Safety state evaluation system for in-service conveyor belt
By real-time monitoring and evaluating the eight influencing factors of in-service conveyor belts, using multi-type detection technology and big data and artificial intelligence models, the problem that traditional sampling methods are difficult to evaluate the safety status of in-service conveyor belts is solved, achieving more accurate safety prediction and management, and reducing accidents and maintenance costs.
Patent Information
- Application Number
- CN202411995326.2
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2024-12-31
- Publication Date
- 2025-03-21
AI Technical Summary
It is difficult for the prior art to effectively evaluate the safety status of in-service conveyor belts. The traditional sampling method has problems such as belt damage, construction difficulty, inaccurate data and inability to fully cover the influencing factors.
Real-time monitoring of eight factors affecting the safety of the conveyor belt is adopted, including internal damage, external damage, external predictable damage, random damage along the line, aging degree, wear degree, temperature distribution and vibration distribution, comprehensive evaluation and early warning are carried out through multi-type detection technology and safety status evaluation model of big data and artificial intelligence.
It realizes an accurate assessment of the safety status of in-service conveyor belts, discovers and solves potential problems in real time, improves the level of safety management, reduces the accident rate, and extends the service life of the conveyor belt, and reduces the cost of repair and replacement.
Smart Images
Figure CN119660289A_ABST
Abstract
Description
Technical Field
[0001] The invention relates to a safety status assessment system for an in-service conveyor belt, belonging to the technical field of safety status assessment for in-service conveyor belts. Background Art
[0002] As the core component of the conveyor, the conveyor belt plays a key role in the entire conveying system. Therefore, real-time monitoring of the safety status of the conveyor belt is the key to ensuring the smooth progress of the entire transportation process. Since there has been no effective method for evaluating the safety status of the conveyor belts that have been put into use (hereinafter referred to as: in-service conveyor belts), it has caused great trouble in actual production. At present, the conveyor belt mainly uses the sampling method to evaluate the safety status of the conveyor belt, which is not applicable to the safety status evaluation of in-service conveyor belts.
[0003] Since the conveyor belts in service have been installed at the actual use site, if the conveyor belt safety status is evaluated through traditional sampling, there are the following problems and difficulties: 1. Sampling on an in-service conveyor belt will cause belt damage, which will lead to the insecurity of the original conveyor belt.
[0004] 2. Sampling on an in-service conveyor belt is difficult to carry out.
[0005] 3. Due to the non-uniform environment in which the conveyor belts in service are located during use, the belt damage distribution is irregular, and the safety status of the sampled part is quite different from that of other parts of the conveyor belt, resulting in the sampling data being unable to accurately reflect the safety status of the entire conveyor belt.
[0006] 4. Traditional sampling methods, including: "Rectangular specimen tensile method", "Rectangular specimen bending test method", "Conical specimen tensile method", "Rubber aging test surface cracking method", etc., cannot fully cover the factors that affect the safety status of conveyor belts in service. Summary of the invention
[0007] The present invention overcomes the shortcomings of the prior art and provides a safety status assessment system for an in-service conveyor belt, which can accurately assess the safety status of the in-service conveyor belt and can discover and solve potential problems in real time.
[0008] In order to solve the above technical problems, the technical solution adopted by the present invention is: an in-service conveyor belt safety status assessment system, which uses real-time monitoring of eight factors affecting the safety of conveyor belts, namely, internal damage of conveyor belts, external damage of conveyor belts, external predictable damage, random damage along the line, aging degree of conveyor belts, wear degree of conveyor belts, temperature distribution and vibration distribution during the operation of conveyor belts, and comprehensively evaluates the current conveyor belts based on the monitoring data, and gives corresponding early warnings and solutions, among which: Internal damage monitoring of conveyor belts: non-destructive monitoring equipment is used to monitor the internal damage of conveyor belts, and the monitoring data is automatically analyzed and processed, including information on joint twitching, deformation, and non-joint fractures. The detection accuracy of joint twitching should not be less than 3mm, and the joint deformation detection should be automatically checked against standard requirements. The fracture detection accuracy should be no less than 3mm. All detection data should be transmitted to the evaluation platform through the API interface; External damage monitoring of conveyor belts: Use visual monitoring equipment to monitor external damage of conveyor belts, and automatically analyze and process monitoring data, including tear depth, width, and length information. The minimum tear detection accuracy is no more than 3mm, the minimum tear width is no more than 2mm, and the minimum tear length is no more than 50mm. It can output a three-dimensional point cloud image of the conveyor belt in real time, identify and judge the damage results in real time, and issue an alarm or shut down the machine based on the results. All detection data should be transmitted to the evaluation platform through the API interface; External predictable damage monitoring: Use fixed visual monitoring equipment to monitor predictable damage at special points of the conveyor belt, and automatically analyze and process the monitoring data, including information on jamming, scratches, local high temperature, and deviation. The false alarm rate is less than 5%, and video images can be output in real time. The results can be identified and judged in real time, and alarms or shutdowns can be issued based on the results. All detection data should be transmitted to the evaluation platform through the API interface; Random damage monitoring along the line: The use of inspection equipment along the line can realize accurate detection of damaging objects or events along the conveyor belt, including: abnormal noise, edge tearing, deviation detection, and timely reporting of abnormal events, with the reporting time being less than 1 second; among them, all detection data should be transmitted to the evaluation platform through the API interface; Monitoring of conveyor belt aging degree: Use visual technology detection equipment to achieve real-time detection of conveyor belt aging. The equipment's recognition accuracy of conveyor belt cracks is no more than 0.1mm, and the visible area is no more than 2cm. By counting the number of cracks on the belt surface and based on the reasoning of the aging model, the current belt aging degree is calculated. According to the set aging degree, an alarm can be issued in real time. Among them, all detection data should be transmitted to the evaluation platform through API; Conveyor belt wear monitoring: 3D point cloud technology detection equipment can be used to realize real-time detection of the thickness of the entire conveyor belt surface. The measurement point interval should be greater than 2mm. Through model reasoning, the change in belt thickness is fed back and processed. If it is greater than the set wear amount, a real-time alarm is issued and the wear position is reported. Among them, all detection data should be transmitted to the evaluation platform through API; Temperature distribution monitoring during conveyor belt operation: Distributed temperature measurement equipment is used to achieve real-time and accurate monitoring of the temperature of the conveyor belt during operation, with positioning accuracy up to centimeters. The system automatically analyzes and processes monitoring data, and can identify and warn of abnormal temperature rise and local overheating. The temperature detection accuracy is not less than 1°C, and it can accurately detect the temperature change and distribution status of the conveyor belt to help prevent failures or damage. Among them, all detection data is transmitted to the evaluation platform in real time through the API interface; the platform combines historical data and algorithms for further analysis to provide support for system decision-making and improve operational safety and reliability; Vibration distribution monitoring during conveyor belt operation: Distributed vibration measuring equipment is used to achieve real-time monitoring of the vibration of the conveyor belt during operation. The system can restore the sound signals generated by vibration at each position, help identify abnormal vibration and abnormal local vibration amplitude, and use AI algorithms to perform data analysis and abnormality identification, automatically mark potential problem locations, and ensure that subtle vibration changes generated during the operation of the conveyor belt rollers can be detected and warned in a timely manner; among them, all monitoring data are transmitted to the evaluation platform through the API interface for subsequent risk assessment and decision support, and further optimize the operation management of the conveyor belt; Comprehensive evaluation: Based on the above factors affecting conveyor belt safety and the corresponding test data, using data models and standard calculation formulas, the strength values, wear grade coefficients, and thickness hazard coefficients of each joint and non-joint part of the conveyor belt are calculated respectively, and the minimum safety value of the conveyor belt is evaluated as a whole, and early warnings and solutions are given.
[0009] Compared with the prior art, the present invention has the following beneficial effects: the present invention introduces advanced multi-type detection technologies, including X-ray detection, visualization detection, radar detection, and optical fiber detection, and can dynamically and real-time detect the safety status of the entire conveyor belt, the entire belt surface, the interior, and the entire life cycle. Based on the safety status assessment model of big data and artificial intelligence, more accurate safety predictions can be achieved. The present invention completes the safety status assessment of the conveyor belt during its normal operation, and also promotes the use of environmentally friendly detection equipment and technologies to reduce the impact on the environment, which can improve the safety management level of in-service conveyor belts, reduce the accident rate, and extend the service life of the conveyor belt, reducing the cost of maintenance and replacement. BRIEF DESCRIPTION OF THE DRAWINGS
[0010] The present invention will be further described below in conjunction with the accompanying drawings.
[0011] Figure 1 It is a schematic diagram of the structural framework of the present invention. DETAILED DESCRIPTION
[0012] The present invention will be further described below in conjunction with specific embodiments.
[0013] like Figure 1As shown, the safety status assessment system of an in-service conveyor belt of the present invention can accurately assess the safety status of an in-service conveyor belt, and it can discover and solve potential problems in real time, including: Conveyor belt damage: internal conveyor belt damage, external conveyor belt damage, external predictable damage, and random damage along the conveyor belt.
[0014] Conveyor belt fatigue: conveyor belt aging degree, conveyor belt wear degree, conveyor belt temperature distribution during operation, and vibration distribution.
[0015] The safety status assessment system for in-service conveyor belts of the present invention can effectively solve the problems of economic losses caused by long-term downtime due to accidents and personnel costs caused by fixed-period maintenance. The system can prevent accidents, reduce downtime, and ensure production continuity and stability. The assessment system also helps to formulate reasonable maintenance plans, extend equipment life, reduce repair and replacement costs, improve economic benefits, and enhance corporate competitiveness. In addition, the assessment system complies with regulatory requirements, provides safety management evidence, improves management level, reduces environmental pollution, saves resources, and protects employee health and safety. Therefore, conducting safety assessments is both necessary and meaningful in many aspects.
[0016] The present invention adopts real-time monitoring of eight factors that affect the safety of the conveyor belt (internal damage of the conveyor belt, external damage of the conveyor belt, external predictable damage, random damage along the line, aging degree of the conveyor belt, wear degree of the conveyor belt, temperature distribution during conveyor belt operation, vibration distribution), and conducts a comprehensive evaluation of the current conveyor belt based on the monitoring data, and gives corresponding early warnings and solutions.
[0017] a. Conveyor belt internal damage monitoring: Use non-destructive monitoring equipment to monitor the internal damage of the conveyor belt, and automatically analyze and process the monitoring data, including information such as joint twitching, deformation, and non-joint fracture. The joint twitching detection accuracy should not be less than 3mm, and the joint deformation detection should be automatically checked against the standard requirements. The fracture detection accuracy should not be less than 3mm. All test data should be transmitted to the evaluation platform through the API interface.
[0018] b. External damage monitoring of conveyor belts: Use visual monitoring equipment to monitor external damage of conveyor belts, and automatically analyze and process monitoring data, including tear depth, width, length and other information. The minimum tear detection accuracy is no more than 3mm, the minimum tear width is no more than 2mm, and the minimum tear length is no more than 50mm. It can output the three-dimensional point cloud image of the conveyor belt in real time, identify and judge the damage results in real time, and issue an alarm or shut down according to the results. All detection data should be transmitted to the evaluation platform through the API interface.
[0019] c. External predictable damage monitoring: Use fixed visual monitoring equipment to monitor predictable damage at special points of the conveyor belt, and automatically analyze and process the monitoring data, including abnormal information such as jamming, scratches, local high temperature, deviation, etc. The false alarm rate is less than 5%, and the video image can be output in real time. The results can be identified and judged in real time, and alarms or shutdowns can be processed according to the results. All detection data should be transmitted to the evaluation platform through the API interface.
[0020] d. Random damage monitoring along the line: The use of inspection equipment along the line can accurately detect potentially damaging objects or events along the conveyor belt, including abnormal noise, edge tearing, deviation, etc., and report abnormal events in a timely manner, with a reporting time of less than 1 second. All detection data should be transmitted to the evaluation platform through the API interface.
[0021] e. Monitoring of conveyor belt aging degree: Use visual technology detection equipment to achieve real-time detection of conveyor belt aging. The equipment's recognition accuracy of conveyor belt cracks is no more than 0.1mm, and the visible area is no more than 2cm. By counting the number of cracks on the belt surface and based on the reasoning of the aging model, the current belt aging degree is calculated. According to the set aging degree, an alarm can be issued in real time. All detection data should be transmitted to the evaluation platform through the API.
[0022] f. Monitoring of conveyor belt wear: Using 3D point cloud technology detection equipment, the full belt thickness of the conveyor belt can be detected in real time. The interval between measurement points should be greater than 2mm. Through model reasoning, the change of belt thickness is fed back and processed. If it is greater than the set wear amount, a real-time alarm is issued and the wear position is reported. All detection data should be transmitted to the evaluation platform through API.
[0023] g. Temperature distribution monitoring during conveyor belt operation: Distributed temperature measurement equipment is used to achieve real-time and accurate monitoring of the temperature during conveyor belt operation, with positioning accuracy up to centimeter level. The system automatically analyzes and processes monitoring data, and can identify and warn of abnormal temperature rise, local overheating and other phenomena. The temperature detection accuracy is not less than 1°C, and it can accurately detect the temperature changes and distribution status of the conveyor belt to help prevent failures or damage. All detection data is transmitted to the evaluation platform in real time through the API interface. The platform combines historical data and algorithms for further analysis to provide support for system decision-making and improve operational safety and reliability.
[0024] h. Vibration distribution monitoring during conveyor belt operation: Distributed vibration measuring equipment is used to monitor the vibration of the conveyor belt in real time during operation. The system can restore the sound signals generated by vibration at each location to help identify abnormal vibrations and abnormal local vibration amplitudes. With the help of AI algorithms, data analysis and abnormality identification are carried out to automatically mark potential problem locations to ensure timely detection and early warning of subtle vibration changes generated during the operation of conveyor belt rollers. All monitoring data is transmitted to the evaluation platform through the API interface for subsequent risk assessment and decision support, further optimizing the operation management of the conveyor belt.
[0025] i. Comprehensive evaluation: Based on the above factors affecting the safety of conveyor belts and the corresponding test data, the data model and standard calculation formula are used to calculate the strength value, wear grade coefficient, and thickness hazard coefficient of each joint and non-joint part of the conveyor belt, and the overall minimum safety value of the conveyor belt is evaluated, and early warnings and solutions are given.
[0026] The present invention is aimed at evaluating the strength of the conveyor belt joint part According to GB / T 9770-2013 standard, the strength evaluation of the joint part of the steel cord conveyor belt mainly refers to the bonding strength A between the steel cord and the covering rubber. The bonding strength is calculated by the following formula: Bonding strength of steel wire rope and covering rubber A (N / mm): A=F / L; A is the bonding strength between the wire rope and the covering rubber, measured in Newton per millimeter (N / mm); F is the adhesive force, in Newton (N); L is the joint bonding length in millimeters (mm). The joint bonding length needs to be automatically calculated by the conveyor belt internal damage monitoring equipment.
[0027] To evaluate the change in bond strength during aging, the following formula can be used to calculate the bond strength at the current aging level:
[0028] is the bonding strength at the current level; is the bond strength before aging; is the bond strength after aging; It is the current aging level. The aging level needs to be automatically calculated by the conveyor belt aging degree monitoring equipment.
[0029] The bonding strength before and after aging refers to 6.5 of GB / T 9770-2013, as shown in the following table:
[0030] The bonding strength calculated above , the adhesion force F of the test section can be further calculated: .
[0031] The present invention is aimed at evaluating the strength of the non-joint part of the conveyor belt. According to GB / T 9770-2013 standard, the evaluation of the longitudinal tensile strength R of the conveyor belt can be done using the following formula: Conveyor belt longitudinal tensile strength R (N / mm): ; is the minimum breaking force of the wire rope, in Newton (kN); C is the number of radial steel wire ropes of the belt, which needs to be automatically calculated by the internal damage monitoring equipment of the conveyor belt; b is the nominal width of the belt, in millimeters (mm). The nominal width needs to be automatically calculated by the conveyor belt external damage monitoring equipment.
[0032] The present invention is aimed at conveyor belt thickness evaluation The thickness evaluation of conveyor belts is mainly used to test their durability and overall structural performance. Generally, the thickness change of conveyor belts will affect their load-bearing capacity and fatigue resistance. In practical applications, the thickness can be compared with the thickness change of new belts to evaluate the degree of aging.
[0033] Thickness evaluation formula:
[0034] is the current thickness of the conveyor belt; is the initial thickness of the conveyor belt; ΔT is the change in thickness.
[0035] The present invention is aimed at evaluating the fatigue degree of conveyor belt The conveyor belt fatigue monitoring is automatically calculated by the conveyor belt aging degree monitoring equipment. The evaluation method refers to the 22 test result evaluation method in GB11206-2009. The cracking grade is divided into the following table: ; It is the current grade corresponding to crack width, crack density, hardness and thickness, with a total of 13 grades.
[0036] The present invention is aimed at the overall safety evaluation of the conveyor belt The overall safety evaluation aims to calculate the belt safety index by evaluating the current status and alarm conditions of the conveyor belt. This index reflects the overall safety status of the conveyor belt and helps determine whether it needs to be replaced or repaired.
[0037] ; is the current belt strength; The value is the number of alarms.
[0038] The embodiments of the present invention are described in detail above in conjunction with the accompanying drawings, but the present invention is not limited to the above embodiments, and various changes can be made within the knowledge scope of ordinary technicians in this field without departing from the purpose of the present invention.
Claims
1. The safety status assessment system for in-service conveyor belts is characterized by: Real-time monitoring of eight factors that affect conveyor belt safety, namely, internal damage to the conveyor belt, external damage to the conveyor belt, external predictable damage, random damage along the line, aging degree of the conveyor belt, wear degree of the conveyor belt, temperature distribution and vibration distribution during conveyor belt operation, and comprehensive evaluation of the current conveyor belt based on the monitoring data, and corresponding early warnings and solutions are given, including: Internal damage monitoring of conveyor belts: non-destructive monitoring equipment is used to monitor the internal damage of conveyor belts, and the monitoring data is automatically analyzed and processed, including information on joint twitching, deformation, and non-joint fractures. The detection accuracy of joint twitching should not be less than 3mm, and the joint deformation detection should be automatically checked against standard requirements. The fracture detection accuracy should be no less than 3mm. All detection data should be transmitted to the evaluation platform through the API interface; External damage monitoring of conveyor belts: Use visual monitoring equipment to monitor external damage of conveyor belts, and automatically analyze and process monitoring data, including tear depth, width, and length information. The minimum tear detection accuracy is no more than 3mm, the minimum tear width is no more than 2mm, and the minimum tear length is no more than 50mm. It can output a three-dimensional point cloud image of the conveyor belt in real time, identify and judge the damage results in real time, and issue an alarm or shut down the machine based on the results. All detection data should be transmitted to the evaluation platform through the API interface; External predictable damage monitoring: Use fixed visual monitoring equipment to monitor predictable damage at special points of the conveyor belt, and automatically analyze and process the monitoring data, including information on jamming, scratches, local high temperature, and deviation. The false alarm rate is less than 5%, and video images can be output in real time. The results can be identified and judged in real time, and alarms or shutdowns can be issued based on the results. All detection data should be transmitted to the evaluation platform through the API interface; Random damage monitoring along the line: The use of inspection equipment along the line can realize accurate detection of damaging objects or events along the conveyor belt, including: abnormal noise, edge tearing, deviation detection, and timely reporting of abnormal events, with the reporting time being less than 1 second; among them, all detection data should be transmitted to the evaluation platform through the API interface; Monitoring of conveyor belt aging degree: Use visual technology detection equipment to achieve real-time detection of conveyor belt aging. The equipment's recognition accuracy of conveyor belt cracks is no more than 0.1mm, and the visible area is no more than 2cm. By counting the number of cracks on the belt surface and based on the reasoning of the aging model, the current belt aging degree is calculated. According to the set aging degree, an alarm can be issued in real time. Among them, all detection data should be transmitted to the evaluation platform through API; Conveyor belt wear monitoring: 3D point cloud technology detection equipment can be used to realize real-time detection of the thickness of the entire conveyor belt surface. The measurement point interval should be greater than 2mm. Through model reasoning, the change in belt thickness is fed back and processed. If it is greater than the set wear amount, a real-time alarm is issued and the wear position is reported. Among them, all detection data should be transmitted to the evaluation platform through API; Temperature distribution monitoring during conveyor belt operation: Distributed temperature measurement equipment is used to achieve real-time and accurate monitoring of the temperature of the conveyor belt during operation, with positioning accuracy up to centimeters. The system automatically analyzes and processes monitoring data, and can identify and warn of abnormal temperature rise and local overheating. The temperature detection accuracy is not less than 1°C, and it can accurately detect the temperature change and distribution status of the conveyor belt to help prevent failures or damage. Among them, all detection data is transmitted to the evaluation platform in real time through the API interface; the platform combines historical data and algorithms for further analysis to provide support for system decision-making and improve operational safety and reliability; Vibration distribution monitoring during conveyor belt operation: Distributed vibration measuring equipment is used to achieve real-time monitoring of the vibration of the conveyor belt during operation. The system can restore the sound signals generated by vibration at each position, help identify abnormal vibration and abnormal local vibration amplitude, and use AI algorithms to perform data analysis and abnormality identification, automatically mark potential problem locations, and ensure that subtle vibration changes generated during the operation of the conveyor belt rollers can be detected and warned in a timely manner; among them, all monitoring data are transmitted to the evaluation platform through the API interface for subsequent risk assessment and decision support, and further optimize the operation management of the conveyor belt; Comprehensive evaluation: Based on the above factors affecting conveyor belt safety and the corresponding test data, using data models and standard calculation formulas, the strength values, wear grade coefficients, and thickness hazard coefficients of each joint and non-joint part of the conveyor belt are calculated respectively, and the minimum safety value of the conveyor belt is evaluated as a whole, and early warnings and solutions are given.