Intelligent tensioning control method and system for belt conveyor

Abstract

The invention relates to the technical field of belt conveyors, and discloses an intelligent tensioning control method and system for a belt conveyor. According to the method and system, a dynamic mathematical model is constructed by collecting the current, rotating speed and torque parameters of a driving motor and combining hydraulic cylinder pressure and turnabout drum bearing force data, and high-precision indirect estimation of belt tension is achieved; a progressive adjustment strategy is adopted, the displacement of the hydraulic cylinder is dynamically adjusted according to tension deviation, mechanical abrasion is reduced, and system reliability and severe environment adaptability are improved. The method has the beneficial effects that the tension control error is reduced, the hydraulic action frequency and the maintenance cost are reduced, and meanwhile, the operation efficiency and safety of the conveyor are improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of belt conveyor, in particular to a kind of belt conveyor intelligent tensioning control method and system, for realizing the indirect monitoring and intelligent adjustment of belt tension. BACKGROUND

[0002] As the core equipment of material continuous transportation, belt conveyor is widely used in mining, port, cement, electric power and logistics industries. Its running stability and efficiency directly affect the overall performance of production line. As a key component of belt conveyor, tensioning device is responsible for maintaining the appropriate pre-tightening force of belt to prevent problems such as slipping, deviation and excessive sag. However, the traditional tensioning technology has significant defects in practical application. Mainly, the traditional tensioning device mainly uses mechanical or manual adjustment mode, which has low adjustment precision, high maintenance cost, poor adaptability and insufficient safety.

[0003] At present, the improvement of belt conveyor tensioning device mainly uses indirect tension calculation and intelligent control algorithm, but it is difficult to meet the high precision requirement due to the lack of full consideration of belt elastic deformation, roller friction and other factors, and frequent maintenance and component replacement are required to increase operation cost.

[0004] To solve the above problems, the present application provides an intelligent tensioning control method and system, which indirectly monitors the belt tension and intelligently adjusts it to ensure that the belt is always in the best working condition, while reducing maintenance cost and improving safety. SUMMARY

[0005] In order to solve the problems of traditional tensioning device, such as relying on direct tension measurement, frequent adjustment and poor environmental adaptability, the present application provides a kind of belt conveyor intelligent tensioning control method and system, which indirectly monitors the belt tension and intelligently adjusts it to ensure that the belt is always in the best working condition, while reducing maintenance cost and improving safety.

[0006] In order to achieve the above purpose, the present application adopts the following technical scheme:

[0007] A kind of belt conveyor intelligent tensioning control method of the present application comprises the following steps:

[0008] S1: Collecting real-time speed and torque parameters of driving motor, combining belt running speed and load characteristics, the tension difference between tight edge and loose edge of belt is deduced through torque and speed;

[0009] S2: Through force sensor installed on tensioning roller, measure the axial and radial force of roller, according to the force balance equation of roller;

[0010] S3: Adopting multi-source data fusion method, constructing data fusion mathematical model to calculate belt tension, and indirectly monitoring tension, judging whether the current tension exceeds the preset tension range according to the measured indirect tension data;

[0011] S4: If the tension exceeds the range, starting intelligent adjustment strategy, dynamically selecting adjustment mode according to tension deviation, selecting gradual adjustment mode, and adjusting monitoring period according to working condition;

[0012] S5: Control execution module drives the tensioning roller to move until the belt tension returns to the target range.

[0013] As a further preferred scheme, step S1 includes: the tension difference relationship of the tight side and the loose side of the belt is:

[0014]

[0015] Wherein, T motor is the motor output torque, F1 and F2 are the belt tight side tension and the belt loose side tension, R is the driving roller radius, T friction is the system friction torque.

[0016] As a further preferred scheme, step S3 includes: the roller force balance equation is:

[0017] The roller radial force equation is: radial F

[0018] The roller axial force equation is: exial F

[0019] Wherein, γ is the belt wrap angle.

[0020] As a further preferred scheme, step S4 further includes: the mathematical model construction method is:

[0021] Based on the dynamics equation of the belt conveyor, the relationship between the driving motor output torque and the belt tension is established:

[0022]

[0023] Wherein, F is the belt tension, k is the pressure-tension conversion coefficient (calibrated by experiment), β is the roller force weight coefficient (dynamically adjusted according to the sensor confidence), P hydraulic is the hydraulic cylinder working pressure, F bearing is the bearing force comprehensive value,

[0024] As a further preferred scheme, the specific process of the dynamic adjustment mode of step S5 is:

[0025] Gradually adjust the tension, when the tension deviation is >10%, the adjustment amount is 50% of the deviation value; when the deviation is 5%-10%, the adjustment amount is 30%; and when the deviation is <5%, no adjustment is triggered;

[0026] The working condition self-adaptive monitoring period is that the monitoring is performed every 5 minutes in the starting stage, the monitoring is performed every 30 minutes in the steady state operation, and the monitoring is performed every 10 minutes in the high-load fluctuation stage.

[0027] The intelligent tension control system of the belt conveyor has the following characteristics:

[0028] (1) Indirect tension monitoring module:

[0029] The drive motor parameter acquisition unit is connected to the motor controller and acquires current, speed and torque data in real time;

[0030] The hydraulic cylinder pressure monitoring unit measures the hydraulic system output pressure through a pressure sensor;

[0031] The roller force monitoring unit includes a three-way force sensor installed on the roller bearing;

[0032] The data fusion unit is used to integrate the motor parameters and the roller force data, and to calculate the tension estimate value through the Kalman filtering algorithm;

[0033] (2) Control module;

[0034] The adaptive control unit dynamically adjusts the PID parameters or the fuzzy control rules according to the deviation between the tension estimate value and the target value;

[0035] The adjustment strategy selection unit supports manual mode, automatic mode and preset working condition mode (such as heavy load, no load and inclined section);

[0036] (3) Execution module;

[0037] The hydraulic drive unit includes a hydraulic cylinder, a servo valve and an accumulator, and is used to drive the tension roller to move;

[0038] The position feedback unit uses a magnetostrictive displacement sensor to monitor the displacement of the tension roller in real time;

[0039] (4) Safety protection module;

[0040] The overload protection unit triggers the pressure relief valve and alarms when the hydraulic system pressure exceeds the set threshold;

[0041] The mechanical limiting unit sets a hard limiting block at both ends of the slide rail to prevent the tension roller from overtraveling;

[0042] (5) Remote interaction module

[0043] Supports Modbus TCP / IP or OPC UA protocols for communication with host computers or cloud platforms;

[0044] It provides functions for querying historical tension data, exporting adjustment records, and diagnosing fault codes.

[0045] As a further preferred option, the hydraulic drive unit is designed as follows:

[0046] It adopts a double-acting hydraulic cylinder with the piston rod rigidly connected to the tensioning roller support; the hydraulic system is equipped with a pressure compensation valve to ensure constant output thrust when the load changes; the servo valve control accuracy reaches ±0.1mm and the response time is less than 50ms.

[0047] As a further preferred option, the workflow of the data fusion unit is as follows: The tension value F is calculated from the motor parameters. mortor Calculated value F of the roller force roller A weighted average is performed, and when the data from a certain sensor is abnormal, it automatically switches to single data source mode.

[0048] Beneficial effects: This invention provides a method and system for intelligent tension control of belt conveyors. Through the innovative combination of indirect tension monitoring, multi-source data fusion and intelligent adjustment strategy, it achieves a comprehensive improvement in the accuracy, lifespan, cost, stability and safety of belt conveyor tensioning devices. It is especially suitable for harsh working conditions such as mines and ports and has significant industry promotion value. Attached Figure Description

[0049] Figure 1 A flowchart illustrating the overall process of intelligent tension control for belt conveyors;

[0050] Figure 2 This is a schematic diagram of the intelligent tension control system.

[0051] Figure 3 This is a flowchart of the intelligent adjustment strategy. Detailed Implementation

[0052] The technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments.

[0053] The present invention provides an intelligent tension control system for a belt conveyor, comprising the following modules:

[0054] (1) Indirect tension monitoring module:

[0055] A driving motor parameter acquisition unit is connected to the motor controller to acquire current, rotating speed and torque data in real time; the belt tension is indirectly calculated by monitoring the current, rotating speed and torque of the driving motor; the feedback data of the motor controller is used to update the tension calculation value in real time;

[0056] A hydraulic cylinder pressure monitoring unit measures the hydraulic system output pressure through a pressure sensor;

[0057] A roller force monitoring unit includes a three-way force sensor installed on the roller bearing; the force sensor is installed on the roller bearing to measure the roller force and indirectly calculate the belt tension;

[0058] A data fusion unit is used to integrate the motor parameters and roller force data, calculate the tension estimate value through the Kalman filtering algorithm, and improve the tension calculation accuracy;

[0059] (2) Control module;

[0060] An adaptive control unit dynamically adjusts the PID parameters or fuzzy control rules according to the deviation between the tension estimate value and the target value;

[0061] An adjustment strategy selection unit supports manual mode, automatic mode and preset working condition mode (such as heavy load, no load and inclined section);

[0062] (3) Execution module;

[0063] A hydraulic drive unit includes a hydraulic cylinder, a servo valve and an accumulator for driving the tensioning roller to move;

[0064] The design of the hydraulic drive unit is as follows:

[0065] A double-acting hydraulic cylinder is used, and the piston rod is rigidly connected with the tensioning roller support; the hydraulic system is equipped with a pressure compensation valve to ensure that the output thrust is constant when the load changes; the control accuracy of the servo valve reaches ±0.1 mm, and the response time is less than 50 ms.

[0066] A position feedback unit uses a magnetostrictive displacement sensor to monitor the displacement of the tensioning roller in real time;

[0067] (4) Safety protection module;

[0068] An overload protection unit triggers a pressure relief valve and alarms when the hydraulic system pressure exceeds the set threshold;

[0069] A mechanical limiting unit sets hard limiting blocks at both ends of the slide rail to prevent the tensioning roller from overtraveling;

[0070] (5) Remote interaction module

[0071] Modbus TCP / IP or OPC UA protocol is supported to communicate with the upper computer or cloud platform;

[0072] Tension history data query, adjustment record export and fault code diagnosis functions are provided.

[0073] A belt conveyor intelligent tensioning control method of the application comprises the following steps:

[0074] S1: Collecting real-time speed and torque parameters of the driving motor, combining belt running speed and load characteristics, and inversely calculating the tension difference between the tight side and the loose side of the belt through torque and speed.

[0075] The tension difference between the tight side and the loose side of the belt is related to:

[0076]

[0077] Where, T motor is the motor output torque, F1 and F2 are the tight side tension and the loose side tension of the belt, R is the driving drum radius, T friction is the system friction torque.

[0078] Load characteristics refer to all system parameters and states that affect the conversion relationship between driving torque and belt tension. Mainly include:

[0079] 1. System resistance (F_friction): including the bearing friction resistance of all rotating parts (drum, idler), the running resistance of the belt on the idler, the slope resistance of the material lifting or descending, etc. This is a reference value that needs to be measured by "empty load calibration";

[0080] 2. Mechanical transmission efficiency (η): the efficiency of transmission components such as reducers and couplings;

[0081] 3. Driving drum radius (R): a key geometric parameter;

[0082] 4. Belt stiffness / elastic modulus: affects the transmission speed of tension fluctuation.

[0083] S2: Measure the axial and radial forces of the tensioning drum through the force sensor installed on the drum, and according to the drum force balance equation;

[0084] The drum force balance equation is:

[0085] The drum radial force equation is: radial F

[0086] The drum axial force equation is: exial F

[0087] Where, θ is the belt wrap angle.

[0088] According to the formula, the belt tight side tension and the belt loose side tension F1·F2 can also be specifically calculated, F1·F2 are two more representative tensions, the tight side tension F1 is a core index of the load and driving state, and the loose side tension F2 is directly embodied as the adjustment effect of the tensioning device and is a direct object of the tensioning device.

[0089] S3 adopts a multi-source data fusion method to construct a data fusion mathematical model to calculate the belt tension and perform indirect tension monitoring, and judges whether the current tension exceeds the preset tension range according to the measured indirect tension data;

[0090] The mathematical model construction method is:

[0091] Based on the dynamics equation of the belt conveyor, a relationship between the output torque of the driving motor and the belt tension is established:

[0092]

[0093] Wherein, f is the belt tension, k is the pressure-tension conversion coefficient (calibrated through experiments), β is the roller force weight coefficient (dynamically adjusted according to the sensor confidence), P hydraulic is the working pressure of the hydraulic cylinder, F bearing is the bearing force comprehensive value,

[0094] The experimental calibration method of the pressure-tension conversion coefficient is determined through offline calibration experiments. The specific method is: under the no-load and constant-speed working condition, the step pressure change ΔP of the hydraulic cylinder is controlled, and the response change ΔF of the belt tension is measured synchronously. The multiple sampling points are sampled within the stroke of the tensioning device, and finally the proportional coefficient k between ΔF and ΔP is fitted through the linear regression algorithm. The coefficient is stored in the memory of the control system and is used for real-time calculation.

[0095] The dynamic adjustment strategy of the roller force weight coefficient β is that the roller force weight coefficient β is dynamically adjusted according to the sensor confidence. The system monitors the short-term mutation rate and the long-term variance of the three-way force sensor readings in real time, and calculates a comprehensive confidence C between 0 and 1. The weight coefficient is dynamically adjusted according to β=β0×C (β0 is the preset basic weight). When it is detected that the reading mutation exceeds the threshold value (such as 20% of the target tension), it is judged as an interference signal, and the β value is immediately significantly reduced, and slowly recovered in the subsequent period of time. This ensures that the data fusion algorithm can still output reliable results when the sensor is interfered or fails, greatly improving the robustness of the system.

[0096] Further, the working process of the data fusion unit is: the tension value F mortor calculated by the motor parameters is compared with the roller force calculated value F rollerWeighted average is performed, and when the data of a sensor is abnormal, the system automatically switches to the single data source mode.

[0097] S4: If the tension exceeds the range, the intelligent adjustment strategy is started, the adjustment mode is dynamically selected according to the tension deviation, the gradual adjustment mode is selected, and the monitoring period is adjusted according to the working condition;

[0098] Gradual adjustment of tension: when the tension deviation is >10%, the adjustment amount is 50% of the deviation value; when the deviation is 5%-10%, the adjustment amount is 30%; and when the deviation is <5%, the adjustment is not triggered;

[0099] Working condition adaptive monitoring period (periodic adjustment): in the belt startup stage, the tension is monitored every 5 minutes, and adjusted as needed; in the normal running stage of the belt, the tension is monitored every 30 minutes in the steady state, and every 10 minutes in the high load fluctuation stage, to avoid frequent adjustment.

[0100] S5: The control execution module drives the tensioning roller to move until the belt tension returns to the target range.

[0101] In addition, a pressure sensor can also be used to measure the working pressure of the hydraulic cylinder, and the force applied by the tensioning roller to the belt is indirectly reflected through the hydraulic cylinder thrust. Combined with the geometric relationship, the belt tension component can be calculated;

[0102] Indirect reaction force formula: F_piston=P*A, P is the working pressure of the hydraulic cylinder (P_hydraulic), and A is the effective area of the hydraulic cylinder (m 2 ).

[0103] Regarding threshold adjustment: the tension range is set to 1000N to 1200N; when the indirect tension data is lower than 1000N or higher than 1200N, the adjustment program is started.

[0104] During the adjustment process, a step-by-step approach is adopted, and the adjustment amount is 10% of the target tension each time, to avoid over-adjustment or oscillation.

[0105] The above is only the preferred specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art can make equivalent replacements or changes to the technical solution and the inventive concept of the present application within the technical scope disclosed by the present application, which should be covered within the protection scope of the present application.

Claims

1. A method for intelligent tension control of a belt conveyor, characterized in that, Includes the following steps: S1: Collect the real-time speed and torque parameters of the drive motor, and combine them with the belt running speed and load characteristics to infer the tension difference between the tight and slack sides of the belt through torque and speed. S2: By using a force sensor installed on the tensioning drum, the axial and radial forces on the drum are measured, and the drum force balance equation is applied. S3: Using a multi-source data fusion method, a data fusion mathematical model is constructed to calculate belt tension and perform indirect tension monitoring. Based on the measured indirect tension data, it is determined whether the current tension exceeds the preset tension range. S4: If the tension exceeds the range, activate the intelligent adjustment strategy, dynamically select the adjustment mode based on the tension deviation, select the gradual adjustment mode, and adjust the monitoring cycle according to the working conditions. S5: The control execution module drives the tensioning roller to move until the belt tension returns to the target range.

2. The intelligent tension control method for a belt conveyor according to claim 1, characterized in that, Step S1 further includes: the tension difference between the tight side and the slack side of the belt is expressed as follows: Among them, T motor The output torque of the motor is given by R, F1 and F2 are the tension of the belt on the tight side and the tension of the belt on the slack side, respectively, and R is the radius of the drive roller. friction This represents the system friction torque.

3. The intelligent tension control method for a belt conveyor according to claim 1, characterized in that, Step S3 further includes: the force balance equation of the drum is: The equation for the radial force on the drum is: F radial =F1+F2 The equation for the axial force on the drum is: F axial =F1·sinθ-F2·sinθ Where θ is the belt wrap angle.

4. The intelligent tension control method for a belt conveyor according to claim 1, characterized in that, Step S4 further includes: the method for constructing the mathematical model is as follows: Based on the dynamic equations of the belt conveyor, the relationship between the output torque of the drive motor and the belt tension is established: Where F is the belt tension, k is the pressure-tension conversion coefficient, β is the roller force weighting coefficient, and P hydraulic F is the working pressure of the hydraulic cylinder. bearing This is the combined bearing force value.

5. The intelligent tension control method for a belt conveyor according to claim 1, characterized in that, The specific process of the dynamic adjustment mode in step S5 is as follows: The tension is gradually adjusted. When the tension deviation is >10%, the adjustment amount is 50% of the deviation value; when the deviation is 5% to 10%, the adjustment amount is 30%; when the deviation is <5%, no adjustment is triggered. Operating condition adaptive monitoring cycle: every 5 minutes during startup, every 30 minutes during steady-state operation, and every 10 minutes during high load fluctuation phase.

6. The intelligent tensioning control system for a belt conveyor according to claim 1, characterized in that, Includes the following modules: (1) Indirect tension monitoring module: The drive motor parameter acquisition unit is connected to the motor controller to acquire current, speed and torque data in real time. The hydraulic cylinder pressure monitoring unit measures the output pressure of the hydraulic system through a pressure sensor. The drum force monitoring unit includes a triaxial force sensor mounted on the drum bearing; The data fusion unit is used to integrate motor parameters and drum force data, and calculate tension estimates using a Kalman filter algorithm. (2) Control module; The adaptive control unit dynamically adjusts the PID parameters or fuzzy control rules based on the deviation between the estimated tension and the target value. The adjustment strategy selection unit supports manual mode, automatic mode and preset working condition mode; (3) Execution module; The hydraulic drive unit, including a hydraulic cylinder, a servo valve, and an accumulator, is used to drive the tensioning roller to move. The position feedback unit uses a magnetostrictive displacement sensor to monitor the displacement of the tensioning roller in real time; (4) Safety protection module; The overload protection unit triggers the pressure relief valve and alarms when the hydraulic system pressure exceeds the set threshold. The mechanical limit unit has hard limit blocks at both ends of the slide rail to prevent the tensioning roller from overtraveling; (5) Remote Interaction Module Supports Modbus TCP / IP or OPC UA protocols for communication with host computers or cloud platforms; It provides functions for querying historical tension data, exporting adjustment records, and diagnosing fault codes.

7. The intelligent tensioning control system for a belt conveyor according to claim 6, characterized in that, The hydraulic drive unit is designed as follows: It adopts a double-acting hydraulic cylinder with the piston rod rigidly connected to the tensioning roller support; the hydraulic system is equipped with a pressure compensation valve to ensure constant output thrust when the load changes; the servo valve control accuracy reaches ±0.1mm and the response time is less than 50ms.

8. The intelligent tensioning control system for a belt conveyor according to claim 6, characterized in that, The workflow of the data fusion unit is as follows: Calculate the tension value F from the motor parameters. mortor Calculated value F of the roller force roller A weighted average is performed, and when the data from a certain sensor is abnormal, it automatically switches to single data source mode.

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