Intelligent monitoring system and method for in-service coal conveying steel trestle

Abstract

The invention discloses an intelligent monitoring system for an in-service coal conveying steel trestle, and the system comprises a sensing system which is in communication connection with a steel truss working state monitoring module, a steel support working state monitoring module, and a rod piece and node welding seam pull crack damage automatic diagnosis module through a data collection and transmission system. The damage assessment and early warning system is in communication connection with a database module and a cloud monitoring platform. The invention further discloses an intelligent monitoring method for the in-service coal conveying steel trestle. The intelligent monitoring method comprises the following steps that the structural characteristics of the steel trestle structure group are obtained; determining detection content; selecting a monitoring point and installing a sensing system; the structure condition of the steel trestle structure group is collected; analyzing and evaluating the structural damage condition of the steel trestle structure group and generating an evaluation result; and determining a monitoring grading early warning risk grade and taking corresponding measures. According to the intelligent monitoring system and method for the in-service coal conveying steel trestle, the working state of the steel trestle is monitored on line in real time, and structural damage and performance degradation are perceived in time.

Description

Technical Field

[0001] The present invention belongs to the technical field of auxiliary equipment for coal conveying steel trestles, and relates to an intelligent monitoring system for in-service coal conveying steel trestles, and also relates to an intelligent monitoring method for in-service coal conveying steel trestles. Background Art

[0002] As an important passage connecting key positions such as coal mining areas and storage sites, and coal bunkers and boilers in power plants, large-span coal conveying steel trestles play an indispensable role in the energy transportation link. In coal mines, coal conveying steel trestles are an important way to efficiently transport the mined coal from the mine entrance to the coal washing workshop or coal storage yard.

[0003] At present, construction monitoring is mainly adopted for coal conveying steel trestles, and this method can reflect the safety status of steel trestles during the construction period. However, for in-service coal conveying steel trestles that have been put into use, the existing intelligent damage identification and early warning systems are not yet mature. This leads to the inability to accurately and reasonably feedback the safety status of steel trestles during the operation period, and it is also difficult to provide a scientific basis for the maintenance, repair, and reinforcement of steel trestles, making it difficult to fundamentally solve the problems exposed during the operation period of coal conveying steel trestles. The structural systems of coal conveying steel trestles in coal mines and power plants are complex, with extremely high requirements for structural safety control, and the geological conditions and surrounding environments are also intricate. Especially, in-service steel trestles are in a harsh environment of high humidity and high corrosiveness for a long time, which is extremely likely to induce structural diseases. At the same time, the long-term operation of belt loads will cause the main structure of the trestle to bear the cyclic effect of loading and unloading, and the characteristics of structural fatigue damage are obvious, which is extremely unfavorable to the deformation and stability of the trestle. In addition, there are many deficiencies in traditional structural health monitoring systems: manual monitoring has strong subjectivity, large measurement errors, and low accuracy; measurement data needs to be processed manually and cannot respond quickly at the initial stage of the deformation of the trestle structure; the integrity is poor, usually only local inspections can be carried out, and it is difficult to detect the deformation of the trestle in places where equipment is difficult to reach; traditional sensors are significantly affected by environmental factors, and their survival rates are low in the environment of the trestle with much moisture, vibration, and electromagnetic interference; the real-time performance is poor, and the networking of all monitoring data along the line is not considered, and the goal of real-time online health monitoring cannot be achieved.

[0004] In summary, the existing technology has the problem that it cannot accurately identify and warn of the structural damage of in-service coal conveying steel trestles. Summary of the Invention

[0005] The purpose of the present invention is to provide an intelligent monitoring system for in-service coal conveying steel trestles, which solves the problem in the existing technology that the structural damage of in-service coal conveying steel trestles cannot be accurately identified and warned.

[0006] The purpose of the present invention is to provide an intelligent monitoring method for in-service coal conveying steel trestles.

[0007] The technical solution adopted by the present invention is an intelligent monitoring system for in-service coal conveying steel trestles, including a sensing system arranged inside the steel trestle structure group. The sensing system is respectively communicatively connected with a steel truss working state monitoring module, a steel support working state monitoring module, and an automatic diagnosis module for tensile cracks in members and node welds through a data acquisition and transmission system. The data acquisition and transmission system, the steel truss working state monitoring module, the steel support working state monitoring module, and the automatic diagnosis module for tensile cracks in members and node welds are respectively communicatively connected with a database module. The database module is communicatively connected with a damage assessment and early warning system, and the damage assessment and early warning system is communicatively connected with a cloud monitoring platform.

[0008] The features of the present invention also lie in: The sensing system includes fiber Bragg grating surface strain gauges, vibration sensors, fiber Bragg grating pH sensors, fiber Bragg grating displacement gauges, and fiber Bragg grating joint settlement gauges; the sensing system obtains the steel structure strain condition, steel structure vibration condition, steel structure corrosion condition, steel structure surface deformation condition, and steel structure settlement condition of the steel trestle structure group.

[0009] The steel truss working state monitoring module conducts real-time assessment on the working states of all components of the steel truss, saves the assessment results to the database module and transmits them to the damage assessment and early warning system; the steel support working state monitoring module conducts real-time assessment on the working states of all components of the steel support, saves the assessment results to the database module and transmits them to the damage assessment and early warning system; the automatic diagnosis module for tensile cracks in members and node welds analyzes and diagnoses the tensile crack conditions of members and node welds, saves the diagnosis results to the database module and transmits them to the damage assessment and early warning system.

[0010] The damage assessment and early warning system comprehensively assesses the structural state of the steel trestle structure group based on the assessment results of the steel truss working state monitoring module and the steel support working state monitoring module and the diagnosis results of the automatic diagnosis module for tensile cracks in members and node welds, sends the assessment results to the cloud monitoring platform, and judges whether to issue early warning information according to the assessment results; the cloud monitoring platform generates a monitoring classification early warning risk level based on the detection classification early warning risk level table and the assessment results sent by the damage assessment and early warning system and takes corresponding measures.

[0011] The monitoring classification early warning risk levels include level I risk level, level II risk level, level III risk level, level IV risk level, and level V risk level; when the monitoring classification early warning risk level is level II risk level or level III risk level, the cloud monitoring platform generates maintenance suggestions or reinforcement suggestions corresponding to the assessment results and feeds back relevant information to maintenance personnel; when the monitoring classification early warning risk level is level IV risk level or level V risk level, the cloud monitoring platform issues an alarm message, generates maintenance suggestions or reinforcement suggestions corresponding to the assessment results and arranges manual maintenance operations.

[0012] Another technical solution adopted by the present invention is an intelligent monitoring method for in-service coal conveying steel trestles, including the following steps: Step 1: Obtain the operating environment, structural characteristics, and structural hazard analysis of the steel trestle structure group; Step 2: Determine the detection content based on the operating environment, structural characteristics, and structural hazard analysis of the steel trestle structure group; Step 3: Select monitoring points and install a sensing system; Step 4: Collect the structural conditions of the steel trestle structure group; Step 5: Analyze and evaluate the structural damage of the steel trestle structure group based on the structural conditions, and generate an evaluation result; Step 6: Determine the monitoring classification warning risk level based on the evaluation result and take corresponding measures.

[0013] The characteristics of another technical solution of the present invention also lie in: The detection content includes static response, dynamic response, and environmental factors; the static response includes structural strain, structural deformation, and structural settlement; the dynamic response includes structural vibration; the environmental factors include humidity and pH value.

[0014] Step 3 includes the following steps: Step 3.1: Select monitoring points; The selection of monitoring points includes the following methods: A1: Establish a finite element model of the steel trestle structure group, and analyze the stress and strain distribution and displacement of the steel trestle structure group; A2: Select components and structural connection points as monitoring points based on the stress and strain distribution and displacement of the steel trestle structure group; B1: Obtain the manual on-site inspection records and patrol history records of the steel trestle structure group; B2: Analyze the corrosion and deformation of components based on the manual on-site inspection records and patrol history records of the steel trestle structure group; B3: Select components as monitoring points based on the corrosion and deformation of components; Step 3.2: Install a sensing system; Step 3.2.1: Grind and clean the installation surface of the detection point; Step 3.2.2: Fix and install the sensors at the monitoring points respectively; Step 3.2.3: Lay optical fibers to connect the sensors with the data acquisition and transmission system.

[0015] Step 4 includes the following steps: The sensing system collects in real time the steel structure strain, vibration, corrosion, surface deformation, and settlement of the steel trestle structure group, and transmits them to the database module, the steel truss working state monitoring module, the steel bracket working state monitoring module, and the automatic diagnosis module for tensile cracks in members and joints through the data acquisition and transmission system respectively. Step five includes the following steps: Step 5.1: The steel truss working state monitoring module and the steel bracket working state monitoring module evaluate the working states of all components of the steel truss and all components of the steel bracket respectively according to the structural conditions of the steel trestle structure group to obtain evaluation results; the automatic diagnosis module for tensile cracks in members and joints diagnoses the tensile crack conditions of members and joints according to the structural conditions of the steel trestle structure group to obtain diagnosis results. Step 5.2: The damage assessment and early warning system comprehensively evaluates the structural state of the steel trestle structure group by combining the evaluation results of the steel truss working state monitoring module and the steel bracket working state monitoring module and the diagnosis results of the automatic diagnosis module for tensile cracks in members and joints, and generates evaluation results.

[0016] The monitoring classification early warning risk levels include level I risk level, level II risk level, level III risk level, level IV risk level, and level V risk level. The evaluation result corresponding to the level I risk level is: Under the combined action of the design load and the monitoring load, the stress and deformation of all components are less than the allowable values specified in the code design, and it will not affect the safety and durability of the structure. The steel trestle is in good condition. The evaluation result corresponding to the level II risk level is: Under the combined action of the design load and the monitoring load, the key components are in good condition, and the stress and deformation of secondary components accounting for less than 10% are greater than 5% of the allowable values specified in the code design, but it does not affect the safety and durability of the structure. It is judged to be in a good state. The evaluation result corresponding to the level III risk level is: Under the combined action of the design load and the monitoring load, the stress and deformation of key components accounting for less than 5% are greater than 5% of the allowable values specified in the code design, and at the same time, the stress and deformation of secondary components accounting for 10% - 20% are greater than 10% of the allowable values specified in the code design. Although it will affect the durability of the structure, it has not endangered the structural safety. It is judged to be in a medium damage state. The evaluation result corresponding to the level IV risk level is: Under the combined action of the design load and the monitoring load, the stress and deformation of key components accounting for less than 10% are greater than 10% of the allowable values specified in the code design, or the fatigue cumulative damage index of key components is between 0.45 - 0.80, and the load-bearing capacity decreases by less than 10%. It has already affected the safety of the structure. It is judged to be in a serious damage state. The evaluation result corresponding to the Class V risk level is as follows: under the combined action of the design load and the monitoring load, the stress and deformation of the key components are greater than 10% of the allowable value specified in the code design, and the damage shows a trend of development and expansion, or the fatigue cumulative damage index of the key components is greater than 0.80, and major damages have occurred, seriously threatening the safety of the structure, which is determined to be in a dangerous state.

[0017] The beneficial effects of the present invention are as follows: through the collected information, comprehensively evaluate the working state and safety of the steel trestle bridge, clarify the key points of structural maintenance management for managers, scientifically guide the use and maintenance of the steel trestle bridge, effectively carry out maintenance, repair and reinforcement work, and save a large amount of later maintenance funds; monitor the working state of the steel trestle bridge in real time online, deeply reveal the evolution process and development trend between the structural stress and deformation and the bearing capacity, detect structural damage and performance degradation in time, and take precautions; can monitor the overall working performance of the structure that is difficult to detect by manual daily inspection, and monitor the parts that are difficult for people to reach, making up for the limitations of manual inspection; after sudden events such as earthquakes, strong winds or other serious accidents, quickly evaluate the steel trestle bridge structure and take effective measures in time, which can bring huge social and economic benefits; comprehensively analyze the data collected by the monitoring system, accumulate engineering experience, and provide a solid guarantee for the operation and maintenance safety of the steel trestle bridge. Brief Description of the Drawings

[0018] Figure 1 It is a flow chart of the intelligent monitoring method for the in-service coal conveying steel trestle bridge of the present invention. Detailed Embodiments

[0019] The present invention will be described in detail below with reference to the drawings and specific embodiments.

[0020] The intelligent monitoring system for the in-service coal conveying steel trestle bridge includes a sensing system arranged inside the steel trestle bridge structure group. The sensing system is respectively connected in communication with a steel truss working state monitoring module, a steel support working state monitoring module, and an automatic diagnosis module for tensile cracks in members and joints through a data acquisition and transmission system. The data acquisition and transmission system, the steel truss working state monitoring module, the steel support working state monitoring module, and the automatic diagnosis module for tensile cracks in members and joints are respectively connected in communication with a database module. The database module is connected in communication with a damage assessment and early warning system, and the damage assessment and early warning system is connected in communication with a cloud monitoring platform.

[0021] The sensing system includes fiber Bragg grating surface strain gauges, vibration sensors, fiber Bragg grating pH sensors, fiber Bragg grating displacement gauges, and fiber Bragg grating joint settlement gauges; the sensing system obtains the steel structure strain condition, steel structure vibration condition, steel structure corrosion condition, steel structure surface deformation condition, and steel structure settlement condition of the steel trestle bridge structure group.

[0022] The working condition monitoring module of the steel truss conducts real-time evaluation on the working conditions of all components of the steel truss, saves the evaluation results to the database module and transmits them to the damage assessment and early warning system; the working condition monitoring module of the steel support conducts real-time evaluation on the working conditions of all components of the steel support, saves the evaluation results to the database module and transmits them to the damage assessment and early warning system; the automatic diagnosis module for tensile cracking damage of members and joints welds analyzes and diagnoses the tensile cracking conditions of members and joints welds, saves the diagnosis results to the database module and transmits them to the damage assessment and early warning system.

[0023] Based on the evaluation results of the working condition monitoring module of the steel truss and the steel support and the diagnosis results of the automatic diagnosis module for tensile cracking damage of members and joints welds, the damage assessment and early warning system comprehensively evaluates the structural state of the steel trestle structure group, sends the evaluation results to the cloud monitoring platform, and determines whether to issue early warning information according to the evaluation results; the cloud monitoring platform generates the monitoring classification early warning risk level and takes corresponding measures based on the detection classification early warning risk level table and the evaluation results sent by the damage assessment and early warning system.

[0024] The monitoring classification early warning risk levels include Level I risk level, Level II risk level, Level III risk level, Level IV risk level, and Level V risk level; when the monitoring classification early warning risk level is Level II risk level or Level III risk level, the cloud monitoring platform generates maintenance suggestions or reinforcement suggestions corresponding to the evaluation results and feeds back relevant information to the maintenance personnel; when the monitoring classification early warning risk level is Level IV risk level or Level V risk level, the cloud monitoring platform issues an alarm message, generates maintenance suggestions or reinforcement suggestions corresponding to the evaluation results and arranges manual maintenance operations.

[0025] An intelligent monitoring method for in-service coal conveying steel trestles, as Figure 1 shown, includes the following steps: Step 1: Obtain the operating environment, structural characteristics, and structural hazard analysis of the steel trestle structure group; Step 2: Determine the detection content based on the operating environment, structural characteristics, and structural hazard analysis of the steel trestle structure group; The detection content includes static response conditions, dynamic response conditions, and environmental factors; the static response conditions include structural strain conditions, structural deformation conditions, and structural settlement conditions; the dynamic response conditions include structural vibration conditions; the environmental factors include humidity and pH value.

[0026] Step 3: Select monitoring points and install a sensing system; Step 3.1: Select monitoring points; The selection of monitoring points includes the following methods: A1: Establish a finite element model of the steel trestle structure group and analyze the stress-strain distribution and displacement conditions of the steel trestle structure group; A2. Select components and structural connection points as monitoring points based on the stress-strain distribution and displacement conditions of the steel trestle structure group; B1. Obtain the manual on-site inspection records and historical inspection records of the steel trestle structure group; B2. Analyze the corrosion and deformation conditions of components based on the manual on-site inspection records and historical inspection records of the steel trestle structure group; B3. Select components as monitoring points based on the corrosion and deformation conditions of components; Step 3.2. Install the sensing system; Step 3.2.1. Grind and clean the installation surface of the detection point; Step 3.2.2. Fix and install the sensors at the monitoring points respectively; Step 3.2.3. Lay optical fibers to connect the sensors with the data acquisition and transmission system.

[0027] Step Four. Collect the structural conditions of the steel trestle structure group; The sensing system collects the steel structure strain conditions, steel structure vibration conditions, steel structure corrosion conditions, steel structure surface deformation conditions, and steel structure settlement conditions of the steel trestle structure group in real time, and transmits them to the database module, steel truss working state monitoring module, steel bracket working state monitoring module, and automatic diagnosis module for tensile cracking damage of bars and node welds respectively through the data acquisition and transmission system; Step Five. Analyze and evaluate the structural damage conditions of the steel trestle structure group based on the structural conditions of the steel trestle structure group, and generate evaluation results; Step 5.1. The steel truss working state monitoring module and the steel bracket working state monitoring module evaluate the working states of all components of the steel truss and all components of the steel bracket respectively based on the structural conditions of the steel trestle structure group to obtain evaluation results; the automatic diagnosis module for tensile cracking damage of bars and node welds diagnoses the tensile cracking conditions of bars and node welds based on the structural conditions of the steel trestle structure group to obtain diagnosis results; Step 5.2. The damage assessment and early warning system comprehensively evaluates the structural state of the steel trestle structure group by combining the evaluation results of the steel truss working state monitoring module and the steel bracket working state monitoring module and the diagnosis results of the automatic diagnosis module for tensile cracking damage of bars and node welds, and generates evaluation results; Step Six. Determine the monitoring classification early warning risk level based on the evaluation results and take corresponding measures.

[0028] The monitoring classification early warning risk levels include Level I risk level, Level II risk level, Level III risk level, Level IV risk level, and Level V risk level; The evaluation result corresponding to the first-level risk level is as follows: Under the combined action of the design load and the monitoring load, the stress and deformation of all components are less than the allowable values specified in the code design, and it will not affect the safety and durability of the structure. The steel trestle is in good condition; The evaluation result corresponding to the second-level risk level is as follows: Under the combined action of the design load and the monitoring load, the key components are in good condition, and the stress and deformation of the secondary components accounting for less than 10% are greater than 5% of the allowable values specified in the code design, but it does not affect the safety and durability of the structure, and it is judged to be in a good state; The evaluation result corresponding to the third-level risk level is as follows: Under the combined action of the design load and the monitoring load, the stress and deformation of the key components accounting for less than 5% are greater than 5% of the allowable values specified in the code design, and at the same time, the stress and deformation of the secondary components accounting for 10%-20% are greater than 10% of the allowable values specified in the code design. Although it will affect the durability of the structure, it has not endangered the structural safety, and it is judged to be in a medium damage state; The evaluation result corresponding to the fourth-level risk level is as follows: Under the combined action of the design load and the monitoring load, the stress and deformation of the key components accounting for less than 10% are greater than 10% of the allowable values specified in the code design, or the fatigue cumulative damage index of the key components is between 0.45 and 0.80, and the bearing capacity drops by less than 10%. It has already affected the safety of the structure, and it is judged to be in a serious damage state; The evaluation result corresponding to the fifth-level risk level is as follows: Under the combined action of the design load and the monitoring load, the stress and deformation of the key components are greater than 10% of the allowable values specified in the code design, and the damage shows a developing and expanding trend, or the fatigue cumulative damage index of the key components is greater than 0.80, and major damage has occurred, seriously threatening the safety of the structure, and it is judged to be in a dangerous state.

[0029] The present invention has successfully overcome the technical shortcomings of the existing coal conveying steel trestle in intelligent damage identification and early warning, achieved the full-process closed-loop management of "perception-diagnosis-decision-repair", and laid a solid foundation for the safe and stable operation of the coal conveying steel trestle. The intelligent monitoring system constructed by the present invention covers a sensing system, a data acquisition and transmission system, a database module, a steel truss working state monitoring module, a steel support working state monitoring module, an automatic diagnosis module for tensile cracks in members and joints, a damage assessment and early warning system, and a cloud monitoring platform.

[0030] The sensing system in the present invention is mainly responsible for accurately acquiring the load and response information necessary for intelligent monitoring of the working state of the structure and automatic diagnosis of structural damage, and provides original data support for the analysis and judgment of subsequent links. The data acquisition and transmission system is responsible for the efficient transmission, proper storage and scientific management of data between the modules of the entire intelligent monitoring system, ensuring that the entire system runs smoothly and in an orderly manner. The steel truss working state monitoring module can carry out real-time and accurate assessment of the working state of all components of the steel truss with the limited point structural response information collected by the sensing system, so that the working condition of the steel truss can be seen at a glance. The steel support working state monitoring module uses the limited point structural response information obtained by the sensing system to realize real-time assessment of the working state of all components of the steel support, and always grasp the operating dynamics of the steel support. The rod and node weld tear damage automatic diagnosis module can quickly and accurately complete the automatic diagnosis of the location of the tear of each rod and node weld when the steel trestle is subjected to harsh working conditions such as corrosion and strong winds, and timely discover potential hidden dangers. Based on the analysis results transmitted from the above three modules, the damage assessment and early warning system conducts a comprehensive and in-depth assessment of the structural status of the steel trestle, and accurately determines whether an early warning is needed based on the status assessment results, providing reliable early warning protection for structural safety. The cloud monitoring platform can not only conduct a scientific and detailed risk level assessment of the steel trestle structure based on damage indicators, and immediately trigger an alarm when the early warning threshold appears, but also generate targeted professional and practical maintenance or reinforcement suggestions to help operation and maintenance personnel carry out maintenance work efficiently.

[0031] The sensing system of the present invention includes a fiber grating surface strain gauge for monitoring the strain of the steel structure, a vibration sensor for monitoring the vibration of the steel structure, a fiber grating pH sensor for monitoring the corrosion of the steel structure, a fiber grating displacement meter for monitoring the surface deformation of the steel structure, and a fiber grating crack meter for monitoring the settlement of the structure, which comprehensively ensures the accuracy and comprehensiveness of data collection.

[0032] The software level of the intelligent monitoring system for the in-service coal-carrying steel trestle includes a structural monitoring subsystem, a storage management subsystem, an alarm evaluation subsystem, and a user interface subsystem. The structural monitoring subsystem is responsible for collecting load source and structural response data information, and performing necessary preprocessing on the acquired data, and then storing it uniformly in the data storage management subsystem. Then, the corresponding statistical analysis work is carried out using the built-in calculation tools of the software, and the comprehensive alarm and status evaluation functions of the system are realized in combination with the pre-set safety thresholds of each characteristic parameter. Finally, the user interface subsystem completes the convenient human-computer interaction work, such as intuitively displaying the monitoring results and evaluation conclusions in the form of charts, and receiving user input commands.

[0033] The cloud monitoring platform of the present invention consists of a monitoring terminal and a corresponding software system. Its core function is to open a convenient window for monitoring personnel in different locations to remotely access the monitoring system, enabling them to view the dynamic changes in the environmental effects and structural response monitoring data during the construction process in real time. At the same time, the platform can also receive the data analysis instructions issued by the monitoring personnel in a timely manner and quickly send the instructions to the structural monitoring subsystem for efficient data processing and analysis.

[0034] The present invention first determines the monitoring locations of the steel trestle structure group through rigorous analysis and calculation, and reasonably arranges sensors. Then, it collects the data of the site monitoring items, enabling the data networking and sharing among the sensing system, the data acquisition and transmission system, the steel truss working state monitoring module, the steel bracket working state monitoring module, the automatic diagnosis module for tensile cracks in members and joints, the damage assessment and early warning system, and the cloud monitoring platform. The data acquisition and transmission can be carried out in a wired or wireless manner to achieve the whole-life cycle health monitoring. The present invention can achieve dynamic real-time monitoring. According to the requirements of hierarchical management, when the risk level reaches level II or level III, the system will feedback to the maintenance personnel for handling; when the risk level reaches level IV or level V, the cloud monitoring platform will dispatch the relevant sites along the line for regulation, or arrange for manual maintenance, providing strong data support for maintenance or reinforcement.

[0035] In the intelligent monitoring method for in-service coal conveying steel trestles of the present invention, based on factors such as the operation environment, structural characteristics, and structural hazard analysis of the steel trestle, the static response, dynamic response, and environmental factors of the structure are monitored. The main monitoring contents are shown in Table 1. During the operation period, if diseases such as cracks and uneven settlement occur, it is necessary to timely increase the monitoring of disease characteristics.

[0036] Table 1 Main monitoring contents

[0037] Example 1 This example presents an intelligent monitoring system for in-service coal conveying steel trestles, including a sensing system arranged inside the steel trestle structure group. The sensing system is respectively connected in communication with the steel truss working state monitoring module, the steel bracket working state monitoring module, and the automatic diagnosis module for tensile cracks in members and joints through the data acquisition and transmission system. The data acquisition and transmission system, the steel truss working state monitoring module, the steel bracket working state monitoring module, and the automatic diagnosis module for tensile cracks in members and joints are respectively connected in communication with the database module. The database module is connected in communication with a damage assessment and early warning system, and the damage assessment and early warning system is connected in communication with a cloud monitoring platform.

[0038] Example 2 This embodiment proposes an intelligent monitoring system for in-service coal conveying steel trestles, including a sensing system arranged inside the steel trestle structure group. The sensing system is respectively communicatively connected with a steel truss working state monitoring module, a steel support working state monitoring module, and an automatic diagnosis module for tensile cracking damage of members and joints through a data acquisition and transmission system. The data acquisition and transmission system, the steel truss working state monitoring module, the steel support working state monitoring module, and the automatic diagnosis module for tensile cracking damage of members and joints are respectively communicatively connected with a database module. The database module is communicatively connected with a damage assessment and early warning system, and the damage assessment and early warning system is communicatively connected with a cloud monitoring platform.

[0039] The sensing system includes fiber Bragg grating surface strain gauges, vibration sensors, fiber Bragg grating pH sensors, fiber Bragg grating displacement gauges, and fiber Bragg grating joint settlement gauges; the sensing system obtains the steel structure strain condition, steel structure vibration condition, steel structure corrosion condition, steel structure surface deformation condition, and steel structure settlement condition of the steel trestle structure group. The steel truss working state monitoring module conducts real-time assessment on the working states of all components of the steel truss, saves the assessment results to the database module, and transmits them to the damage assessment and early warning system; the steel support working state monitoring module conducts real-time assessment on the working states of all components of the steel support, saves the assessment results to the database module, and transmits them to the damage assessment and early warning system; the automatic diagnosis module for tensile cracking damage of members and joints analyzes and diagnoses the tensile cracking condition of members and joints, saves the diagnosis results to the database module, and transmits them to the damage assessment and early warning system.

[0040] Embodiment 3 This embodiment proposes an intelligent monitoring system for in-service coal conveying steel trestles, including a sensing system arranged inside the steel trestle structure group. The sensing system is respectively communicatively connected with a steel truss working state monitoring module, a steel support working state monitoring module, and an automatic diagnosis module for tensile cracking damage of members and joints through a data acquisition and transmission system. The data acquisition and transmission system, the steel truss working state monitoring module, the steel support working state monitoring module, and the automatic diagnosis module for tensile cracking damage of members and joints are respectively communicatively connected with a database module. The database module is communicatively connected with a damage assessment and early warning system, and the damage assessment and early warning system is communicatively connected with a cloud monitoring platform.

[0041] The damage assessment and early warning system comprehensively assesses the structural state of the steel trestle structure group based on the assessment results of the steel truss working condition monitoring module and the steel bracket working condition monitoring module, and the diagnosis results of the automatic diagnosis module for tensile cracking damage of members and node welds. It sends the assessment results to the cloud monitoring platform and determines whether to send early warning information according to the assessment results. The cloud monitoring platform generates a monitoring classification early warning risk level and takes corresponding measures based on the detection classification early warning risk level table and the assessment results sent by the damage assessment and early warning system. The monitoring classification early warning risk levels include level I risk level, level II risk level, level III risk level, level IV risk level, and level V risk level. When the monitoring classification early warning risk level is level II risk level or level III risk level, the cloud monitoring platform generates maintenance suggestions or reinforcement suggestions corresponding to the assessment results and feeds back relevant information to the maintenance personnel. When the monitoring classification early warning risk level is level IV risk level or level V risk level, the cloud monitoring platform sends an alarm message, generates maintenance suggestions or reinforcement suggestions corresponding to the assessment results, and arranges manual maintenance operations.

[0042] Embodiment 4 This embodiment proposes an intelligent monitoring method for in-service coal conveying steel trestles, as Figure 1 shown, including the following steps: Step 1: Obtain the operating environment, structural characteristics, and structural hazard analysis of the steel trestle structure group; Step 2: Determine the detection content based on the operating environment, structural characteristics, and structural hazard analysis of the steel trestle structure group; The detection content includes static response conditions, dynamic response conditions, and environmental factors. The static response conditions include structural strain conditions, structural deformation conditions, and structural settlement conditions. The dynamic response conditions include structural vibration conditions. The environmental factors include humidity and pH value.

[0043] Step 3: Select monitoring points and install a sensing system; Step 3.1: Select monitoring points; The selection of monitoring points includes the following methods: A1: Establish a finite element model of the steel trestle structure group and analyze the stress-strain distribution and displacement conditions of the steel trestle structure group; A2: Select components and structural connection points as monitoring points based on the stress-strain distribution and displacement conditions of the steel trestle structure group; B1: Obtain the manual on-site inspection records and patrol history records of the steel trestle structure group; B2: Analyze the corrosion and deformation conditions of components based on the manual on-site inspection records and patrol history records of the steel trestle structure group; B3: Select components as monitoring points based on the corrosion and deformation conditions of components; Step 3.2: Install a sensing system; Step 3.2.1: Grind and clean the installation surface of the detection points; Step 3.2.2: Fix and install the sensors at the monitoring points respectively; Step 3.2.3: Lay out the optical fibers to connect the sensors with the data acquisition and transmission system.

[0044] Step Four: Collect the structural conditions of the steel trestle structure group; Step Five: Analyze and evaluate the structural damage of the steel trestle structure group based on the structural conditions of the steel trestle structure group, and generate an evaluation result; Step Six: Determine the monitoring classification early warning risk level according to the evaluation result and take corresponding measures.

[0045] Embodiment 5 This embodiment proposes an intelligent monitoring method for in-service coal conveying steel trestles, as Figure 1 shown, including the following steps: Step One: Obtain the operating environment, structural characteristics and structural hazard analysis of the steel trestle structure group; Step Two: Determine the detection content based on the operating environment, structural characteristics and structural hazard analysis of the steel trestle structure group; Step Three: Select the monitoring points and install the sensing system; Step Four: Collect the structural conditions of the steel trestle structure group; The sensing system continuously collects the steel structure strain, steel structure vibration, steel structure corrosion, steel structure surface deformation, and steel structure settlement of the steel trestle structure group, and transmits them to the database module, steel truss working state monitoring module, steel support working state monitoring module, and automatic diagnosis module for tensile cracks in members and joints through the data acquisition and transmission system respectively; Step Five: Analyze and evaluate the structural damage of the steel trestle structure group based on the structural conditions of the steel trestle structure group, and generate an evaluation result; Step 5.1: The steel truss working state monitoring module and the steel support working state monitoring module respectively evaluate the working states of all components of the steel truss and all components of the steel support based on the structural conditions of the steel trestle structure group to obtain evaluation results; the automatic diagnosis module for tensile cracks in members and joints diagnoses the tensile crack conditions of members and joints based on the structural conditions of the steel trestle structure group to obtain diagnosis results; Step 5.2: The damage assessment and early warning system comprehensively evaluates the structural state of the steel trestle structure group by combining the evaluation results of the steel truss working state monitoring module and the steel support working state monitoring module and the diagnosis results of the automatic diagnosis module for tensile cracks in members and joints, and generates an evaluation result; Step Six: Determine the monitoring classification early warning risk level according to the evaluation result and take corresponding measures.

[0046] Example 6 This example presents an intelligent monitoring method for in-service coal conveyor steel trestles, as Figure 1 shown, including the following steps: Step 1: Obtain the operating environment, structural characteristics, and structural hazard analysis of the steel trestle structure group; Step 2: Determine the inspection content based on the operating environment, structural characteristics, and structural hazard analysis of the steel trestle structure group; The inspection content includes static response conditions, dynamic response conditions, and environmental factors; the static response conditions include structural strain conditions, structural deformation conditions, and structural settlement conditions; the dynamic response conditions include structural vibration conditions; the environmental factors include humidity and pH value.

[0047] Step 3: Select monitoring points and install a sensing system; Step 4: Collect the structural conditions of the steel trestle structure group; Step 5: Analyze and evaluate the structural damage of the steel trestle structure group based on the structural conditions and generate an evaluation result; Step 6: Determine the monitoring classification warning risk level based on the evaluation result and take corresponding measures.

[0048] The monitoring classification warning risk levels are Level I risk level, Level II risk level, Level III risk level, Level IV risk level, and Level V risk level.

[0049] In this embodiment of the present invention, the principle for selecting monitoring points is as follows: By establishing a finite element model of the trestle, simulating the working load of the belt conveyor and wind load conditions, deeply analyzing the stress-strain distribution and displacement status of the trestle, and then selecting components with larger stress-strain and structural connection points with larger displacements. Based on on-site manual inspections and historical inspection records, select components with larger corrosion damage or deformation for local deformation monitoring. Use distributed fiber optic sensors to monitor the strain distribution over a period of time, select the area with the largest strain change amplitude, and then combine with manual inspections to determine the locations that need to be locally monitored in key areas in the future.

[0050] The content detected by the sensing system in this embodiment of the present invention includes the following aspects: 1. Structural strain conditions: Since the strain value at the strain monitoring point takes the strain value when the strain gauge is installed as the initial strain, only the strain change value in the subsequent stage can be monitored, while the true structural strain is the sum of the original structural strain and the subsequent monitored strain . Therefore, the strain warning mechanism needs to combine simulation analysis to calculate the original strain of the structure at the monitoring point under its own dead load, and then add the monitored strain . This value should be less than or equal to the allowable strain of the material , that is , where represents the true value of the current structural strain; represents the structural strain of the original structure under the action of dead load; represents the strain measured by the strain gauge; represents the allowable strain of the structural material. 2. Structural deformation conditions: The deformation monitoring of the steel trestle mainly monitors the settlement, horizontal displacement and inclination of the structure, which are mainly manifested in key parts such as the mid-span of the steel truss, the top of the column and the foundation of the steel support. The offset of the above parts is monitored in real time to obtain the time history change rate of the offset. When the change rate is too large or the offset exceeds the limit, a support offset warning should be issued. 3. Structural vibration conditions: The steel trestle is in a vibration environment of the belt conveyor working load for a long time, and there is a risk of excessive accumulation and mutation of load effects. Taking the vibration response monitoring of the trestle as an example, vibration sensors are arranged on the lower chord of the trestle. Generally, only 3Hz low-frequency data is collected. When the vibration reaches the set amplitude, triggered high-frequency 100Hz data is collected. The high-frequency vibration signals collected by the vibration sensors can reflect the dynamic response law of the structure. When the dynamic response law is abnormal, it can reflect the changes in the structural stiffness and mode. At this time, a warning is issued and manual inspection is arranged. 4. Structural corrosion conditions: The iron oxide generated by corrosion is a basic oxide, which will form a basic environment in a humid environment. Therefore, fiber Bragg grating pH sensors are arranged, and by monitoring the acidity and alkalinity of the area, the possible corrosion conditions can be analyzed and evaluated. When the monitored environment changes to a basic environment, a corrosion warning can be issued to remind the maintenance personnel to conduct inspections.

[0051] In the embodiments of the present invention, various fiber Bragg grating sensors are used to form a sensing system, and the strain sensing characteristics of the fiber Bragg grating are used as a test carrier to generate data information of various test items. The sensors are summarized in Table 2 below.

[0052] Table 2 Statistical Table of Monitoring Instruments

[0053] The installation process of the sensing system is as follows: ① Surface treatment: First, the installation surface needs to be treated. Use sandpaper or a grinding tool to grind the installation surface rough to enhance the adhesion between the sensor and the structure surface. Then use a cleaning tool to clean the surface to remove impurities such as oil stains and dust, and ensure that the surface is clean and dry.

[0054] ② Sensor installation: For the adhesive fiber Bragg grating strain sensor, apply an appropriate amount of special adhesive (such as epoxy resin glue) on the treated surface. Carefully place the sensor at the predetermined position and use a fixture or other fixing device to keep the sensor in the correct position until the adhesive cures. For sensors installed mechanically, use screws, clamps, etc. to firmly fix the sensor on the pipeline or other equipment according to the installation structure of the sensor. During the installation process, pay attention to avoiding excessive bending or stretching of the optical fiber. Generally, the minimum bending radius of the optical fiber should meet the technical requirements of the sensor.

[0055] ③ Optical fiber wiring and connection: Route the optical fiber according to the designed circuit, and avoid scratching the optical fiber with sharp objects or squeezing it with heavy objects. If the optical fiber needs to be extended, use an optical fiber fusion splicer for high-quality optical fiber fusion splicing. Connect the end of the optical fiber to the signal acquisition device or demodulator, ensure a firm connection, and keep the interface of the optical fiber clean to prevent dust and other contaminants from affecting signal transmission.

[0056] ④ Inspection and debugging after installation: Turn on the signal acquisition system and check whether signals from the fiber Bragg grating sensors can be received. Observe the signal intensity and stability, compare with the baseline data recorded before installation, and check whether parameters such as wavelength are within a reasonable range. If the signal is abnormal, check whether the sensor is installed correctly, whether the optical fiber connection is good, and whether the parameter settings of the signal acquisition device are appropriate, and make corresponding adjustments and repairs.

[0057] In the embodiment of the present invention, in the health status monitoring work of the steel trestle, an optical fiber grating demodulator is used to achieve real-time online monitoring. All fiber Bragg grating sensors are connected to the monitoring station through a multi-core main transmission optical cable, so that the data acquisition of all optical path signals is completed in the monitoring station. Subsequently, with the help of the server and the Internet, remote data sharing is realized, and then a complete data acquisition and transmission system is constructed. Based on the data transmitted from the monitoring station, a comprehensive and in-depth evaluation of the structural state of the steel trestle is carried out, and according to the state evaluation results, a monitoring classification early warning risk level is issued.

Claims

1. An intelligent monitoring system for in-service coal transport steel trestle, characterized in that: The invention comprises a sensor system arranged inside the steel trestle structure group, wherein the sensor system is respectively connected to the steel truss working state monitoring module, the steel support working state monitoring module, and the rod and node weld crack damage automatic diagnosis module through the data acquisition and transmission system, the data acquisition and transmission system, the steel truss working state monitoring module, the steel support working state monitoring module, and the rod and node weld crack damage automatic diagnosis module are respectively connected to the database module, the database module is connected to the damage assessment and early warning system, and the damage assessment and early warning system is connected to the cloud monitoring platform.

2. The intelligent monitoring system for an in-service coal transport steel trestle according to claim 1 is characterized in that: The sensing system includes a fiber grating surface strain gauge, a vibration sensor, a fiber grating PH sensor, a fiber grating displacement meter and a fiber grating gap meter; the sensing system obtains the steel structure strain, steel structure vibration, steel structure corrosion, steel structure surface deformation and steel structure settlement of the steel trestle structure group.

3. The intelligent monitoring system for an in-service coal transport steel trestle according to claim 1 is characterized in that: The steel truss working status monitoring module performs real-time evaluation on the working status of all components of the steel truss, saves the evaluation results to the database module and transmits them to the damage assessment and early warning system; the steel support working status monitoring module performs real-time evaluation on the working status of all components of the steel support, saves the evaluation results to the database module and transmits them to the damage assessment and early warning system; the rod and node weld tensile crack damage automatic diagnosis module analyzes and diagnoses the tensile crack conditions of the rod and node welds, saves the diagnosis results to the database module and transmits them to the damage assessment and early warning system.

4. The intelligent monitoring system for an in-service coal transport steel trestle according to claim 1 is characterized in that: The damage assessment and early warning system comprehensively assesses the structural status of the steel trestle structure group based on the assessment results of the steel truss working status monitoring module and the steel support working status monitoring module and the diagnosis results of the rod and node weld tensile damage automatic diagnosis module, sends the assessment results to the cloud monitoring platform, and determines whether to issue an early warning information based on the assessment results; The cloud monitoring platform generates a monitoring graded warning risk level and takes corresponding measures based on the detection graded warning risk level table and the assessment results sent by the damage assessment and warning system.

5. The intelligent monitoring system for an in-service coal transport steel trestle according to claim 4 is characterized in that: The monitoring and grading early warning risk levels include level I risk level, level II risk level, level III risk level, level IV risk level, and level V risk level; When the monitoring graded early warning risk level is level II risk level or level III risk level, the cloud monitoring platform generates maintenance suggestions or reinforcement suggestions corresponding to the assessment results and feedbacks relevant information to the maintenance personnel; when the monitoring graded early warning risk level is level IV risk level or level V risk level, the cloud monitoring platform issues an alarm message, generates maintenance suggestions or reinforcement suggestions corresponding to the assessment results and arranges manual maintenance operations.

6. An intelligent monitoring method for an in-service coal transport steel trestle, characterized in that: The intelligent monitoring system for an in-service coal transport steel trestle described in any one of claims 1 to 5 comprises the following steps: Step 1: Obtain the operating environment, structural characteristics and structural hazard analysis of the steel trestle structure group; Step 2: Determine the inspection content based on the operating environment, structural characteristics and structural hazard analysis of the steel trestle structure group; Step 3: Select monitoring points and install the sensor system; Step 4: Collect the structural information of the steel trestle structure group; Step 5: Analyze and evaluate the structural damage of the steel trestle structure group according to the structural condition of the steel trestle structure group, and generate an evaluation result; Step 6: Determine the monitoring and warning risk level based on the assessment results and take corresponding measures.

7. The intelligent monitoring method for an in-service coal transport steel trestle according to claim 6 is characterized in that: The detection content includes static response, dynamic response, and environmental factors; the static response includes structural strain, structural deformation, and structural settlement; the dynamic response includes structural vibration; and the environmental factors include humidity and pH value.

8. The intelligent monitoring method for an in-service coal transport steel trestle according to claim 6 is characterized in that: The step three comprises the following steps: Step 3.1, select monitoring points; The selection of the monitoring points includes the following methods: A1. Establish a finite element model of the steel trestle structure group and analyze the stress-strain distribution and displacement of the steel trestle structure group; A2. Select components and structural connection points as monitoring points based on the stress-strain distribution and displacement conditions of the steel trestle structure group; B1. Obtain the manual on-site survey records and inspection history records of the steel trestle structure group; B2. Analyze the corrosion and deformation of components based on the manual on-site survey records and inspection history records of the steel trestle structure group; B3. Select components as monitoring points based on their corrosion and deformation conditions; Step 3.2, install the sensor system; Step 3.2.1, polish and clean the installation surface of the detection point; Step 3.2.2, install the sensors at the monitoring points respectively; Step 3.2.3: Lay out optical fiber to connect the sensor to the data acquisition and transmission system.

9. The intelligent monitoring method for an in-service coal transport steel trestle according to claim 6, characterized in that: The step 4 comprises the following steps: The sensor system collects the steel structure strain, steel structure vibration, steel structure corrosion, steel structure surface deformation, and steel structure settlement of the steel trestle structure group in real time, and transmits them to the database module, steel truss working status monitoring module, steel support working status monitoring module, and rod and node weld tensile crack damage automatic diagnosis module through the data acquisition and transmission system; The step five comprises the following steps: Step 5.1, the steel truss working state monitoring module and the steel support working state monitoring module respectively evaluate the working state of all components of the steel truss and the working state of all components of the steel support according to the structural conditions of the steel trestle structure group to obtain evaluation results; the rod and node weld tensile crack damage automatic diagnosis module diagnoses the rod and node weld tensile crack damage according to the structural conditions of the steel trestle structure group to obtain diagnosis results; Step 5.2, the damage assessment and early warning system combines the assessment results of the steel truss working status monitoring module and the steel support working status monitoring module and the diagnosis results of the rod and node weld tensile damage automatic diagnosis module to comprehensively assess the structural status of the steel trestle structure group and generate an assessment result.

10. The intelligent monitoring method for an in-service coal transport steel trestle according to claim 6, characterized in that: The monitoring and grading early warning risk levels include level I risk level, level II risk level, level III risk level, level IV risk level, and level V risk level; The assessment results corresponding to the above-mentioned risk level I are: under the combined effect of the design load and the monitoring load, the stress and deformation of all components are less than the allowable value of the specification design, and will not affect the safety and durability of the structure, and the steel trestle is in good condition; The assessment results corresponding to the risk level II are: under the combined effect of the design load and the monitoring load, the key components are in good condition, the stress and deformation of the secondary components within 10% are greater than 5% of the allowable value of the specification design, but do not affect the safety and durability of the structure, and are judged to be in good condition; The assessment result corresponding to the risk level III is: under the combined effect of the design load and the monitoring load, the stress and deformation of the key components accounting for less than 5% are greater than 5% of the allowable value of the specification design, and at the same time, the stress and deformation of the secondary components accounting for 10%-20% are greater than 10% of the allowable value of the specification design. Although it will affect the durability of the structure, it has not yet endangered the safety of the structure and is judged to be in a medium damage state; The assessment results corresponding to the IV risk level are: under the combined effect of the design load and the monitoring load, the stress and deformation of the key components accounting for less than 10% are greater than 10% of the allowable value of the specification design, or the fatigue cumulative damage index of the key components is between 0.45 and 0.80, and the bearing capacity decreases by less than 10%, which has affected the safety of the structure and is judged to be in a serious damage state; The assessment results corresponding to the Grade V risk level are: under the combined effect of the design load and the monitoring load, the stress and deformation of the key components are greater than 10% of the allowable value of the design specification, and the damage shows a trend of development and expansion, or the fatigue cumulative damage index of the key components is greater than 0.80, major damage has occurred, seriously threatening the safety of the structure, and it is judged to be in a dangerous state.

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