Test device and test method for long-term test of dynamic change of conveyor belt performance

Abstract

This invention discloses a testing device and method for long-term dynamic testing of conveyor belt performance, belonging to the technical field of conveyor belt performance testing equipment. It involves connecting three conveyors in series to form a closed material circulation loop, and then using three conveyor belts of different specifications for dynamic cyclic testing. Two independent frequency-controlled feeding hoppers, combined with independent speed regulation and tensioning structures for the three conveyors, simulate load differences, speed impacts, and multi-dimensional working condition changes at multiple transfer nodes. The invention integrates a full-path distributed data acquisition unit and a data detection and control system, incorporating algorithms such as multi-source synchronous calibration, segmented wear calculation, impact-coupled fatigue prediction, and working condition correlation analysis to achieve long-term dynamic monitoring and performance evolution analysis of multiple indicators such as conveyor belt wear, fatigue, and joint life. This invention is widely applicable to the research and development verification and factory testing of various conveyor belts.

Description

Technical Field

[0001] This invention relates to the field of conveyor belt performance testing equipment, and in particular to a long-term testing device and method for dynamic changes in conveyor belt performance that can realize multi-condition simulation, multi-specification parallel operation, and dynamic monitoring of all indicators. Background Technology

[0002] Conveyor belts, as the core load-bearing component of continuous bulk material conveying systems, are widely used in coal mines, port logistics, building materials, chemicals, power, and metallurgy. Their key indicators, such as tensile strength, abrasion resistance, tear resistance, dynamic fatigue performance, and joint reliability, directly determine the operational safety and service life of the conveying system. With the development of conveying systems towards longer distances, larger capacities, and steeper angles, the performance requirements for conveyor belts under complex conditions such as multi-stage transfers and variable load impacts have increased dramatically, but existing testing technologies can no longer meet these demands.

[0003] Currently, there are two main methods for conveyor belt performance testing. The first is offline static testing machines, such as universal tensile testing machines and Akron abrasion testing machines. These can only perform single-performance tests on small, cut samples and cannot simulate the dynamic coupling effects of alternating tension, material impact, and continuous bending in actual operation. The second is dynamic testing devices that have emerged in recent years, such as single-conveyor-belt closed-loop wear test benches. These install a single conveyor belt in a loop and test its wear life under constant load. However, such devices can only test one conveyor belt at a time, and the loop only has one transfer stage, making it impossible to reproduce the multi-stage transfers and the material accumulation and tensile impact caused by speed mismatch at transfer points in industrial settings. There is also a twin-roller bending fatigue test bench, which can simulate alternating bending stress, but without material involvement, it cannot assess the comprehensive performance degradation under the coupling effects of wear and impact.

[0004] Crucially, when comparing the performance of conveyor belts of various specifications or materials, the existing solutions can only be completed through multiple independent tests. This not only significantly increases the testing cycle but also makes it difficult to maintain consistency in environmental variables such as temperature, humidity, and material batches, resulting in severely unreliable comparative data. Furthermore, how to simultaneously load and collect data from multiple conveyors under independent and differentiated chemical conditions within the same material loop, and to monitor the long-term dynamic evolution of their performance online, has long been an unsolved technical challenge in this field. Summary of the Invention

[0005] This invention overcomes the shortcomings of existing technologies and provides a test device and method for long-term dynamic testing of conveyor belt performance. Within the same test cycle, multiple conveyor belts of different specifications or materials are subjected to parallel dynamic comparative tests under gradient chemical conditions covering long-distance wear, transitional transfer impact, and extreme working conditions. The performance evolution is monitored in real time, and the remaining life is estimated. At the same time, it overcomes the difficulties of independent control of multiple conveyors in a closed loop and differential chemical condition loading.

[0006] To solve the above-mentioned technical problems, the technical solution adopted by the present invention is as follows: a test device for long-term dynamic testing of conveyor belt performance, which includes a test bench main frame, a first conveyor, a second conveyor, a third conveyor, a first hopper, a second hopper, a power drive system, a working condition simulation component, a return guide trough, and a data detection and control system.

[0007] The first, second, and third conveyors are arranged in series along the material conveying direction on the main frame of the test bench. The feed end of the second conveyor corresponds to the area below the discharge end of the first conveyor, and the feed end of the third conveyor corresponds to the area below the discharge end of the second conveyor. The discharge end of the third conveyor is connected to the area above the feed end of the first conveyor via the return guide chute, forming a closed material circulation conveying loop. Each of the three conveyors has a conveyor belt to be tested mounted on its frame. The three conveyors simultaneously serve as the carrier for material transfer and conveying and as the test objects for conveyor belt performance testing. This allows for parallel dynamic cyclic testing of three conveyor belts of different specifications and materials within the same test cycle. This design breaks through the limitations of traditional single-belt testing. The three conveyor belts operate under identical material flow, environmental conditions, and operating history, eliminating the environmental differences introduced by traditional multiple independent tests, and ensuring the scientific rigor and comparability of the parallel comparison of the conveyor belts.

[0008] The first hopper is positioned above the inlet of the first conveyor, and the second hopper is positioned above the inlet of the second conveyor. Both the first and second hoppers are equipped with adjustable-flow feeding mechanisms at their lower outlets. These two feeding mechanisms are independently controlled to adjust the material supply per unit time of the corresponding conveyors, simulating load differences, load fluctuations, and material impact conditions at different inlet nodes in a multi-stage transfer system. This dual-hopper independent control structure allows for the introduction of controllable differentiated material flows at the beginning and end of the loop, overcoming the limitation of traditional single-hopper systems that only provide a single feeding mode. Based on actual simulation requirements, a third hopper can be added above the inlet of the third conveyor to further expand the simulation capabilities of multi-stage transfer.

[0009] The power drive system is electrically connected to the drive motors of the three conveyors, allowing independent control of the start / stop time, running direction, and running speed of each conveyor. It supports independent commissioning and operation of a single machine as well as sequential start / stop of multiple machines. By setting the speed difference between the three conveyors, it can reproduce material impact, slippage, and accumulation conditions at multi-level transfer points in industrial settings. By configuring each conveyor with an independent vector frequency converter and a built-in speed difference impact simulation mode, the speed difference effect at transfer points can be quantitatively reproduced, solving the problem of traditional constant speed testing devices neglecting key degradation factors.

[0010] The operating condition simulation component includes conveyor belt tension adjustment mechanisms, each corresponding to one of the three conveyors, for independently adjusting the tension of each conveyor belt under test to simulate service conditions under different tension states. Independent tensioning allows the three conveyor belts to operate under their respective optimal or extreme tension, and different types of tensioning mechanisms can be combined for parallel comparison in the same test.

[0011] The data detection and control system integrates multiple sets of data acquisition units. These data acquisition units are respectively arranged at the output end of the drive motor of each conveyor, at the tensioning and redirecting roller, at the transfer docking position, and above the belt surface of the unloaded section. They are used to collect the conveyor operating parameters, dynamic mechanical parameters of the conveyor belt, and belt surface wear parameters in real time, and to perform time-series calibration, storage, performance evolution analysis, and automatic generation of detection reports on the collected multi-source data.

[0012] The main frame of the test bench is further configured as follows: the frame is constructed by welding H-beams and channel steel, with pre-embedded anchor bolt holes at the bottom. The overall foundation length is between 8m and 12m, and the foundation width for the silo installation area is between 3m and 5m. The frame surface is treated for corrosion and rust prevention. Within the frame plane, the installation areas are sequentially divided according to the material circulation direction: the first conveyor installation area, the second conveyor installation area, the third conveyor installation area, the first silo installation area, and the second silo installation area. The central axis of each installation area coincides with the conveying center line of the corresponding conveyor. The frame mounting supports in each installation area have pre-reserved multi-position length adjustment holes and multi-specification bandwidth installation interfaces. The length adjustment holes have adjustment steps from 200mm to 800mm, and the bandwidth installation interfaces are compatible with various bandwidth specifications ranging from 650mm to 1400mm, allowing for simultaneous adjustment of the assembly parameters of the three conveyors.

[0013] The first, second, and third conveyors form a tiered three-level test zone. The first conveyor is the primary long-distance conveying zone, with a conveyor support inclination angle of 10° to 35°, adaptable to the conveyor belt to be tested with a bandwidth of 800mm to 1200mm, a total body length of 10m to 25m, a drive roller diameter of 400mm to 1000mm, a redirecting roller diameter of 300mm to 800mm, and is equipped with a variable frequency drive motor with a power of 5kW to 22kW, undertaking the main material lifting and long-distance continuous wear simulation functions in the circulation loop. The second conveyor is a two-stage transition transfer zone, with a conveyor support inclination angle of 15° to 40°. It is suitable for test conveyor belts with a bandwidth of 800mm to 1200mm, a total body length of 6m to 16m, a drive roller diameter of 300mm to 800mm, and a redirecting roller diameter of 200mm to 600mm. It is equipped with a variable frequency drive motor with a power of 3kW to 15kW, replicating the effect of abrupt changes in inclination angle and material impact on the conveyor belt between the two transfer stages. The third conveyor is a three-stage extreme condition test zone, with a conveyor support inclination angle of 15° to 40°. It is suitable for test conveyor belts with a bandwidth of 650mm to 1200mm, a total body length of 4m to 12m, a drive roller diameter of 200mm to 600mm, and a redirecting roller diameter of 150mm to 500mm. It is equipped with a variable frequency drive motor with a power of 3kW to 15kW, used for verifying the anti-slip performance, wear resistance, and material throughput performance of large-angle patterned conveyor belts. The primary long-distance transport zone, the secondary transitional transfer zone, and the tertiary extreme condition test zone together form the tilt gradient and bandwidth gradient, simultaneously generating three different degradation-dominant modes in a single test of a single device.

[0014] Both the first and second silos are constructed with square pyramidal steel structures, with wear-resistant linings on the inner walls. The effective volume of the first silo ranges from 8 m³ to 25 m³, while that of the second silo ranges from 2 m³ to 12 m³. The cone angle of the silos is set from 45° to 70°. Both silos utilize variable frequency controlled belt feeders with adjustable flow rates. The feed belt width matches the corresponding conveyor width, and the feed flow rate can be adjusted from 0 t / h to 500 t / h with an adjustment accuracy of no less than ±5%. The two feed mechanisms can be independently set to simulate steady-state loads, step load fluctuations, periodic off-center loads, and material impact conditions at different feeding nodes in a multi-stage transfer system.

[0015] In terms of the specific control of the power drive system, each of the three conveyor drive motors is equipped with an independent vector frequency converter, enabling stepless speed regulation control of the three conveyors within the range of 0.3m / s to 8m / s, with a speed regulation accuracy of no less than ±0.05m / s. The system has a built-in speed difference impact simulation mode, which allows setting the speed difference between the first and second conveyors, and between the second and third conveyors. Material accumulation at the transfer point is simulated when the downstream conveyor speed is lower than the upstream conveyor, and tensile impact of the material at the transfer point is simulated when the downstream conveyor speed is higher than the upstream conveyor. The system supports single-machine independent start-stop debugging mode and multi-machine linked start-stop control mode. In the linked mode, the conveyors start sequentially according to the preset start-up sequence and stop sequentially according to the preset stop-down sequence.

[0016] Regarding tension adjustment, each of the three conveyors is equipped with an independent tension adjustment mechanism at its tail deflector roller. These three mechanisms can independently set the tension parameters, with an adjustment range covering 2kN to 80kN and an adjustment accuracy of no less than ±1kN. The conveyor belt tension adjustment mechanisms for the three conveyors can be configured in the same or different combinations, including spiral tensioning mechanisms, hydraulic tensioning mechanisms, or counterweight tensioning mechanisms. The operating condition simulation component can also be equipped with an ambient temperature control enclosure, covering the outside of the three conveyors. The enclosure contains an electric heating unit and a cooling unit, allowing adjustment of the test environment temperature within the range of -30℃ to 80℃.

[0017] In terms of data acquisition, the data acquisition unit specifically includes a speed sensor, a torque sensor, a tension detection module, a laser wear detection probe, an environmental temperature and humidity monitoring module, and an impact acceleration sensor. The speed and torque sensors are coaxially mounted at the coupling between the drive motor output shaft and the drive drum of each conveyor, used to collect real-time speed, output torque, and power parameters of the drive end. The tension detection module is located at the bearing seat of the tensioning redirecting drum at the tail of each conveyor, using a bearing seat-type tension sensor to collect real-time belt tension, tension fluctuation amplitude, and peak impact tension data. The laser wear detection probe is installed above the belt surface under the unloaded section of each conveyor, using a non-contact laser displacement detection principle with a sampling frequency of not less than 50Hz, used to collect data on belt cover thickness changes and surface wear morphology. The environmental temperature and humidity monitoring module is located around each transfer point and on the air inlet side of the test bench, used to collect ambient temperature and humidity data. The impact acceleration sensor is located at the receiving idler roller frame at each transfer point, used to collect belt surface vibration acceleration data generated by the impact of falling material. The various acquisition units together form a full-path data acquisition network covering the drive end, the bearing section, the transfer impact point, and the unloaded section, fully matching the full working path of multi-level cyclic conveying.

[0018] In terms of data processing and output, the data detection and control system is also configured to: synchronously store the operating parameters of the three conveyors and the corresponding performance test data of the conveyor belts according to a unified timestamp, and establish a time-series database with a one-to-one correspondence between operating conditions and performance; automatically generate a horizontal performance comparison report of the three conveyor belts to be tested, including at least one of wear rate comparison, fatigue damage comparison, tension fluctuation comparison, and joint life prediction comparison; output the correlation curves of the corresponding operating parameters and performance indicators of each conveyor, and fully record the dynamic evolution of performance during long-term testing; support the screening, querying, exporting and tracing of test data, and realize full-process data traceability and quantitative comparison of multi-sample parallel testing.

[0019] At the advanced analytics level, the data monitoring and control system has five built-in algorithm modules.

[0020] The multi-source synchronous calibration module performs the following steps: using the encoder pulse signal of the drive motor of the first conveyor as the global time reference, it calculates the transmission delay of materials arriving at each level of the conveyor sequentially based on the material conveying distance and belt speed of the multi-stage transfer; it aligns all sensor data of the second and third conveyors to the same time axis through linear interpolation, with a timing synchronization accuracy of not less than 15ms; and it performs dynamic temperature drift compensation on the raw sampling data of the tension sensor and torque sensor based on the real-time ambient temperature. The compensation calculation formula is as follows: , In the formula, This represents the sensor measurement value after correction at time t. This represents the original sampled value of the sensor at time t, and k represents the temperature drift coefficient pre-calibrated by the sensor at the factory. The unit of this coefficient is related to the unit of measurement value and temperature of the sensor being calibrated. For example, k for a tension sensor is in kN / ℃, and k for a torque sensor is in N·m / ℃. This represents the ambient temperature at time t. This represents the reference temperature calibrated at the sensor's factory. A sliding median filtering algorithm is used to smooth high-frequency jump points in the laser wear detection data and tension detection data, outputting a standardized time-series dataset.

[0021] The segmented dynamic wear calculation module is used to perform the following steps: Divide the conveyor sections of the three conveyors into three wear test intervals. For each test interval, using a fixed operating mileage interval as a statistical unit, calculate the average thickness attenuation of the conveyor belt cover layer within that statistical unit to obtain the wear rate per unit mileage under the current operating conditions of that interval.

[0022] In the formula, This represents the wear rate per unit mileage in the j-th statistical unit of the i-th test interval, in mm / km; , These are the average thicknesses of the strip surface at the end of the (j-1)th and jth statistical units, respectively; This represents the operating mileage of a single statistical unit; it collects the average material load, belt speed, and tilt angle parameters corresponding to each statistical unit, and uses a multiple linear regression model to fit the wear rate prediction model based on multiple sets of wear rate samples under different working conditions. , In the formula, Let be the material load per unit length in the i-th interval. Let be the conveyor belt speed in the i-th interval. , , The fitting coefficients are dynamically updated; the total operating mileage of the entire circuit is accumulated, and the total cumulative wear amount and the wear rate ratio of each interval are dynamically updated by combining the wear rate prediction model of each interval, and the wear and operating condition correlation curve is output.

[0023] The fatigue life prediction module for transfer impact coupling performs the following steps: It uses the rainflow counting method to perform stress cycle statistics on the calibrated tension time-series data, and simultaneously superimposes the peak impact stress collected by the impact acceleration sensors at each transfer point to generate a composite fatigue load spectrum containing steady-state cyclic stress and transfer impact stress; based on Miner's linear cumulative damage theory, it calculates the cumulative fatigue damage degree of the conveyor belt body and the joint area. , In the formula, D represents the cumulative fatigue damage degree, and n represents the total number of stress amplitude levels. This represents the actual number of cycles for the i-th stress amplitude. This indicates the limit number of cycles for the corresponding conveyor belt material under this stress amplitude; by combining the cumulative running time and the current degree of damage, the remaining fatigue life of the conveyor belt body and the remaining dynamic life of the joint under the current working conditions are estimated, and the fatigue damage evolution rate of three conveyor belts of different specifications or materials is compared.

[0024] The multi-condition combination correlation analysis module is used to perform the following steps: collect multi-dimensional condition parameters of three conveyors, including inclination angle, material load, running speed, and tension force, throughout the entire test cycle; use a clustering algorithm to cluster the condition parameter combinations and identify several typical condition combination intervals; for each typical condition combination interval, establish a mapping model between the condition parameter combination and the performance degradation rate, and quantify the influence weight of each condition parameter on the wear rate and fatigue decay; based on real-time condition data and the mapping model, generate a dynamic evolution map of conveyor belt performance as the condition changes.

[0025] The graded interlocking shutdown protection module is used to perform the following steps: preset multi-level operation warning thresholds, including at least the prompt level, warning level, alarm level, and severe fault level, corresponding to various monitoring objects such as slippage deviation, tension over-limit, wear over-limit, and motor temperature over-limit; monitor the status of each parameter in real time, and trigger the corresponding level of warning and record it when the parameter reaches the corresponding threshold; when the monitored parameter reaches the severe fault level threshold, automatically link the power drive system and feeding mechanism to perform interlocking shutdown according to the preset sequence, first stopping the feeding, and then stopping each conveyor level in the opposite order to the conveying direction.

[0026] Furthermore, the data detection and control system is configured with differentiated sampling strategies for different types of conveyor belts: for fabric core conveyor belts, the tension sampling frequency is set to 30Hz to 80Hz, and the wear sampling frequency is set to 60Hz to 150Hz. Samples are tested after the test cycle or periodically, and the test indicators cover full-thickness tensile strength, interlaminar bond strength, elongation at break, bending fatigue performance, transverse strength, and joint dynamic life; for steel cord core conveyor belts, the tension sampling frequency is set to 60Hz to 150Hz, and the joint displacement sampling frequency is set to 120Hz to 300Hz. Samples are tested after the test cycle or periodically, and the test indicators cover steel wire rubber bond strength, overall tensile strength, joint dynamic life, and transverse tear resistance. The system is compatible with parallel testing of both types of conveyor belts within the same test cycle, and automatically matches the corresponding sampling strategies and analysis algorithms.

[0027] Based on the above-described apparatus, the present invention also provides a long-term test method for dynamic changes in conveyor belt performance, using the test apparatus described in any of the above-described embodiments. The method includes the following steps: acquiring the parameters of the conveyor belt to be tested, the parameters of the test material, the preset working condition parameters, and the inherent parameters of the equipment to generate a set of basic test parameters; setting multiple combinations of working conditions according to the test objectives to generate a candidate working condition sequence, and sequentially adjusting the feeding flow rate of the first and second hoppers, the conveyor running speed, the conveyor belt tension, and the ambient temperature to construct different test working conditions; starting the feeding mechanisms of the three conveyors and the first and second hoppers to enter a cyclic test state, and synchronously collecting the operating parameters of each conveyor and the performance parameters of the conveyor belt through the full-path data acquisition unit to generate an original time-series dataset; performing multi-source synchronous calibration and filtering and denoising on the original time-series dataset to obtain a standardized test dataset; based on the standardized test dataset, calculating the dynamic wear rate, fatigue cumulative damage degree, and remaining life prediction of the conveyor belt under each working condition to generate dynamic performance evolution data; comparing the performance data of different working conditions and different samples to determine the optimal conveyor belt scheme and optimal operating condition parameters; automatically generating a test report after the test, storing the full-process test data, responding to data query requests, and outputting test results.

[0028] In practical implementation, the steps for generating the basic test parameter set include: obtaining parameters such as the type, bandwidth, number of layers, cover layer thickness, and joint type of the conveyor belt to be tested; obtaining parameters such as the density, particle size, angle of repose, and abrasion properties of the test material; reading the inherent parameters of the equipment, including conveyor length, roller diameter, rated power, sensor range, calibration coefficient, rated belt speed, and hopper volume; setting the test operating condition range, including the feed flow rate range, belt speed range, tension range, and ambient temperature range; and combining and storing the above parameters. The steps for constructing different test operating conditions include: determining the operating condition variables, including feed flow rate, belt speed, tension, and ambient temperature; generating a candidate operating condition sequence using orthogonal experimental design or uniform design methods; adjusting the parameters of each operating condition sequentially according to the sequence; and after each set of operating conditions is adjusted and stabilized for a preset period of time, entering the formal data acquisition stage. The steps for generating the original time-series dataset include: starting each conveyor sequentially according to the linkage start-stop logic, and starting each feeding mechanism after the operation is stable; synchronously collecting all sensor data using the encoder signal of the first conveyor as the trigger reference; continuously collecting data at a preset sampling frequency to generate the original time-series dataset, and storing it according to a preset segmentation strategy. The steps for obtaining the standardized test dataset include: aligning the sampling data of the second and third conveyors to the time axis of the first conveyor based on the material transfer delay of the multi-level transfer; compensating for temperature drift in the tension and torque sensor data based on the real-time ambient temperature; using a sliding median filter algorithm for noise reduction, and performing unit unification and outlier removal. The steps for generating dynamic performance evolution data include: statistically analyzing the wear per unit mileage for each conveyor segment, fitting a correlation model between wear rate and operating parameters, and cumulatively calculating the total wear; using the rainflow counting method to count the number of tension cycles, superimposing the transfer impact stress, calculating the cumulative fatigue damage, and estimating the remaining fatigue life and joint remaining life; generating dynamic performance evolution curves and data tables for the conveyor belt.

[0029] The beneficial effects of this invention compared to the prior art are: 1. This invention utilizes a closed-loop circulation system formed by three conveyors connected in series, and a structure where different conveyor belts are installed on each of the three conveyors. This enables parallel comparative testing of multiple conveyor belts within the same test cycle, eliminating the comparison deviations caused by uncontrollable environmental variables in multiple independent tests. By independently feeding materials to the feed ends of the first and second conveyors from the first and second hoppers, and configuring an adjustable flow feeding mechanism, the load differences and fluctuations at different nodes in a multi-stage transfer system can be independently simulated, improving the realism of the operating condition simulation.

[0030] 2. This invention, by equipping each conveyor with an independent vector frequency converter and incorporating a speed difference impact simulation mode, allows for the quantitative setting of the upstream and downstream speed difference, replicating material accumulation and tensile impact at transfer points, thus making wear and fatigue life assessments more realistic. By creating a gradient-based three-level test zone with parameters such as inclination angle, length, and drum diameter, it can simultaneously cover three degradation modes—long-range wear, transitional impact, and extreme operating conditions—in a single test, providing more comprehensive performance comparison data.

[0031] 3. This invention achieves a transformation from traditional downtime measurement to dynamic real-time monitoring by utilizing a full-path data acquisition network distributed across the drive end, tensioning end, transfer point, and unloaded section, combined with online monitoring via a laser wear detection probe. Through built-in algorithm modules such as multi-source synchronous calibration, segmented wear calculation, impact-coupled fatigue prediction, and multi-condition correlation analysis, the acquired data can be processed with high precision, outputting wear-condition correlation curves, remaining life estimates, and dynamic evolution maps, providing data support for engineering decisions.

[0032] 4. This invention ensures safe long-term unattended operation through a graded interlocking shutdown protection module and a reverse step-by-step shutdown strategy. Through differentiated sampling strategies and modular framework design, it can be adapted to different types of conveyor belts, such as fabric cores and steel cord cores, as well as various belt width specifications, improving the versatility and economy of the device. Attached Figure Description

[0033] The present invention will now be further described with reference to the accompanying drawings.

[0034] Figure 1 This is a schematic diagram of the front structure of the present invention; Figure 2 This is a schematic diagram of the left-side structure of the present invention; Figure 3 This is a schematic diagram of the right-side structure of the present invention; Figure 4 This is a top view of the structure of the present invention; Figure 5 This is a three-dimensional structural diagram of the present invention; Figure 6 This is a schematic diagram of the testing process of the present invention;

[0035] Figure 7 This is a schematic diagram of the dynamic wear calculation and fatigue life prediction process of the present invention.

[0036] In the diagram: 1 is the first conveyor, 2 is the second conveyor, 3 is the third conveyor, 4 is the first silo, 5 is the second silo, 6 is the return feed chute, and 7 is the main frame of the test bench. Detailed Implementation

[0037] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without creative effort are within the scope of protection of the present invention.

[0038] Example 1, see Figures 1 to 5 This invention proposes a testing device for long-term dynamic testing of conveyor belt performance. Through a three-stage series closed-loop architecture, multi-dimensional operating condition simulation, and intelligent data analysis, it achieves long-term accurate testing of conveyor belt performance dynamics and parallel comparison of multiple samples. In this embodiment, for ease of description, the first conveyor 1 is referred to as the main transfer conveyor, the second conveyor 2 as the auxiliary transfer conveyor, and the third conveyor 3 as the test conveyor; however, the scope of protection of this invention is not limited thereto.

[0039] The test device consists of a test bench main frame 7, a main transfer conveyor, an auxiliary transfer conveyor, a test conveyor, a first silo 4, a second silo 5, a power drive system, a working condition simulation component, a return material guide trough 6, and a data detection and control system. The whole device is arranged in an indoor test site and fixed to the ground foundation by anchor bolts.

[0040] The main frame 7 of the test bench is constructed from Q235B H-beams and channel steel, welded together to form an integrated load-bearing structure. The overall foundation length of the frame can be selected between 8m and 12m; in this embodiment, it is 9.3m. The foundation width of the main conveying area is 2.2m, and the foundation width of the silo installation area is 3.8m, which can be adjusted within the range of 3m to 5m. The total height of the frame is 7.2m. Twelve sets of pre-embedded anchor bolt holes with a diameter of φ42mm are provided at the bottom of the frame, matching M36 anchor bolts. After shot blasting to remove rust, the frame surface is coated with epoxy zinc-rich primer and polyurethane topcoat. The frame plane is divided sequentially according to the material circulation direction into the main conveyor installation area, auxiliary transfer conveyor installation area, test conveyor installation area, first silo installation area, and second silo installation area. The central axis of each installation area strictly coincides with the conveying center line of the corresponding conveyor, with a coaxiality deviation not exceeding 2mm. Each installation area has a rack mounting bracket with multiple length adjustment holes, and the adjustment step can be selected from 200mm to 800mm. This embodiment uses a 500mm step. At the same time, multiple bandwidth installation interfaces are reserved to adapt to bandwidth specifications from 650mm to 1400mm, specifically including four types: 800mm, 850mm, 1000mm, and 1200mm.

[0041] The main transfer conveyor, auxiliary transfer conveyor, and test conveyor are connected in series along the material conveying direction. The inclination angle of the main transfer conveyor is adjustable from 10° to 35°, and in this embodiment it is set to 22°, with the lower end being the feed end and the higher end being the discharge end. The inclination angle of the auxiliary transfer conveyor is adjustable from 15° to 40°, and in this embodiment it is set to 29°, with its feed end located directly below the discharge end of the main transfer conveyor. The inclination angle of the test conveyor is also set to 29°, with its feed end located directly below the discharge end of the auxiliary transfer conveyor, and its discharge end extending above the feed end of the main transfer conveyor through a closed return guide trough 6, thus forming a complete closed material circulation conveying loop. Each of the three conveyors has a conveyor belt to be tested mounted on its frame, enabling parallel dynamic cyclic testing of three conveyor belts of different specifications and materials within the same test cycle.

[0042] In this embodiment, three conveyors form a gradient three-level test zone, with the specific parameters as follows: Main transfer conveyor (first-level long-distance conveying zone): conveyor support inclination angle 22°, adaptable bandwidth 1000mm, total body length 16.6m, drive drum diameter 630mm (rubber-coated), redirecting drum diameter 500mm, load-bearing section idler spacing 1200mm, unloaded section idler spacing 3000mm, equipped with an 11kW variable frequency drive motor.

[0043] Auxiliary transfer conveyor (secondary transition transfer zone): inclination angle 29°, bandwidth 1000mm, total body length 10.6m, drive drum diameter 500mm, redirecting drum diameter 400mm, bearing section idler roller spacing 1000mm, equipped with a 7.5kW variable frequency drive motor.

[0044] Test conveyor (Level 3 extreme working condition test zone): Inclination angle 29°, bandwidth 850mm, total body length 7.05m, drive roller diameter 400mm, redirecting roller diameter 320mm, bearing section idler roller spacing 800mm, equipped with a 7.5kW variable frequency drive motor.

[0045] Both the first silo 4 and the second silo 5 are constructed of Q235B steel plate welded into a square pyramid structure with a wall thickness of 10mm and an inner lining of 16mm thick high-manganese steel wear-resistant plate. The effective volume of the first silo 4 is 12m³, and the effective volume of the second silo 5 is 5m³. The cone angle of both silos is 55°. A frequency converter belt feeder is installed at the lower outlet of each silo, with a feed flow rate ranging from 0t / h to 500t / h and an adjustment accuracy of ±5%. The two feeders are independently controlled. Depending on the actual simulation requirements, a third silo can be added above the feed end of the test conveyor to further expand the simulation capability of multi-stage transfer.

[0046] The power drive system consists of a control network comprised of three independent vector frequency converters, three variable frequency motors, and a PLC. The speed range is 0.3 m / s to 8 m / s with an accuracy of ±0.05 m / s, and it includes a built-in speed difference impact simulation mode. The operating condition simulation component includes three independent tension adjustment mechanisms with a tension force of 2 kN to 80 kN and an accuracy of ±1 kN. Optional mechanisms include screw, hydraulic, or counterweight tensioning. The device can be equipped with an ambient temperature control enclosure with a temperature adjustment range of -30℃ to 80℃.

[0047] The data acquisition unit includes a speed sensor, a torque sensor, a tension detection module, a laser wear detection probe, an ambient temperature and humidity monitoring module, and an impact acceleration sensor. The speed and torque sensors are coaxially mounted at the coupling between the output shaft of each conveyor's drive motor and the drive drum. The tension detection module is located at the bearing housing of the tensioning redirecting drum at the tail of each conveyor. The laser wear detection probe is positioned above the lower belt surface of each conveyor's unloaded section, employing a non-contact laser displacement detection principle with a sampling frequency of no less than 50Hz. The ambient temperature and humidity monitoring module is located around each transfer point. The impact acceleration sensor is located at the receiving idler roller frame at each transfer point. All these acquisition units together form a full-path data acquisition network.

[0048] Example 2, as Figure 6 , Figure 7 As shown, the data detection and control system is divided into an acquisition layer, a processing layer, and an application layer. The processing layer uses an Advantech IPC-610H industrial PC as its core, connected to various sensors via a PCIe data acquisition card (model: NI PCIe-6363, 16 analog inputs, sampling rate 2MS / s). The dedicated testing software deployed on the industrial PC adopts a hybrid programming architecture of LabVIEW and Python. LabVIEW is responsible for data acquisition, timing synchronization, and the human-machine interface, while Python is responsible for advanced algorithm calculations. The specific control methods, calculation processes, and examples of each algorithm module are as follows.

[0049] (I) Control flow of the multi-source synchronous calibration module This module is driven by a timing loop structure in LabVIEW. It uses the Z-phase pulse (one pulse per revolution) of the encoder of the first conveyor drive motor as the hardware trigger signal and connects to the data acquisition card through the PFI0 interface to realize a global time reference.

[0050] Step 1: Material transfer delay calculation and data alignment The time delay is calculated based on the device geometry and real-time belt speed. This includes the horizontal distance of material falling from the discharge end of the first conveyor to the inlet end of the second conveyor. =1.2m, the horizontal distance of the drop from the discharge end of the second conveyor to the feed end of the third conveyor. =1.5m. At a certain moment, the belt speed of the first conveyor is... =2.5m / s, the belt speed of the second conveyor =3.0m / s, then: = / =0.48s, = / =0.50s, The LabVIEW program shifts all sensor data buffers of the second conveyor backwards as a whole. The data from the third conveyor is shifted backward. + =0.98s. Then, a linear interpolation algorithm is used to resample the data of each channel to a uniform time grid of 100Hz to ensure that the timing synchronization accuracy is not less than 15ms. During interpolation, if the target time point is located between two original sampling points, linear interpolation is used; if it is outside the range, nearest neighbor extrapolation is used.

[0051] Step 2: Dynamic compensation for temperature drift Both tension and torque sensors incorporate a temperature drift coefficient k, provided by the manufacturer's calibration certificate. Taking a certain tension sensor as an example, k = 0.05 kN / ℃, with a reference temperature... =20℃. The LabVIEW program reads the temperature value from the ambient temperature and humidity monitoring module in real time. The compensation amount is updated every second. =35℃, original sampled value When the current is 25.3 kN, the compensated value is: =25.3-0.05×(35-20)=24.55kN.

[0052] The calculation is performed in the formula node of LabVIEW, and the compensated value is stored in the correction data queue.

[0053] Step 3: Sliding Median Filtering. For both laser wear detection and tension detection data, a sliding median filter with a window width of 5 is used. In LabVIEW, the "Median Filter" function is called, and the window radius is set to 2. For continuously sampled sequences... Output median The filter processes the next set of data after sliding the window through one point. This filtering effectively removes high-frequency jump points caused by instantaneous impacts from material particles or frame vibrations, while retaining the true trends in wear and tension changes. The filtered, standardized time-series dataset is then sent to the Python analysis engine via TCP / IP.

[0054] (II) Control Flow of Segmented Dynamic Wear Calculation Module This module is implemented using Python scripts and performs offline or online computations by reading standardized time-series datasets.

[0055] Step 1: Interval Division and Statistical Unit Setting The conveyor sections of the three conveyors were mapped to three wear test intervals: interval 1 corresponds to the main transfer section, interval 2 corresponds to the auxiliary transfer section, and interval 3 corresponds to the test section. Statistical unit length. This is set via a configuration file, with a default value of 100m (conveyor belt running mileage). When the cumulative running mileage of the conveyor belt reaches... When the value is an integer multiple of the wear count, a wear statistics check is triggered.

[0056] Step 2: Calculation of average thickness of the strip surface At the end of the statistical unit, the laser wear detection probe collects thickness values ​​at 20 equally spaced points within the corresponding strip area of ​​the unit. (k=1...20), after removing the maximum and minimum values, take the average to get For example, at the end of the 5th statistical unit, the average thickness of interval 1 is... =5.82mm, at the end of the 6th unit =5.79mm.

[0057] Step 3: Calculation of wear rate per unit mileage Program calculation: =(5.82-5.79) / 0.1=0.30mm / km. This value, along with the current operating parameters (average material load),... Average belt speed ,inclination Stored in the wear rate sample database.

[0058] Step 4: Fitting the prediction model using multiple linear regression Regression analysis is triggered when the sample size reaches a preset value (e.g., 30 groups). Python calls the LinearRegression class from the scikit-learn library to... , As the independent variable, The program fits the data to the dependent variable. The program outputs the fit coefficients. , , and coefficient of determination As experimental data accumulates, the model is automatically refitted weekly, dynamically updating the model coefficients. For example, the model for interval 2 might be updated to... =0.012 +0.085 -0.056.

[0059] Step 5: Cumulative Wear and Critical Range Location Total mileage S and cumulative wear in each section The system is continuously updated by the program. The wear percentage of each interval is displayed on the HMI in the form of a pie chart. When the wear percentage of a certain interval exceeds 50% and is significantly higher than that of other intervals, the interval is determined to be a critical wear interval, and the system automatically marks it and prompts "Critical Wear Interval: Auxiliary Transfer Impact Section".

[0060] (III) Control flow of the impact-coupled fatigue life prediction module This module is also implemented in Python, and combines real-time tension and acceleration data pushed by LabVIEW.

[0061] Step 1: Generation of composite fatigue load spectrum LabVIEW receives each tension data point and simultaneously checks the corresponding impact acceleration sensor signal. When the peak acceleration exceeds a threshold (set to 2g), it is considered a transfer impact event. This peak impact stress is determined by the formula... Conversion, among which The equivalent mass of the material above the receiving roller and the belt surface. This represents the cross-sectional area of ​​the conveyor belt. The peak impact stress and the corresponding tension value are included in the stress-time history.

[0062] The rainflow counting method in Python uses the open-source library rainflow to count the stress time history after merging impact stresses and output the stress amplitudes. and the corresponding number of loops .

[0063] Step 2: Calculation of cumulative damage The system has a built-in database of SN curves for common conveyor belt materials (from standards such as GB / T 7984 and enterprise test data). For EP200 fabric core belts, the limiting number of cycles under a stress amplitude of 10kN is specified. =2×10⁶. The program obtains the value by looking up a table or interpolating based on the actual stress amplitude. Then calculate the damage component at that amplitude. The total damage degree D is obtained by summing the values ​​of each amplitude.

[0064] For example, within a certain statistical period, a stress amplitude of 10 kN is cyclicated 5000 times, and 20 kN is cyclicated 200 times, corresponding to a limit number of cycles of 2 × 10⁻⁶. 6 and 5×10 5 Then the periodic damage ΔD = 5000 / (2 × 10) 6 )+200 / (5×10 5 =0.0025 + 0.0004 = 0.0029.

[0065] Step 3: Comparison of Remaining Life Estimation and Trends Total damage over cumulative runtime T It is obtained by summing up the damage from each cycle. Remaining life. The program updates the remaining life estimate hourly and plots the damage evolution curves and life decay curves for the three conveyor belts. By comparing the curve slopes with the current values, it is easy to see which belt has better fatigue resistance.

[0066] (iv) Control flow of the multi-condition combination correlation analysis module This module runs offline after the experiment ends or after a preset amount of data is reached, and is implemented by the scikit-learn library in Python.

[0067] Step 1: Data Standardization and Clustering The operating parameters (tilt angle θ, material load F, belt speed v, tension force T) for the entire test cycle are extracted to form a feature matrix, and Z-score normalization is used to eliminate the units of measurement values. Then, the KMeans clustering algorithm is called, setting the number of clusters K to cycle from 2 to 10, calculating the profile coefficient and elbow rule index, and automatically determining the optimal K value. In this example, the optimal K=5, which divides the operating condition combination into 5 typical intervals, such as "low load and low speed", "high load and high speed", and "impact load".

[0068] Step 2: Mapping Modeling and Influence Weight Quantification For each type of operating condition range, a mapping model is trained using the operating condition parameters as independent variables and the performance degradation rate (wear rate or damage accumulation rate) as the dependent variable, employing a random forest regression algorithm (RandomForestRegressor, n_estimators=100). After model training, the importance weights of each feature are output. For example, an analysis result might show: "In the impact load range, belt speed importance 0.45, load importance 0.35, tilt angle importance 0.15, tension force importance 0.05."

[0069] Step 3: Generation of Dynamic Evolution Map Based on a trained model, the program generates a heatmap or surface plot of performance degradation rate with two main influencing factors as coordinate axes. This plot is rendered using Python's matplotlib library and then uploaded back to a LabVIEW HMI for display. Users can visually see under which combination of operating conditions the performance degradation is fastest.

[0070] (v) Control logic of the graded interlocking shutdown protection module This module runs on a Siemens S7-1200 PLC and communicates with the industrial computer and frequency converter via PROFINET, ensuring that the PLC can still independently perform protection even if the industrial computer fails.

[0071] Step 1: Presetting and Monitoring Early Warning Thresholds Four threshold levels are set via the HMI. Taking slippage deviation as an example: Warning level 5% (when the actual belt speed deviates from the set value by more than 5%, the yellow indicator light on the HMI illuminates and the event is recorded); Early warning level 10% (orange light flashes, buzzer sounds a low-frequency alarm); Alarm level 15% (red light flashes, buzzer sounds a high-frequency alarm, an HMI confirmation window pops up, allowing manual ignoring or stopping the machine); Critical fault level 20% (triggers interlock shutdown). The PLC calculates the actual belt speed every 100ms using the encoder signal and compares it with the set value.

[0072] Step 2: Interlocking shutdown execution sequence When any monitored parameter reaches the critical fault level threshold, the PLC immediately executes the following uninterruptible interlocking sequence: ① 0ms: Cut off the enable signal of the frequency converter of the feeder in the first and second hoppers, and stop feeding; ② Delay 2s: Send a stop command to the frequency converter of the test conveyor (third conveyor) to slow it down and stop; ③ 3s delay (cumulative 5s): Send a stop command to the frequency converter of the auxiliary transfer conveyor (second conveyor); ④ Delay 5s (cumulative 10s): Send a stop command to the inverter of the main transfer conveyor (first conveyor).

[0073] This stepped reverse shutdown logic (first stopping the feed, then stopping each conveyor in the reverse material flow direction) ensures that material in the loop is emptied segment by segment, avoiding accumulation that could cause localized overload damage to the conveyor belt. Simultaneously, the PLC records the instantaneous values ​​of all parameters for the shutdown event and stores them in the fault log.

[0074] (vi) Overall coordination of data acquisition and control The core of the entire system's control lies in the collaboration between the PLC and the industrial computer. The PLC handles the real-time start / stop logic, speed closed-loop control, and interlocking protection; the industrial computer handles data acquisition, complex algorithm calculations, and human-machine interaction. The two exchange setpoints and status data via the OPC UA protocol. All sensor analog signals are processed by a signal conditioning module (4-20mA to 0-10V) before being connected to the data acquisition card. The LabVIEW program reads each channel at a 100Hz loop frequency, timestamps the data, and stores it in a circular buffer for real-time display and analysis by the Python engine. During long-term testing, the buffer data is written to an HDF5 fragment file every hour, with the file name including the test number and time range for easy traceability.

[0075] Through the detailed control process described above, this device achieves fully automated operation from data acquisition and processing to performance evaluation and safety protection.

[0076] Example 3, taking a three-belt parallel comparative test as an example, involves mounting three conveyor belts to be tested onto the main transfer conveyor, auxiliary transfer conveyor, and test conveyor, respectively, and entering the parameters of each belt into the system. An orthogonal design method is used to generate a sequence of operating conditions including low load / low speed, medium load / medium speed, and high load / high speed. Upon startup, the system starts in the order of main transfer conveyor → auxiliary transfer conveyor → test conveyor. After stabilization, the feeders of the two hoppers are activated, and the test cycle begins. Data is stored according to a preset segmentation strategy. After multi-source synchronous calibration and filtering, each algorithm module analyzes and processes the data, automatically generating a comparison report and evolution curves. After the test, the system is shut down according to the established procedure, and a formal test report is generated and stored.

[0077] In Example 4, under the conditions of maintaining a feed flow rate of 150 t / h, an ambient temperature of 25℃, and a tension of 20 kN, three conveyors were set to operate at the same speed of 2.5 m / s (condition A) and at differentiated speeds (main transfer 2.5 m / s, auxiliary transfer 2.0 m / s, and test 3.0 m / s, condition B), respectively, for 72 hours each. Under condition A, the 72-hour wear and standard deviation of tension fluctuation of the three conveyors were at a low level. Under condition B, because the auxiliary transfer machine's speed was lower than that of the main transfer machine, periodic material accumulation occurred at its feed end, and the peak impact acceleration increased from 3g to 8g; the test machine's speed was higher than that of the auxiliary transfer machine, and the material was quickly dragged after falling onto the test machine's belt surface. After 72 hours, the wear of the three conveyors increased by 75%, 37.5%, and 87%, respectively, and the tension fluctuation also increased significantly. The results show that the conveyor belt exhibits the least performance degradation under impact conditions, and all comparisons can be completed in a single continuous test, avoiding the problems of long cycles and uncontrollable environmental variables associated with traditional multiple independent tests.

[0078] The above description is merely a preferred embodiment of the present invention and is not intended to limit the invention. Various modifications and variations can be made to the present invention by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A testing apparatus for long-term dynamic testing of conveyor belt performance changes, characterized in that, It includes the main frame of the test bench (7), the first conveyor (1), the second conveyor (2), the third conveyor (3), the first silo (4), the second silo (5), the power drive system, the working condition simulation component, the return material guide chute (6), and the data detection and control system; The first conveyor (1), the second conveyor (2), and the third conveyor (3) are arranged in series on the main frame (7) of the test bench along the material conveying direction. The feed end of the second conveyor (2) corresponds to the lower part of the discharge end of the first conveyor (1), and the feed end of the third conveyor (3) corresponds to the lower part of the discharge end of the second conveyor (2). The discharge end of the third conveyor (3) is connected to the upper part of the feed end of the first conveyor (1) through the return guide trough (6), forming a closed material circulation conveying loop. Each of the three conveyors is equipped with a conveyor belt to be tested. The three conveyors simultaneously serve as the carrier for material transfer and conveying and the test object for conveyor belt performance testing. They can simultaneously conduct parallel dynamic cyclic testing on three conveyor belts of different specifications and materials within the same test cycle. The first silo (4) is located above the feed end of the first conveyor (1), and the second silo (5) is located above the feed end of the second conveyor (2). The lower discharge ports of the first silo (4) and the second silo (5) are equipped with flow-adjustable feeding mechanisms. The two feeding mechanisms are independently controlled and used to independently adjust the material supply per unit time of the corresponding conveyor, simulating the load difference, load fluctuation and material impact conditions of different feed nodes in the multi-stage transfer system. The power drive system is electrically connected to the drive motors of the three conveyors respectively, and is used to independently control the start and stop time, running direction and running speed of each conveyor. It supports independent debugging and operation of a single machine and the sequential start and stop of multiple machines. The material impact, slippage and accumulation conditions at multi-level transfer points in the industrial field can be reproduced by setting the speed difference of the three conveyors. The working condition simulation component includes conveyor belt tension adjustment mechanisms set for three conveyors respectively, which are used to independently adjust the tension of each conveyor belt to be tested and simulate service conditions under different tension states. The data detection and control system integrates multiple sets of data acquisition units. These data acquisition units are respectively arranged at the output end of the drive motor of each conveyor, at the tensioning and redirecting roller, at the transfer docking position, and above the belt surface of the unloaded section. They are used to collect the conveyor operating parameters, dynamic mechanical parameters of the conveyor belt, and belt surface wear parameters in real time, and to perform time-series calibration, storage, performance evolution analysis, and automatic generation of detection reports on the collected multi-source data.

2. The testing apparatus for long-term dynamic testing of conveyor belt performance according to claim 1, characterized in that, The first conveyor (1), the second conveyor (2), and the third conveyor (3) form a gradient three-level test partition: The first conveyor (1) is a first-level long-distance conveying section with a conveyor support inclination angle of 10° to 35°. It is compatible with a test conveyor belt with a bandwidth of 800mm to 1200mm. The total length of the machine body is 10m to 25m. The diameter of the active roller is 400mm to 1000mm, the diameter of the redirecting roller is 300mm to 800mm, and it is equipped with a variable frequency drive motor with a power of 5kW to 22kW. It undertakes the main material lifting and long-distance continuous wear simulation functions in the circulation loop. The second conveyor (2) is a secondary transition transfer zone. The conveyor support inclination angle is 15° to 40°. It is compatible with the test conveyor belt with a bandwidth of 800mm to 1200mm. The total length of the machine body is 6m to 16m. The diameter of the drive roller is 300mm to 800mm and the diameter of the redirecting roller is 200mm to 600mm. It is equipped with a variable frequency drive motor with a power of 3kW to 15kW to reproduce the effect of the sudden change in inclination angle between the two transfer stages and the impact of material falling on the conveyor belt. The third conveyor (3) is a three-level extreme working condition test zone. The conveyor support inclination angle is 15° to 40°. It is compatible with the test conveyor belt with a bandwidth of 650mm to 1200mm. The total length of the machine body is 4m to 12m. The diameter of the drive roller is 200mm to 600mm. The diameter of the redirecting roller is 150mm to 500mm. It is equipped with a variable frequency drive motor with a power of 3kW to 15kW. It is used to verify the anti-slip performance, wear resistance and material passage performance of the large-angle patterned conveyor belt. The primary long-distance conveying zone, the secondary transitional transfer zone, and the tertiary extreme condition test zone together form the inclination gradient and bandwidth gradient, which fully reproduces the cumulative impact of the working condition changes of the multi-stage transfer system on the performance of the conveyor belt, and supports parallel comparative tests of conveyor belts of different specifications and materials.

3. The testing apparatus for long-term dynamic testing of conveyor belt performance changes according to claim 1, characterized in that, The control methods and functions of the power drive system include: Each of the three conveyors is equipped with an independent vector frequency converter for its drive motor, enabling stepless speed regulation control of the three conveyors within the range of 0.3m / s to 8m / s, with a speed regulation accuracy of not less than ±0.05m / s. The system has a built-in speed difference impact simulation mode, which can set the speed difference between the first conveyor (1) and the second conveyor (2) and between the second conveyor (2) and the third conveyor (3). The system simulates material accumulation at the transfer point by the downstream conveyor speed being lower than the upstream conveyor speed, and simulates material stretching impact at the transfer point by the downstream conveyor speed being higher than the upstream conveyor speed. The system supports both stand-alone independent start-stop and debugging mode and multi-machine linkage start-stop control mode. In linkage mode, the conveyors start sequentially according to the preset start-up order and stop sequentially according to the preset stop-down order.

4. The testing apparatus for long-term dynamic testing of conveyor belt performance according to claim 1, characterized in that, The data acquisition unit includes a speed sensor, a torque sensor, a tension detection module, a laser wear detection probe, an ambient temperature and humidity monitoring module, and an impact acceleration sensor. The speed sensor and torque sensor are coaxially mounted at the coupling between the output shaft of the drive motor and the drive drum of each conveyor, and are used to collect real-time speed, output torque and power parameters of the drive end; The tension detection module is located at the bearing seat of the tail tensioning and redirecting roller of each conveyor. It adopts a bearing seat type tension sensor to collect real-time tension force, tension fluctuation amplitude and impact tension peak data of the belt. The laser wear detection probe is installed above the belt surface of the unloaded section of each conveyor. It adopts the non-contact laser displacement detection principle and the sampling frequency is not less than 50Hz. It is used to collect data on the thickness change of the belt cover layer and the surface wear morphology. The environmental temperature and humidity monitoring module is arranged around each transfer point and on the air inlet side of the test bench to collect environmental temperature and humidity data. The impact acceleration sensors are arranged at the receiving roller frame at each transfer point to collect the surface vibration acceleration data generated by the impact of falling material. The various acquisition units together form a full-path data acquisition network covering the drive end, the bearing section, the transfer impact point, and the unloaded section, fully matching the full working path of multi-level cyclic conveying.

5. The testing apparatus for long-term dynamic testing of conveyor belt performance according to claim 4, characterized in that, The data detection and control system has a built-in multi-source synchronous calibration module, which is used to perform the following steps: Using the encoder pulse signal of the drive motor of the first conveyor (1) as the global time reference, the transmission delay of the material arriving at each level of the conveyor is calculated according to the material conveying distance and belt speed of the multi-level transfer. All sensor data of the second conveyor (2) and the third conveyor (3) are aligned to the same time axis through linear interpolation, and the timing synchronization accuracy is not less than 15ms. Dynamic temperature drift compensation is performed on the raw sampling data of the tension sensor and torque sensor based on real-time ambient temperature. The compensation calculation formula is as follows: In the formula, This represents the sensor measurement value after correction at time t. The value represents the original sampled value of the sensor at time t, and k represents the temperature drift coefficient pre-calibrated by the sensor at the factory. The unit of this coefficient is related to the unit of measurement value and temperature of the sensor being calibrated. For example, k for a tension sensor is in kN / ℃, and k for a torque sensor is in N·m / ℃. This represents the ambient temperature at time t. This indicates the reference temperature calibrated at the factory for the sensor; A sliding median filtering algorithm is used to smooth high-frequency jump points in laser wear detection data and tension detection data, and output a standardized time series dataset.

6. The testing apparatus for long-term dynamic testing of conveyor belt performance according to claim 1, characterized in that, The data detection and control system has a built-in segmented dynamic wear calculation module, which is used to perform the following steps: The three conveyors were divided into three wear test intervals based on their conveying sections. For each test interval, a fixed operating mileage interval was used as a statistical unit. The average thickness attenuation of the conveyor belt cover layer within that statistical unit was calculated to obtain the wear rate per unit mileage under the current operating conditions of that interval. In the formula, This represents the wear rate per unit mileage in the j-th statistical unit of the i-th test interval, in mm / km; , These are the average thicknesses of the strip surface at the end of the (j-1)th and jth statistical units, respectively; This represents the mileage of a single statistical unit. The average material load, belt speed, and tilt angle parameters within each statistical unit were collected. Based on wear rate samples under multiple combinations of different working conditions, a multiple linear regression model was used to fit the wear rate prediction model. ， In the formula, Let be the material load per unit length in the i-th interval. Let be the conveyor belt speed in the i-th interval. , , These are the dynamically updated fitting coefficients; The total operating mileage of the entire circuit is accumulated, and the total accumulated wear amount and the wear rate ratio of each section are dynamically updated by combining the wear rate prediction model of each section, and the wear and operating condition correlation curve is output.

7. The testing apparatus for long-term dynamic testing of conveyor belt performance according to claim 5, characterized in that, The data detection and control system has a built-in fatigue life prediction module for impact coupling, which is used to perform the following steps: The stress cycle statistics of the calibrated tension time series data were statistically analyzed by rain flow counting method, and the peak values ​​of impact stress collected by the impact acceleration sensor at each transfer point were superimposed to generate a composite fatigue load spectrum that includes steady-state cyclic stress and transfer impact stress. Based on Miner's linear cumulative damage theory, the cumulative fatigue damage at the conveyor belt body and joint is calculated: ， In the formula, D represents the cumulative fatigue damage degree, and n represents the total number of stress amplitude levels. This represents the actual number of cycles for the i-th stress amplitude. This indicates the limit number of cycles for the conveyor belt material under this stress amplitude; By combining the cumulative running time and the current degree of damage, the remaining fatigue life of the conveyor belt and the remaining dynamic life of the joint under the current working conditions are estimated, and the fatigue damage evolution rate of three conveyor belts of different specifications or materials is compared.

8. The testing apparatus for long-term dynamic testing of conveyor belt performance according to claim 1, characterized in that, The data detection and control system has a built-in tiered interlocking shutdown protection module, used to perform the following steps: Preset multi-level operation warning thresholds, including at least prompt level, warning level, alarm level and serious fault level, corresponding to multiple monitoring objects such as slippage deviation, tension over-limit, wear over-limit and motor temperature over-limit; Real-time monitoring of the status of each parameter; when a parameter reaches the corresponding threshold, an alert of the corresponding level is triggered and recorded. When the monitored parameters reach the severe fault level threshold, the automatic linkage power drive system and feeding mechanism will execute interlocked shutdown according to the preset timing sequence, first stopping the feeding, and then stopping each conveyor in the order opposite to the conveying direction.

9. The testing apparatus for long-term dynamic testing of conveyor belt performance according to claim 1, characterized in that, The data detection and control system is configured with differentiated sampling strategies for different types of conveyor belts: For fabric core conveyor belts, the tension sampling frequency is set to 30Hz to 80Hz, and the wear sampling frequency is set to 60Hz to 150Hz. The samples are tested after the test cycle or periodically. The test indicators cover full thickness tensile strength, interlayer bond strength, elongation at break, bending fatigue performance, transverse strength, and joint dynamic life. For steel wire rope conveyor belts, the tension sampling frequency is set to 60Hz to 150Hz, and the joint displacement sampling frequency is set to 120Hz to 300Hz. The samples are tested after the test cycle or periodically. The test indicators cover the steel wire rubber bonding strength, overall tensile strength, joint dynamic life and transverse tear resistance. It can simultaneously detect two types of conveyor belts in the same test cycle and automatically match the corresponding sampling strategy and analysis algorithm.

10. A long-term test method for dynamic changes in conveyor belt performance, characterized in that, Using the testing apparatus as described in any one of claims 1 to 9, the method comprises the following steps: Acquire parameters of the conveyor belt to be tested, parameters of the test material, preset working condition parameters and inherent parameters of the equipment, and generate a set of basic test parameters; Multiple sets of working conditions are set according to the test objectives to generate candidate working condition sequences. The feed flow rate of the first and second hoppers, the running speed of the conveyor, the tension of the conveyor belt and the ambient temperature are adjusted in turn to construct different test conditions. Start the feeding mechanisms of the three conveyors and the first and second hoppers to enter the cyclic test state. The operating parameters of each conveyor and the performance parameters of the conveyor belt are collected synchronously through the full path data acquisition unit to generate the original time series dataset. Multi-source synchronization calibration and filtering denoising were performed on the original time-series dataset to obtain a standardized test dataset. Based on standardized test datasets, the dynamic wear rate, cumulative fatigue damage, and remaining life prediction of the conveyor belt under various working conditions are calculated to generate dynamic performance evolution data. By comparing the performance data of different working conditions and different samples, the optimal conveyor belt scheme and the optimal operating parameters are determined. After the test is completed, a test report is automatically generated, the test data of the entire process is stored, data query requests are responded to, and test results are output.

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