a different meaning:

[0147] ,express right The rate of change itself has no direct effect;

[0148] ,express right direct impact of

[0149] , represents the effect of the chain's stiffness-to-mass ratio on the change in tension;

[0150] , represents the effect of damping coefficient on tension change;

[0151] Input matrix B:

[0152] Indicates strain Effects of chain tension changes; external loads With strain It is linearly related and converted into load through the stiffness coefficient K of the chain:

[0153]

[0154] Therefore, the strain Directly affects the rate of change of tension , and for tension itself has no direct effect; therefore, the input matrix B is designed as:

[0155]

[0156] in, Indicates the strain through the load Effect on rate of change of tension;

[0157] Step S323 specifically includes:

[0158] Construct the observation equation:

[0159]

[0160] Among them, C is the observation matrix, which represents the relationship between chain tension T(t) and stress δ(t); D is the input matrix of the observation equation, which represents the strain The relationship between and stress; the observation matrix C and the input matrix D of the observation equation are constructed respectively;

[0161] Observation matrix C:

[0162] The stress of a chain is usually proportional to the chain tension; assuming that the proportionality constant between the stress and tension of the chain is , it can be expressed as:

[0163]

[0164] in is the proportionality coefficient between chain tension and stress, obtained by finite element analysis;

[0165] Input matrix D:

[0166] strain Directly affects the stress state of the chain, and thus affects the stress of the chain ; There is also a linear relationship between strain and stress, and the proportionality constant is , it can be expressed as:

[0167]

[0168] in, It is the proportionality factor between strain and stress, measured by strain-stress testing or the stress-strain curve of a material;

[0169] Step S324 specifically includes:

[0170] Design virtual sensors, including prediction and update steps;

[0171] Construct the prediction equation:

[0172] According to the tension estimate at the last moment and input ; Predict the chain tension at the current moment :

[0173]

[0174] Wherein, A is the state transfer matrix described in step S322; B is the input matrix of the state equation described in step S322;

[0175] Construct the update equation:

[0176] Update the predicted strain data based on the observed stress data:

[0177]

[0178] Wherein, C is the observation matrix described in step S323; Kalman gain matrix The calculation formula is:

[0179]

[0180] in, is the predicted error covariance matrix, R is the covariance of the observation noise, given by the system dynamics model and initially set to 0.0001, and C is the observation matrix, is the transpose of the observation matrix C;

[0181] State covariance matrix update:

[0182]

[0183] Through real-time updates of the virtual sensor, the virtual sensor can accurately estimate the tension at each location on the chain.

[0184] Further, the step S4 specifically includes:

[0185] S41: Build a verification platform for the effectiveness of virtual sensors, namely the chain tension monitoring experimental platform, install strain gauges and accelerometers at different positions of the chain (the contact point of the sprocket, the midpoint and the end of the chain) to monitor the actual stress and strain of the chain.

[0186] S42: Collect actual strain and stress data in real time and use them as input data for virtual sensor estimation results. Configure data acquisition interfaces to ensure that high-frequency data is obtained from strain gauges and accelerometers in real time and transmitted to the central control system for processing.

[0187] S43: Virtual sensors designed based on Kalman filtering estimate the tension at each point through stress and strain data from finite element analysis. By comparing the actual measured values ​​with the virtual sensor estimates, the error is calculated and the parameters of the Kalman filter are adjusted to make the virtual sensor estimates closer to the actual values. The difference between the sensor and the virtual sensor is calibrated through repeated experiments, and the performance of the Kalman filter is gradually improved.

[0188] S44: Verify the estimation accuracy of the virtual sensor under different loads and working conditions by real-time monitoring and recording the tension at different positions of the chain. Verify the accuracy of the virtual sensor's dynamic response to the chain drive system by simulating different working conditions.

[0189] S45: Integrate the virtual sensor with other monitoring and control systems of the chain drive system to form a complete tension monitoring solution.

[0190] Embodiment 2

[0191] like Figure 2 This embodiment provides a tension monitoring device for a chain transmission system with multiple components coupled, which is used to implement the method of embodiment 1. The device is configured in a chain transmission system monitoring device, and can monitor the working status of each component of the chain transmission system in real time, predict and display the chain link tension of the system, and issue a warning signal in time.

[0192] Reference Figure 2 ,The device includes three-dimensional modeling and simulation module, data acquisition and preprocessing module, tension calculation and polygon effect analysis module, finite element analysis and virtual sensor design module, experimental verification and data storage module, decision rule and early warning module, remote access and management interface module.

[0193] The 3D modeling and simulation module uses Creo 3D design software to build a 3D model of a multi-component coupled chain transmission system to ensure that the model can accurately reflect the meshing relationship between the chain and the sprocket. ADAMS dynamic analysis software is used for simulation to obtain the changing law of chain tension.

[0194] The data acquisition and preprocessing module uses a high-precision tension sensor in the experimental environment to record the actual tension value of the chain; the collected data is cleaned to remove noise and abnormal values;

[0195] The tension calculation and polygon effect analysis module performs tension analysis and calculation on the chain of a multi-component coupled chain drive system, and analyzes the polygon effect of the chain drive system, providing a theoretical basis for the dynamic characteristics analysis of the multi-component coupled chain drive system;

[0196] Finite element analysis and virtual sensor design module: perform finite element analysis on key components of the chain drive system to determine the installation location and parameters of the virtual sensor; based on the finite element analysis results, design a state observer to estimate the tension distribution of the chain at different positions in real time;

[0197] The experimental verification and data storage module builds a virtual sensor verification platform, including strain sensors, data acquisition systems and communication modules, to conduct experimental verification to ensure that the system can accurately monitor chain tension; the experimental data and real-time monitoring data are recorded in the system database for subsequent analysis and monitoring;

[0198] The decision-making rules and early warning module sets the upper and lower thresholds of chain tension according to system design requirements and safety standards; monitors the chain tension in real time. When the tension value exceeds the preset upper threshold, the system automatically triggers a fault warning and prompts the operator to perform inspection and maintenance;

[0199] The remote access and management interface module provides a remote access interface, allowing managers to view and manage recorded data at any time through a browser; encrypts transmitted and stored data, and sets up a multi-layer identity authentication mechanism to ensure data security.

[0200] Embodiment 3

[0201] As attached Figure 3 As shown, this embodiment provides a tension monitoring system for a chain transmission system coupled with multiple components, which is used to implement the method of embodiment 1, including:

[0202] The chain drive system status data acquisition equipment consists of multiple sensors, including temperature sensors, vibration sensors, speed sensors and load sensors installed on the chain, gears and bearings; all sensors are connected to the central control unit and can collect and transmit the operating data of each component of the chain drive system in real time;

[0203] Multi-component coupling status data processing and analysis equipment, which can monitor the operating status of each component in real time, including temperature, pressure, and speed parameters. The equipment can integrate data from different components to form a unified data stream;

[0204] 3D modeling and simulation equipment, to establish a 3D model of a multi-component coupled chain drive system, to ensure that the model can accurately reflect the meshing relationship between the chain and the sprocket; to simulate the dynamic characteristics of the chain;

[0205] The tension test equipment of the virtual sensor includes finite element analysis and virtual sensor design modules. The finite element analysis is performed on the key components of the chain transmission system to determine the installation position and parameters of the virtual sensor. Based on the finite element analysis results, a state observer is designed to estimate the tension distribution of the chain at different positions in real time.

[0206] Experimental verification and data storage equipment, build a virtual sensor verification platform, including strain sensors, data acquisition systems and communication modules, conduct experimental verification to ensure that the system can accurately monitor chain tension; record experimental data and real-time monitoring data into the system database for subsequent analysis and monitoring;

[0207] Early warning equipment sets the upper and lower limits of chain tension according to system design requirements and safety standards; monitors chain tension in real time. When the tension value exceeds the preset upper limit, the system automatically triggers a fault warning and prompts the operator to perform inspection and maintenance;

[0208] Remote access and management equipment provides a remote access interface, allowing managers to view and manage recorded data at any time through a browser; encrypt transmitted and stored data, and set up a multi-layer identity authentication mechanism to ensure data security.

[0209] Finally, the above are only embodiments of the present invention and the technical principles applied. Those skilled in the art will understand that the present invention is not limited to the specific embodiments described herein, because various obvious changes, readjustments and alternatives are possible without departing from the scope of protection of the present invention. Therefore, although the above embodiments describe the present invention in detail, the present invention is not limited to the above embodiments. Without exceeding the scope of the present invention, more other equivalent embodiments may also be included. The scope of the present invention is defined by the claims.

Claims

1. A tension monitoring method for a chain drive system with multiple components coupled, comprising the following steps: S1: Analyze and calculate the tension of the chain of the multi-component coupled chain drive system, and analyze the polygonal effect of the chain drive system, providing a theoretical basis for the dynamic characteristics analysis of the multi-component coupled chain drive system; S2: Modeling and simulation analysis of the dynamics of the chain drive system with multiple components coupled, simulating different working conditions, analyzing the variation law of chain tension under corresponding working conditions, obtaining the dynamic load of the sprocket and chain, and providing load basis for the finite element analysis of the chain drive system; S3: Perform finite element analysis on the multi-component coupled chain drive system and design a virtual chain tension sensor to estimate the chain tension distribution at different locations; including: S32: By constructing a state observer and designing a chain virtual sensor, the chain tension at different positions can be estimated, which includes four sub-steps: S321: define state variables of the state observer; S322: constructing a state prediction equation of a state observer; S323: constructing a state update equation of a state observer; S324: Design virtual sensor based on state observer; Step S322 specifically includes: Construct the equation of state: , where the state transfer matrix A: represents the relationship between tension and tension change rate; the tension of the chain and its rate of change The relationship between is consistent with a typical second-order vibration system model, in which the mass m, damping coefficient J and stiffness coefficient K of the chain affect the change of tension; the state transfer matrix A can be expressed as: ； Input matrix B represents strain Impact on chain tension changes; input matrix B is designed as: ； Step S323 specifically includes: Construct the observation equation: , where C is the observation matrix, which represents the relationship between chain tension T(t) and stress δ(t); D is the input matrix of the observation equation, which represents the strain Relationship with stress; The observation matrix C is: ,in is the proportionality coefficient between chain tension and stress, obtained by finite element analysis; The input matrix D is: ,in, It is the proportionality factor between strain and stress, measured by strain-stress testing or the stress-strain curve of a material; S4: Build a verification platform for the effectiveness of virtual sensors to verify the effectiveness of the chain tension monitoring solution based on virtual sensors.

2. A tension monitoring method for a chain transmission system with multiple components coupled as claimed in claim 1, characterized in that: Step S1 specifically includes: S11: Get basic parameters of chain drive system, including chain type , Number of sprocket teeth , Chain link length , Material properties , and integrated into data set P; based on data set P, the chain tension calculation method is used to accurately calculate the pre-tension of the chain drive system under static conditions , as the initial condition for subsequent dynamic modeling and simulation analysis; S12: Based on the geometric structure and kinematics of the chain drive system, a chain dynamics model is constructed when the chain is running without load. , pre-tension the chain As input parameter, use Simulate the motion trajectory and tension distribution of the chain under no-load conditions; take the point where the chain contacts the sprocket, the middle point of the chain, and the end of the chain as key points, and record the tension data of each key point in detail. , where n represents the number of key points; S13: Based on the uniform load requirements of the system design, a uniformly distributed load is applied. , while pre-tensioning the chain As input parameters; simulate the movement of the chain under uniform load and accurately record the tension data of each key point ; S14: Based on this, a polygon effect correction factor is introduced , combined with the tension data D when no load and uniform load and , construct the modified kinetic model ;use Simulate the movement of the chain under the influence of the polygon effect and record the tension fluctuation data in detail ; Polygon Effect Correction Factor Defined as: =1+ ,in, is the magnitude of the correction factor, indicating the strength of the polygon effect, is the periodic fluctuation of the contact between the chain and the sprocket, is the phase shift.

3. A tension monitoring method for a chain transmission system with multiple components coupled as claimed in claim 2, characterized in that: Step S2 specifically includes: S21: Use 3D solid model software to build a 3D solid model based on the actual size and structure of the chain drive system ;from Extracting geometric dimensions , including sprocket diameter, chain width, and chain link length, denoted as data set G; S22: Based on dataset G and the modified kinetic model obtained in step S1 , combined with multi-body dynamics theory, to build a dynamic model of the chain transmission system ; According to the system characteristics and operating conditions, set the model parameters, including friction coefficient μ, damping coefficient J, stiffness coefficient K, and chain mass m; S23: Set different working conditions, including speed v, load F and temperature CT, as simulation input; use simulation software to run the dynamic model , simulate the dynamic behavior of the chain drive system under different working conditions; output the dynamic load data of the sprocket and chain ,in Represents the ith dynamic load and chain tension change data ,in represents the tension change data of the i-th chain, and q represents the dimension of different working conditions.

4. A tension monitoring method for a chain transmission system with multiple components coupled as claimed in claim 3, characterized in that: The finite element analysis of the multi-component coupled chain drive system described in step S3 specifically includes: S31: The kinetic model obtained in step S2 Or import the geometric data of key components into the finite element analysis software; mesh the key components to ensure mesh quality and calculation accuracy; according to the dynamic load data obtained in step S2 and chain tension change data , run finite element analysis and calculate the stress matrix of key components ,in represents the stress value of the i-th grid element at the j-th grid element; the strain matrix ;in represents the strain value of the i-th grid element at the j-th grid element.

5. A tension monitoring method for a chain transmission system with multiple components coupled as claimed in claim 1, characterized in that: Step S321 specifically includes: Design state variables: Select the tension T at a point on the chain as the state variable, analyze the tension at this point, and the tension changes with time. changes, so record it as ; Design the input variables of the state observer: The strain results of the finite element analysis As input ; Design the observed variables of the state observer: transform the stress results of finite element analysis into As an observed variable ; Set the initial value of the state variable of the state observer to be pretension , covariance The initial value is the identity matrix.

6. A tension monitoring method for a chain transmission system with multiple components coupled as claimed in claim 1, characterized in that: Step S324 specifically includes: Design virtual sensors, including prediction and update steps; Construct the prediction equation: According to the tension estimate at the last moment and input ; Predict the chain tension at the current moment : , wherein A is the state transfer matrix described in step S322; B is the input matrix of the state equation described in step S322; Construct the update equation: Update the predicted strain data based on the observed stress data: , where C is the observation matrix described in step S323; Kalman gain matrix The calculation formula is: ,in, is the predicted error covariance matrix, R is the covariance of the observation noise, given by the system dynamics model and initially set to 0.0001, and C is the observation matrix, is the transpose of the observation matrix C; State covariance matrix update: , through real-time updates of the virtual sensor, the virtual sensor can accurately estimate the tension at each position on the chain.

7. A tension monitoring method for a chain transmission system with multiple components coupled as claimed in claim 1, characterized in that: Step S4 specifically includes: S41: Build a verification platform for the effectiveness of virtual sensors, i.e., a chain tension monitoring experimental platform, install strain gauges and acceleration sensors at the contact points of the sprockets, the midpoints and ends of the chains, and monitor the actual stress and strain of the chains; S42: collecting actual strain and stress data in real time and using them as input data for virtual sensor estimation results; Configure the data acquisition interface to ensure that high-frequency data is obtained from strain gauges and accelerometers in real time and transmitted to the central control system for processing; S43: Virtual sensor designed based on Kalman filter estimates the tension at each point through stress and strain data from finite element analysis; By comparing the actual measured value with the virtual sensor's estimated value, the error is calculated and the parameters of the Kalman filter are adjusted to make the virtual sensor's estimated value closer to the actual value; the difference between the sensor and the virtual sensor is calibrated through repeated experiments, and the performance of the Kalman filter is gradually improved; S44: Verify the estimation accuracy of the virtual sensor under different loads and working conditions by real-time monitoring and recording the tension at different positions of the chain; verify the accuracy of the virtual sensor's dynamic response to the chain drive system by simulating different working conditions; S45: Integrate the virtual sensor with other monitoring and control systems of the chain drive system to form a complete tension monitoring solution.

8. A tension monitoring device for a chain drive system with multiple components coupled, characterized in that: include: The 3D modeling and simulation module builds a 3D model of a multi-component coupled chain drive system to reflect the meshing relationship between the chain and the sprocket; simulates the dynamic characteristics of the chain under different working conditions to obtain the variation law of the chain tension; simulates different working conditions, analyzes the variation law of the chain tension under the corresponding working conditions, obtains the dynamic load of the sprocket and chain, and provides a load basis for the finite element analysis of the chain drive system; The data acquisition and preprocessing module uses a high-precision tension sensor in the experimental environment to record the actual tension value of the chain, and a strain gauge and an acceleration sensor to monitor the actual stress and strain of the chain; the collected data is cleaned to remove noise and outliers; The tension calculation and polygon effect analysis module performs tension analysis and calculation on the chain of a multi-component coupled chain drive system, and analyzes the polygon effect of the chain drive system, providing a theoretical basis for the dynamic characteristics analysis of the multi-component coupled chain drive system; Finite element analysis and virtual sensor design module, which performs finite element analysis on key components of the chain drive system and determines the installation location and parameters of the virtual sensor; Based on the finite element analysis results, a state observer is designed to estimate the tension distribution of the chain at different positions in real time; including: S32: By constructing a state observer and designing a chain virtual sensor, the chain tension at different positions can be estimated, which includes four sub-steps: S321: define state variables of the state observer; S322: constructing a state prediction equation of a state observer; S323: constructing a state update equation of a state observer; S324: Design virtual sensor based on state observer; Step S322 specifically includes: Construct the equation of state: , where the state transfer matrix A: represents the relationship between tension and tension change rate; the tension of the chain and its rate of change The relationship between is consistent with a typical second-order vibration system model, in which the mass m, damping coefficient J and stiffness coefficient K of the chain affect the change of tension; the state transfer matrix A can be expressed as: ； Input matrix B represents strain Impact on chain tension changes; input matrix B is designed as: ； Step S323 specifically includes: Construct the observation equation: , where C is the observation matrix, which represents the relationship between chain tension T(t) and stress δ(t); D is the input matrix of the observation equation, which represents the strain Relationship with stress; The observation matrix C is: ,in is the proportionality coefficient between chain tension and stress, obtained by finite element analysis; The input matrix D is: ,in, It is the proportionality factor between strain and stress, measured by strain-stress testing or the stress-strain curve of a material; The experimental verification and data storage module builds a virtual sensor verification platform, including strain sensors, data acquisition systems and communication modules, to conduct experimental verification to ensure that the system can accurately monitor chain tension; the experimental data and real-time monitoring data are recorded in the system database for subsequent analysis and monitoring; The decision-making rules and early warning module sets the upper and lower thresholds of chain tension according to system design requirements and safety standards; monitors the chain tension in real time. When the tension value exceeds the preset upper threshold, the system automatically triggers a fault warning and prompts the operator to perform inspection and maintenance; The remote access and management interface module provides a remote access interface, allowing managers to view and manage recorded data at any time through a browser; encrypts transmitted and stored data, and sets up a multi-layer identity authentication mechanism to ensure data security.

9. A tension monitoring system for a chain drive system with multiple components coupled, characterized in that: include: The chain drive system status data acquisition equipment consists of multiple sensors, including temperature sensors, vibration sensors, speed sensors and load sensors installed on the chain, gears and bearings; all sensors are connected to the central control unit and can collect and transmit the operating data of each component of the chain drive system in real time; Multi-component coupling status data processing and analysis equipment, which can monitor the operating status of each component in real time, including temperature, pressure, and speed parameters. The equipment can integrate data from different components to form a unified data stream; 3D modeling and simulation equipment, to establish a 3D model of a multi-component coupled chain drive system, to ensure that the model can accurately reflect the meshing relationship between the chain and the sprocket; to simulate the dynamic characteristics of the chain; Virtual sensor tension test equipment, including finite element analysis and virtual sensor design modules, performs finite element analysis on key components of the chain drive system and determines the installation location and parameters of the virtual sensor; Based on the finite element analysis results, a state observer is designed to estimate the tension distribution of the chain at different positions in real time; including: S32: By constructing a state observer and designing a chain virtual sensor, the chain tension at different positions can be estimated, which includes four sub-steps: S321: define state variables of the state observer; S322: constructing a state prediction equation of a state observer; S323: constructing a state update equation of a state observer; S324: Design virtual sensor based on state observer; Step S322 specifically includes: Construct the equation of state: , where the state transfer matrix A: represents the relationship between tension and tension change rate; the tension of the chain and its rate of change The relationship between is consistent with a typical second-order vibration system model, in which the mass m, damping coefficient J and stiffness coefficient K of the chain affect the change of tension; the state transfer matrix A can be expressed as: ； Input matrix B represents strain Impact on chain tension changes; input matrix B is designed as: ； Step S323 specifically includes: Construct the observation equation: , where C is the observation matrix, which represents the relationship between chain tension T(t) and stress δ(t); D is the input matrix of the observation equation, which represents the strain Relationship with stress; The observation matrix C is: ,in is the proportionality coefficient between chain tension and stress, obtained by finite element analysis; The input matrix D is: ,in, It is the proportionality factor between strain and stress, measured by strain-stress testing or the stress-strain curve of a material; Experimental verification and data storage equipment, build a virtual sensor verification platform, including strain sensors, data acquisition systems and communication modules, conduct experimental verification to ensure that the system can accurately monitor chain tension; record experimental data and real-time monitoring data into the system database for subsequent analysis and monitoring; Early warning equipment sets the upper and lower limits of chain tension according to system design requirements and safety standards; monitors chain tension in real time. When the tension value exceeds the preset upper limit, the system automatically triggers a fault warning and prompts the operator to perform inspection and maintenance; Remote access and management equipment provides a remote access interface, allowing managers to view and manage recorded data at any time through a browser; encrypt transmitted and stored data, and set up a multi-layer identity authentication mechanism to ensure data security.

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