Coal conveying belt deviation detection system based on line anchor mechanism

Abstract

The present application relates to the technical field of industrial conveying equipment safety monitoring, in particular to a coal conveying belt deviation detection system based on a line anchor mechanism, comprising: a sensing and collecting module that collects dynamic tension waveform and contact point displacement data of a flexible line anchor device; a mechanical analysis module that inversely deduces lateral contact force based on a tension transmission model and separates transient mechanical disturbance and continuous extrusion behavior using time-frequency domain decomposition; a damage assessment module that constructs a friction heat energy integral model, calculates damage energy accumulation value and maps it into a risk assessment index; a hierarchical control module that executes early warning or emergency stop instructions according to index and threshold comparison; the present application effectively prevents thermal aging and fire hazards caused by long-term micro-friction of the belt edge.

Description

Technical Field

[0001] This invention relates to the field of safety monitoring technology for industrial conveying equipment, specifically a coal conveyor belt deviation detection system based on a line anchor mechanism. Background Technology

[0002] In the operation and maintenance of coal conveyor belts, belt misalignment detection mainly relies on point-type limit switches arranged at intervals along the belt to provide feedback on the physical contact status of the belt edge. However, existing technology has significant drawbacks:

[0003] First, due to the use of a discrete point layout, there are detection blind spots between sensors, making it impossible to build a continuous sensing network without blind spots along the entire line, and making it difficult to capture local deviation behavior in long-distance conveyor lines. Second, the traditional detection logic is only based on the instantaneous geometric displacement threshold trigger, lacking the ability to analyze the time-frequency characteristics of mechanical signals, and cannot distinguish between transient mechanical disturbances caused by coal flow impact and joint collisions and continuous friction that causes substantial damage, resulting in frequent false alarms in the system under normal vibration conditions, reducing production efficiency. In addition, the existing solution ignores the energy dimension in the contact process, cannot quantify the accumulation of frictional heat damage, and is difficult to detect hidden wear risks with small force values ​​but long durations, and cannot accurately warn of belt edge thermal aging and fire hazards caused by long-term micro-friction.

[0004] Therefore, how to eliminate detection blind spots, accurately remove interference signals under complex working conditions, and effectively assess the risk of cumulative damage has become an urgent technical problem to be solved. Summary of the Invention

[0005] To solve the above-mentioned technical problems, the present invention provides a coal conveyor belt deviation detection system based on a line anchor mechanism. Specifically, the technical solution of the present invention includes:

[0006] The sensing and acquisition module is configured to collect real-time status data of the flexible anchor devices arranged along the conveyor belt. The real-time status data includes dynamic tension waveform data inside the anchor and contact point displacement data, and obtains the raw sensing dataset.

[0007] The mechanical analysis module is configured to perform decoupled analysis on the original sensor dataset. Based on the preset tension transmission model, it uses dynamic tension waveform data to infer the lateral contact force acting on the anchor line and performs time-frequency domain decomposition on the lateral contact force. It separates the high-frequency component with a frequency higher than the preset cutoff frequency as transient mechanical disturbance and the low-frequency DC component with a frequency lower than the preset cutoff frequency as continuous extrusion behavior, generating a mechanical feature vector containing the high-frequency component and the low-frequency DC component.

[0008] The damage assessment module is configured to perform thermal damage accumulation calculation on the low-frequency DC component in the mechanical feature vector, construct a frictional heat energy integral model, introduce time dimension integration, calculate the damage energy accumulation value of the belt edge under continuous contact state, and map the damage energy accumulation value to the risk assessment index of the current operating state.

[0009] The hierarchical control module is configured to execute hierarchical control strategies based on the risk assessment index, with preset safety thresholds and shutdown thresholds, where the shutdown threshold is greater than the safety threshold. When the risk assessment index is less than the safety threshold, the system maintains normal operation and records data. When the risk assessment index is greater than or equal to the safety threshold but less than the shutdown threshold, a maintenance warning signal is generated. When the risk assessment index is greater than or equal to the shutdown threshold, an emergency stop command is triggered.

[0010] Preferably, the sensing and acquisition module includes:

[0011] The physical architecture unit, used to construct the flexible anchor sensor array, includes flexible cables with preset pretension arranged on both sides of the conveyor belt, and rolling contact wheels set at preset intervals as support nodes for the flexible cables. The rolling contact wheels are floatingly mounted on the frame through elastic damping components, so that the nodes can generate measurable radial micro-displacement when subjected to force. The rolling contact wheels, as the physical contact medium with the edge of the belt, are configured to convert the lateral thrust of the belt into the axial tension of the flexible cable.

[0012] The data acquisition unit includes a tension sensor installed at the end of the online anchor and a micro-displacement sensor at the node, as well as an environmental humidity sensor arranged along the entire conveyor belt. The environmental humidity sensor is configured to collect real-time environmental humidity data of the conveyor belt's operating environment, and is configured to simultaneously collect dynamic tension waveform data, contact point displacement data and real-time environmental humidity data, and transmit the dynamic tension waveform data, contact point displacement data and real-time environmental humidity data to the mechanical analysis module after marking them with a unified timestamp.

[0013] Preferably, the mechanical analysis module includes:

[0014] The tension inversion unit is configured to calculate the magnitude and location of the lateral contact force based on the tension transfer model. The tension transfer model characterizes the geometric and mechanical relationship between the lateral contact force and the tension change, contact point displacement data, and elastic coefficient of the flexible cable in the dynamic tension waveform data.

[0015] The frequency domain filtering unit is configured to perform spectrum analysis to distinguish between harmless jitter and harmful belt misalignment. It sets a cutoff frequency and identifies the dynamic tension waveform data components in the original sensor dataset with frequencies higher than the cutoff frequency as high-frequency components caused by coal flow impact or joint collision, and identifies the signal components with frequencies lower than the cutoff frequency as low-frequency DC components caused by belt misalignment and compression.

[0016] Preferably, the mechanical analysis module further includes:

[0017] The modal recognition unit is used to identify the physical causes of belt misalignment. It performs periodic analysis on the low-frequency DC component. If the low-frequency DC component exhibits sinusoidal fluctuation characteristics that are strongly correlated with the conveyor belt's operating cycle, it is determined to be periodic misalignment caused by the eccentricity of the rollers or idlers. If the low-frequency DC component exhibits non-periodic step-up characteristics, it is determined to be sudden misalignment caused by uneven material distribution or structural deformation.

[0018] Preferably, the damage assessment module includes:

[0019] The energy integration unit is configured to quantify the physical damage risk of the belt edge, obtain the real-time running speed of the belt and the preset friction coefficient, combine the low-frequency DC component output by the mechanical analysis module, calculate the product of the lateral contact force, real-time running speed and friction coefficient, and integrate the product in the time domain to obtain the cumulative heat value generated by friction at the belt edge, which is used as the cumulative damage energy value.

[0020] The state mapping unit is configured to map the cumulative damage energy value to a visualized risk assessment index. It has a preset function relating damage energy to the tolerance limit of the belt material. This function is configured to increase the value of the risk assessment index in a non-linear manner when the cumulative damage energy value is greater than or equal to 80% of the tolerance limit of the material.

[0021] Preferably, the hierarchical control module includes:

[0022] The transient filtering unit is configured to pre-screen high-frequency components before the damage assessment module calculates the risk assessment index. In response to the high-frequency components separated by the mechanical analysis module, it is configured to determine that a transient mechanical disturbance occurs when the amplitude of the high-frequency component exceeds a preset trigger threshold and the duration is less than a preset impact time threshold. The data for this time period will not be included in the energy integration calculation of the damage assessment module, and only the disturbance event will be recorded. Otherwise, if the duration is greater than or equal to the preset impact time threshold, the high-frequency component will be included in the calculation of the risk assessment index.

[0023] Preferably, the hierarchical control module further includes:

[0024] The trend prediction unit is configured to process the risk assessment index between the safety threshold and the shutdown threshold, calculate the rate of change of the risk assessment index over time, and if the rate of change is positive and the variance of the rate of change is less than the preset stable value, predict the remaining time to reach the shutdown threshold based on the current rate of change, and generate a maintenance warning signal containing the suggested inspection location at the current moment. The suggested inspection location is calculated by the mechanical analysis module based on the contact point displacement data.

[0025] Preferably, the system further includes an adaptive calibration module, which is used to dynamically correct the reference parameters of the sensing and acquisition module. During the period when the conveyor belt is unloaded and running smoothly, the static tension value of the flexible anchor is monitored. If the static tension value drifts due to changes in ambient temperature or cable creep, the reference tension parameters in the tension transmission model are automatically updated to eliminate the influence of environmental factors on the detection accuracy.

[0026] The present invention has the following advantages:

[0027] 1. This system constructs a flexible line anchor sensor array through physical architecture units, forming a continuous sensing network without blind spots along the conveyor belt. It uses flexible cables to convert the lateral thrust of the belt into axial tension, and uses rolling contact wheels as the physical contact medium with the belt edge, effectively solving the detection vacuum zone and wear problems of traditional point switches. Combined with a timestamp synchronization mechanism to align tension and displacement data, it eliminates phase errors caused by transmission delay, providing a high-fidelity data foundation for the accurate perception of local deviation events in long-distance coal conveying corridors.

[0028] 2. This system uses the mechanical analysis module to perform time-frequency domain decomposition and transient filtering unit. It uses a preset tension transmission model to back-calculate the lateral contact force, accurately separating the high-frequency transient disturbances caused by coal flow impact or joint impact and the low-frequency DC component caused by continuous extrusion. By setting trigger thresholds and impact time thresholds to filter random noise, it fundamentally distinguishes between harmless mechanical vibrations and harmful deviation friction, ensuring that the system only triggers a response when there is a real and continuous fault, which significantly improves the anti-interference capability of detection and production continuity.

[0029] 3. This system introduces a frictional heat energy integral model through a damage assessment module, abandoning the traditional paradigm that only focuses on geometric displacement. Instead, it quantifies the energy accumulation during the contact process and calculates the damage energy accumulation value of the belt edge by combining the real-time running speed of the belt with the preset friction coefficient. This can sensitively capture the hidden wear risk with small force values ​​but long duration, effectively preventing thermal aging and fire hazards caused by long-term micro-friction of the belt, and extending the service life of the belt while ensuring safety.

[0030] 4. This system achieves intelligent fault diagnosis and proactive management through modal recognition and trend prediction units. It distinguishes the physical causes of roller eccentricity and material unevenness based on the periodic analysis of low-frequency signals, and predicts the remaining time to reach the downtime threshold based on the rate of change of the risk index. At the same time, it uses contact point displacement data to calculate suggested inspection locations, enabling maintenance personnel to carry targeted tools directly to the core fault area, greatly shortening the fault diagnosis time and improving the overall maintenance efficiency. Attached Figure Description

[0031] Figure 1This is a structural diagram of the system of the present invention. Detailed Implementation

[0032] The following combination Figure 1 The present invention will be further illustrated by describing a preferred embodiment in detail.

[0033] like Figure 1 As shown, the coal conveyor belt deviation detection system based on the line anchor mechanism includes: a sensing and acquisition module, configured to collect real-time status data of the flexible line anchor devices arranged along the conveyor belt. The real-time status data includes dynamic tension waveform data and contact point displacement data inside the line anchor, and obtains the original sensor dataset.

[0034] The mechanical analysis module is configured to perform decoupled analysis on the original sensor dataset. Based on the preset tension transmission model, it uses dynamic tension waveform data to infer the lateral contact force acting on the anchor line and performs time-frequency domain decomposition on the lateral contact force. It separates the high-frequency component with a frequency higher than the preset cutoff frequency as transient mechanical disturbance and the low-frequency DC component with a frequency lower than the preset cutoff frequency as continuous extrusion behavior, generating a mechanical feature vector containing the high-frequency component and the low-frequency DC component.

[0035] The damage assessment module is configured to perform thermal damage accumulation calculation on the low-frequency DC component in the mechanical feature vector, construct a frictional heat energy integral model, introduce time dimension integration, calculate the damage energy accumulation value of the belt edge under continuous contact state, and map the damage energy accumulation value to the risk assessment index of the current operating state.

[0036] The hierarchical control module is configured to execute hierarchical control strategies based on the risk assessment index, with preset safety thresholds and shutdown thresholds, where the shutdown threshold is greater than the safety threshold. When the risk assessment index is less than the safety threshold, the system maintains normal operation and records data. When the risk assessment index is greater than or equal to the safety threshold but less than the shutdown threshold, a maintenance warning signal is generated. When the risk assessment index is greater than or equal to the shutdown threshold, an emergency stop command is triggered.

[0037] This embodiment details the overall architecture and operating logic of the system, aiming to solve the problem that point switches in the prior art cannot distinguish between transient belt vibration and continuous destructive friction. The system activates the sensing and acquisition module, which does not rely on discrete contact switches, but instead uses a continuous mechanical sensing mechanism, i.e., a flexible anchor device, arranged in parallel along the entire length or key sections of the conveyor belt to capture the contact behavior of the belt edge in real time. The raw sensor dataset output by this module contains two types of key time-series data: dynamic tension waveform data reflecting the time sequence of axial force on the flexible cable, and contact point displacement data reflecting the geometric position of the anchor stress point. The dynamic tension waveform data is acquired by a tension sensor installed at the end of the anchor, and the contact point displacement data is acquired by a micro-displacement sensor arranged at the support node.

[0038] The mechanical analysis module performs decoupling analysis on the above dataset. This process is based on a preset tension transmission model and uses dynamic tension waveform data to infer the lateral contact force acting on the anchor. On this basis, in order to distinguish between harmless disturbances and harmful deviations, the module uses fast Fourier transform or wavelet transform techniques to decompose the lateral contact force in the time and frequency domain. The system sets a preset cutoff frequency and identifies signals with frequencies higher than the cutoff frequency as high-frequency components, which physically represent transient mechanical disturbances caused by coal flow impact and joint collision.

[0039] Simultaneously, signals with frequencies below the cutoff frequency are identified as low-frequency DC components, whose physical meaning is a quasi-static force characterizing the continuous compression of the anchor line after belt misalignment. The system generates a mechanical feature vector containing these two components. The damage assessment module then intervenes, abandoning the traditional approach of focusing only on geometric displacement and instead focusing on energy accumulation. It performs thermal damage accumulation calculation on the low-frequency DC component in the mechanical feature vector. By constructing a frictional heat energy integral model and introducing time dimension integration, the accumulated damage energy value of the belt edge under continuous contact is calculated. This value is mapped to a normalized risk assessment index, which intuitively reflects the degree of proximity of the current operating state to the physical failure of the belt. The hierarchical control module executes a hierarchical control strategy based on this index: in response to the index being less than the safety threshold, the system is determined to be in a safe or slightly disturbed state, maintaining normal operation and recording data.

[0040] When the index is between the safety threshold and the shutdown threshold, it is determined that there is a risk of continuous wear and tear, and a maintenance warning signal is generated to prompt inspection during non-production periods; when the index exceeds the shutdown threshold, it is determined that there is an imminent risk of tearing or fire, and an emergency stop command is immediately triggered.

[0041] This embodiment achieves a fundamental shift in detection paradigm in coal conveyor belt scenarios by constructing a closed-loop logic from perception to control. Unlike traditional switch quantity detection, which can only provide feedback on whether there is contact, this solution uses time-frequency domain decomposition technology to accurately eliminate false alarm signals caused by coal block impact, ensuring the high availability of the system. At the same time, the introduction of a thermal damage integral model enables the system to capture hidden wear risks that have small force values ​​but long durations, effectively preventing thermal aging and fire hazards caused by long-term micro-friction at the belt edge. This significantly extends the service life of expensive belt assets while ensuring the continuity of coal conveying operations.

[0042] The sensing and acquisition module includes: a physical architecture unit for constructing a flexible anchor sensor array, comprising flexible cables with preset pretension arranged on both sides of the conveyor belt, and rolling contact wheels set at preset intervals as support nodes for the flexible cables. The rolling contact wheels are floatingly mounted on the frame through elastic damping components, enabling the nodes to generate measurable radial micro-displacement when subjected to force. The rolling contact wheels, as the physical contact medium with the edge of the belt, are configured to convert the lateral thrust of the belt into the axial tension of the flexible cables.

[0043] The data acquisition unit includes a tension sensor installed at the end of the online anchor and a micro-displacement sensor at the node, as well as an environmental humidity sensor arranged along the entire conveyor belt. The environmental humidity sensor is configured to collect real-time environmental humidity data of the conveyor belt's operating environment, and is configured to simultaneously collect dynamic tension waveform data, contact point displacement data and real-time environmental humidity data, and transmit the dynamic tension waveform data, contact point displacement data and real-time environmental humidity data to the mechanical analysis module after marking them with a unified timestamp.

[0044] This embodiment describes in detail the hardware configuration and signal acquisition mechanism of the sensing and acquisition module; the physical architecture unit constructs a flexible wire anchor sensing array, the core of which is to use steel wire rope or special fiber rope with high tensile strength and low creep characteristics as flexible cable and arrange them on both sides of the conveyor belt; the system applies a preset pre-tension force to the cable, such as 500N, to ensure the sensitivity of signal transmission and prevent slack.

[0045] A rolling contact wheel is set at a preset distance on the cable; the rolling contact wheel serves as the physical contact medium with the edge of the belt, and its outer surface is covered with wear-resistant rubber. When the belt deviates and comes into contact, it can convert sliding friction into rolling friction, reducing the wear of the belt by the detection device itself. At the same time, through the geometric structure, it converts the lateral thrust of the belt into the axial tension increment of the flexible cable; the data acquisition unit is responsible for the digital acquisition of signals.

[0046] S-shaped tension and compression sensors are installed at the tensioning device at the end of the online anchor to collect dynamic tension waveform data at a sampling frequency of not less than 100Hz; at the same time, micro-displacement sensors are arranged at the support nodes of the online anchor to collect contact point displacement data to help determine the deformation position of the cable; in order to eliminate the phase error caused by transmission delay, all sensor data are marked with a uniform timestamp before transmission to ensure that the subsequent mechanical analysis module can accurately align tension fluctuations and displacement changes.

[0047] This embodiment constructs a continuous sensing network without blind spots along the conveyor belt through an array arrangement of flexible cables and rolling contact wheels. The introduction of pre-tension gives the cables string-like physical properties, enabling them to transmit minute contact forces from a distance to the sensor end with high fidelity, solving the problem of detection vacuum zones between traditional point sensors. In addition, the timestamp synchronization mechanism provides a time-domain alignment reference for multi-source heterogeneous data through a unified clock source, eliminating phase errors caused by transmission delays and ensuring strict alignment of multi-source heterogeneous data in the time domain. This provides a reliable data foundation for subsequent accurate mechanical inversion, especially in long-distance coal conveying corridors, where this architecture greatly enhances the ability to detect local deviation events.

[0048] The mechanical analysis module includes a tension inversion unit, configured to calculate the magnitude and location of the lateral contact force based on the tension transmission model. The tension transmission model characterizes the geometric and mechanical relationship between the lateral contact force and the tension change in the dynamic tension waveform data, the contact point displacement data, and the elastic coefficient of the flexible cable.

[0049] The frequency domain filtering unit is configured to perform spectrum analysis to distinguish between harmless jitter and harmful belt misalignment. It sets a cutoff frequency and identifies the dynamic tension waveform data components in the original sensor dataset with frequencies higher than the cutoff frequency as high-frequency components caused by coal flow impact or joint collision, and identifies the signal components with frequencies lower than the cutoff frequency as low-frequency DC components caused by belt misalignment and compression.

[0050] This embodiment elaborates on the core algorithm logic of the mechanical analysis module, focusing on the specific mathematical modeling of the geometric and mechanical relationship based on the elastic coefficient involved in the embodiment, to ensure the inversion accuracy;

[0051] The tension inversion unit executes a physically constrained iterative solution algorithm to accurately calculate the magnitude of the lateral contact force. Contact position The unit pre-stores the tensile stiffness of the flexible cable. Its unit is Newton, and the effective stress-bearing length of the cable. The calculation process is as follows:

[0052] The system acquires initial parameters and defines the computational domain; it iterates through the real-time readings of all node micro-displacement sensors and identifies the two adjacent nodes with the largest readings. and As the current stress span, obtain the spacing of that stress span. Read the original values ​​of node displacements. ,in, A function is selected for maxima to extract the maximum deformation from the displacements of two adjacent nodes. To overcome the displacement measurement deviation caused by the micro-displacement sensors being placed at the support end and contact occurring at mid-span, the system introduces a correction coefficient based on beam deflection theory to calculate the effective displacement at the contact point. The calculation formula is as follows:

[0053]

[0054] Among them, among them, This is the corrected real-time effective displacement of the contact point. At the current sampling time, This is a preset stiffness coupling factor, for example, 200 N / m; it should be noted here that in the formula... and The product divided by tension The dimensionless number was then obtained, which ensured the dimensionality consistency of the correction term;

[0055] Stiffness coupling factor The specific method for obtaining the value is as follows: During the system debugging phase, on-site calibration is performed. A standard lateral load is applied at the mid-span of the flexible cable. The actual displacement value of the contact point is measured using an external high-precision laser rangefinder. Simultaneously, the original readings of the node displacement sensor and the real-time tension value of the tension sensor are recorded. The beam deflection correction formula is then used to calculate the value in reverse. This value is used to eliminate system errors caused by differences in cable material and varying installation tightness;

[0056] This step ensures that the geometric parameters used for inversion accurately reflect the maximum deformation at the contact point; simultaneously, the end tension sensor values ​​are read. ;

[0057] Construct a set of geometric-mechanical coupled equations; based on the principles of continuum mechanics, the calculation basis for elastic elongation needs to be modified; the modified Hooke's Law equation is defined as:

[0058]

[0059] in, This is the real-time tension value. The preset initial static tension for the flexible cable. This refers to the tensile stiffness of the cable; the effective stress length is introduced here. Defined as when there is no lateral contact force In the initial state of action, the cable length corresponds to the natural length of the cable between the effective tension points at both ends of the current stress span, rather than the total physical length of the cable. This is because although the tension increment caused by local lateral contact is transmitted, the resulting elastic elongation mainly depends on the effective length of the stressed segment. Using the total length for calculation would significantly overestimate the actual elastic elongation, leading to a fundamental deviation in the subsequent coupling equations. The geometric equations remain unchanged, i.e., the calculation formula is:

[0060]

[0061] in, Based on contact position Local geometric elongation of the cable;

[0062] Solve for the contact position Constructing nonlinear equations:

[0063]

[0064] The solution is obtained using the Newton-Raphson iterative method, and the iterative formula is as follows:

[0065]

[0066] Where the derivative function for:

[0067]

[0068] The result obtained through iterative solution It can accurately match the tension increment and local geometric deformation of the stressed section;

[0069] Calculate lateral contact forces; based on determined... and Calculate the cable deflection angle and substitute it into the balance equation. The calculation formula is as follows:

[0070]

[0071] This formula utilizes The function (arctangent function) calculates the geometric deflection angle of the cable after it is compressed, and then... The function (sine function) will measure the real-time tension. The force is decomposed into lateral components, thus allowing the total lateral contact force to be fitted. ;

[0072] The frequency domain filtering unit performs spectrum analysis and sets the cutoff frequency to . The low-frequency DC component was extracted using a fourth-order Butterworth low-pass filter and used as the input for subsequent damage assessment.

[0073] The mechanical analysis module also includes a modal recognition unit, used to identify the physical causes of belt misalignment. It performs periodic analysis on the low-frequency DC component. If the low-frequency DC component exhibits sinusoidal fluctuation characteristics strongly correlated with the conveyor belt's operating cycle, it is determined to be periodic misalignment caused by roller or idler eccentricity. If the low-frequency DC component exhibits non-periodic step-up characteristics, it is determined to be sudden misalignment caused by uneven material distribution or structural deformation. Strong correlation means that the cross-correlation coefficient between the fluctuation sequence of the low-frequency DC component and the theoretical periodic sequence calculated based on belt speed is greater than a preset correlation threshold, such as 0.85.

[0074] This embodiment details the diagnostic logic of the modal recognition unit, aiming to identify the physical causes behind belt deviation. This unit performs periodic analysis on the low-frequency DC component extracted in the preceding steps, including calculating its autocorrelation function. Simultaneously, the system acquires the conveyor belt's operating cycle parameters. Based on this, the system executes feature matching logic: specifically, for the determination of sinusoidal fluctuation characteristics, the system performs a spectral peak matching algorithm, performing a Fast Fourier Transform (FFT) on the low-frequency DC component to extract the main peak frequency where its power spectral density is maximum. And calculate the fundamental frequency of the conveyor belt, the calculation formula is:

[0075]

[0076] in, For the real-time running speed of the belt, The belt circumference is obtained, and the idler diameter is also obtained. Calculate the fundamental frequency of the idler roller rotation. Perform the following hierarchical judgment:

[0077] like If so, the system determines that the fault source is the conveyor drum eccentricity or uneven joint.

[0078] like If so, the system determines that the fault source is roller eccentricity;

[0079] If any of the above conditions are met, it is confirmed that the fault exhibits a sinusoidal fluctuation characteristic that is strongly correlated with the operating cycle. Such faults usually originate from manufacturing defects or uneven wear of mechanical parts.

[0080] Conversely, the response to the low-frequency DC component exhibits a non-periodic step-up characteristic, meaning the force suddenly increases and remains at a certain level. The specific criterion is: calculate the first-order difference sequence of the low-frequency DC component. ,like The amplitude exceeds the preset mutation threshold If so, it is determined to be a step jump;

[0081] threshold It is not set arbitrarily, but is obtained through an adaptive statistical method: real-time calculation of the past hour. Standard deviation and update ,Should The principle ensures that the false alarm rate is less than one in a million. The system determines that the source of the fault is uneven material distribution, such as incorrect coal drop point, or sudden deviation caused by structural deformation.

[0082] This embodiment elevates deviation detection from a simple alarm to a fault diagnosis dimension. By analyzing the time-series characteristics of mechanical signals, the system can distinguish between periodic faults caused by geometric defects in the equipment itself and sudden faults caused by process operations. In actual operation and maintenance scenarios, this means that maintenance personnel can directly carry targeted tools to the site based on system prompts. For example, for periodic deviations, they can directly check the roller bearings, and for sudden deviations, they can check the location of the coal drop pipe, thereby greatly shortening the fault diagnosis time and improving operation and maintenance efficiency.

[0083] The damage assessment module includes: an energy integration unit, configured to quantify the physical damage risk of the belt edge, obtain the real-time operating speed of the belt and the preset friction coefficient, combine the low-frequency DC component output by the mechanical analysis module, calculate the product of the lateral contact force, real-time operating speed and friction coefficient, and integrate the product in the time domain to obtain the cumulative heat value generated by friction at the belt edge, which is used as the cumulative damage energy value; and a state mapping unit, configured to map the cumulative damage energy value into a visualized risk assessment index, with a preset function relating damage energy to the belt material's tolerance limit. This function is configured to increase the risk assessment index value in a non-linear manner when the cumulative damage energy value is greater than or equal to 80% of the material's tolerance limit.

[0084] This embodiment details the calculation process of the damage assessment module, particularly clarifying the physical boundary conditions and reset logic for energy integration to address the integration divergence problem under continuous operation. The energy integration unit introduces a frictional heat energy integration model, and the calculation formula is as follows:

[0085]

[0086] in, The damage energy accumulation value, in physical terms, is the cumulative frictional work within a single contact event.

[0087] The integration start time is dynamically triggered by the system logic; when the low-frequency DC component output by the mechanics analysis module... When the value jumps from zero to a preset dead zone threshold, such as above 10N, it is marked as... And reset ;like If the value is zeroed and remains above the cooldown confirmation time (e.g., 60 seconds), the integration of the current event ends. This logic ensures that the model performs risk assessment only for a single consecutive deviation event, preventing irrelevant interference from historical data.

[0088] This is the current deadline for integral calculation; The heat conversion efficiency coefficient is set to 1.0 based on the adiabatic temperature rise assumption, which assumes that the frictional work is completely converted into internal energy and there is no heat loss. This setting is intended to assess the fire risk under the most unfavorable working conditions and is in line with the redundancy design principle of the safety monitoring system.

[0089] This is the filtered lateral contact force;

[0090] For the time variable in the integral transform, corresponding to Historical moment value;

[0091] For real-time speed control; To avoid the lag of the lookup table method under drastic environmental fluctuations, the system uses a humidity-friction coupling attenuation equation for real-time calculation of the dynamic friction coefficient; and reads the environmental humidity sensor values ​​in real time. The unit is %RH, and the formula is:

[0092]

[0093] in, The preset minimum limiting friction coefficient is set to a value of [value missing]. This is used to prevent calculation results from being distorted in extremely humid environments; The constant 0.02 is the humidity exponential decay coefficient obtained through experimental fitting. Its physical meaning is to reflect the tribomechanical property that the thickness of the adsorption film formed by water molecules at the rubber-metal interface increases exponentially with the increase of humidity, thereby reducing the microscopic contact area. It is used to characterize the sensitivity of the friction coefficient to changes in humidity. For humidity sensitivity, this formula quantifies the nonlinear weakening effect of the water film on friction; the constant 50 is a preset environmental relative humidity benchmark threshold, used to define the initial humidity level at which the formation of the water film has a significant nonlinear effect on the friction coefficient.

[0094] The state mapping unit will Mapped to risk assessment index The calculation formula is as follows:

[0095]

[0096] in, The cumulative value of damage energy With the material's endurance limit The ratio, i.e. ; The material's tolerance limit was determined by measuring the total heat endothermic reaction of the belt cover rubber using differential scanning calorimetry in the laboratory. For example... J; Denominator ( Normalization factor, coefficient The scaling factor is used to combine the two factors to scale the risk assessment index. Mapped to Within the dimensionless closed interval, so that maintenance personnel can understand it intuitively; The nonlinear growth factor is used; by combining adiabatic integral and nonlinear mapping, this model can issue a high-level warning in advance when the temperature at the edge of the belt has not yet reached the ignition point but the accumulated energy has approached the damage threshold.

[0097] The graded control module includes a transient filtering unit, configured to pre-screen high-frequency components before the damage assessment module calculates the risk assessment index. In response to the high-frequency components separated by the mechanical analysis module, the unit is configured to determine a transient mechanical disturbance when the amplitude of the high-frequency component exceeds a preset trigger threshold and the duration is less than a preset impact time threshold. Data for this time period will not be included in the energy integration calculation of the damage assessment module, and only the disturbance event will be recorded. Otherwise, if the duration is greater than or equal to the preset impact time threshold, the high-frequency component will be included in the calculation of the risk assessment index.

[0098] This embodiment details the logical judgment process of the transient filtering unit, aiming to prevent unnecessary accidental shutdowns. The system sets a trigger threshold and an impact time threshold. The trigger threshold is obtained by collecting background vibration noise from the conveyor belt during its no-load operation during the system initialization phase and calculating the standard deviation of the noise signal. And set the trigger threshold to To filter out 99.7% of random noise;

[0099] The impact time threshold is determined based on physical experiments and set to 200ms. This value corresponds to the maximum time span during which the largest permissible coal block will generate impulse when it falls freely and impacts the anchor. The unit monitors the amplitude and duration of the high-frequency component in real time. Based on this, the system performs strict condition judgment: if the amplitude of the high-frequency component exceeds the trigger threshold and the duration is less than the impact time threshold, the system determines that the high-frequency component is a transient mechanical disturbance, such as a large piece of coal hitting the anchor.

[0100] For such judgments, the system will ignore the event, that is, it will not trigger a shutdown command or increase the risk assessment index, but will only record a disturbance event in the background log. Conversely, if the response duration exceeds the threshold, it indicates that the high-frequency vibration is persistent and may be caused by tearing and slapping. The system will then include the high-frequency component in the calculation of the risk assessment index.

[0101] In this embodiment, the system acts as an intelligent filter in the hierarchical control system. In coal conveying sites, it is common for coal blocks to roll or for the machine frame to vibrate. Traditional switches often malfunction frequently as a result, which seriously affects the efficiency of power plants and other continuous production enterprises. This solution uses dual logic thresholds of amplitude and time, and sets thresholds based on statistical principles to accurately filter out occasional transient interferences. This ensures that only real continuous faults will trigger alarms or shutdowns. This design maximizes the operating efficiency and production continuity of the equipment while ensuring safety.

[0102] The hierarchical control module also includes a trend prediction unit, configured to process the risk assessment index between the safety threshold and the shutdown threshold, calculate the rate of change of the risk assessment index over time, and if the rate of change is positive and the variance of the rate of change is less than the preset stable value, then predict the remaining time to reach the shutdown threshold based on the current rate of change, and generate a maintenance warning signal containing the suggested inspection location at the current moment. The suggested inspection location is calculated by the mechanical analysis module based on the contact point displacement data.

[0103] This embodiment details the prediction logic of the trend prediction unit. This unit calculates the rate of change of the risk assessment index within the warning range over time. The system verifies the stability of this rate of change by calculating its variance. To accurately quantify this variance, the system employs a sliding window statistical method, constructing a window of length [length missing]. First-in, first-out (FIFO) cache queue; parameters It is not selected arbitrarily, but rather based on the system sampling frequency. Compared with the preset trend observation window time Commonly determined, that is, satisfying the relational expression For example, when the system sampling frequency And when it is necessary to observe data from the past minute to ensure statistical significance, that is... Then determine ;

[0104] System storage recently The rate of change of each sampling point is used to calculate the variance of the data in the queue in real time. Only when Continuously below the preset stable value, for example Only when the rate of change is stable is the prediction error due to instantaneous disturbances eliminated. Based on this, the system executes a prediction algorithm: If the rate of change is positive and the variance of the fluctuation meets the above stability conditions, indicating that the fault is worsening with a stable trend, the system predicts the remaining time to reach the shutdown threshold based on the current rate of change. The calculation formula is as follows:

[0105]

[0106] in, The remaining time is calculated and its physical meaning is the estimated time until an emergency stop is triggered. The shutdown threshold is derived from preset parameters; This is the current risk assessment index, calculated in real time. The rate of change is exponential, derived from differential calculations, and is the average rate of change within the sliding window.

[0107] While generating warning signals, the system combines the contact point displacement data provided by the mechanical analysis module to calculate and push suggested inspection locations. To overcome the random errors of single-point calculations and adapt to dynamic situations where deviation points may cross nodes, the suggested inspection locations are... The calculation formula is derived using the torque-weighted centroid algorithm that incorporates global coordinates:

[0108]

[0109] in, For at any time System-locked starting node of the force span The global mileage coordinates are calculated along the direction of the conveyor belt, with the center line of the conveyor head drive roller as the origin, and the unit is meters. This represents the local contact position relative to the starting node at that moment. The corresponding lateral force; the algorithm ensures that even if the conveyor belt drifts along the deviated area, the warning coordinates can still accurately focus on the physical location with the most concentrated force and the greatest risk of damage by converting local coordinates into global coordinates in real time for weighting.

[0110] The system also includes an adaptive calibration module, which dynamically corrects the reference parameters of the sensing and acquisition module. During periods when the conveyor belt is unloaded and running smoothly, it monitors the static tension value of the flexible anchor. If the static tension value drifts due to changes in ambient temperature or cable creep, it automatically updates the reference tension parameters in the tension transfer model to eliminate the influence of environmental factors on detection accuracy.

[0111] This embodiment details the closed-loop correction mechanism of the adaptive calibration module, focusing on solving the quantization identification and parameter update logic under no-load and stable operation conditions, thus avoiding the lag of manual calibration. The system connects to the conveyor's PLC control system via the OPC protocol to acquire the main motor current in real time. Real-time running speed of the belt The system defines the trigger condition for the calibration window as the simultaneous fulfillment of the following three logical criteria:

[0112] No-load criterion: ,in, The preset no-load current threshold is set to 30% of the rated current on the nameplate of the main drive motor to confirm that there is no coal flow load on the conveyor belt.

[0113] Steady speed criterion: That is, the speed fluctuation is within 5%, excluding dynamic interference during the start-stop phase. The rated operating speed of the belt;

[0114] Steady-state criterion: within the duration window Variance of flexible anchor tension data Ensure there is no mechanical disturbance or human touch;

[0115] In other words, the aforementioned period when the conveyor belt is unloaded and running smoothly is defined as: within a preset time window. Inside, the real-time running speed of the belt relative to the belt's rated operating speed The fluctuation deviation rate remained below 5%, and the variance of the flexible anchor tension data was... Less than 1.0;

[0116] When the system is in the calibration window, the module performs drift monitoring: calculating the average tension within the current window. ; Calculate the mean and the current model baseline tension. The deviation ratio is calculated using the following formula:

[0117]

[0118] like As a preset drift threshold, and the ambient temperature sensor displays temperature changes. ,in, If the absolute value of the difference between the current real-time temperature and the ambient temperature recorded when the system last performed a baseline parameter update is used, then parameter drift is determined to have occurred.

[0119] The system performs an update operation: the baseline parameters are updated using the Exponentially Weighted Moving Average (EWMA) method, with the following formula:

[0120]

[0121] Among them, among them, For the updated model reference tension parameters, The original model reference tension parameters were used before the update. The update factor is an empirically set exponential weighted smoothing coefficient with a value of 0.1. This value is selected based on a trade-off between response speed and stability. It can track ambient temperature drift on an hourly scale while effectively suppressing random measurement noise on a sub-second scale. This smoothing update strategy prevents drastic changes in the baseline caused by a single misjudgment, thereby eliminating the impact of environmental factors on detection accuracy.

[0122] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention.

Claims

1. A coal conveying belt deviation detection system based on line anchor mechanism, characterized in that, include: The sensing and acquisition module is configured to collect real-time status data of the flexible anchor devices arranged along the conveyor belt. The real-time status data includes dynamic tension waveform data and contact point displacement data inside the anchor, and obtains the raw sensing dataset. The mechanical analysis module is configured to perform decoupled analysis on the original sensor dataset. Based on the preset tension transmission model, it uses dynamic tension waveform data to infer the lateral contact force acting on the anchor line and performs time-frequency domain decomposition on the lateral contact force. It separates the high-frequency component with a frequency higher than the preset cutoff frequency as transient mechanical disturbance and the low-frequency DC component with a frequency lower than the preset cutoff frequency as continuous compression behavior, and generates a mechanical feature vector containing the high-frequency component and the low-frequency DC component. The damage assessment module is configured to perform thermal damage accumulation calculation on the low-frequency DC component in the mechanical feature vector, construct a frictional heat energy integral model, introduce time dimension integration, calculate the damage energy accumulation value of the belt edge under continuous contact state, and map the damage energy accumulation value to the risk assessment index of the current operating state. The hierarchical control module is configured to execute hierarchical control strategies based on a risk assessment index, with preset safety thresholds and shutdown thresholds, wherein the shutdown thresholds are greater than the safety thresholds. When the risk assessment index is less than the safety threshold, the system continues to operate normally and records data; when the risk assessment index is greater than or equal to the safety threshold but less than the shutdown threshold, a maintenance warning signal is generated. When the risk assessment index is greater than or equal to the shutdown threshold, an emergency stop command is triggered. The sensing and acquisition module includes: The physical architecture unit, used to construct a flexible wire anchor sensor array, includes flexible cables with preset pretension arranged on both sides of the conveyor belt, and rolling contact wheels arranged at preset intervals as support nodes for the flexible cables. The rolling contact wheels are floatingly mounted on the frame through elastic damping components, so that the nodes can generate measurable radial micro-displacement when subjected to force. The rolling contact wheels, as the physical contact medium with the edge of the belt, are configured to convert the lateral thrust of the belt into the axial tension of the flexible cables. The data acquisition unit includes a tension sensor installed at the end of the online anchor and a micro-displacement sensor at the node, as well as an environmental humidity sensor arranged along the entire conveyor belt. The environmental humidity sensor is configured to collect real-time environmental humidity data of the conveyor belt's operating environment, and is configured to simultaneously collect dynamic tension waveform data, contact point displacement data, and real-time environmental humidity data. The dynamic tension waveform data, contact point displacement data, and real-time environmental humidity data are then marked with a unified timestamp and transmitted to the mechanical analysis module.

2. The coal conveying belt misalignment detection system based on line anchor mechanism according to claim 1, wherein, The mechanics analysis module includes: The tension inversion unit is configured to calculate the magnitude and location of the lateral contact force based on the tension transfer model, which characterizes the geometric and mechanical relationship between the lateral contact force and the tension change, contact point displacement data, and elastic coefficient of the flexible cable in the dynamic tension waveform data. The frequency domain filtering unit is configured to perform spectrum analysis to distinguish between harmless jitter and harmful belt misalignment. It sets a cutoff frequency and identifies the dynamic tension waveform data components in the original sensor dataset with frequencies higher than the cutoff frequency as high-frequency components caused by coal flow impact or joint collision, and identifies the signal components with frequencies lower than the cutoff frequency as low-frequency DC components caused by belt misalignment and compression.

3. The coal conveying belt misalignment detection system based on line anchor mechanism according to claim 2, characterized in that, The mechanics analysis module also includes: The modal recognition unit is used to identify the physical causes of belt misalignment. It performs periodic analysis on the low-frequency DC component. If the low-frequency DC component exhibits sinusoidal fluctuation characteristics that are strongly correlated with the conveyor belt's operating cycle, it is determined to be periodic misalignment caused by the eccentricity of the rollers or idlers. If the low-frequency DC component exhibits non-periodic step-up characteristics, it is determined to be sudden misalignment caused by uneven material distribution or structural deformation.

4. The coal conveying belt misalignment detection system based on line anchor mechanism of claim 1, wherein, The damage assessment module includes: The energy integration unit is configured to quantify the physical damage risk of the belt edge, obtain the real-time running speed of the belt and the preset friction coefficient, combine the low-frequency DC component output by the mechanical analysis module, calculate the product of the lateral contact force, real-time running speed and friction coefficient, and integrate the product in the time domain to obtain the cumulative heat value generated by friction at the belt edge, which is used as the cumulative damage energy value. The state mapping unit is configured to map the cumulative damage energy value to a visualized risk assessment index. It has a preset function relating damage energy to the tolerance limit of the belt material. This function is configured to increase the value of the risk assessment index in a non-linear manner when the cumulative damage energy value is greater than or equal to 80% of the tolerance limit of the material.

5. The coal conveyor belt misalignment detection system based on line anchor mechanism of claim 1, wherein, The hierarchical management and control module includes: The transient filtering unit is configured to pre-screen high-frequency components before the damage assessment module calculates the risk assessment index. In response to the high-frequency components separated by the mechanical analysis module, it is configured to determine that a transient mechanical disturbance occurs when the amplitude of the high-frequency component exceeds a preset trigger threshold and the duration is less than a preset impact time threshold. The data for this time period will not be included in the energy integration calculation of the damage assessment module, and only the disturbance event will be recorded. Otherwise, if the duration is greater than or equal to the preset impact time threshold, the high-frequency component will be included in the calculation of the risk assessment index.

6. The coal conveyor belt misalignment detection system based on line anchor mechanism of claim 1, wherein, The hierarchical management module also includes: The trend prediction unit is configured to process the risk assessment index between the safety threshold and the shutdown threshold, calculate the rate of change of the risk assessment index over time, and if the rate of change is positive and the variance of the rate of change is less than a preset stable value, predict the remaining time to reach the shutdown threshold based on the current rate of change, and generate a maintenance warning signal containing suggested inspection locations at the current moment. The suggested inspection locations are calculated by the mechanical analysis module based on the contact point displacement data.

7. The coal conveyor belt misalignment detection system based on line anchor mechanism of claim 1, wherein, Also includes: The adaptive calibration module is used to dynamically correct the reference parameters of the sensing and acquisition module. During periods when the conveyor belt is unloaded and running smoothly, it monitors the static tension value of the flexible anchor. If the static tension value drifts due to changes in ambient temperature or cable creep, it automatically updates the reference tension parameters in the tension transfer model to eliminate the influence of environmental factors on detection accuracy.

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