A joint inspection method and system applied to a conveying belt

Abstract

The present application relates to the technical field of conveying belt inspection, and particularly relates to a combined inspection method and system applied to a conveying belt, comprising the following steps: step 1, synchronous acquisition of conveying belt parameters; step 2, dynamic evaluation of internal hidden troubles of a vulcanization joint; step 3, static electricity accumulation determination and static electricity intervention: dynamically setting a static electricity threshold value according to a combination condition of humidity H and coal dust concentration C; and step 4, closed-loop verification and model optimization. By collecting multi-dimensional data of ultrasonic echoes, surface temperature fields, steel wire rope core magnetic fields and environmental parameters inside the vulcanization joint of the conveying belt, and performing fusion analysis based on edge computing, hidden faults such as internal bubbles of the vulcanization joint and steel wire rope corrosion can be accurately identified, the problem that a single detection method cannot capture multiple physical field related hidden troubles is effectively solved, and the accuracy and comprehensiveness of hidden trouble identification are improved.

Description

Technical Field

[0001] This invention relates to the field of conveyor belt inspection technology, and in particular to a combined inspection method and system for conveyor belts. Background Technology

[0002] Underground conveyor belts in coal mines are core equipment in the coal transportation system, and their operational stability directly determines mine production efficiency and operational safety. Traditional conveyor belt inspection methods combine visual scanning with mechanical transmission.

[0003] For example, the patent application number CN202511021900.9 proposes a joint intelligent conveying system based on three-dimensional inspection, which mainly uses structures such as vision sensors, cleaning mechanisms and inspection units to achieve mechanical transmission combined with visual scanning to complete the inspection of the conveyor belt.

[0004] However, when existing integrated intelligent conveying systems are applied in complex and harsh environments such as high humidity, strong dust, load fluctuations, and sudden temperature changes in underground mines, the following drawbacks exist:

[0005] First, the complex and harsh underground environment of coal mines can easily cause hidden problems in conveyor belts. Existing patents use visual sensors (with built-in wide-angle cameras and cameras on the outer surface of the belt), which cannot penetrate the rubber layer of the conveyor belt and cannot identify hidden defects inside. They can only detect visible problems such as surface tears and damage, which does not meet the operation and maintenance needs of early detection and early warning in underground coal mines.

[0006] Secondly, visual inspection is susceptible to the effects of high dust and high humidity underground, resulting in a low accuracy rate of defect identification. In addition, the airflow cleaning method in the solution cannot solve the problem of dust adhering to the sensor lens or lens fogging in humid environments, which further aggravates the detection error.

[0007] Based on this, after research and design, a new method and system for joint inspection of conveyor belts in complex coal mine environments was designed for operation in complex and harsh environments such as high humidity, strong dust, load fluctuations and sudden temperature changes. Summary of the Invention

[0008] To solve one of the above-mentioned technical problems, the present invention adopts the following technical solution: a joint inspection method for conveyor belts, comprising the following steps: Step 1, synchronous acquisition of conveyor belt parameters: in the monitoring section of the middle section of the lower conveyor belt, several parameter monitoring modules are set up at intervals to synchronously acquire: ultrasonic echo signal of vulcanized joint (amplitude, phase), surface temperature field (temperature gradient), magnetic field signal of steel wire rope core (amplitude attenuation, phase shift), static electricity on the surface of the conveyor belt (instantaneous value, rate of change) and environmental parameters (humidity, temperature, coal dust concentration).

[0009] Step 2, Dynamic Assessment of Internal Hazards in Vulcanized Joints: Based on the edge computing unit, the data collected in Step 1 is fused and analyzed to achieve dynamic assessment of air bubbles and wire rope corrosion inside the vulcanized joints. Specifically, air bubbles are identified when the ultrasonic echo phase difference is >5° and the thermal imaging temperature difference is >3℃, with a volume >5mm. 3 Furthermore, points less than 3mm from the wire rope core are considered high-risk for air bubbles; based on the model's corrosion rate... Where a is the influence coefficient of electromagnetic induction amplitude attenuation rate on corrosion rate, b is the correction coefficient of ambient humidity on corrosion rate, and c is the system basic attenuation correction term; where ΔA is the electromagnetic induction amplitude attenuation rate, and R > 15% is the corrosion risk point; risk value = (bubble volume / V) max )×3+(corrosion rate / R max )×2; where, V max R represents the maximum permissible hazardous value for the bubble volume. max The maximum permissible hazard value for corrosion rate is defined as follows: a risk value > 80 indicates a Level 1 hazard, 50-80 indicates a Level 2 hazard, and < 50 indicates a Level 3 hazard.

[0010] Step 3, Static Electricity Accumulation Judgment and Static Electricity Intervention: The static electricity threshold is dynamically set based on the combination of humidity (H) and coal dust concentration (C). Specific operating steps include: when H > 85%RH and C > 10mg / m³, the threshold is 20kV / m; when 60%RH ≤ H ≤ 85%RH and 5mg / m³ ≤ C ≤ 10mg / m³, the threshold is 25kV / m; when H < 60%RH and C < 5mg / m³, the threshold is 30kV / m; when the static electric field strength is ≥ the corresponding threshold for 3 consecutive seconds, or the rate of change is > 8kV / (m•s), it is judged as a risk of static electricity accumulation. The ionization wind is immediately started and the conveyor belt speed is controlled to decrease until the static electric field strength drops to below 80% of the corresponding threshold.

[0011] Step 4: Closed-loop verification and model optimization.

[0012] Based on any of the above technical solutions, a further optimization is made: when using edge computing units to fuse and analyze the data collected in step 1 to achieve dynamic evaluation of internal bubbles and wire rope corrosion in vulcanized joints, a data fusion processing method using ultrasonic echo signals and infrared thermal imaging temperature is adopted. The specific steps are as follows: aligning ultrasonic echo signals and temperature data from the same detection area using timestamps; denoising the ultrasonic signals using wavelet transform algorithms; and establishing an echo phase difference-temperature gradient mapping relationship using a BP neural network. The bubble volume calculation formula is: V = 5 × k × (ΔA / ΔA0). 3 k is the material adaptation calibration coefficient (value 0.8-1.2), ΔA is the target echo amplitude attenuation, and ΔA0 is the standard bubble amplitude attenuation.

[0013] Based on any of the above technical solutions, a further optimization is made: the construction process of the electromagnetic induction amplitude attenuation rate-ambient humidity correlation model in step 2 is as follows: In a laboratory setting, simulating steel wire rope core samples with a humidity range of 40%-95%RH and a corrosion rate of 0%-30%, more than 300 sets of electromagnetic induction data are collected. The least squares method is used to fit the model coefficients, and the model fit goodness R... 2 ≥0.92; the initial amplitude of ΔA is the reference value of the electromagnetic induction amplitude when the conveyor belt is newly installed.

[0014] Based on any of the above technical solutions, the following optimization is made: when dynamically setting the electrostatic threshold in step 3 according to the combination of humidity H and coal dust concentration C, the following steps are also included: ambient temperature correction. When the ambient temperature T > 35℃, the electrostatic safety threshold is lowered by 5% on the original basis; when T < 5℃, the threshold is raised by 5%; the coal dust concentration C distinguishes between particle sizes (0.5-1μm, 1-5μm, 5-10μm), and when the proportion of 0.5-1μm particle size is > 60%, the threshold is further lowered by 3kV / m.

[0015] Based on any of the above technical solutions, the following optimization is made: the ion wind parameters of the electrostatic eliminator in step 3 are: ion balance ≤ ±5V, jet flow rate 0.5-1m³ / h. 3 / min, the spray angle can be adaptively adjusted within the range of 30°-60° by the edge computing unit; the conveyor belt speed reduction is achieved by linkage with the frequency conversion control system of the drive motor, and the speed reduction process continues until the electrostatic field strength stabilizes and reaches the standard, after which the original speed is restored.

[0016] Based on any of the above technical solutions, the following optimizations are made: The specific steps for closed-loop verification and model optimization are as follows:

[0017] On-site verification: For the primary and secondary hazard points marked in step 2, an explosion-proof endoscope was used to penetrate deep into the vulcanized joint to verify the actual volume of the bubbles and the degree of corrosion of the wire rope, and to correct the coefficients of the corrosion rate calculation model in step 2.

[0018] Model optimization: The electrostatic intervention effect data (changes in electrostatic field strength before and after intervention, intervention time) from step 3 are correlated with the corresponding environmental parameters, and the threshold adjustment rules of step 3 are updated.

[0019] Data archiving: Upload all collected data, evaluation results, intervention records and verification data to the cloud database to form a full life cycle archive of each conveyor belt from potential hazards to intervention and verification.

[0020] Based on any of the above technical solutions, the following optimization is made: In step 1, the monitoring section is set along the conveying direction of the conveyor belt and has a length of 5-8m. The monitoring modules of each parameter are arranged at intervals along the length of the conveyor belt, and the ultrasonic detection points in each monitoring module are set every 20mm along the width of the vulcanized joint. The overlap with the projection position of the electromagnetic induction coil is ≥90%, and the vulcanization transition zone within 50mm of the joint edge is avoided.

[0021] Based on any of the above technical solutions, a further optimization is made as follows: the parameter monitoring module includes an inner detection component disposed above the lower conveyor belt and an outer detection component disposed below the lower conveyor belt. The inner detection component is used to collect the inner layer parameters of the conveyor belt, and the outer detection component is used to collect the outer layer parameters of the conveyor belt. Both ends of the inner detection component and the outer detection component extend along the width direction of the conveyor belt and are fixedly installed on the corresponding positioning mechanism.

[0022] Based on any of the above technical solutions, a further optimization is made as follows: the positioning mechanism includes a horizontally fixed connecting plate seat, a fixing screw tube welded to the inner end of the connecting plate seat, an external threaded post screwed into the internal threaded cavity of the fixing screw tube, the upper end of the external threaded post being bolted and fixed to the corresponding end of the internal detection component, and the lower end being bolted and fixed to the corresponding end of the external detection component.

[0023] Based on any of the above technical solutions, a further optimization is made as follows: the internal detection component includes a first plate frame disposed above the lower conveyor belt, a first through groove is provided in the middle of the first plate frame along the width direction of the conveyor belt, a first collector is installed in the first through groove, both ends of the first plate frame are bent into first angle steel parts, the first angle steel parts cooperate with the upper threaded section of the external threaded column through threaded holes thereon, and first anti-loosening nuts are screwed on the outer side walls of the external threaded column on the upper and lower sides of the horizontal section of the first angle steel parts, and the two first anti-loosening nuts cooperate to clamp the first angle steel parts.

[0024] Based on any of the above technical solutions, a further optimization is made as follows: the external detection component includes a second plate frame disposed below the lower conveyor belt, a second through groove is provided in the middle of the second plate frame along the width direction of the conveyor belt, a second collector is installed in the second through groove, both ends of the second plate frame are bent into second angle steel parts, the second angle steel parts cooperate with the lower threaded section of the external threaded column through threaded holes thereon, and second anti-loosening nuts are screwed on the outer side walls of the external threaded column on the upper and lower sides of the horizontal section of the second angle steel parts, and the two second anti-loosening nuts cooperate to clamp the second angle steel parts.

[0025] Based on any of the above technical solutions, a further optimization is made: both the first data collector and the second data collector are connected to the controller configured on the ground via signal connection.

[0026] Based on any of the above technical solutions, a further optimization is made as follows: the first collector includes a plurality of ultrasonic detectors fixedly installed in the first through groove, and an infrared thermal imager is installed in the first through groove between two adjacent ultrasonic detectors.

[0027] The second data acquisition device includes several low-frequency electromagnetic induction coils fixedly installed in the second through slot, and explosion-proof electrostatic field sensors are respectively installed in the second through slot between two adjacent ultrasonic detectors.

[0028] Based on any of the above technical solutions, a further optimization is made as follows: each of the ultrasonic detectors and each of the infrared thermal imagers are installed in the first through groove through a quick-adjustment base at their corresponding positions; each of the low-frequency electromagnetic induction coils and each of the explosion-proof electrostatic field sensors are installed in the second through groove through a quick-adjustment base at their corresponding positions.

[0029] Based on any of the above technical solutions, a further optimization is made as follows: the quick-adjustment base includes a vertical column installed vertically inside the first through groove or the second through groove. A clamping plate is fixed at the bottom of each vertical column. The top of each vertical column is used to fix an ultrasonic detector, an infrared thermal imager, a low-frequency electromagnetic induction coil, or an explosion-proof electrostatic field sensor. A clamping spring is sleeved on the outer wall of the vertical column between the clamping plate and the first or second plate frame. The bottom of the clamping spring is fixed to the top of the clamping plate, and the top of the spring abuts against the bottom of the first or second plate frame.

[0030] Based on any of the above technical solutions, a further optimization is made: the specific steps for synchronously collecting the following parameter data include:

[0031] Internal structural data of vulcanized joint: An ultrasonic detector is set up along the width of the conveyor belt, with a detection point set every 20mm along the width of the vulcanized joint. An ultrasonic signal with a frequency of 2-5MHz is emitted, and the ultrasonic echo signal inside the vulcanized joint is collected. The echo amplitude and phase information are recorded. At the same time, an infrared thermal imager is used to collect the surface temperature field distribution of the vulcanized joint at a frame rate of 10fps to obtain temperature gradient data.

[0032] Steel wire rope core status data: Low-frequency electromagnetic induction coils are laid out along the width of the conveyor belt. When the conveyor belt is running, the coils generate an alternating magnetic field that passes through the steel wire rope core. The amplitude attenuation signal and phase shift signal of the magnetic field after passing through the steel wire rope core are collected.

[0033] Electrostatic data of conveyor belt surface: Explosion-proof electrostatic field sensors are used and deployed at intervals of 0.3m along the width direction of the conveyor belt to collect the electrostatic field strength on the surface of the conveyor belt in real time. The sampling frequency is 10Hz, and the instantaneous value and rate of change of the electrostatic field strength are recorded.

[0034] Environmental parameter data: Externally configured explosion-proof temperature and humidity sensors and coal dust concentration sensors are used to collect real-time relative humidity, ambient temperature and coal dust concentration in the inspection area.

[0035] The present invention also provides a joint inspection system for conveyor belts. The system includes several parameter monitoring modules arranged at intervals along the length of the conveyor belt. Each parameter monitoring module is equipped with a timestamp synchronizer and is used to synchronously collect ultrasonic echo signals inside the vulcanized joint of the conveyor belt, surface temperature field data, wire rope core magnetic field signals, surface electrostatic field data, and temperature, humidity and coal dust concentration parameters of the inspection area, and realize the above-mentioned joint inspection method.

[0036] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0037] 1. This invention simultaneously collects multi-dimensional data such as ultrasonic echoes inside the vulcanized joint of the conveyor belt, surface temperature field, magnetic field of the wire rope core, and environmental parameters, and performs fusion analysis based on edge computing. It can accurately identify hidden faults such as air bubbles inside the vulcanized joint and corrosion of the wire rope, effectively solving the problem that traditional single detection methods are difficult to capture hidden dangers related to multiple physical fields, and greatly improving the accuracy and comprehensiveness of hidden danger identification.

[0038] 2. This invention addresses harsh working conditions such as high humidity and high coal dust by constructing a dynamic judgment model for electrostatic accumulation based on real-time environmental parameters. Combined with ion wind elimination and conveyor belt speed reduction intervention, it can promptly prevent and control the risk of electrostatic accumulation on the conveyor belt, avoid safety accidents caused by electrostatic discharge, and significantly improve the safety and stability of conveyor belt operation.

[0039] 3. This invention designs a closed-loop verification and model optimization process. By correcting the hidden danger assessment threshold and the corrosion calculation model coefficients, and combining the effect of electrostatic intervention to update the dynamic threshold rules, the inspection scheme can adapt to different working conditions, maintain high inspection accuracy in the long term, reduce maintenance deviations caused by insufficient adaptability to working conditions, and reduce later operation and maintenance costs.

[0040] 4. This invention does not require large-scale hardware modifications to existing conveyor belt systems. It can adapt to harsh environments such as underground coal mines by optimizing parameter acquisition logic and analysis methods. The inspection process is clear and the operation is reproducible, which facilitates rapid implementation and promotion in industrial scenarios. At the same time, the generated full life cycle archive of the conveyor belt can guide equipment selection and maintenance planning, thereby improving the overall operation and management efficiency of the conveyor belt. Attached Figure Description

[0041] To more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the accompanying drawings used in the description of the specific embodiments or the prior art will be briefly introduced below. In all the drawings, similar elements or components are generally identified by similar reference numerals. In the drawings, the elements or components are not necessarily drawn to scale.

[0042] Figure 1 This is a flowchart illustrating the joint inspection method of the present invention.

[0043] Figure 2 This is a schematic diagram showing the relative positional relationship between the parameter monitoring module and the conveyor belt in the installed state of this invention.

[0044] Figure 3 This is a partial three-dimensional structural diagram of the parameter monitoring module of the present invention in its first state.

[0045] Figure 4 This is a partial three-dimensional structural diagram of the second state of the parameter monitoring module of the present invention.

[0046] In the diagram, 1. Connecting plate base; 2. Fixing screw tube; 3. External threaded column; 4. First plate frame; 5. First through groove; 6. First angle steel part; 7. First anti-loosening nut; 8. Second plate frame; 9. Second through groove; 10. Second angle steel part; 11. Second anti-loosening nut; 12. Ultrasonic detector; 13. Infrared thermal imager; 14. Low frequency electromagnetic induction coil; 15. Explosion-proof electrostatic field sensor; 16. Quick-adjustment base; 17. Vertical column; 18. Clamping plate; 19. Clamping spring; 20. Conveyor belt. Detailed Implementation

[0047] The embodiments of the technical solution of the present invention will now be described in detail with reference to the accompanying drawings. The following embodiments are only used to more clearly illustrate the technical solution of the present invention, and are therefore merely examples and should not be used to limit the scope of protection of the present invention. The specific structure of the present invention is as follows: Figures 1-4 As shown in the image.

[0048] Example 1: To solve one of the above-mentioned technical problems, the technical solution adopted by the present invention is: a joint inspection method for conveyor belts, including the following steps: Step 1, synchronous acquisition of conveyor belt parameters: Several sets of parameter monitoring modules, each equipped with a timestamp synchronizer, are set up at intervals in the monitoring section extending along the length of the lower conveyor belt in the middle section, and the following parameter data are acquired synchronously: ultrasonic echo signals inside the vulcanized joint are acquired and the echo amplitude and phase information are recorded; the temperature field distribution on the surface of the vulcanized joint is acquired and the temperature gradient data is obtained; the amplitude attenuation signal and phase shift signal after the magnetic field passes through the steel wire rope core are acquired; the electrostatic field strength on the surface of the conveyor belt is acquired in real time and the instantaneous value and rate of change of the electrostatic field strength are recorded; the real-time relative humidity, ambient temperature and coal dust concentration of the inspection area are acquired.

[0049] It should be explained that the air bubbles inside the vulcanized joint in this step simultaneously alter the ultrasonic propagation path (causing abrupt changes in echo phase) and heat conduction efficiency (forming a local temperature gradient). Corrosion of the wire rope affects electromagnetic induction characteristics (attenuation of magnetic field amplitude), while static electricity accumulation is directly related to ambient humidity and coal dust concentration (humidity affects charge dissipation rate, and coal dust particle size determines charging capacity). These parameters are collected synchronously because a single parameter cannot fully reflect latent faults (e.g., relying solely on ultrasound can easily lead to misjudgments of air bubbles and impurities). Using timestamp synchronization ensures strict data correspondence, complying with the spatiotemporal matching requirements for multi-dimensional testing in the "Test Method for Steel Wire Rope Core Conveyor Belt Joints".

[0050] This optimization scheme mainly involves deploying several interval-based parameter monitoring modules in layers to cover internal structure, core status, surface static electricity, and environmental parameters, thereby achieving full feature capture of hidden hazards in the conveyor belt; and utilizing the differences in the sensitivity of different physical fields to faults (ultrasound is sensitive to bubbles, electromagnetic fields are sensitive to corrosion, and static electricity is sensitive to the environment) to construct a multi-dimensional data matrix.

[0051] It breaks through the limitations of traditional single detection (such as the missed detection rate of >40% for bubbles detected by infrared alone), improves the coverage of latent fault features, and provides a data benchmark for subsequent fusion analysis, avoiding model distortion caused by missing parameters.

[0052] It should also be explained that the specific spacing standard for the parameter monitoring modules set in several intervals is as follows: combining the conveyor belt running speed (1-2m / s) and the parameter sampling frequency (e.g., 10Hz for electrostatic sensors, 1310fps for infrared thermal imagers), the modules are arranged at intervals of 1-1.5m along the length of the conveyor belt, and the ultrasonic detection points and electromagnetic coil positions of each group of modules are aligned along the width of the conveyor belt (deviation <5mm) to ensure that multiple parameters on the same cross-section can be correlated and analyzed. Its working principle is: by covering the length direction with intervals and aligning the positions in the width direction, three-dimensional data acquisition of the monitoring section is achieved, avoiding missed detections due to excessively large intervals (e.g., when the interval is >2m, the conveyor belt moves 1-2m within 1s, easily missing instantaneous fault signals) or data redundancy due to excessively small intervals (increasing the computational load).

[0053] Step 2, Dynamic Assessment of Internal Hazards in Vulcanized Joints: Based on edge computing units, the data collected in Step 1 is fused and analyzed to achieve dynamic assessment of internal air bubbles and wire rope corrosion in vulcanized joints. The specific steps are as follows:

[0054] 2.1 Internal bubble identification: The ultrasonic echo signal and infrared thermal imaging temperature data are fused. When the phase difference of the ultrasonic echo is >5° and the thermal imaging temperature difference of the corresponding area is >3℃, it is determined that there is an internal bubble.

[0055] The bubble volume was further calculated using the ultrasonic echo amplitude attenuation rate. When the bubble volume was greater than 5 mm³ and the distance from the bubble center to the wire rope core was less than 3 mm, it was marked as a bubble risk point.

[0056] 2.2 Calculation of Wire Rope Corrosion Rate: An electromagnetic induction amplitude attenuation rate-ambient humidity correlation model is established. The electromagnetic induction amplitude attenuation rate ΔA (ΔA = (initial amplitude - real-time amplitude) / initial amplitude × 100%) collected in step 1 and the real-time relative humidity H are substituted into the corrosion rate judgment model. The corrosion rate is then determined according to the model. Where a is the influence coefficient of electromagnetic induction amplitude attenuation rate on corrosion rate, b is the correction coefficient of ambient humidity on corrosion rate, and c is the system basic attenuation correction term; where ΔA is the electromagnetic induction amplitude attenuation rate, and R > 15% is the corrosion risk point; risk value = (bubble volume / V) max )×3+(corrosion rate / R max )×2; where, V max R represents the maximum permissible hazardous value for the bubble volume. max The maximum permissible hazard value for corrosion rate is defined as follows: a risk value > 80 indicates a Level 1 hazard, 50-80 indicates a Level 2 hazard, and < 50 indicates a Level 3 hazard.

[0057] When the corrosion rate R > 15%, it is marked as a corrosion risk point for the wire rope.

[0058] ① Symbol a (Influence coefficient of electromagnetic induction amplitude attenuation rate on corrosion rate): Six groups of coal mine steel wire rope core samples (diameter 12mm, material 6×19S+FC) with different corrosion rates (0%, 5%, 10%, 15%, 20%, 25%) were selected. The electromagnetic induction amplitude attenuation rate ΔA was measured under the same humidity (60%RH). Through linear fitting, it was found that: for every 1% increase in corrosion rate, ΔA increases by an average of 0.02%, and the goodness of fit R²=0.94. Therefore, a=0.02 was determined.

[0059] ② Symbol b (correction factor for the effect of ambient humidity on corrosion rate): In the laboratory, a humidity gradient of 40%-95%RH was simulated (each 5%RH is a gradient). A 72-hour corrosion test was conducted on the same uncorroded steel wire rope core sample. It was found that for every 1% increase in humidity, the corrosion rate increased by an average of 0.015% / day. Moreover, this change was positively correlated with the electromagnetic attenuation rate. Therefore, b = 0.015.

[0060] ③ Symbol c (system basic attenuation correction term): Select 10 new conveyor belts (model ST1600) and measure the basic attenuation rate of their rubber layer for 50kHz electromagnetic signals. The average value is 0.52%. Take an approximate value to offset the basic attenuation and determine C=-0.5. The error after actual measurement correction is <3%.

[0061] 2.3 Based on the bubble volume of the bubble risk point and the corrosion rate of the wire rope corrosion risk point, the risk value is calculated by bubble volume × 3 + corrosion rate × 2, and the priority of the hidden dangers is ranked: when the risk value is > 80, it is a first-level hidden danger; when it is 50-80, it is a second-level hidden danger; and when it is < 50, it is a third-level hidden danger.

[0062] It should be explained that this step is based on the physical characteristics of material defects: bubbles, as hollow defects, will cause a sudden change in the phase of ultrasonic echoes. At the same time, due to thermal insulation, a temperature difference will be formed on the surface. The coupling judgment of the two conforms to the mapping relationship between defects and multiphysics response.

[0063] The formula for calculating bubble volume is based on the cubic relationship between defect volume and echo attenuation in acoustic theory, and the k-value (0.8-1.2) is calibrated using standard bubble experiments. Additionally, a humidity variable is introduced into the corrosion model, as high humidity accelerates the electrochemical corrosion of the wire rope, ensuring a model fit R² ≥ 0.92 and guaranteeing its statistical validity.

[0064] In the risk ranking, bubbles have a higher weight because bubbles close to the wire rope core will directly weaken the structural strength, which is consistent with the engineering assessment logic of failure-induced risk.

[0065] This optimization scheme primarily achieves accurate hazard assessment through multi-feature fusion combined with quantitative modeling. It utilizes the complementarity of ultrasound and infrared to identify bubbles (reducing the false positive rate of single-mode ultrasound detection), while simultaneously combining electromagnetic induction with the correlation of environmental parameters to calculate corrosion rate (improving the environmental adaptability of corrosion assessment). The advantages are: improved bubble identification accuracy and reduced corrosion rate calculation error; and the provision of quantitative basis for maintenance resource allocation through risk priority ranking (e.g., prioritizing shutdown for level-one hazards), avoiding over- or under-maintenance.

[0066] It is also necessary to clarify the physical meaning of the model coefficients (0.02, 0.015, -0.5): 0.02 is the coefficient of influence of electromagnetic induction amplitude attenuation rate on corrosion rate. When the corrosion rate of the wire rope increases, the electromagnetic induction amplitude attenuation rate increases (because corrosion reduces the cross-sectional area of ​​the wire rope, increasing the resistance to magnetic field penetration); 0.015 is the correction coefficient for environmental humidity on corrosion rate. Based on conventional corrosion kinetic experiments, for every 1% increase in RH, the corrosion rate of the wire rope increases by an average of 0.015% (high humidity accelerates electrochemical corrosion); -0.5 is a system correction term used to offset the basic attenuation of the magnetic field by the conveyor belt rubber layer (when the new conveyor belt is uncorroded, the rubber layer will cause an amplitude attenuation of about 0.5%, which needs to be eliminated by the correction term). Its working principle is to quantify the correlation between electromagnetic characteristics, corrosion degree, and environmental influence through coefficients, avoiding miscalculation of corrosion rate due to neglecting the basic attenuation of the rubber layer or the influence of humidity.

[0067] Step 3, Dynamic Determination and Intervention of Static Electricity Accumulation in Conveyor Belts under High Humidity Environments:

[0068] Combining the environmental parameters and electrostatic data from step 1, a dynamic judgment model for electrostatic accumulation is constructed and early intervention is implemented:

[0069] 3.1 Dynamic threshold setting: The electrostatic safety threshold is adjusted using real-time relative humidity H and coal dust concentration C as variables.

[0070] When H > 85%RH and C > 10mg / m³, the electrostatic safety threshold is lowered to 20kV / m.

[0071] When 60%RH≤H≤85%RH and 5mg / m³≤C≤10mg / m³, the electrostatic safety threshold is set to 25kV / m;

[0072] When H < 60%RH and C < 5mg / m³, the electrostatic safety threshold is raised to 30kV / m;

[0073] It also needs to be explained that the measurement of coal dust concentration C uses a specific particle size differentiation method: an explosion-proof laser particle size analyzer (measurement range 0.1-100μm) is used to collect the coal dust particle size distribution in real time, dividing C into three concentration ranges: 0.5-1μm, 1-5μm, and 5-10μm. The threshold is lowered by 3kV / m only when the proportion of 0.5-1μm particles is >60% (because coal dust of this particle size has a large specific surface area, and its charge per unit mass is three times that of 5-10μm coal dust, making it more prone to electrostatic discharge). Its working principle is to distinguish the differences in the charging capacity of coal dust of different particle sizes using the particle size analyzer, avoiding the misjudgment of high concentrations of large-particle coal dust as high electrostatic risk (e.g., when the concentration of 5-10μm coal dust is >10mg / m³, the charging capacity is weak, and there is no need to lower the threshold), ensuring the accuracy of the dynamic threshold.

[0074] 3.2 Determination of electrostatic accumulation: When the electrostatic field strength on the conveyor belt surface is greater than or equal to the dynamic threshold for 3 consecutive seconds, or the rate of change of electrostatic field strength is greater than 8 kV / (m•s), it is determined to be a risk of electrostatic accumulation.

[0075] 3.3 Early intervention: Activate the externally preset static elimination device to spray ion wind onto the surface of the conveyor belt, while controlling the conveyor belt running speed to decrease by 20% until the electrostatic field strength drops to below 80% of the dynamic threshold.

[0076] It should be explained that this step is based on the fundamental laws of electrostatics: the air breakdown field strength decreases with increasing humidity. High coal dust concentrations (C > 10 mg / m³) exacerbate static electricity accumulation due to the increased number of charged dust particles. Therefore, the dynamic threshold adapts to environmental changes (compliant with the "Electrostatic Protection Standard"). The determination of a static electricity change rate > 8 kV / (m·s) is more effective than a simple static threshold in detecting potential spark risks in cases of sudden situations such as conveyor belt misalignment and friction (where the transient charging rate is much higher than the steady-state rate).

[0077] Among the intervention measures, ion wind can neutralize surface charge, and deceleration can reduce the rate of triboelectric charging (because the amount of triboelectric charge is positively correlated with speed). The two work together to significantly shorten the static electricity elimination time.

[0078] This optimization scheme primarily achieves dynamic prevention and control of electrostatic risks through environmental adaptation thresholds combined with synergistic intervention. Its working principle is based on the influence of humidity and coal dust on electrostatic dissipation, dynamically adjusting safety thresholds, capturing sudden risks by combining transient change rates, and then eliminating hidden dangers through synergistic ion neutralization and deceleration. Compared to fixed thresholds, this reduces the false alarm rate and improves intervention response speed. Furthermore, deceleration not only reduces the generation of new static electricity but also provides a longer window for ion wind action, thus improving electrostatic elimination efficiency.

[0079] Step 4, closed-loop verification and model optimization, the specific steps are as follows: On-site verification: For the primary and secondary hidden danger points marked in step 2, use an explosion-proof endoscope to go deep into the vulcanized joint to verify the actual volume of the bubbles and the degree of corrosion of the wire rope, and correct the bubble identification phase difference threshold and corrosion rate calculation model coefficients in step 2.

[0080] Model optimization: The electrostatic intervention effect data (changes in electrostatic field strength before and after intervention, intervention time) from step 3 are correlated with the corresponding environmental parameters, and the threshold adjustment rules of step 3 are updated.

[0081] Data archiving: Upload all collected data, evaluation results, intervention records and verification data to the cloud database to form a full life cycle archive of each conveyor belt from potential hazards to intervention and verification.

[0082] It should be explained that this step is based on a data-driven iterative scientific method: Explosion-proof endoscope inspections provide the model with realistic labels, and feedback is used to correct the phase difference threshold for bubble identification (e.g., actual bubbles are often accompanied by a phase difference >6°, so the threshold is optimized from 5° to 6°) and the corrosion model coefficients (the coefficients are fine-tuned due to differences in humidity characteristics in different mines), which conforms to the optimization logic of supervised learning in machine learning. By combining model optimization with intervention effect data (e.g., when the proportion of high coal dust particles is >60%, the threshold needs to be lowered by 3kV / m), the dynamic rules are made more adaptable to specific working conditions. The full lifecycle archive formed by data archiving provides a benchmark case for the analysis of similar conveyor belt failures, which complies with the "Conveyor Belt Full Lifecycle Management Standard".

[0083] This optimization scheme primarily constructs a self-optimization mechanism through on-site inspection feedback, model iteration, and data accumulation. The theoretical model is corrected using field measurement data, and a dynamic rule base adapted to different working conditions is formed through the accumulation of multiple batches of data. The scheme's accuracy continuously improves with application time, reducing long-term maintenance costs. Furthermore, the full lifecycle archive can provide reverse guidance for conveyor belt selection (e.g., in areas prone to air bubbles, conveyor belts with low-bubble-rate vulcanization processes are prioritized), achieving closed-loop optimization of inspection, selection, and maintenance.

[0084] Based on any of the above technical solutions, a further optimization is made: the specific steps for data fusion processing using ultrasonic echo signals and infrared thermal imaging temperature are as follows: ultrasonic echo signals and temperature data from the same detection area are aligned using timestamps; the ultrasonic signals are denoised using a wavelet transform algorithm; and a BP neural network is used to establish an echo phase difference-temperature gradient mapping relationship. The bubble volume calculation formula is: V = 5 × k × (ΔA / ΔA0). 3k is the material adaptation calibration coefficient (value 0.8-1.2), ΔA is the target echo amplitude attenuation, and ΔA0 is the amplitude attenuation of the standard bubble (the standard bubble volume is 5mm³ and the actual detection has an allowable error range of 4mm³ < V < 6mm³).

[0085] It should be explained that the combination of signal processing and machine learning is effective: wavelet transform algorithms can effectively filter out ultrasonic noise generated by downhole mechanical vibrations, conforming to the specifications of the "Guideline for Application of Wavelet Transform in Signal Processing"; BP neural networks learn feature mapping relationships through training samples to solve the nonlinear correlation between ultrasonic phase difference and temperature gradient. Specifically, in the bubble volume formula, the cubic relationship of ΔA / ΔA0 is based on the cubic ratio of the scattering cross-sectional area and volume of a spherical defect in acoustic theory. The k value is determined through routine calibration experiments on conveyor belts of different materials (rubber, polyurethane), ensuring applicability to various types of conveyor belts. Feasibility has been verified through laboratory and field comparisons.

[0086] This optimization scheme primarily enhances the anti-interference capability and computational accuracy of bubble recognition. It purifies the original signal through wavelet denoising, then uses a neural network to uncover the deep correlation between ultrasonic and infrared features, and finally quantifies the bubble volume based on acoustic theory. The advantages include reducing the false positive rate caused by noise interference in the ultrasonic signal and improving the accuracy of volume calculation. Furthermore, the neural network model can automatically adapt to different thicknesses of the vulcanization layer (5-15mm) without requiring manual parameter adjustments, thus reducing the difficulty of on-site operation.

[0087] Based on any of the above technical solutions, a further optimization is made: the construction process of the electromagnetic induction amplitude attenuation rate-ambient humidity correlation model in step 2 is as follows: In a laboratory setting, simulating steel wire rope core samples with a humidity range of 40%-95%RH and a corrosion rate of 0%-30%, more than 300 sets of electromagnetic induction data are collected. The least squares method is used to fit the model coefficients, and the model fit goodness R... 2 ≥0.92; the initial amplitude of ΔA is the reference value of the electromagnetic induction amplitude when the conveyor belt is newly installed.

[0088] It should be explained that this optimization scheme combines statistics with corrosion kinetics: 40%-95%RH covers the typical humidity range downhole, and 0%-30% rust rate covers the entire stage from new rope to severe rust. The statistical large sample requirement is met by using 300 sets of data (the fitting results are stable when the sample size is >200).

[0089] The least squares method was used to solve for the model coefficients (0.02, 0.015, -0.5) by minimizing the sum of squared errors, ensuring the model's optimal fit to the experimental data. Controlling R² ≥ 0.92 indicates that the model can explain over 92% of the corrosion rate variation (far exceeding the industry-standard R² = 0.75). Furthermore, the initial amplitude was taken from the baseline value at the time of new installation to avoid baseline drift caused by conveyor belt aging, complying with the requirements of the "Performance Testing Specification for Steel Wire Rope Conveyor Belts".

[0090] This optimization scheme primarily aims to improve the accuracy and environmental adaptability of corrosion rate calculation. A large-sample experiment establishes a quantitative relationship between electromagnetic attenuation, humidity, and corrosion rate. Statistical methods ensure the model's universality, and the newly installed baseline value eliminates the influence of baseline drift. This reduces corrosion rate calculation errors and improves applicability in high-humidity mines. Furthermore, the model can backward calculate the remaining life of the wire rope core (based on the corrosion rate growth rate), providing a basis for preventative replacement.

[0091] Based on any of the above technical solutions, the following optimization is made: when dynamically setting the electrostatic threshold in step 3 according to the combination of humidity H and coal dust concentration C, the following steps are also included: ambient temperature correction. When the ambient temperature T > 35℃, the electrostatic safety threshold is lowered by 5% on the original basis; when T < 5℃, the threshold is raised by 5%; the coal dust concentration C distinguishes between particle sizes (0.5-1μm, 1-5μm, 5-10μm), and when the proportion of 0.5-1μm particle size is > 60%, the threshold is further lowered by 3kV / m.

[0092] It should be explained that this optimization scheme is based on the influence of temperature and dust particle size on electrostatics: when the temperature is >35℃, the degree of air ionization increases and the breakdown field strength decreases, so the threshold is lowered; when the temperature is <5℃, the dryness of the air increases and the charge retention capacity is enhanced, so the threshold is raised (in accordance with the "Influence of Environmental Factors on Electrostatics"). 0.5-1μm coal dust has a larger specific surface area (more than 10 times that of 5-10μm dust) and a stronger charging capacity (3 times higher charge per unit mass), so when its proportion exceeds 60%, the threshold needs to be further lowered.

[0093] This optimization scheme primarily refines the impact of environmental factors on the electrostatic threshold, improving the accuracy of judgment. Its working principle involves introducing a temperature correction term to compensate for changes in air ionization, differentiating dust particle sizes to adapt to different charging capacities, and making the threshold more closely match actual working conditions. Its advantages include: improved adaptability of the dynamic threshold to complex environments, increased risk identification accuracy in high-temperature, high-humidity, and fine-dust environments; and particle size data that can guide the selection of dust removal equipment, reducing electrostatic risks at the source.

[0094] Based on any of the above technical solutions, the following optimization is made: the ion wind parameters of the electrostatic eliminator in step 3 are: ion balance ≤ ±5V, jet flow rate 0.5-1m³ / h.3 / min, the spray angle can be adaptively adjusted within the range of 30°-60° by the edge computing unit; the conveyor belt speed reduction is achieved by linkage with the frequency conversion control system of the drive motor, and the speed reduction process continues until the electrostatic field strength stabilizes and reaches the standard, after which the original speed is restored.

[0095] It should be explained that this optimization scheme is based on the synergistic principle of ion neutralization and motion control: an ion balance of ≤±5V ensures that no new charge is introduced after neutralization (avoiding overcompensation), and a reasonable injection flow rate of 0.5-1m³ / min can cover the entire width of the conveyor belt (1-2m) without scattering coal dust. The adaptive adjustment of the injection angle is based on electrostatic field distribution data (real-time analysis of field strength hotspots by the edge computing unit), and a range of 30°-60° ensures that the ion wind acts vertically on high field strength areas (improving neutralization efficiency). Speed ​​reduction is achieved in conjunction with the frequency conversion system, controlling the charge generation by changing the friction contact time (a 20% reduction in speed results in approximately a 36% reduction in charge generation), conforming to the square law of velocity for triboelectric charging.

[0096] This optimization scheme primarily improves the efficiency and targeting of static electricity elimination. Its working principle involves precisely controlling ion wind parameters to ensure neutralization effectiveness, adaptively adjusting the angle to focus on high-risk areas, and simultaneously reducing speed to minimize static charge generation at the source. The advantages include shorter static electricity elimination time and lower energy consumption (due to reduced ineffective consumption from directional spraying); additionally, the ion wind also has a mild dust removal effect (removing loose coal dust from the surface), indirectly reducing the subsequent static electricity accumulation rate.

[0097] Based on any of the above technical solutions, the following optimization is made: In step 1, the monitoring section is set along the conveying direction of the conveyor belt and has a length of 5-8m, and an ultrasonic detection point is set every 20mm along the width direction of the vulcanized joint, with a coincidence of ≥90% with the projection position of the electromagnetic induction coil, avoiding the vulcanization transition zone within 50mm of the joint edge.

[0098] It should be explained that this optimization scheme is based on the engineering logic of fault distribution and detection coverage: the 5-8m monitoring section covers the joint and the fatigue-prone areas on both sides (conveyor belt faults are concentrated on both sides of the joint), thus ensuring that no hidden dangers are missed. Ultrasonic testing points are placed every 20mm, corresponding to the wire rope core spacing (15-25mm), achieving precise coverage of individual wire ropes (avoiding blind spots). A projection overlap of ≥90% ensures that ultrasonic and electromagnetic data correspond to the same spatial location (reducing data correlation errors). The 50mm transition zone is avoided because the vulcanization layer thickness gradually changes in the transition zone, easily generating interference signals, which complies with the "Conveyor Belt Joint Detection Area Division Specification".

[0099] This optimization scheme mainly optimizes the layout of the detection area and points to improve the spatial correlation of data; it determines the monitoring length based on the high-fault area, sets detection points according to the core spacing, and ensures the comparability of multi-source data through spatial alignment.

[0100] Based on any of the above technical solutions, a further optimization is made: the specific steps for synchronously collecting the following parameter data include:

[0101] Internal structure data of vulcanized joint: An ultrasonic detector 12 is set up along the width of the conveyor belt, with a detection point set every 20mm along the width of the vulcanized joint. An ultrasonic signal with a frequency of 2-5MHz is emitted, and the ultrasonic echo signal inside the vulcanized joint is collected. The echo amplitude and phase information are recorded. At the same time, an infrared thermal imager 13 is used to collect the surface temperature field distribution of the vulcanized joint at a frame rate of 10fps to obtain temperature gradient data.

[0102] Steel wire rope core status data: Low-frequency electromagnetic induction coils 14 are laid out along the width of the conveyor belt. When the conveyor belt is running, the coils generate an alternating magnetic field that passes through the steel wire rope core. The amplitude attenuation signal and phase shift signal of the magnetic field after passing through the steel wire rope core are collected.

[0103] Electrostatic data of conveyor belt surface: Explosion-proof electrostatic field sensors 15 are deployed at intervals of 0.3m along the width direction of the conveyor belt to collect the electrostatic field strength on the surface of the conveyor belt in real time. The sampling frequency is 10Hz, and the instantaneous value and rate of change of the electrostatic field strength are recorded.

[0104] Environmental parameter data: Externally configured explosion-proof temperature and humidity sensors and coal dust concentration sensors are used to collect real-time relative humidity, ambient temperature and coal dust concentration in the inspection area.

[0105] It should be explained that the 2-5MHz ultrasonic frequency is adapted to the thickness of the vulcanized layer (5-15mm), and the 10fps infrared frame rate ensures the capture of dynamic temperature changes (each frame covers a distance of 0.1-0.2m when the conveyor belt speed is 1-2m / s); the low-frequency electromagnetic coil (50-100kHz) has strong penetration capability into the rubber layer (attenuation rate <10%), making it suitable for detecting internal steel wire ropes; the 0.3m interval and 10Hz sampling rate can capture the differences in electrostatic field distribution in the width direction of the conveyor belt; the explosion-proof sensor complies with coal mine safety standards, and the real-time data meets the requirements for dynamic threshold adjustment. The acquisition frequency of each parameter is matched with the conveyor belt speed to avoid data redundancy or loss.

[0106] This optimization scheme mainly clarifies the acquisition details of each parameter to ensure data quality and correlation; it matches equipment parameters according to the physical characteristics of different parameters (such as ultrasonic frequency and detection depth, electromagnetic frequency and penetration capability) and sets the acquisition interval according to spatial and temporal characteristics.

[0107] Example 2: Compared with Example 1, this example also includes the following technical features:

[0108] Based on any of the above technical solutions, a further optimization is made as follows: the parameter monitoring module includes an inner detection component disposed above the lower conveyor belt and an outer detection component disposed below the lower conveyor belt. The inner detection component is used to collect the inner layer parameters of the conveyor belt, and the outer detection component is used to collect the outer layer parameters of the conveyor belt. Both ends of the inner detection component and the outer detection component extend along the width direction of the conveyor belt and are fixedly installed on the corresponding positioning mechanism.

[0109] It should be explained that the upper and lower surfaces of the conveyor belt have different characteristics (the upper surface, in contact with the coal flow, is prone to dust accumulation, while the lower surface, in contact with the idlers, is prone to wear). The inner detection component (upper) focuses on the internal (ultrasonic) and surface temperature (infrared) of the vulcanized joint, while the outer detection component (lower) focuses on the steel wire rope core (electromagnetic) and surface static electricity, which conforms to the principle of matching the detection target with the spatial position. The positioning mechanism ensures the stability of the relative position between the component and the conveyor belt, avoiding changes in the detection distance caused by vibration.

[0110] This optimization scheme primarily enhances the targeting of inspections through a spatially layered layout. Different inspection tasks are assigned based on the functional differences between the upper and lower surfaces of the conveyor belt, and a positioning mechanism ensures inspection stability. Parallel data acquisition across upper and lower layers improves inspection efficiency while avoiding functional conflicts between individual components. Furthermore, the layered layout facilitates individual maintenance (e.g., dust accumulation on upper-layer components can be cleaned separately without affecting the operation of lower layers), reducing downtime for maintenance.

[0111] It should also be noted that the spacing adjustment is achieved through the external threaded post 3 of the positioning mechanism. Rotating the external threaded post 3 clockwise simultaneously brings the inner and outer components closer to the conveyor belt, while rotating it counterclockwise moves them further apart. Its working principle is to ensure the detection equipment is at the optimal working distance by fixing the spacing, balancing signal strength and equipment safety, and avoiding detection errors caused by improper spacing.

[0112] Based on any of the above technical solutions, the following optimization is made: the positioning mechanism includes a horizontally fixed connecting plate seat 1, a fixing screw tube 2 is welded to the inner end of the connecting plate seat 1, an external threaded post 3 is screwed into the internal threaded cavity of the fixing screw tube 2, the upper end of the external threaded post 3 is bolted and fixed to the corresponding end of the internal detection component, and the lower end is bolted and fixed to the corresponding end of the external detection component.

[0113] It should be explained that the connecting plate base 1 provides a horizontal reference, and the threaded engagement (accuracy M12×1.5) between the fixed screw tube 2 and the external threaded column 3 achieves a height adjustment of ±0.1mm, ensuring that the distance between the inner / outer detection components and the conveyor belt meets the design value (meets the optimal distance for ultrasonic testing).

[0114] This optimization scheme mainly achieves precise positioning and convenient adjustment of the detection components. Its working principle is to achieve fine-tuning of height through threaded transmission. The rigid structure ensures stable position under vibration environment. The threaded adjustment can be adapted to conveyor belts of different thicknesses without replacing the positioning mechanism, thus improving the versatility of the equipment.

[0115] Based on any of the above technical solutions, a further optimization is made as follows: the internal detection component includes a first plate frame 4 disposed above the lower conveyor belt, a first through groove 5 disposed in the middle of the first plate frame 4 along the width direction of the conveyor belt, a first collector installed in the first through groove 5, both ends of the first plate frame 4 are bent into first angle steel parts 6, the first angle steel parts 6 cooperate with the upper threaded section of the external threaded column 3 through threaded holes thereon, and first anti-loosening nuts 7 are screwed onto the outer side walls of the external threaded column 3 on the upper and lower sides of the horizontal section of the first angle steel parts 6, and the two first anti-loosening nuts 7 cooperate to clamp the first angle steel parts 6.

[0116] It should be explained that the first plate frame 4 adopts an angle steel bending structure to ensure rigidity in the downhole vibration environment; the first through groove 5 provides an installation benchmark for the data collector (ensuring straightness error <0.2mm / m); the threaded hole and the external threaded column 3 cooperate to achieve horizontal fine adjustment; and the double anti-loosening nuts prevent loosening by axial clamping (preload force 50-80N).

[0117] This optimization scheme mainly improves the structural stability and installation accuracy of the internal detection components; it reduces deformation through a rigid frame, ensures positional stability under vibration by using double nuts to prevent loosening, and provides a linear installation reference for each data acquisition unit through the through slot.

[0118] Based on any of the above technical solutions, a further optimization is made as follows: the external detection component includes a second plate frame 8 disposed below the lower conveyor belt, a second through groove 9 disposed in the middle of the second plate frame 8 along the width direction of the conveyor belt, a second collector installed in the second through groove 9, both ends of the second plate frame 8 are bent into second angle steel parts 10, the second angle steel parts 10 cooperate with the lower threaded section of the external threaded column 3 through threaded holes thereon, and second anti-loosening nuts 11 are screwed onto the outer side walls of the external threaded column 3 on the upper and lower sides of the horizontal section of the second angle steel parts 10, and the two second anti-loosening nuts 11 cooperate to clamp the second angle steel parts 10.

[0119] It should be explained that the structural design was strengthened to address the characteristics of the lower environment (which is susceptible to the vibration of the idler rollers): the thickness of the second plate frame 8 was increased by 2mm (compared to the first plate frame 4), the bending stiffness was improved, and it was adapted to the periodic impact of the lower idler rollers (the impact frequency was matched with the conveyor belt speed).

[0120] The parallelism error between the second through groove 9 and the first through groove 5 is controlled to be less than 0.3 mm / m to ensure spatial alignment of the upper and lower data collectors. The double anti-loosening nuts also adopt pre-tightening force control to adapt to higher vibration intensity below.

[0121] This optimization scheme is mainly adapted to the complex environment of the lower layer, ensuring the stability and data alignment of the external detection components, resisting the impact of the idler rollers by strengthening the structure, and ensuring spatial matching with the internal components by high-precision installation; in addition, the second plate frame 8 can also serve as an auxiliary detection benchmark for abnormal wear of the idler rollers (indirectly judging the idler roller offset by the change of the position of the data collector).

[0122] Based on any of the above technical solutions, a further optimization is made: both the first data collector and the second data collector are connected to the controller configured on the ground via signal connection.

[0123] It should be explained that the ground controller (using an existing PLC or industrial computer) receives and analyzes the data from the data acquisition unit in real time, and must comply with the requirements of GB / T26333-2010 "Network Security for Industrial Control Systems". Signal connections use fiber optic transmission (immune to electromagnetic interference) or intrinsically safe cables to ensure signal integrity in the complex electromagnetic environment downhole (interference from motors and frequency converters).

[0124] This optimization scheme mainly realizes centralized data processing and remote control; through a stable signal transmission link, the distributed data is aggregated to the controller for unified analysis and decision-making; the controller can also link with the mine production system (such as conveyor belt start / stop and alarm) to realize an automated closed loop of detection-decision-execution.

[0125] Based on any of the above technical solutions, the following further optimization is made: the first collector includes a plurality of ultrasonic detectors 12 fixedly installed in the first through groove 5, and an infrared thermal imager 13 is installed in the first through groove 5 between two adjacent ultrasonic detectors 12.

[0126] The second data acquisition device includes several low-frequency electromagnetic induction coils 14 fixedly installed in the second through slot 9, and explosion-proof electrostatic field sensors 15 are respectively installed in the second through slot 9 between two adjacent ultrasonic detectors 12.

[0127] It should be explained that in the first data acquisition unit, the ultrasonic detector 12 (point detection) and the infrared thermal imager 13 (area detection) are arranged alternately to ensure that the ultrasonic detector can accurately detect key areas, while the infrared detector can cover the entire width (without blind spots). The distance between the two detectors matches the width of the conveyor belt (for example, 5 sets are set for a 1m wide conveyor belt).

[0128] In the second data acquisition unit, the electromagnetic coils and electrostatic sensors are positioned corresponding to the upper-level ultrasonic and infrared sensors (spatial alignment error <5mm), ensuring the correlation of multiple parameters within the same area and meeting the analysis requirements for spatial data consistency. Furthermore, all equipment selected is explosion-proof, suitable for downhole environments.

[0129] This optimization scheme mainly optimizes the layout of the data acquisition devices to achieve functional complementarity and data alignment; by alternating the arrangement of point and area detection devices, the detection accuracy and coverage are balanced, and spatial alignment ensures the comparability of multiple parameters; in addition, the alternating layout reduces electromagnetic interference between devices.

[0130] Based on any of the above technical solutions, the following further optimizations are made: each of the ultrasonic detectors 12 and each of the infrared thermal imagers 13 are installed in the first through groove 5 through the quick-adjustment base 16 at their corresponding positions; each of the low-frequency electromagnetic induction coils 14 and each of the explosion-proof electrostatic field sensors 15 are installed in the second through groove 9 through the quick-adjustment base 16 at their corresponding positions.

[0131] It should be explained that the quick-adjustment base 16 allows for three-dimensional fine adjustment (X / Y / Z axes) of ±3mm, ensuring that the ultrasonic probe is vertically aligned with the conveyor belt and the infrared lens is accurately focused.

[0132] Based on any of the above technical solutions, a further optimization is made as follows: the quick-adjustment base 16 includes a vertical column 17 vertically installed inside the first through groove 5 or the second through groove 9. A clamping plate 18 is fixed at the bottom of each vertical column 17. The top of the vertical column 17 is used to fix and install an ultrasonic detector 12, an infrared thermal imager 13, a low-frequency electromagnetic induction coil 14, or an explosion-proof electrostatic field sensor 15, respectively. A clamping spring 19 is sleeved on the outer wall of the vertical column 17 between the clamping plate 18 and the first plate frame 4 or the second plate frame 8. The bottom of the clamping spring 19 is fixed to the top of the clamping plate 18, and the top of the spring abuts against the bottom of the first plate frame 4 or the bottom of the second plate frame 8.

[0133] It should be explained that the retaining spring 19 provides continuous preload to ensure the relative position of the vertical column 17 and the plate frame is stable (without gaps or wobbling), while allowing an elastic displacement of ±0.5mm (to absorb high-frequency vibrations). The vertical column 17 adopts a precision guide structure (fitting clearance <0.05mm) to ensure straightness during adjustment (deviation <0.01mm / mm), meeting the verticality requirements of the ultrasonic probe. The material used is stainless steel (304), which is resistant to downhole moisture and corrosion.

[0134] This optimization scheme primarily achieves stable installation and vibration damping of the data logger through elastic pre-tensioning. Its working principle involves pre-tensioning the spring 19 to eliminate gaps, ensuring adjustment accuracy through precision guidance, and absorbing vibration energy through elastic displacement. Furthermore, the spring pre-tensioning force can be adjusted via compression (to accommodate data loggers of different weights), improving the versatility of the base.

[0135] It should also be noted that when the conveyor belt vibration causes displacement of the first plate frame 4 / second plate frame 8, the clamping spring 19 offsets part of the displacement by compression or extension, ensuring that the relative distance between the corresponding collector and the conveyor belt changes by less than 0.1mm. Vibration buffering is achieved by utilizing the elastic deformation of the spring, preventing the collector from vibrating violently with the plate frame due to rigid connection.

[0136] The present invention also provides a joint inspection system for conveyor belts. The system includes several parameter monitoring modules arranged at intervals along the length of the conveyor belt. Each parameter monitoring module is equipped with a timestamp synchronizer and is used to synchronously collect ultrasonic echo signals inside the vulcanized joint of the conveyor belt, surface temperature field data, wire rope core magnetic field signals, surface electrostatic field data, and temperature, humidity and coal dust concentration parameters of the inspection area, and realize the above-mentioned joint inspection method.

[0137] The above embodiments are only used to illustrate the technical solutions of the present invention, and are not intended to limit it. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some or all of the technical features therein. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention. For those skilled in the art, any alternative improvements or transformations made to the implementation of the present invention fall within the protection scope of the present invention.

[0138] Any aspects of this invention not described in detail are well-known to those skilled in the art.

Claims

1. A joint inspection method for conveyor belts, characterized in that, Includes the following steps: Step 1, synchronous acquisition of conveyor belt parameters: Several parameter monitoring modules are set up at intervals in the monitoring section of the middle section of the lower conveyor belt to synchronously acquire: ultrasonic echo signal of vulcanized joint, surface temperature field, magnetic field signal of steel wire rope core, static electricity and environmental parameters of conveyor belt surface; Step 2, Dynamic Assessment of Internal Hazards in Vulcanized Joints: Based on the edge computing unit, the data collected in Step 1 is fused and analyzed to achieve dynamic assessment of air bubbles and wire rope corrosion inside the vulcanized joints. Specifically, air bubbles are identified when the ultrasonic echo phase difference is >5° and the thermal imaging temperature difference is >3℃, with a volume >5mm. 3 Furthermore, a distance of less than 3mm from the wire rope core is considered a risk point for air bubbles. Corrosion rate based on model Where a is the influence coefficient of electromagnetic induction amplitude attenuation rate on corrosion rate, b is the correction coefficient of ambient humidity on corrosion rate, and c is the system basic attenuation correction term; where ΔA is the electromagnetic induction amplitude attenuation rate, and R > 15% is the corrosion risk point; risk value = (bubble volume / V) max )×3+(corrosion rate / R max )×2; where, V max R represents the maximum permissible hazardous value for the bubble volume. max This represents the maximum permissible hazardous value for the corrosion rate. Among them, a risk value ≥ 1.8 is identified as a Level 1 hazard point, a risk value ≤ 1.0 and < 1.8 is identified as a Level 2 hazard point, and a risk value < 1.0 is identified as a Level 3 hazard point. Step 3, Static Electricity Accumulation Judgment and Static Electricity Intervention: The static electricity threshold is dynamically set based on the combination of humidity (H) and coal dust concentration (C). Specific operating steps include: when H > 85%RH and C > 10mg / m³, the threshold is 20kV / m; when 60%RH ≤ H ≤ 85%RH and 5mg / m³ ≤ C ≤ 10mg / m³, the threshold is 25kV / m; when H < 60%RH and C < 5mg / m³, the threshold is 30kV / m; when the static electric field strength is ≥ the corresponding threshold for 3 consecutive seconds, or the rate of change is > 8kV / (m•s), it is judged as a risk of static electricity accumulation. The ionization wind is immediately started and the conveyor belt speed is controlled to decrease until the static electric field strength drops to below 80% of the corresponding threshold. Step 4: Closed-loop verification and model optimization.

2. The joint inspection method for conveyor belts according to claim 1, characterized in that, When the data collected in step 1 is fused and analyzed based on the edge computing unit to achieve dynamic evaluation of the internal bubbles of the vulcanized joint and the corrosion of the steel wire rope, the data fusion processing method of ultrasonic echo signal and infrared thermal imaging temperature is adopted. The specific steps are as follows: the ultrasonic echo signal and temperature data of the same detection area are aligned by timestamp, the ultrasonic signal is denoised by wavelet transform algorithm, and the echo phase difference-temperature gradient mapping relationship is established by BP neural network. The formula for calculating the bubble volume is: V = 5 × k × (ΔA / ΔA0) 3 k is the material adaptation calibration coefficient, ΔA is the target echo amplitude attenuation, and ΔA0 is the standard bubble amplitude attenuation.

3. The joint inspection method for conveyor belts according to claim 2, characterized in that, The initial amplitude of ΔA is the reference value of the electromagnetic induction amplitude when the conveyor belt is newly installed.

4. The joint inspection method for conveyor belts according to claim 3, characterized in that, When dynamically setting the electrostatic threshold based on the combination of humidity H and coal dust concentration C in step 3, the following steps are also included: ambient temperature correction: when the ambient temperature T > 35℃, the electrostatic safety threshold is lowered by 5% based on the original value; when T < 5℃, the threshold is raised by 5%; the coal dust concentration C is differentiated by particle size, and when the proportion of 0.5-1μm particles is > 60%, the threshold is further lowered by 3kV / m.

5. The joint inspection method for conveyor belts according to claim 4, characterized in that, The parameters for the ion wind in step 3 are: ion balance ≤ ±5V, jet flow rate 0.5-1m. 3 / min, the spray angle is adaptively adjusted within the range of 30°-60° by the edge calculation unit; The conveyor belt speed reduction is achieved through linkage with the frequency conversion control system of the drive motor. The speed reduction process continues until the electrostatic field strength stabilizes and reaches the standard, after which the original speed is restored.

6. The joint inspection method for conveyor belts according to claim 5, characterized in that: The specific steps for closed-loop validation and model optimization are as follows: On-site verification: For the primary and secondary hazard points marked in step 2, an explosion-proof endoscope was used to penetrate deep into the vulcanized joint to verify the actual volume of the bubbles and the degree of corrosion of the wire rope, and to correct the coefficients of the corrosion rate calculation model in step 2. Model optimization: Correlate the effect data of electrostatic intervention in step 3 with the corresponding environmental parameters; Data archiving: Upload all collected data, evaluation results, intervention records and verification data to the cloud database to form a full life cycle archive of each conveyor belt from potential hazards to intervention and verification.

7. A joint inspection method for conveyor belts according to claim 6, characterized in that, In step 1, the monitoring section is set along the conveying direction of the conveyor belt and has a length of 5-8m. Ultrasonic detection points are set every 20mm along the width of the vulcanized joint, with a coincidence of ≥90% with the projection position of the electromagnetic induction coil, avoiding the vulcanization transition zone within 50mm of the joint edge.

8. A joint inspection method for conveyor belts according to claim 7, characterized in that: The specific steps for synchronously collecting the following parameter data include: Internal structural data of vulcanized joint: An ultrasonic detector is installed along the width of the conveyor belt to collect ultrasonic echo signals inside the vulcanized joint and record the echo amplitude and phase information; at the same time, an infrared thermal imager is used to obtain temperature gradient data. Steel wire rope core status data: Low-frequency electromagnetic induction coils are laid out along the width of the conveyor belt. When the conveyor belt is running, the coils generate an alternating magnetic field that passes through the steel wire rope core. The amplitude attenuation signal and phase shift signal of the magnetic field after passing through the steel wire rope core are collected. Electrostatic data of conveyor belt surface: An explosion-proof electrostatic field sensor is used to collect the electrostatic field strength of the conveyor belt surface in real time and record the instantaneous value and rate of change of the electrostatic field strength; Environmental parameter data: Externally configured explosion-proof temperature and humidity sensors and coal dust concentration sensors are used to collect real-time relative humidity, ambient temperature and coal dust concentration in the inspection area.

9. A joint inspection system for conveyor belts, characterized in that, The system includes several parameter monitoring modules spaced apart along the length of the conveyor belt. Each parameter monitoring module is equipped with a timestamp synchronizer and is used to synchronously collect ultrasonic echo signals inside the vulcanized joint of the conveyor belt, surface temperature field data, wire rope core magnetic field signals, surface electrostatic field data, and temperature, humidity and coal dust concentration parameters of the inspection area. The system is applied in the joint inspection method described in any one of claims 1-8.

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