Chain breakage fault pre-judging device and method of scraper conveyor and scraper conveyor

Abstract

The invention relates to the technical field of scraper conveyors, in particular to a chain breakage fault pre-judging device and method for a scraper conveyor and the scraper conveyor, and the device comprises a controller, a first insertion plate and a first magnetic sensor fixed to the first insertion plate; a first opening is formed in a conveying groove of the scraper conveyor, the first inserting plate is inserted into the first opening and located on the side, close to a chain way, of the chain, and the first magnetic sensor is fixed to the first inserting plate and opposite to one side of the chain. In the running process of a chain of the scraper conveyor, the first magnetic sensor collects a first magnetic characteristic signal of a chain ring passing through the first magnetic sensor; the controller is connected with the first magnetic sensor, analyzes the damage state of the chain ring according to the first magnetic characteristic signal, and prejudges whether the chain ring is a chain breakage fault risk point or not based on the damage state. Therefore, the chain damage of the scraper conveyor is pre-judged in advance, so that the early warning and preventive maintenance of the chain breakage fault are realized, and the operation reliability and safety of the scraper conveyor are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of scraper conveyors, and particularly relates to a device and method for predicting chain breakage faults of a scraper conveyor and a scraper conveyor. Background Art

[0002] A scraper conveyor is a continuous conveying device that uses a chain as a load-bearing and traction mechanism and pushes materials along a closed trough through scrapers. Its core principle is to drive the scrapers with the chain and push the materials from one end to the other end in a fixed trough body, and it is widely used in fields such as mines, metallurgy, chemical industry, food processing, and coal mining. In the production system of a coal mine fully mechanized mining face, as the core transportation equipment, the stable operation of the chain drive system of the scraper conveyor directly determines the coal mining efficiency and production safety. However, chain breakage accidents restrict the reliable operation of the scraper conveyor. Once a chain breakage occurs, the coal mining face will be forced to stop operating comprehensively, which not only seriously reduces production efficiency but may also cause secondary damages such as sprocket jamming and scraper deformation due to the failure not being detected in time, further expanding the equipment damage degree and maintenance cost.

[0003] In some scenarios, the chain breakage monitoring technology of the scraper conveyor mainly focuses on the breakpoint detection after the occurrence of chain breakage faults. Among them, the image recognition method installs a camera at the tail of the machine and judges the chain breakage based on the movement continuity and inclination state of the scraper within the monitoring area of the camera, and it is difficult to predict the chain breakage fault of the scraper conveyor chain before it occurs. The above monitoring means belong to the detection of chain breakage after the occurrence of chain breakage faults, and it is difficult to effectively identify chain defects in the early damage stages such as chain wear and fatigue cracks, so it is difficult to achieve early warning and preventive maintenance of faults, resulting in high shutdown risks and economic damages caused by sudden chain breakage in production. Thus, the above monitoring means are difficult to predict the chain damage of the scraper conveyor in advance, so it is difficult to achieve early warning and preventive maintenance of chain breakage faults, resulting in low reliability and safety of the operation of the scraper conveyor. Summary of the Invention

[0004] In order to solve the technical problems that it is difficult to predict the chain damage of the scraper conveyor in advance, so it is difficult to achieve early warning and preventive maintenance of chain breakage faults, resulting in low reliability and safety of the operation of the scraper conveyor, the purpose of the present invention is to provide a device and method for predicting chain breakage faults of a scraper conveyor and a scraper conveyor, and the specific technical solutions adopted are as follows:

[0005] In a first aspect, an embodiment of the present invention discloses a device for predicting chain breakage faults of a scraper conveyor. The scraper conveyor includes a sprocket, a chain, and scrapers. The chain is formed by connecting multiple chain links, and the scrapers are distributed at intervals on the chain. When the sprocket rotates, it drives the chain and the scrapers to move. The device for predicting chain breakage faults includes: a controller, a first plug plate, and a first magnetic sensor fixed to the first plug plate; a first opening is provided in the conveying trough of the scraper conveyor, the first plug plate is inserted into the first opening and is located on the side of the chain close to the chain path, the first magnetic sensor is fixed to the first plug plate and is opposite to one side of the chain; during the operation of the chain of the scraper conveyor, the first magnetic sensor is used to collect the first magnetic characteristic signal of the chain link passing through the first magnetic sensor; the controller is connected to the first magnetic sensor and is used to analyze the damage state of the chain link according to the first magnetic characteristic signal and predict whether the chain link is a risk point of chain breakage fault based on the damage state.

[0006] In a second aspect, an embodiment of the present invention discloses a method for predicting chain breakage faults of a scraper conveyor. Based on the device for predicting chain breakage faults of the scraper conveyor mentioned in the first aspect, the method for predicting chain breakage faults includes: obtaining the first magnetic characteristic signal of the chain link passing through the first magnetic sensor, and the first magnetic sensor is opposite to one side of the chain link; analyzing the damage state of the chain link according to the first magnetic characteristic signal and predicting whether the chain link is a risk point of chain breakage fault based on the damage state.

[0007] In a third aspect, an embodiment of the present invention discloses a scraper conveyor, including: a sprocket, a chain, and scrapers. The chain is formed by connecting multiple chain links, and the scrapers are distributed at intervals on the chain. When the sprocket rotates, it drives the chain and the scrapers to move. It further includes a device for predicting chain breakage faults. The device for predicting chain breakage faults includes: a controller, a first plug plate, and a first magnetic sensor fixed to the first plug plate; a first opening is provided in the conveying trough of the scraper conveyor, the first plug plate is inserted into the first opening and is located on the side of the chain close to the chain path, the first magnetic sensor is fixed to the first plug plate and is opposite to one side of the chain; during the operation of the chain of the scraper conveyor, the first magnetic sensor is used to collect the first magnetic characteristic signal of the chain link passing through the first magnetic sensor; the controller is connected to the first magnetic sensor and is used to analyze the damage state of the chain link according to the first magnetic characteristic signal and predict whether the chain link is a risk point of chain breakage fault based on the damage state.

[0008] In the embodiment of the present invention, by installing the first plug board and the first magnetic sensor at the first opening of the conveying trough of the scraper conveyor, it is possible to continuously and real-time collect the first magnetic characteristic signals of the chain links during the operation of the chain, ensuring that the state information of the chain links can be obtained in a timely manner at every moment of the chain operation, greatly improving the timeliness and effectiveness of monitoring. The controller analyzes the damage state of the chain links based on the first magnetic characteristic signals and pre-judges the risk points of chain faults. Through in-depth analysis of the magnetic characteristic signals, potential damage states such as early fatigue cracks and wear of the chain links can be identified, and the chain links at risk of chain breakage can be pre-judged in advance with a high prediction accuracy, preventing chain breakage faults before they occur and effectively avoiding sudden accidents caused by chain breakage.

[0009] Since the risk points of chain faults can be accurately pre-judged, a maintenance plan can be formulated in advance according to the prediction results, and the shutdown and maintenance time can be reasonably arranged to avoid unplanned shutdowns caused by sudden chain breaks. The number of production interruptions and the shutdown and maintenance duration are reduced, ensuring the continuous and stable operation of the scraper conveyor, significantly improving production efficiency, and reducing economic losses caused by equipment failures. Further, by detecting chain link damage in advance and pre-judging the risk points of chain faults, measures such as replacing chain links can be taken in a timely manner to reduce the probability of chain breakage accidents, avoid safety hazards such as material splashing and equipment component detachment caused by chain breakage, create a safer working environment for operators, and ensure personnel safety. In this way, the damage of the chain of the scraper conveyor is pre-judged in advance, so as to realize early warning and preventive maintenance of chain breakage faults, and improve the operation reliability and safety of the scraper conveyor. Description of the Drawings

[0010] Figure 1 It is a schematic cross-sectional structure diagram of a scraper conveyor provided by an embodiment of the present invention.

[0011] Figure 2 It is a schematic side structure diagram of a scraper conveyor provided by an embodiment of the present invention.

[0012] Figure 3 It is a schematic structure diagram of a chain breakage fault prediction device provided by an embodiment of the present invention.

[0013] Figure 4 It is a schematic flow diagram of a chain breakage fault prediction method provided by an embodiment of the present invention. Detailed Embodiments

[0014] In order to further elaborate on the technical means and effects adopted by the present invention to achieve the intended invention purpose, the following specifically describes, in conjunction with the accompanying drawings and preferred embodiments, a chain-breaking fault prediction device, method and scraper conveyor according to the present invention, including its specific implementation manners, structures, features and effects, as follows. In the following description, different "one embodiment" or "another embodiment" do not necessarily refer to the same embodiment. In addition, the specific features, structures or characteristics in one or more embodiments can be combined in any suitable form.

[0015] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by those skilled in the technical field to which the present invention belongs.

[0016] The following specifically describes the specific solutions of a chain-breaking fault prediction device, method and scraper conveyor provided by the present invention in conjunction with the accompanying drawings.

[0017] As Figures 1 to 3 shown, Figure 1 FIG. is a schematic cross-sectional structure diagram of a scraper conveyor provided by an embodiment of the present invention, Figure 2 FIG. is a schematic side structure diagram of a scraper conveyor provided by an embodiment of the present invention, Figure 3 FIG. is a schematic structural diagram of a chain-breaking fault prediction device provided by an embodiment of the present invention. As Figure 1 shown, the scraper conveyor includes: a sprocket 10, a chain 11 and a scraper 12. The chain 11 is formed by connecting a plurality of chain links. Scrapers 12 are distributed at intervals on the chain 11. When the sprocket 10 rotates, it drives the chain 11 and the scraper 12 to move. The scraper conveyor further includes a chain-breaking fault prediction device, and the chain-breaking fault prediction device includes: a controller (not shown in the figure), a first plug board 20 and a first magnetic sensor 30 fixed to the first plug board 20. A first opening 40 is provided in the conveying trough of the scraper conveyor. The first plug board 20 is inserted into the first opening 40 and is located on the side of the chain close to the chain track. The first magnetic sensor 30 is fixed to the first plug board 20 and is opposite to one side of the chain. During the operation of the chain of the scraper conveyor, the first magnetic sensor 30 is used to collect the first magnetic characteristic signal of the chain link passing by the first magnetic sensor 30. The controller is connected to the first magnetic sensor 30 and is used to analyze the damage state of the chain link according to the first magnetic characteristic signal and predict whether the chain link is a risk point of chain-breaking fault based on the damage state.

[0018] Specifically, the axis of the sprocket 10 is rigidly connected to the driving motor through a coupling, and precise circular motion is achieved under the drive of the motor output torque. The chain 11, as a power transmission component, is composed of multiple high-strength alloy steel chain links connected by a precise hinged process. The adjacent chain links are connected by the interference fit of the pin shaft and the chain link hole, and the fit tolerance is controlled within ±0.02 mm to ensure the stability and reliability of the chain operation. The scraper 12 is in a rectangular plate structure, made of high manganese steel, with card slots matching the chain 11 arranged on both sides, and is fixedly connected to the chain 11 through high-strength bolts. The interval distance between adjacent scrapers 12 is determined according to the material characteristics and conveying requirements, usually between 300 - 500 mm, to ensure the continuity and efficiency of material conveying.

[0019] Further, the conveying trough can be the middle trough or the transition trough of the scraper conveyor. A first opening and the second opening in the following embodiments are provided on the middle trough or the transition trough to fix and install the plug plate. In the embodiment of the present invention in the figure, the transition trough is taken as an example, and the first opening and the second opening in the following embodiments are provided at the positions shown in the figure of the transition trough.

[0020] In the embodiment of the present invention, to effectively prevent the chain breakage fault of the chain and ensure the safe and stable operation of the scraper conveyor, the embodiment of the present invention provides a device for predicting the chain breakage fault. This device mainly consists of a controller, a first plug plate 20, and a first magnetic sensor 30. The conveying trough, as the track carrier for the operation of the chain 11, is welded and formed at the bottom with high-strength wear-resistant steel plates, and a rectangular first opening 40 is provided on the side wall close to the chain path. The size of the first opening 40 is precisely matched with the first plug plate 20, its width is 2 - 3 mm larger than the width of the first plug plate 20, and a mounting gap of 1 - 2 mm is reserved in the height direction to facilitate the smooth insertion and removal of the first plug plate 20. The first plug plate 20 is made of stainless steel and has an L-shaped structure. The vertical part is inserted into the first opening 40, and the horizontal part extends towards the chain 11. A special sensor mounting groove is machined on the surface of the horizontal part of the first plug plate 20, and the groove depth and width are customized according to the external dimensions of the first magnetic sensor 30 to ensure that the first magnetic sensor 30 can be firmly embedded and maintain a stable working posture. It should be noted that according to the number of chains, a corresponding number of first magnetic sensors 30 can be provided on the first plug plate 20. For example, if the number of chains is 2, two first magnetic sensors 30 can be provided on the first plug plate 20, that is Figure 3 the structure shown in. Each chain link of the two chains can pass through the corresponding first magnetic sensor 30 in sequence.

[0021] Further, the types of the first magnetic sensor 30 include, but are not limited to, Hall effect sensors, magnetoresistive sensors, magnetic induction sensors, magnetic integrated sensors, etc., which can quickly and accurately capture the weak changes in the magnetic field of the link passing through the first magnetic sensor 30. The perpendicular distance between the sensing surface of the first magnetic sensor 30 and the surface of the chain 11 on the side close to the first magnetic sensor 30 is controlled between 5 - 8 mm. This distance can not only ensure the effective induction of the link magnetic field by the first magnetic sensor 30, but also avoid the mechanical collision of the first magnetic sensor 30 caused by the close distance during the operation of the chain. The first magnetic sensor 30 can be connected to the controller through a high-temperature resistant and interference-resistant shielded cable. The cable adopts a double shielding structure, with a metal braided mesh on the outer layer and an aluminum foil shielding layer on the inner layer, effectively suppressing electromagnetic interference and ensuring the accuracy and stability of signal transmission.

[0022] Further, after the scraper conveyor starts to operate, the teeth of the sprocket 10 mesh with the links of the chain 11, driving the chain 11 to perform a cyclic movement in the chain path of the conveying trough. During the operation of the chain 11, each link passes through the sensing area of the first magnetic sensor 30 in sequence. Due to inevitable defects such as internal stress and microscopic cracks in the production, manufacturing, installation, and use processes of the link, these defects will cause changes in the magnetic permeability of the local area of the link. When the link passes through the first magnetic sensor 30, if there are damage defects, the magnetic field distribution will be distorted, and the first magnetic sensor 30 converts this magnetic field change into an electrical signal, that is, the first magnetic characteristic signal.

[0023] Further, the collected first magnetic characteristic signal contains rich link state information, but at the same time is mixed with environmental noise and interference signals generated by equipment operation. The controller internally integrates a multi-level signal processing system. First, the original first magnetic characteristic signal is preprocessed through a hardware filter circuit. The hardware filter circuit uses a second-order Butterworth low-pass filter with a cut-off frequency set at 5 kHz to effectively filter out high-frequency noise. The signal after hardware filtering enters the digital signal processing module, which uses the fast Fourier transform algorithm to convert the time-domain signal into a frequency-domain signal and extract the frequency characteristics of the first magnetic characteristic signal. At the same time, the wavelet transform algorithm is used to perform multi-scale decomposition on the signal to obtain the signal detail characteristics at different resolutions.

[0024] Further, as an optional embodiment of the present invention, the controller analyzes the damage state of the chain link based on the first magnetic feature signal, and predicts whether the chain link is a risk point of chain breakage based on the damage state, including: comparing the first magnetic feature signal with the normal magnetic feature signal of the chain link in the normal state. If the first magnetic feature signal is inconsistent with the normal magnetic feature signal, it is determined that the chain link is damaged; comparing the first magnetic feature signal with the damaged magnetic feature signals of chain links of different damage types to determine the damage type corresponding to the first magnetic feature signal; respectively calculating the first difference between the first magnetic feature signals of the same chain link of each damage type in adjacent monitoring periods, and taking the first difference as the damage deterioration degree of the chain link of each damage type, and taking the signal intensity of the first magnetic feature signal of the chain link of each damage type monitored in a single period as the damage degree of the chain link; selecting the maximum damage deterioration degree or the maximum damage degree from each damage type, and taking the chain link corresponding to the maximum damage deterioration degree as the risk point of chain breakage of the corresponding damage type, or taking the chain link corresponding to the maximum damage degree as the risk point of chain breakage of the corresponding damage type.

[0025] Specifically, in the embodiment of the present invention, feature extraction is performed on the first magnetic feature signal. Based on the extracted signal features, the chain link damage analysis algorithm built into the controller is combined with the pre-established chain link damage feature database for comparative analysis. The chain link damage feature database is accumulated through a large number of experiments and actual operation data, and includes magnetic feature signal samples of chain links with different damage types and different degrees of damage of each damage type, as well as magnetic feature signal samples of the chain link in the normal state. For example, for fatigue cracks, when the energy in a specific frequency band (such as 1-3 kHz) in the first magnetic feature signal is significantly enhanced and the waveform shows sharp pulse characteristics, combined with the magnetic feature signal samples of the chain link in the normal state and the magnetic feature signal samples of chain links with different degrees of damage of various damage types in the chain link damage feature database, it can be determined that the chain link has fatigue cracks, and by calculating the energy amplitude of this frequency band or comparing it with the magnetic feature signal samples of chain links with different degrees of damage under fatigue crack damage, the length and depth of the crack can be estimated.

[0026] Further, in the actual comparison process, the Euclidean distance algorithm is used to calculate the difference degree between the collected first magnetic feature signal and the normal magnetic feature signal. For the first magnetic feature signal S1 and the normal magnetic feature signal S01, the calculation formula for its difference degree D1 is: where n is the number of sampling points of the first magnetic feature signal, S1 i and S01 i are the values of the first magnetic feature signal and the normal magnetic feature signal at the i-th sampling point respectively. If D1 exceeds the set threshold, it is determined that the first magnetic feature signal is inconsistent with the normal magnetic feature signal, that is, the chain link is damaged.

[0027] Further, after determining that a link is damaged, the first magnetic characteristic signal and the second magnetic characteristic signal are further compared with the damaged magnetic characteristic signals of links with different damage types to determine the damage type. The damaged magnetic characteristic signals of links with different damage types are also obtained through a large number of experiments. In the experiments, common damage types such as fatigue cracks, wear, and plastic deformation of the links are artificially created, and the corresponding magnetic characteristic signals are collected to establish a damaged magnetic characteristic signal database. When comparing, a feature matching algorithm is used. First, key features are extracted from the first magnetic characteristic signal, such as the peak value, valley value, frequency components, waveform complexity, etc. For the fatigue crack damage type, its magnetic characteristic signal usually shows obvious peaks in a specific frequency band, while the magnetic characteristic signal of wear damage shows a decrease in amplitude and an increase in low-frequency components. Then, the extracted features are matched with the feature templates of each damage type in the database, and the similarity score is calculated. For example, the cosine similarity algorithm is used to calculate the cosine value between the feature vector of the first magnetic characteristic signal collected and the feature template vector of a certain damage type. The closer the cosine value is to 1, the higher the similarity. Finally, the damage type with the highest similarity score is determined as the damage type of the link.

[0028] Further, in order to facilitate the statistics of the monitoring period of the links in the embodiments of the present invention, the initial origin of the chain operation is first set, and the purpose is to accurately locate the fault risk points. When the scraper conveyor leaves the factory (in the case of no chain cut, the total number of scrapers is set to A), the scraper conveyor is powered on and run. The first scraper passing through the monitoring point is used as the initial operation point, and when A scrapers are counted, that is, when the chain runs one week, it is used as a monitoring period.

[0029] Further, in the embodiments of the present invention, after obtaining the first magnetic characteristic signal through monitoring, let f(X mn ) represent the first magnetic characteristic signal at the position Xmn of the link. That is, the degree of damage deterioration of each link can be expressed by the following formula:

[0030] Δf(X mn ) = f2(X mn ) - f1(X mn )

[0031] In the above formula, Δf(X mn ) represents the first difference between the first magnetic characteristic signals of the link at the position Xmn in adjacent monitoring periods, that is, the degree of damage deterioration. f2(X mn ) represents the first magnetic characteristic signal of the link at the position Xmn in the second monitoring period, and f1(X mn ) represents the first magnetic characteristic signal of the link at the position Xmn in the first monitoring period.

[0032] Further, in the embodiment of the present invention, the maximum value is selected from the damage deterioration degrees of all the chain links as the chain link with the fastest damage deterioration. The formula Δf max (X mn ) = max{Δf(X mn )} is used to represent that Δf max (X mn ) represents the maximum value among the damage deterioration degrees of all the chain links.

[0033] Further, in the embodiment of the present invention, the following formula is used to select the maximum value from the signal intensities of the first magnetic characteristic signals monitored for all the chain links in a single cycle as the maximum damage degree, which is specifically represented by the following formula: f max (X mn ) = max{f(X mn )}, where f max (X mn ) represents the maximum value among the signal intensities of the first magnetic characteristic signals monitored for all the chain links in a single cycle.

[0034] Further, the maximum damage deterioration degree or the maximum damage degree is selected from each type of damage, and the chain link corresponding to the maximum damage deterioration degree is used as the broken chain fault risk point for the corresponding type of damage, or the chain link corresponding to the maximum damage degree is used as the broken chain fault risk point for the corresponding type of damage. This broken chain fault risk point is the position point where a broken chain may occur.

[0035] Specifically, after calculating the damage deterioration degrees and damage degrees of all the chain links, the chain link corresponding to the maximum damage deterioration degree or the maximum damage degree is selected from the chain links of each type of damage as the broken chain fault risk point for the corresponding type of damage. If the device is more sensitive to the damage development trend and pays more attention to the rapid deterioration of the chain link damage, then the chain link corresponding to the maximum damage deterioration degree is selected as the broken chain fault risk point. For example, in some scraper conveyors running continuously under high load, even if the current damage degree of the chain link is relatively light, but if the damage deterioration speed is very fast, a broken chain fault may also be triggered in a short time. If the device focuses more on evaluating the current damage severity of the chain link, then the chain link corresponding to the maximum damage degree is selected as the broken chain fault risk point. For some scraper conveyors with a relatively slow running speed and a relatively stable load, the chain link with a larger current damage degree is more likely to break. By determining the broken chain fault risk point, these high-risk chain links can be monitored intensively, replaced in advance or other maintenance measures can be taken, effectively reducing the probability of the broken chain fault of the scraper conveyor.

[0036] Further, as an optional embodiment of the present invention, after the controller selects the maximum damage deterioration degree or the maximum damage degree from each type of damage, and takes the link corresponding to the maximum damage deterioration degree as the chain break fault risk point corresponding to the corresponding damage type, or takes the link corresponding to the maximum damage degree as the chain break fault risk point corresponding to the corresponding damage type, the controller is further configured to control the scraper conveyor to stop and send the link corresponding to the maximum damage deterioration degree or the position of the link corresponding to the maximum damage degree to the host computer when the maximum damage deterioration degree exceeds the first threshold or the maximum damage degree exceeds the second threshold, so as to remind the maintenance personnel to maintain the link corresponding to the maximum damage deterioration degree or the link corresponding to the maximum damage degree.

[0037] Specifically, the values of the first threshold and the second threshold can be determined according to the actual situation, and the embodiments of the present invention do not limit this here. When the maximum damage deterioration degree exceeds the first threshold or the maximum damage degree exceeds the second threshold, it indicates that the link may break. Therefore, the controller controls the scraper conveyor to stop and sends the link corresponding to the maximum damage deterioration degree or the position of the link corresponding to the maximum damage degree to the host computer, so that the maintenance personnel can conduct maintenance inspections on the link to avoid losses caused by the sudden chain break of the scraper conveyor. Among them, the position of the link can be the position of the scraper closest to the link marked in advance, and the position of the scraper is marked in advance.

[0038] Further, as an optional embodiment of the present invention, the chain break fault prediction device further includes: a second plug plate 21 and a second magnetic sensor 31 fixed to the second plug plate 21; the conveying trough is provided with a second opening 41, the second plug plate 21 is inserted into the second opening 41 and is located on the other side of the chain away from the chain track, and the second magnetic sensor 31 is fixed to the second plug plate 21 and is opposite to the other side of the chain; during the operation of the chain of the scraper conveyor, the second magnetic sensor 31 is used to collect the second magnetic characteristic signal of the link passing through the second magnetic sensor 31; the controller is connected to both the first magnetic sensor 30 and the second magnetic sensor 31, and is configured to analyze the damage state of the link according to the first magnetic characteristic signal and the second magnetic characteristic signal of the same link, and predict whether the link is a chain break fault risk point based on the damage state.

[0039] Specifically, to further improve the accuracy and reliability of chain link damage detection, the broken chain fault prediction device is also provided with a second plug board 21 and a second magnetic sensor 31 fixed to the second plug board 21. A second opening 41 is provided on the other side of the conveying trough opposite to the first opening 40. The second opening 41 and the first opening 40 are coaxial in the horizontal direction, and the distance between them is adapted to the width of the chain 11. The second plug board 21 and the first plug board 20 are designed with the same stainless steel material and L-shaped structure. Its vertical part is inserted into the second opening 41, and the horizontal part extends towards the chain 11, so that the other side of each link in the chain is opposite to the second magnetic sensor 31. In this way, the first magnetic sensor 30 and the second magnetic sensor 31 can collect the magnetic characteristic signals on both sides of the same link, so as to perform defect prediction on both sides of the link, and improve the prediction accuracy and reliability of link defects. It should be noted that the structure between the second plug board 21 and the second magnetic sensor 31 is the same as the structure between the first plug board 20 and the first magnetic sensor 30, and the embodiments of the present invention will not repeat the same parts here.

[0040] Further, as the track carrier for the operation of the chain 11, the bottom of the conveying trough is formed by welding high-strength wear-resistant steel plates, and a rectangular second opening 41 is provided on the side wall close to the chain path. The size of the second opening 41 is precisely matched with the second plug board 21. Its width is 2-3 mm larger than the width of the first plug board 20, and a 1-2 mm installation gap is reserved in the height direction to facilitate the smooth insertion and removal of the second plug board 21. The second plug board 21 is made of stainless steel and has an L-shaped structure. Its vertical part is inserted into the second opening 41, and the horizontal part extends towards the chain 11. A special sensor installation groove is machined on the surface of the horizontal part of the second plug board 21. The groove depth and groove width are customized according to the outer dimensions of the first magnetic sensor 30 to ensure that the first magnetic sensor 30 can be firmly embedded and maintain a stable working posture.

[0041] Further, the types of the second magnetic sensor 31 include but are not limited to Hall effect sensors, magnetoresistive sensors, magnetic induction sensors, magnetic integrated sensors, etc., which can quickly and accurately capture the weak changes in the magnetic field of the link passing through the second magnetic sensor 31. The vertical distance between the sensing surface of the second magnetic sensor 31 and the surface of the chain 11 close to the second magnetic sensor 31 is controlled between 5-8 mm. This distance can not only ensure the effective induction of the link magnetic field by the second magnetic sensor 31, but also avoid the second magnetic sensor 31 being mechanically collided during the operation of the chain due to too close a distance. The second magnetic sensor 31 can be connected to the controller through a high-temperature resistant and anti-interference shielded cable. The cable adopts a double shield structure, with a metal braided mesh on the outer layer and an aluminum foil shield layer on the inner layer, effectively suppressing electromagnetic interference and ensuring the accuracy and stability of signal transmission.

[0042] Further, after the scraper conveyor starts running, the teeth of the sprocket 10 mesh with the links of the chain 11, driving the chain 11 to perform a cyclic motion in the chain path of the conveying trough. During the operation of the chain 11, one side of each link close to the second magnetic sensor 31 sequentially passes through the sensing area of the second magnetic sensor 31. Due to inevitable defects such as internal stress and microscopic cracks in the production, manufacturing, installation, and use of the links, these defects will cause changes in the magnetic permeability of the local part of the link. When the link passes through the second magnetic sensor 31, if there are damage defects, the magnetic field distribution will be distorted, and the second magnetic sensor 31 converts this magnetic field change into an electrical signal, that is, the second magnetic characteristic signal.

[0043] Further, the collected second magnetic characteristic signal contains rich link state information on the side of the link close to the second magnetic sensor 31, but at the same time, it is also mixed with environmental noise and interference signals generated by the operation of the equipment. The controller integrates a multi-level signal processing system inside. First, the original second magnetic characteristic signal is preprocessed through a hardware filtering circuit. The hardware filtering circuit uses a second-order Butterworth low-pass filter, and the cut-off frequency is set to 5 kHz to effectively filter out high-frequency noise. The signal after hardware filtering enters the digital signal processing module. This module uses the fast Fourier transform algorithm to convert the time-domain signal into a frequency-domain signal and extract the frequency characteristics of the second magnetic characteristic signal. At the same time, the wavelet transform algorithm is used to perform multi-scale decomposition on the signal to obtain the signal detail characteristics at different resolutions.

[0044] Further, as an optional embodiment of the present invention, the controller analyzes the damage state of the link according to the first magnetic feature signal and the second magnetic feature signal, and predicts whether the chain is a risk point of chain breakage based on the damage state, including: comparing the first magnetic feature signal and the second magnetic feature signal with the normal magnetic feature signals of the links in the normal state respectively. If the first magnetic feature signal and / or the second magnetic feature signal is inconsistent with the normal magnetic feature signal, it is determined that the link is damaged; comparing the first magnetic feature signal and the second magnetic feature signal with the damaged magnetic feature signals of the links of different damage types to determine the damage types corresponding to the first magnetic feature signal and the second magnetic feature signal; when the damage types corresponding to the first magnetic feature signal and the second magnetic feature signal of the same link are the same, calculate the second difference between the first magnetic feature signals of the same link in adjacent monitoring periods and the third difference between the second magnetic feature signals respectively, perform weighted superposition on the second difference and the third difference to obtain a first superposition value, and use the first superposition value as the damage deterioration degree of the link; perform weighted superposition on the signal intensity of the first magnetic feature signal and the signal intensity of the second magnetic feature signal of the link to obtain a second superposition value, and use the second superposition value of the link monitored in a single period as the damage degree of the link; select the maximum damage deterioration degree or the maximum damage degree from the links of each damage type, and use the link corresponding to the maximum damage deterioration degree as the risk point of chain breakage of the corresponding damage type, or use the link corresponding to the maximum damage degree as the risk point of chain breakage of the corresponding damage type.

[0045] Specifically, in the embodiment of the present invention, feature extraction is respectively performed on the first magnetic feature signal and the second magnetic feature signal. Based on the extracted signal features, the link damage analysis algorithm built in the controller is combined with the pre-established link damage feature database for comparative analysis. The link damage feature database is accumulated through a large number of experiments and actual operation data, and includes magnetic feature signal samples of damaged links of different damage types and different degrees of each damage type, as well as magnetic feature signal samples of links in the normal state. For example, for fatigue cracks, when the energy in a specific frequency band (such as 1 - 3 kHz) in the first magnetic feature signal and / or the second magnetic feature signal is significantly enhanced, and the waveform shows sharp pulse characteristics, combined with the magnetic feature signal samples of the links in the normal state and the magnetic feature signal samples of the damaged links of different damage types and different degrees in the link damage feature database, it can be determined that the link has fatigue cracks, and by calculating the energy amplitude of this frequency band or comparing it with the magnetic feature signal samples of the damaged links of each degree under fatigue crack damage, the length and depth of the crack can be estimated.

[0046] Further, during the operation of the scraper conveyor, the first magnetic sensor and the second magnetic sensor continuously collect the first magnetic characteristic signal and the second magnetic characteristic signal of the chain link. To preliminarily determine whether the chain link is damaged, it is necessary to compare the collected signals with the normal magnetic characteristic signals of the chain link in the normal state. The acquisition of the normal magnetic characteristic signals is based on a large number of brand-new and unused chain links. Under the same detection conditions (detection distance, environmental temperature, etc.), the first magnetic sensor and the second magnetic sensor are used to collect signals, and statistical analysis is performed on these signals to calculate statistical quantities such as their average values and standard deviations, so as to determine the reference range of the normal magnetic characteristic signals. For example, the amplitude range of the normal magnetic characteristic signals is [X1, X2], and the frequency range is [Y1, Y2].

[0047] During the actual comparison process, the Euclidean distance algorithm is used to calculate the difference degree between the collected first magnetic characteristic signal and the second magnetic characteristic signal and the normal magnetic characteristic signals. For the first magnetic characteristic signal S1 and the normal magnetic characteristic signal S01, the calculation formula for its difference degree D1 is: where n is the number of sampling points of the first magnetic characteristic signal, S1 i and S01 i are the signal intensity values of the first magnetic characteristic signal and the normal magnetic characteristic signal at the i-th sampling point respectively. Similarly, according to the same calculation method of the difference degree D1, the difference degree D2 between the second magnetic characteristic signal S2 and the normal magnetic characteristic signal S02 is calculated. If D1 exceeds the set threshold, and / or D2 exceeds the set threshold, it is determined that the first magnetic characteristic signal and / or the second magnetic characteristic signal is inconsistent with the normal magnetic characteristic signal, that is, the chain link is damaged.

[0048] Further, after determining that the link is damaged, the first magnetic characteristic signal and the second magnetic characteristic signal are further compared with the damaged magnetic characteristic signals of links of different damage types to determine the damage type. The damaged magnetic characteristic signals of links of different damage types are also obtained through a large number of experiments. In the experiments, common damage types such as fatigue cracks, wear, and plastic deformation of the links are artificially created, and the corresponding magnetic characteristic signals are collected to establish a damaged magnetic characteristic signal database. When comparing, a feature matching algorithm is used. First, key features are extracted from the first magnetic characteristic signal and the second magnetic characteristic signal, such as the peak value, valley value, frequency component, waveform complexity, etc. of the signal. For the fatigue crack damage type, its magnetic characteristic signal usually shows obvious peaks in a specific frequency band, while the magnetic characteristic signal of wear damage shows a decrease in amplitude and an increase in low-frequency components. Then, the extracted features are matched with the feature templates of each damage type in the database to calculate the similarity score. For example, the cosine similarity algorithm is used to calculate the cosine value between the feature vectors of the first magnetic characteristic signal and the second magnetic characteristic signal collected and the feature template vector of a certain damage type. The closer the cosine value is to 1, the higher the similarity. Finally, the damage type with the highest similarity score is determined as the damage type of the link indicated by the first magnetic characteristic signal and the second magnetic characteristic signal.

[0049] Further, when the damage types of the same link corresponding to the first magnetic characteristic signal and the second magnetic characteristic signal are the same, in order to evaluate the development trend of the link damage, it is necessary to calculate the damage deterioration degree of the link. The specific process is as follows: Calculate the second difference ΔS1 between the first magnetic characteristic signals of the same link in adjacent monitoring periods and the third difference ΔS2 between the second magnetic characteristic signals respectively. When calculating the difference, the signal can be first normalized to eliminate the influence of the signal amplitude difference on the calculation result. For example, the Z-score normalization method is used to convert the signal into a standard normal distribution with a mean of 0 and a standard deviation of 1. In order to comprehensively consider the changes of the first magnetic characteristic signal and the second magnetic characteristic signal, the second difference ΔS1 and the third difference ΔS2 are weighted and superimposed. The determination of the weight is based on factors such as the installation position of the magnetic sensor, the detection accuracy, and the link structure characteristics. Through a large number of experiments and data analysis, the weight w1 of the second difference ΔS1 of the first magnetic characteristic signal and the weight w2 of the third difference between the second magnetic characteristic signals are determined, and w1 + w2 = 1. Then the first superimposed value D is: D = w1×ΔS1 + w2×ΔS2, and this first superimposed value is used as the damage deterioration degree of the link, reflecting the change rate of the link damage over time.

[0050] Further, in addition to the degree of damage deterioration, it is also necessary to determine the degree of damage of the chain link within a single monitoring cycle. The signal intensities of the first magnetic characteristic signal and the second magnetic characteristic signal of the chain link are weighted and superimposed to obtain a second superimposed value. The signal intensity can be represented by calculating the root mean square value (RMS) of the signal, the first magnetic characteristic signal intensity The second magnetic characteristic signal intensity where n is the number of sampling points of the first magnetic characteristic signal or the second magnetic characteristic signal. S1 i represents the signal intensity value of the i-th first magnetic characteristic. S2 i represents the signal intensity value of the i-th second magnetic characteristic.

[0051] Further, according to the sensor performance and the actual detection effect in the embodiments of the present invention, the weight w3 of the signal intensity of the first magnetic characteristic signal and the weight w4 of the signal intensity of the second magnetic characteristic signal are determined, and w3 + w4 = 1. Then the second superimposed value M is: M = w3 × I1 + w4 × I2. This second superimposed value is used as the degree of damage of the chain link in a single cycle monitoring, reflecting the current severity of the damage of the chain link.

[0052] Further, after calculating the degree of damage deterioration and the degree of damage of all chain links, the chain link corresponding to the maximum degree of damage deterioration or the maximum degree of damage among the chain links of each damage type is selected as the chain break fault risk point for the corresponding damage type. If the equipment is more sensitive to the damage development trend and pays more attention to the rapid deterioration of the chain link damage, the chain link corresponding to the maximum degree of damage deterioration is selected as the chain break fault risk point. For example, in some scraper conveyors with high-load continuous operation, even if the current damage degree of the chain link is relatively light, if the damage deterioration speed is very fast, a chain break fault may be triggered in a short time. If the equipment focuses more on evaluating the current severity of the chain link damage, the chain link corresponding to the maximum degree of damage is selected as the chain break fault risk point. For some scraper conveyors with relatively slow operating speed and relatively stable load, the chain link with a larger current damage degree is more likely to have a chain break. By determining the chain break fault risk point, key monitoring, early replacement or other maintenance measures can be targeted at these high-risk chain links, effectively reducing the probability of chain break faults occurring in the scraper conveyor.

[0053] Further, as an optional embodiment of the present invention, after the controller selects the maximum damage deterioration degree or the maximum damage degree from each damage type according to the first magnetic feature signal and the second magnetic feature signal, and takes the link corresponding to the maximum damage deterioration degree as the broken chain fault risk point of the corresponding damage type, or takes the link corresponding to the maximum damage degree as the broken chain fault risk point of the corresponding damage type, the controller is further configured to control the scraper conveyor to stop and send the link corresponding to the maximum damage deterioration degree or the position of the link corresponding to the maximum damage degree to the host computer when the maximum damage deterioration degree exceeds the first threshold or the maximum damage degree exceeds the second threshold, so as to remind the maintenance personnel to maintain the link corresponding to the maximum damage deterioration degree or the link corresponding to the maximum damage degree.

[0054] Specifically, the values of the first threshold and the second threshold can be determined according to the actual situation, and the embodiments of the present invention do not limit this here. When the maximum damage deterioration degree exceeds the first threshold or the maximum damage degree exceeds the second threshold, it indicates that the chain link may break. Therefore, the scraper conveyor is controlled to stop and the position of the link corresponding to the maximum damage deterioration degree or the link corresponding to the maximum damage degree is sent to the host computer, so that the maintenance personnel can conduct maintenance inspections on the link to avoid losses caused by sudden chain breakage of the scraper conveyor. Among them, the position of the chain link can be the position of the scraper closest to the chain link marked in advance, and the position of the scraper is marked in advance.

[0055] In the embodiment of the present invention, by installing the first plug board and the first magnetic sensor at the first opening of the conveying trough of the scraper conveyor, the first magnetic characteristic signal of the chain link can be continuously and real-time collected during the operation of the chain, ensuring that the state information of the chain link can be obtained in a timely manner at every moment during the operation of the chain, greatly improving the timeliness and effectiveness of monitoring. The controller analyzes the damage state of the chain link based on the first magnetic characteristic signal and pre-judges the risk points of chain faults. Through in-depth analysis of the magnetic characteristic signal, potential damage states such as early fatigue cracks and wear of the chain link can be identified, and the chain links at risk of chain breakage can be pre-judged in advance with high accuracy, preventing chain breakage faults before they occur and effectively avoiding sudden accidents caused by chain breakage. Since the risk points of chain faults can be accurately pre-judged, a maintenance plan can be formulated in advance according to the pre-judgment results, and the shutdown and maintenance time can be reasonably arranged to avoid unplanned shutdowns caused by sudden chain breaks. The number of production interruptions and the shutdown and maintenance duration are reduced, ensuring the continuous and stable operation of the scraper conveyor, significantly improving production efficiency, and reducing economic losses caused by equipment failures. Further, by detecting chain link damage in advance and pre-judging the risk points of chain faults, measures such as replacing chain links can be taken in a timely manner to reduce the probability of chain breakage accidents and avoid safety hazards such as material splashing and equipment component detachment caused by chain breakage, creating a safer working environment for operators and ensuring personnel safety. In this way, the damage of the chain of the scraper conveyor is pre-judged in advance, so as to realize early warning and preventive maintenance of chain breakage faults, and improve the operation reliability and safety of the scraper conveyor.

[0056] Based on the same inventive concept, the embodiment of the present invention also provides a method for pre-judging chain breakage faults of a scraper conveyor, as Figure 4 shown in Figure 4 FIG. is a schematic flow chart of a method for pre-judging chain breakage faults of a scraper conveyor provided by an embodiment of the present invention. The present invention provides a method for pre-judging chain breakage faults of a scraper conveyor, which can be executed by the controller of the scraper conveyor described above, and includes the following steps:

[0057] Step S401, obtain the first magnetic characteristic signal of the chain link passing through the first magnetic sensor, and the first magnetic sensor is opposite to one side of the chain link.

[0058] Step S402, analyze the damage state of the chain link according to the first magnetic characteristic signal, and pre-judge whether the chain link is a risk point of chain breakage fault based on the damage state.

[0059] Optionally, analyzing the damage state of the chain link according to the first magnetic feature signal and predicting whether the chain link is a risk point of chain breakage based on the damage state includes: comparing the first magnetic feature signal with the normal magnetic feature signal of the chain link in the normal state. If the first magnetic feature signal is inconsistent with the normal magnetic feature signal, it is determined that the chain link is damaged; comparing the first magnetic feature signal with the damaged magnetic feature signals of chain links with different damage types to determine the damage type corresponding to the first magnetic feature signal; respectively calculating the first difference between the first magnetic feature signals of the same chain link of each damage type in adjacent monitoring periods, taking the first difference as the damage deterioration degree of the chain link of each damage type, and taking the signal intensity of the first magnetic feature signal of the chain link of each damage type monitored in a single period as the damage degree of the chain link; selecting the maximum damage deterioration degree or the maximum damage degree from each damage type, taking the chain link corresponding to the maximum damage deterioration degree as the risk point of chain breakage of the corresponding damage type, or taking the chain link corresponding to the maximum damage degree as the risk point of chain breakage of the corresponding damage type.

[0060] Optionally, after selecting the maximum damage deterioration degree or the maximum damage degree from each damage type, taking the chain link corresponding to the maximum damage deterioration degree as the risk point of chain breakage of the corresponding damage type, or taking the chain link corresponding to the maximum damage degree as the risk point of chain breakage of the corresponding damage type, the method further includes: in the case where the maximum damage deterioration degree exceeds the first threshold or the maximum damage degree exceeds the second threshold, controlling the scraper conveyor to stop and sending the position of the chain link corresponding to the maximum damage deterioration degree or the chain link corresponding to the maximum damage degree to the upper computer to remind the maintenance personnel to maintain the chain link corresponding to the maximum damage deterioration degree or the chain link corresponding to the maximum damage degree.

[0061] Optionally, the position of the chain link is the position of the scraper closest to the chain link, and the position of the scraper is pre-marked.

[0062] Optionally, the method for predicting chain breakage failure further includes: obtaining the second magnetic feature signal of the chain link passing through the second magnetic sensor, and the second magnetic sensor is opposite to the other side of the chain link; analyzing the damage state of the chain link according to the first magnetic feature signal and predicting whether the chain link is a risk point of chain breakage based on the damage state includes: analyzing the damage state of the chain link according to the first magnetic feature signal and the second magnetic feature signal of the same chain link, and predicting whether the chain is a risk point of chain breakage based on the damage state.

[0063] Optionally, analyzing the damage state of the link based on the first magnetic feature signal and the second magnetic feature signal, and predicting whether the chain is a risk point of chain breakage based on the damage state includes: respectively comparing the first magnetic feature signal and the second magnetic feature signal with the normal magnetic feature signal of the link in the normal state. If the first magnetic feature signal and / or the second magnetic feature signal is inconsistent with the normal magnetic feature signal, it is determined that the link is damaged; comparing the first magnetic characteristic signal and the second magnetic feature signal with the damage magnetic feature signals of links with different damage types to determine the damage types corresponding to the first magnetic feature signal and the second magnetic feature signal; when the damage types corresponding to the first magnetic feature signal and the second magnetic feature signal of the same link are the same, respectively calculating the second difference between the first magnetic feature signals of the same link in adjacent monitoring periods and the third difference between the second magnetic feature signals, and performing weighted superposition on the second difference and the third difference to obtain a first superposition value, and taking the first superposition value as the damage deterioration degree of the link; performing weighted superposition on the signal intensity of the first magnetic feature signal and the signal intensity of the second magnetic feature signal of the link to obtain a second superposition value, and taking the second superposition value monitored by the link in a single period as the damage degree of the link; selecting the maximum damage deterioration degree or the maximum damage degree from the links of each damage type, and taking the link corresponding to the maximum damage deterioration degree as the risk point of chain breakage for the corresponding damage type, or taking the link corresponding to the maximum damage degree as the risk point of chain breakage for the corresponding damage type.

[0064] It should be noted that the method for predicting chain breakage faults of the scraper conveyor provided in the embodiments of the present invention is based on the same inventive concept as the scraper conveyor in the above embodiments. Therefore, the specific implementation of this embodiment can refer to the implementation of the aforementioned scraper conveyor and has the same or similar beneficial effects, and the repeated parts will not be elaborated.

[0065] Further, based on the same inventive concept, the embodiments of the present invention also provide a device for predicting chain breakage faults of a scraper conveyor, the above Figure 3 is a structural schematic diagram of a device for predicting chain breakage faults provided in an embodiment of the present invention. The scraper conveyor includes a sprocket, a chain, and a scraper. The chain is formed by connecting a plurality of links, and the scraper is distributed at intervals on the chain. When the sprocket rotates, it drives the chain and the scraper to move. It is characterized in that the device for predicting chain breakage faults includes: a controller, a first plug board, and a first magnetic sensor fixed to the first plug board; a first opening is provided in the conveying trough of the scraper conveyor, the first plug board is inserted into the first opening and is located on the side of the chain close to the chain path, and the first magnetic sensor is fixed to the first plug board and is opposite to one side of the chain; during the operation of the chain of the scraper conveyor, the first magnetic sensor is used to collect the first magnetic feature signal of the link passing through the first magnetic sensor; the controller is connected to the first magnetic sensor and is used to analyze the damage state of the link based on the first magnetic feature signal and predict whether the link is a risk point of chain breakage based on the damage state.

[0066] Optionally, the chain break fault prediction device further includes: a second plugboard and a second magnetic sensor fixed to the second plugboard; the conveying trough is provided with a second opening, the second plugboard is inserted into the second opening and is located on the other side of the chain away from the chain path, the second magnetic sensor is fixed to the second plugboard and is opposite to the other side of the chain; during the operation of the chain of the scraper conveyor, the second magnetic sensor is used to collect the second magnetic characteristic signal of the chain link passing through the second magnetic sensor; the controller is connected to both the first magnetic sensor and the second magnetic sensor, and is used to analyze the damage state of the chain link according to the first magnetic characteristic signal and the second magnetic characteristic signal of the same chain link, and predict whether the chain link is a risk point of chain break fault based on the damage state.

[0067] It should be noted that the chain break fault prediction device of the scraper conveyor provided in the embodiment of the present invention is based on the same application concept as the scraper conveyor in the above embodiment. Therefore, the specific implementation of this embodiment can refer to the implementation of the aforementioned scraper conveyor, and has the same or similar beneficial effects. The repeated parts will not be described again.

[0068] It should be noted that the above sequence of the embodiments of the present invention is only for description and does not represent the superiority or inferiority of the embodiments. The processes depicted in the drawings do not necessarily require the specific order or continuous order shown to achieve the desired result. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0069] Each embodiment in this specification is described in a progressive manner. The same or similar parts between the embodiments can be referred to each other. Each embodiment focuses on the differences from other embodiments.

Claims

1. A device for predicting the chain breakage fault of a scraper conveyor, the scraper conveyor comprising a sprocket, a chain and a scraper, the chain being connected by a plurality of chain links, the scraper being distributed at intervals on the chain, and the sprocket driving the chain and the scraper to move when rotating, characterized in that, The chain break fault prediction device includes: a controller, a first plug board, and a first magnetic sensor fixed to the first plug board; A first opening is provided in the conveying trough of the scraper conveyor. The first plug board is inserted into the first opening and is located on the side of the chain close to the chain track. The first magnetic sensor is fixed to the first plug board and is opposite to one side of the chain; During the operation of the chain of the scraper conveyor, the first magnetic sensor is used to collect the first magnetic characteristic signal of the chain link passing through the first magnetic sensor; The controller is connected to the first magnetic sensor and is used to analyze the damage state of the chain link according to the first magnetic characteristic signal, and predict whether the chain link is a risk point of chain break fault based on the damage state.

2. The chain breakage fault prediction device for the scraper conveyor according to claim 1, characterized in that, The chain break fault prediction device further includes: a second plug board and a second magnetic sensor fixed to the second plug board; A second opening is provided in the conveying trough. The second plug board is inserted into the second opening and is located on the other side of the chain away from the chain track. The second magnetic sensor is fixed to the second plug board and is opposite to the other side of the chain; During the operation of the chain of the scraper conveyor, the second magnetic sensor is used to collect the second magnetic characteristic signal of the chain link passing through the second magnetic sensor; The controller is connected to both the first magnetic sensor and the second magnetic sensor and is used to analyze the damage state of the chain link according to the first magnetic characteristic signal and the second magnetic characteristic signal of the same chain link, and predict whether the chain link is a risk point of chain break fault based on the damage state.

3. A method for predicting the chain breakage fault of a scraper conveyor, characterized in that, Based on the chain break fault prediction device of the scraper conveyor according to claim 1 or 2, the chain break fault prediction method includes: Obtain the first magnetic characteristic signal of the chain link passing through the first magnetic sensor. The first magnetic sensor is opposite to one side of the chain link; Analyze the damage state of the chain link according to the first magnetic characteristic signal, and predict whether the chain link is a risk point of chain break fault based on the damage state.

4. The method for predicting the chain breakage fault of the scraper conveyor according to claim 3, characterized in that, The analyzing the damage state of the chain link according to the first magnetic characteristic signal and predicting whether the chain link is a risk point of chain break fault based on the damage state includes: Compare the first magnetic characteristic signal with the normal magnetic characteristic signal of the chain link in the normal state. If the first magnetic characteristic signal is inconsistent with the normal magnetic characteristic signal, it is determined that the chain link is damaged; Compare the first magnetic characteristic signal with the damage magnetic characteristic signals of the chain links of different damage types to determine the damage type corresponding to the first magnetic characteristic signal; Calculate the first difference between the first magnetic characteristic signals of the same chain link of each damage type in adjacent monitoring periods respectively, and use the first difference as the damage deterioration degree of the chain link of each damage type. Use the signal intensity of the first magnetic characteristic signal of the chain link of each damage type monitored in a single period as the damage degree of the chain link; Select the maximum damage deterioration degree or the maximum damage degree from each damage type, and use the chain link corresponding to the maximum damage deterioration degree as the risk point of chain break fault of the corresponding damage type, or use the chain link corresponding to the maximum damage degree as the risk point of chain break fault of the corresponding damage type.

5. The method for predicting the chain breakage fault of the scraper conveyor according to claim 4, wherein After selecting the maximum damage deterioration degree or the maximum damage degree from each damage type, and taking the link corresponding to the maximum damage deterioration degree as the broken chain fault risk point for the corresponding damage type, or taking the link corresponding to the maximum damage degree as the broken chain fault risk point for the corresponding damage type, the method further includes: In the case where the maximum damage deterioration degree exceeds the first threshold or the maximum damage degree exceeds the second threshold, control the scraper conveyor to stop and send the link corresponding to the maximum damage deterioration degree or the position of the link corresponding to the maximum damage degree to the host computer, so as to remind the maintenance personnel to maintain the link corresponding to the maximum damage deterioration degree or the link corresponding to the maximum damage degree.

6. The method for predicting the chain breakage fault of the scraper conveyor according to claim 5, characterized in that, The position of the link is the position of the scraper closest to the link, and the position of the scraper is marked in advance.

7. The method for predicting the chain breakage fault of the scraper conveyor according to claim 3, characterized in that, The broken chain fault prediction method further includes: Obtain the second magnetic characteristic signal of the link passing through the second magnetic sensor, and the second magnetic sensor is opposite to the other side of the link; The analyzing the damage state of the link according to the first magnetic characteristic signal and predicting whether the link is a broken chain fault risk point based on the damage state includes: Analyze the damage state of the link according to the first magnetic characteristic signal and the second magnetic characteristic signal of the same link, and predict whether the chain is a broken chain fault risk point based on the damage state.

8. The method for predicting the chain breakage fault of the scraper conveyor according to claim 7, characterized in that, The analyzing the damage state of the link according to the first magnetic characteristic signal and the second magnetic characteristic signal and predicting whether the chain is a broken chain fault risk point based on the damage state includes: Compare the first magnetic characteristic signal and the second magnetic characteristic signal with the normal magnetic characteristic signal of the link in the normal state respectively. If the first magnetic characteristic signal and / or the second magnetic characteristic signal is inconsistent with the normal magnetic characteristic signal, it is determined that the link is damaged; Compare the first magnetic characteristic signal and the second magnetic characteristic signal with the damage magnetic characteristic signals of the links of different damage types to determine the damage types corresponding to the first magnetic characteristic signal and the second magnetic characteristic signal; In the case where the damage types corresponding to the first magnetic characteristic signal and the second magnetic characteristic signal of the same link are the same, calculate the second difference between the first magnetic characteristic signals of the same link in adjacent monitoring periods and the third difference between the second magnetic characteristic signals respectively, perform weighted superposition on the second difference and the third difference to obtain a first superposition value, and take the first superposition value as the damage deterioration degree of the link; Perform weighted superposition on the signal intensity of the first magnetic characteristic signal and the signal intensity of the second magnetic characteristic signal of the link to obtain a second superposition value, and take the second superposition value of the link monitored in a single period as the damage degree of the link; Select the maximum damage deterioration degree or the maximum damage degree from the links of each damage type, and take the link corresponding to the maximum damage deterioration degree as the broken chain fault risk point for the corresponding damage type, or take the link corresponding to the maximum damage degree as the broken chain fault risk point for the corresponding damage type.

9. A scraper conveyor, comprising: Sprocket, chain and scraper, the chain is connected by a plurality of chain links, the scrapers are distributed at intervals on the chain, and when the sprocket rotates, it drives the chain and the scraper to move. It is characterized in that it further includes a broken chain fault prediction device, and the broken chain fault prediction device includes: a controller, a first plug board and a first magnetic sensor fixed to the first plug board; A first opening is provided in the conveying trough of the scraper conveyor, the first plug board is inserted into the first opening and is located on the side of the chain close to the chain path, and the first magnetic sensor is fixed to the first plug board and is opposite to one side of the chain; During the operation of the chain of the scraper conveyor, the first magnetic sensor is used to collect the first magnetic characteristic signal of the chain link passing through the first magnetic sensor; The controller is connected to the first magnetic sensor, and is used to analyze the damage state of the chain link according to the first magnetic characteristic signal, and predict whether the chain link is a risk point of broken chain fault based on the damage state.

10. The method for predicting the chain breakage fault of the scraper conveyor according to claim 9, characterized in that, The broken chain fault prediction device further includes: a second plug board and a second magnetic sensor fixed to the second plug board; A second opening is provided in the conveying trough, the second plug board is inserted into the second opening and is located on the other side of the chain away from the chain path, and the second magnetic sensor is fixed to the second plug board and is opposite to the other side of the chain; During the operation of the chain of the scraper conveyor, the second magnetic sensor is used to collect the second magnetic characteristic signal of the chain link passing through the second magnetic sensor; The controller is connected to both the first magnetic sensor and the second magnetic sensor, and is used to analyze the damage state of the chain link according to the first magnetic characteristic signal and the second magnetic characteristic signal of the same chain link, and predict whether the chain link is a risk point of broken chain fault based on the damage state.

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