Chain breakage fault prediction device and method for flight conveyer and flight conveyer

Abstract

The present application relates to the technical field of scraper conveyor, and particularly relates to a chain breakage fault pre-judgment device and method of a scraper conveyor and the scraper conveyor, the device comprising: a controller, a first plug plate and a first magnetic sensor fixed to the first plug plate; a conveying groove of the scraper conveyor is provided with a first opening, the first plug plate is inserted into the first opening and located at a side of the chain close to a chain channel, and the first magnetic sensor is fixed to the first plug plate and opposite to one side of the chain; in the process of running of the chain of the scraper conveyor, the first magnetic sensor collects a first magnetic characteristic signal of a chain link passing through the first magnetic sensor; the controller is connected with the first magnetic sensor, the damage state of the chain link is analyzed according to the first magnetic characteristic signal, and whether the chain link is a chain breakage fault risk point is pre-judged based on the damage state. In this way, the present application pre-judges the chain damage of the scraper conveyor in advance, so as to realize the early warning and preventive maintenance of the chain breakage fault, and the operation reliability and safety of the scraper conveyor are improved.

Description

Technical Field

[0001] This invention relates to the field of scraper conveyor technology, specifically to a scraper conveyor chain breakage fault prediction device, method, and scraper conveyor. Background Technology

[0002] A scraper conveyor is a continuous conveying device that uses a chain as its load-bearing and traction mechanism, and scrapers to push materials along a closed trough. Its core principle is to use the chain to drive scrapers, pushing materials from one end to the other within a fixed trough. It is widely used in mining, metallurgy, chemical, grain processing, and coal mining industries. In coal mine fully mechanized mining face production systems, the scraper conveyor, as a core transportation device, directly determines coal mining efficiency and production safety through the stable operation of its chain drive system. However, chain breakage accidents restrict the reliable operation of scraper conveyors. Once a chain breakage occurs, the coal mining face will be forced to shut down completely, severely reducing production efficiency and potentially causing secondary damage such as sprocket jamming and scraper deformation if the fault is not detected in time, further increasing the extent of equipment damage and maintenance costs.

[0003] In some scenarios, chain breakage monitoring technology for scraper conveyors primarily focuses on detecting the break point after a chain breakage failure has occurred. Image recognition methods, which use cameras installed at the tail of the conveyor to determine chain breakage based on the continuity of scraper movement and tilt within the camera's monitoring area, are insufficient for predicting chain breakage failures before they occur. These monitoring methods detect chain breakage after it has happened, failing to effectively identify chain defects in the early stages of damage such as wear and fatigue cracks. This hinders early warning and preventative maintenance, resulting in high downtime risks and economic losses due to sudden chain breaks during production. Consequently, these monitoring methods cannot predict chain damage in advance, hindering early warning and preventative maintenance, leading to low reliability and safety in scraper conveyor operation. Summary of the Invention

[0004] To address the technical problem of difficulty in predicting chain damage in scraper conveyors, thus hindering early warning and preventative maintenance of chain breakage and resulting in low reliability and safety of scraper conveyor operation, this invention aims to provide a chain breakage prediction device, method, and scraper conveyor. The specific technical solution adopted is as follows:

[0005] In a first aspect, embodiments of the present invention disclose a chain breakage prediction device for a scraper conveyor. The scraper conveyor includes a sprocket, a chain, and scrapers. The chain is composed of multiple chain links connected together, and scrapers are distributed at intervals on the chain. When the sprocket rotates, it drives the chain and scrapers to move. The chain breakage prediction device includes: a controller, a first insert plate, and a first magnetic sensor fixed to the first insert plate. The conveying trough of the scraper conveyor has a first opening. The first insert plate is inserted into the first opening and located on the side of the chain near the chain path. The first magnetic sensor is fixed to the first insert 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 based on the first magnetic characteristic signal, and predict whether the chain link is a chain breakage risk point based on the damage state.

[0006] Secondly, embodiments of the present invention disclose a chain breakage prediction method for a scraper conveyor. Based on the chain breakage prediction device for a scraper conveyor mentioned in the first aspect, the chain breakage prediction method includes: acquiring a first magnetic feature signal of a chain link passing through a first magnetic sensor, wherein the first magnetic sensor is opposite to one side of the chain link; analyzing the damage state of the chain link based on the first magnetic feature signal, and predicting whether the chain link is a chain breakage risk point based on the damage state.

[0007] Thirdly, embodiments of the present invention disclose a scraper conveyor, comprising: a sprocket, a chain, and scrapers. The chain is composed of multiple chain links connected together, and scrapers are spaced apart on the chain. When the sprocket rotates, it drives the chain and scrapers to move. The conveyor also includes a chain breakage prediction device, comprising: a controller, a first insert plate, and a first magnetic sensor fixed to the first insert plate. The conveying trough of the scraper conveyor has a first opening. The first insert plate is inserted into the first opening and located on the side of the chain near the chain path. The first magnetic sensor is fixed to the first insert plate and faces 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 based on the first magnetic characteristic signal, and predict whether the chain link is a chain breakage risk point based on the damage state.

[0008] This invention, by installing a first insert plate and a first magnetic sensor at the first opening of the scraper conveyor trough, enables continuous real-time acquisition of the first magnetic characteristic signal of the chain links during chain operation. This ensures timely acquisition of chain link status information at every moment of chain operation, greatly improving the timeliness and effectiveness of monitoring. The controller analyzes the chain link damage state based on the first magnetic characteristic signal and predicts potential chain failure points. Through in-depth analysis of the magnetic characteristic signal, it can identify early fatigue cracks, wear, and other potential damage states of the chain links, predicting chain links at risk of breakage in advance with high accuracy. This prevents chain breakage failures before they occur, effectively avoiding sudden accidents caused by chain breakage.

[0009] By accurately predicting chain failure risk points, maintenance plans can be developed in advance based on the predictions, allowing for reasonable scheduling of downtime for repairs and avoiding unplanned shutdowns due to sudden chain breakage. This reduces the number of production interruptions and downtime for repairs, ensuring the continuous and stable operation of the scraper conveyor, significantly improving production efficiency, and reducing economic losses caused by equipment failures. Furthermore, early detection of chain link damage and prediction of chain failure risk points allows for timely replacement of chain links, reducing the probability of chain breakage accidents and preventing safety hazards such as material splashing and equipment part detachment caused by chain breakage. This creates a safer working environment for operators and protects personnel safety. Thus, by predicting chain damage in advance, early warning and preventative maintenance of chain breakage faults are achieved, improving the reliability and safety of scraper conveyor operation. Attached Figure Description

[0010] Figure 1 This is a cross-sectional structural diagram of a scraper conveyor provided in an embodiment of the present invention.

[0011] Figure 2 This is a side view of a scraper conveyor provided in an embodiment of the present invention.

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

[0013] Figure 4 This is a flowchart illustrating a chain breakage fault prediction method provided in an embodiment of the present invention. Detailed Implementation

[0014] To further illustrate the technical means and effects adopted by the present invention to achieve its intended purpose, the following, in conjunction with the accompanying drawings and preferred embodiments, details the specific implementation, structure, features, and effects of a scraper conveyor chain breakage prediction device, method, and scraper conveyor according to the present invention. In the following description, different "one embodiment" or "another embodiment" do not necessarily refer to the same embodiment. Furthermore, 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 one of ordinary skill in the art to which this invention pertains.

[0016] The following description, in conjunction with the accompanying drawings, details a chain breakage prediction device and method for a scraper conveyor, as well as a specific solution for the scraper conveyor provided by the present invention.

[0017] like Figures 1 to 3 As shown, Figure 1 This is a cross-sectional structural diagram of a scraper conveyor provided in an embodiment of the present invention. Figure 2 This is a side view of a scraper conveyor according to an embodiment of the present invention. Figure 3 This is a schematic diagram of the structure of a chain breakage fault prediction device provided in an embodiment of the present invention, as shown below. Figure 1 As shown, the scraper conveyor includes a sprocket 10, a chain 11, and scrapers 12. The chain 11 is composed of multiple chain links connected together, and scrapers 12 are spaced apart on the chain 11. When the sprocket 10 rotates, it drives the chain 11 and scrapers 12 to move. The scraper conveyor also includes a chain breakage prediction device, which includes a controller (not shown in the figure), a first insert plate 20, and a first magnetic sensor 30 fixed to the first insert plate 20. The conveying trough of the scraper conveyor has a first opening 40. The first insert plate 20 is inserted into the first opening 40 and located on the side of the chain near the chain track. The first magnetic sensor 30 is fixed to the first insert plate 20 and faces one side of the chain. During the operation of the scraper conveyor chain, the first magnetic sensor 30 is used to collect the first magnetic characteristic signal of the chain link passing through 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 based on the first magnetic characteristic signal, and predict whether the chain link is a chain breakage risk point based on the damage state.

[0018] Specifically, the sprocket 10 is rigidly connected to the drive motor via a coupling, achieving precise circular motion under the drive of the motor's output torque. The chain 11, as the power transmission component, is composed of multiple high-strength alloy steel links connected by a precision hinge process. Adjacent links are connected by an interference fit between pins and link holes, with the tolerance controlled within ±0.02mm to ensure the stability and reliability of the chain operation. The scraper 12 has a rectangular plate structure made of high-manganese steel, with grooves on both sides matching the chain 11. It is fixedly connected to the chain 11 by high-strength bolts. The spacing between adjacent scrapers 12 is determined according to the material characteristics and conveying requirements, typically between 300-500mm, to ensure the continuity and efficiency of material conveying.

[0019] Furthermore, the conveying trough can be a central trough or a transition trough of a scraper conveyor. A first opening and a second opening (described later in the embodiments) are formed on the central trough or the transition trough to fix and install the insert plate. In the figure, the embodiment of the present invention takes a transition trough as an example, and the first opening and the second opening (described later in the embodiments) are formed at the positions shown in the transition trough illustration.

[0020] To effectively prevent chain breakage and ensure the safe and stable operation of scraper conveyors, this invention provides a chain breakage prediction device. The device mainly consists of a controller, a first insert plate 20, and a first magnetic sensor 30. The conveyor trough, serving as the track carrier for the chain 11, has its bottom welded from high-strength wear-resistant steel plate. A rectangular first opening 40 is formed on the side wall near the chain track. The dimensions of the first opening 40 precisely match those of the first insert plate 20, with its width being 2-3 mm larger and a 1-2 mm height clearance reserved for easy insertion and removal of the first insert plate 20. The first insert plate 20 is made of stainless steel and has an L-shaped structure. Its vertical portion inserts into the first opening 40, while its horizontal portion extends towards the chain 11. A dedicated sensor mounting groove is machined on the horizontal surface of the first insert plate 20. The groove depth and width are customized according to the dimensions of the first magnetic sensor 30 to ensure that the first magnetic sensor 30 is firmly embedded and maintains a stable working posture. It is worth noting that, depending on the number of chains, a corresponding number of first magnetic sensors 30 can be set on the first insert plate 20. For example, if there are two chains, two first magnetic sensors 30 can be set on the first insert plate 20. Figure 3 The structure shown is such that each link of the two chains can pass sequentially through the corresponding first magnetic sensor 30.

[0021] Furthermore, the type of the first magnetic sensor 30 includes, but is not limited to, Hall effect sensors, magnetoresistive sensors, magnetic induction sensors, and magnetic integrated sensors, which can quickly and accurately capture the slight changes in the magnetic field of the chain link passing through the first magnetic sensor 30. The vertical distance between the sensing surface of the first magnetic sensor 30 and the surface of the chain 11 closest to the first magnetic sensor 30 is controlled between 5-8 mm. This distance ensures that the first magnetic sensor 30 can effectively sense the magnetic field of the chain link while avoiding mechanical collisions to the first magnetic sensor 30 due to excessive distance during chain operation. The first magnetic sensor 30 can be connected to the controller via a high-temperature resistant, anti-interference shielded cable. The cable adopts a double shielding structure, with an outer layer of metal braided mesh and an inner layer of aluminum foil shielding, effectively suppressing electromagnetic interference and ensuring the accuracy and stability of signal transmission.

[0022] Furthermore, after the scraper conveyor starts operating, the teeth of the sprocket 10 mesh with the links of the chain 11, driving the chain 11 to circulate within the conveying trough. During the operation of the chain 11, each link sequentially passes through the sensing area of ​​the first magnetic sensor 30. Due to unavoidable defects such as internal stress and microcracks during manufacturing, installation, and use, the magnetic permeability of the link changes locally. When a link passes the first magnetic sensor 30, if it has damage defects, its magnetic field distribution will be distorted. The first magnetic sensor 30 converts this magnetic field change into an electrical signal, i.e., the first magnetic characteristic signal.

[0023] Furthermore, the acquired first magnetic feature signal contains rich chain state information, but it is also mixed with environmental noise and interference signals generated by equipment operation. The controller integrates a multi-level signal processing system. First, the original first magnetic feature signal is preprocessed through a hardware filtering circuit. The hardware filtering circuit uses a second-order Butterworth low-pass filter with a cutoff frequency set to 5kHz, effectively filtering out high-frequency noise. The signal after hardware filtering enters the digital signal processing module, which uses a fast Fourier transform algorithm to convert the time-domain signal into a frequency-domain signal and extract the frequency features of the first magnetic feature signal. At the same time, a wavelet transform algorithm is used to decompose the signal at multiple scales to obtain signal detail features at different resolutions.

[0024] Furthermore, as an optional embodiment of the present invention, the controller analyzes the damage state of the chain link based on the first magnetic characteristic signal and predicts whether the chain link is a potential point of chain breakage based on the damage state, including: comparing the first magnetic characteristic signal with the normal magnetic characteristic signal of the chain link under normal conditions; if the first magnetic characteristic signal is inconsistent with the normal magnetic characteristic signal, it is determined that the chain link is damaged; comparing the first magnetic characteristic signal with the damaged magnetic characteristic signals of chain links of different damage types to determine the damage type corresponding to the first magnetic characteristic signal; calculating the first difference between the first magnetic characteristic signals of the same chain link of each damage type in adjacent monitoring cycles, using the first difference as the degree of damage degradation of the chain link of each damage type, and using the signal strength of the first magnetic characteristic signal of the chain link of each damage type monitored in a single cycle as the degree of damage of the chain link; selecting the maximum degree of damage degradation or the maximum degree of damage from each damage type, and using the chain link corresponding to the maximum degree of damage degradation as the potential point of chain breakage for the corresponding damage type, or using the chain link corresponding to the maximum degree of damage as the potential point of chain breakage for the corresponding damage type.

[0025] Specifically, in this embodiment of the invention, features are extracted from the first magnetic characteristic signal. Based on the extracted signal features, the chain link damage analysis algorithm built into the controller is compared and analyzed in conjunction with a pre-established chain link damage feature database. The chain link damage feature database is accumulated through a large amount of experimental and actual operation data, and includes magnetic characteristic signal samples of chain links with different damage types and different degrees of damage for each type, as well as magnetic characteristic signal samples of chain links under normal conditions. For example, for fatigue cracks, when the energy in a specific frequency range (e.g., 1-3kHz) in the first magnetic characteristic signal is significantly enhanced, and the waveform exhibits sharp pulse characteristics, by combining the magnetic characteristic signal samples of chain links under normal conditions with the magnetic characteristic signal samples of chain links with different degrees of damage for various damage types in the chain link damage feature database, it can be determined that there is a fatigue crack in the chain link. By calculating the energy amplitude of this frequency range or comparing it with the magnetic characteristic 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] Furthermore, in the actual comparison process, the Euclidean distance algorithm is used to calculate the difference between the acquired first magnetic feature signal and the normal magnetic feature signal. The formula for calculating the difference D1 between the first magnetic feature signal S1 and the normal magnetic feature signal S01 is as follows: Where n is the number of sampling points for the first magnetic characteristic signal, S1 i and S01 i These are the values ​​of the first magnetic characteristic signal and the normal magnetic characteristic signal at the i-th sampling point, respectively. If D1 exceeds the set threshold, it is determined that the first magnetic characteristic signal and the normal magnetic characteristic signal are inconsistent, that is, the chain link is damaged.

[0027] Furthermore, after confirming damage to the chain link, the first magnetic characteristic signal and the second magnetic feature signal are compared with the damage magnetic feature signals of chain links with different damage types to determine the damage type. The damage magnetic feature signals of chain links with different damage types were also obtained through extensive experiments. In these experiments, common damage types such as fatigue cracks, wear, and plastic deformation of the chain links were artificially created, and corresponding magnetic feature signals were collected to establish a damage magnetic feature signal database. A feature matching algorithm was used for comparison. First, key features, such as peak values, valley values, frequency components, and waveform complexity, were extracted from the first magnetic feature signal. For fatigue crack damage, the magnetic feature signal usually shows a significant peak in a specific frequency range, while the magnetic feature signal of wear damage shows a decrease in amplitude and an increase in low-frequency components. Then, the extracted features were matched with feature templates for each damage type in the database to calculate a similarity score. For example, the cosine similarity algorithm was used to calculate the cosine value between the feature vector of the collected first magnetic feature signal 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 was determined as the damage type of the chain link.

[0028] Furthermore, in order to facilitate the statistical analysis of the chain link monitoring cycle, this embodiment of the invention first sets the initial origin of the chain operation, with the aim of accurately locating the fault risk point. When the scraper conveyor leaves the factory (without chain cutting, the total number of scrapers is set to A), the scraper conveyor is powered on and started. The first scraper that passes through the monitoring point is taken as the initial running point. Counting the number of scrapers that pass through, i.e., the chain runs one revolution, is considered a monitoring cycle.

[0029] Furthermore, in this embodiment of the invention, after obtaining the first magnetic characteristic signal, f(X) mn The first magnetic characteristic signal at position Xmn of the chain link represents the degree of damage and deterioration of each chain link, which 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 f2(Xmn) represents the first difference between the first magnetic characteristic signals of the chain link's location Xmn in adjacent monitoring cycles, indicating the degree of damage and degradation. mn f1(X) represents the first magnetic characteristic signal at the location Xmn of the chain link during the second monitoring cycle. mn ) represents the first magnetic characteristic signal of the chain link location Xmn in the first monitoring cycle.

[0032] Furthermore, in this embodiment of the invention, the maximum value among all chain links' damage and degradation levels is selected as the chain link with the fastest damage and degradation. The formula Δf is used. max (X mn )=max{Δf(X mn )} represents Δf max (X mn ) represents the maximum value among all chain links in terms of damage and deterioration.

[0033] Furthermore, in this embodiment of the invention, the maximum value of the signal intensity of the first magnetic characteristic signal monitored in a single cycle of all chain links is selected as the maximum damage degree using the following formula, specifically expressed as: f max (X mn )=max{f(X mn )}, where f max (X mn ) represents the maximum signal strength of the first magnetic characteristic signal of all links in a single cycle of monitoring.

[0034] Furthermore, for each damage type, the maximum degree of damage degradation or the maximum damage level is selected. The link corresponding to the maximum degree of damage degradation is designated as the chain breakage risk point for that damage type, or the link corresponding to the maximum damage level is designated as the chain breakage risk point for that damage type. This chain breakage risk point is the location where chain breakage may occur.

[0035] Specifically, after calculating the degree of damage and degradation of all chain links, the chain link with the highest degree of damage or the chain link corresponding to the highest degree of damage is selected as the chain breakage risk point for each damage type. If the equipment is more sensitive to the damage development trend and focuses more on the rapid deterioration of chain link damage, then the chain link corresponding to the highest degree of damage is selected as the chain breakage risk point. For example, in some high-load, continuously operating scraper conveyors, even if the current degree of damage to the chain link is relatively minor, if the rate of damage degradation is rapid, it may still lead to a chain breakage failure in a short period of time. If the equipment focuses more on assessing the current severity of damage to the chain link, then the chain link corresponding to the highest degree of damage is selected as the chain breakage risk point. For some scraper conveyors with slower operating speeds and relatively stable loads, chain links with a higher degree of damage are more likely to break. By identifying the chain breakage risk points, these high-risk chain links can be monitored in a targeted manner, replaced in advance, or other maintenance measures can be taken, effectively reducing the probability of chain breakage failures in scraper conveyors.

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

[0037] Specifically, the values ​​of the first and second thresholds can be determined according to actual conditions, and this embodiment of the invention does not limit them. When the maximum damage deterioration exceeds the first threshold or the maximum damage exceeds the second threshold, it indicates that the chain link may break. Therefore, the scraper conveyor is stopped, and the position of the chain link corresponding to the maximum damage deterioration, or the position of the chain link corresponding to the maximum damage, is sent to the host computer. This allows maintenance personnel to perform maintenance and troubleshooting on the chain link, preventing losses caused by sudden chain breakage of the scraper conveyor. The position of the chain link can be the pre-marked position of the scraper closest to the chain link, and the scraper position is pre-marked.

[0038] Furthermore, as an optional embodiment of the present invention, the chain breakage fault prediction device further includes: a second insert plate 21 and a second magnetic sensor 31 fixed to the second insert plate 21; the conveying groove has a second opening 41, the second insert plate 21 is inserted into the second opening 41 and located on the other side of the chain away from the chain path, and the second magnetic sensor 31 is fixed to the second insert plate 21 and opposite to the other side of the chain; during the operation of the scraper conveyor chain, the second magnetic sensor 31 is used to collect the second magnetic characteristic signal of the chain 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 used to analyze the damage state of the chain link based on the first magnetic characteristic signal and the second magnetic characteristic signal of the same chain link, and predict whether the chain link is a chain breakage fault risk point based on the damage state.

[0039] Specifically, to further improve the accuracy and reliability of chain link damage detection, the chain breakage prediction device also adds a second insert plate 21 and a second magnetic sensor 31 fixed to the second insert plate 21. The conveying trough has a second opening 41 on the opposite side from 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 insert plate 21 and the first insert plate 20 are made of the same stainless steel and have an L-shaped structure design. Its vertical part is inserted into the second opening 41, and its horizontal part extends towards the chain 11, so that the other side of each chain 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 magnetic characteristic signals from both sides of the same chain link, thereby predicting defects on both sides of the chain link and improving the accuracy and reliability of chain link defect prediction. It is worth noting that the structure between the second insert plate 21 and the second magnetic sensor 31 is the same as the structure between the first insert plate 20 and the first magnetic sensor 30. The similarities will not be described again in this embodiment of the invention.

[0040] Furthermore, the conveyor trough, serving as the track carrier for the chain 11, has its bottom welded from high-strength wear-resistant steel plate, and a rectangular second opening 41 is formed on the side wall near the chain track. The dimensions of the second opening 41 precisely match those of the second insert plate 21, with its width being 2-3 mm wider than that of the first insert plate 20, and a 1-2 mm installation gap reserved in the height direction to facilitate the smooth insertion and removal of the second insert plate 21. The second insert plate 21 is made of stainless steel and has an L-shaped structure, with its vertical portion inserted into the second opening 41 and its horizontal portion extending towards the chain 11. A dedicated sensor mounting groove is machined on the surface of the horizontal portion of the second insert plate 21, with the groove depth and width 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.

[0041] Furthermore, the type of the second magnetic sensor 31 includes, but is not limited to, Hall effect sensors, magnetoresistive sensors, magnetic induction sensors, and magnetic integrated sensors, which can quickly and accurately capture the slight changes in the magnetic field of the chain 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 closest to the second magnetic sensor 31 is controlled between 5-8 mm. This distance ensures that the second magnetic sensor 31 can effectively sense the magnetic field of the chain link while avoiding mechanical collisions to the second magnetic sensor 31 caused by the chain running due to excessive distance. The second magnetic sensor 31 can be connected to the controller through a high-temperature resistant, anti-interference shielded cable. The cable adopts a double shielding structure, with an outer metal braided mesh and an inner aluminum foil shielding layer, effectively suppressing electromagnetic interference and ensuring the accuracy and stability of signal transmission.

[0042] Furthermore, after the scraper conveyor starts operating, the teeth of the sprocket 10 mesh with the links of the chain 11, driving the chain 11 to circulate within the conveying trough. During the operation of the chain 11, each link passes sequentially through the sensing area of ​​the second magnetic sensor 31 on the side closest to it. Due to unavoidable defects such as internal stress and microcracks during manufacturing, installation, and use, the magnetic permeability of the link changes locally. When a link passes the second magnetic sensor 31, if it has damage defects, its magnetic field distribution will be distorted. The second magnetic sensor 31 converts this magnetic field change into an electrical signal, i.e., the second magnetic characteristic signal.

[0043] Furthermore, the acquired second magnetic feature signal contains rich chain state information on the side of the chain closest to the second magnetic sensor 31, but it is also mixed with environmental noise and interference signals generated by equipment operation. The controller integrates a multi-level signal processing system. First, the original second magnetic feature signal is preprocessed through a hardware filtering circuit. The hardware filtering circuit uses a second-order Butterworth low-pass filter with a cutoff frequency set to 5kHz, effectively filtering out high-frequency noise. The signal after hardware filtering enters the digital signal processing module, which uses a fast Fourier transform algorithm to convert the time-domain signal into a frequency-domain signal and extract the frequency features of the second magnetic feature signal. At the same time, a wavelet transform algorithm is used to decompose the signal at multiple scales to obtain signal detail features at different resolutions.

[0044] Furthermore, as an optional embodiment of the present invention, the controller analyzes the damage state of the chain links based on the first magnetic characteristic signal and the second magnetic characteristic signal, and predicts whether the chain is at risk of breakage based on the damage state, including: comparing the first magnetic characteristic signal and the second magnetic characteristic signal with the normal magnetic characteristic signal of the chain links under normal conditions; if the first magnetic characteristic signal and / or the second magnetic characteristic signal are inconsistent with the normal magnetic characteristic signal, then it is determined that the chain link is damaged; comparing the first magnetic characteristic signal and the second magnetic characteristic signal with the damage magnetic characteristic signals of chain links with different damage types to determine the damage type corresponding to the first magnetic characteristic signal and the second magnetic characteristic signal; when the damage type of the same chain link corresponding to the first magnetic characteristic signal and the second magnetic characteristic signal is the same, Calculate the second difference between the first magnetic characteristic signal and the third difference between the second magnetic characteristic signal of the same chain link in adjacent monitoring periods. Weight the second and third differences to obtain a first superimposed value, which is used as the degree of damage and degradation of the chain link. Weight the signal intensity of the first and second magnetic characteristic signals of the chain link to obtain a second superimposed value, which is used as the degree of damage of the chain link in a single monitoring period. Select the chain link with the maximum degree of damage or the maximum damage level from the chain links of each damage type. Use the chain link with the maximum degree of damage or the chain link with the maximum damage level as the chain breakage risk point for the corresponding damage type.

[0045] Specifically, in this embodiment of the invention, features are extracted from both the first and second magnetic feature signals. Based on the extracted signal features, the chain link damage analysis algorithm built into the controller is compared and analyzed in conjunction with a pre-established chain link damage feature database. The chain link damage feature database is accumulated through extensive experimental and actual operational data, containing magnetic feature signal samples of chain links with different damage types and varying degrees of damage for each type, as well as magnetic feature signal samples of chain links under normal conditions. For example, for fatigue cracks, when a significant increase in energy occurs in a specific frequency range (e.g., 1-3kHz) in the first and / or second magnetic feature signals, and the waveform exhibits sharp pulse characteristics, the presence of a fatigue crack in the chain link can be determined by combining the magnetic feature signal samples of the chain link under normal conditions with the magnetic feature signal samples of chain links with varying degrees of damage for different damage types in the chain link damage feature database. Furthermore, the length and depth of the crack can be estimated by calculating the energy amplitude of this frequency range or by comparing it with the magnetic feature signal samples of chain links with varying degrees of fatigue crack damage.

[0046] Furthermore, during the operation of the scraper conveyor, the first and second magnetic sensors continuously collect the first and second magnetic characteristic signals of the chain links. To preliminarily determine whether the chain links are damaged, the collected signals need to be compared with the normal magnetic characteristic signals of the chain links under normal conditions. The acquisition of normal magnetic characteristic signals is based on a large number of brand-new, unused chain links. Under the same detection conditions (detection distance, ambient temperature, etc.), signals are collected using the first and second magnetic sensors, and these signals are statistically analyzed to calculate their average value, standard deviation, and other statistical quantities, thereby determining 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] In the actual comparison process, the Euclidean distance algorithm is used to calculate the difference between the acquired first and second magnetic feature signals and the normal magnetic feature signal. The formula for calculating the difference D1 between the first magnetic feature signal S1 and the normal magnetic feature signal S01 is as follows: Where n is the number of sampling points for the first magnetic characteristic signal, S1 i and S01 i These 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, using the same calculation method as 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 a set threshold, and / or D2 exceeds a set threshold, it is determined that the first magnetic characteristic signal and / or the second magnetic characteristic signal are inconsistent with the normal magnetic characteristic signal, i.e., the chain link is damaged.

[0048] Furthermore, after confirming damage to the chain links, the first and second magnetic characteristic signals are compared with the damage magnetic characteristic signals of chain links with different damage types to determine the damage type. The damage magnetic characteristic signals of chain links with different damage types were also obtained through extensive experiments. In these experiments, common damage types such as fatigue cracks, wear, and plastic deformation of the chain links were artificially created, and corresponding magnetic characteristic signals were collected to establish a damage magnetic characteristic signal database. A feature matching algorithm was used for comparison. First, key features, such as peak values, valley values, frequency components, and waveform complexity, were extracted from the first and second magnetic characteristic signals. For fatigue crack damage, the magnetic characteristic signal typically shows a significant peak in a specific frequency range, while the magnetic characteristic signal of wear damage shows a decrease in amplitude and an increase in low-frequency components. Then, the extracted features were matched with feature templates for each damage type in the database to calculate a similarity score. For example, a cosine similarity algorithm was used to calculate the cosine value between the feature vectors of the collected first and second magnetic characteristic signals and the feature template vector of a certain damage type. The closer the cosine value is to 1, the higher the similarity. Ultimately, the damage type with the highest similarity score was determined to be the damage type of the chain link indicated by the first magnetic feature signal and the second magnetic feature signal.

[0049] Furthermore, when the damage types of the same chain link corresponding to the first and second magnetic characteristic signals are the same, it is necessary to calculate the degree of damage degradation of the chain link in order to assess the development trend of chain link damage. The specific process is as follows: Calculate the second difference ΔS1 between the first magnetic characteristic signals and the third difference ΔS2 between the second magnetic characteristic signals of the same chain link in adjacent monitoring periods. When calculating the differences, the signals can be standardized first to eliminate the influence of signal amplitude differences on the calculation results. For example, the Z-score standardization method can be used to convert the signals 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 and second magnetic characteristic signals, the second difference ΔS1 and the third difference ΔS2 are weighted and superimposed. The weights are determined based on factors such as the installation position of the magnetic sensor, the detection accuracy, and the structural characteristics of the chain link. 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 signal are determined, and w1 + w2 = 1. The first superposition value D is: D = w1 × ΔS1 + w2 × ΔS2. This first superposition value is used as the degree of damage and deterioration of the chain link, reflecting the rate of change of chain link damage over time.

[0050] Furthermore, in addition to determining the degree of damage and degradation, it is also necessary to determine the extent of damage to the chain links within a single monitoring cycle. The signal intensities of the first and second magnetic characteristic signals of the chain links are weighted and superimposed to obtain a second superimposed value. The signal intensity can be represented by calculating the root mean square (RMS) value of the signal. Second magnetic characteristic signal intensity Where n is the number of sampling points for the first magnetic characteristic signal or the second magnetic characteristic signal. S1 i S2 represents the signal strength value of the i-th first magnetic feature. i This represents the signal strength value of the i-th second magnetic feature.

[0051] Furthermore, in this embodiment of the invention, based on sensor performance and actual detection results, the weight w3 of the signal intensity of the first magnetic feature signal and the weight w4 of the signal intensity of the second magnetic feature signal are determined, and w3 + w4 = 1. Then, the second superposition value M is: M = w3 × I1 + w4 × I2. This second superposition value is used as the degree of damage to the chain link in a single cycle of monitoring, reflecting the current severity of damage to the chain link.

[0052] Furthermore, after calculating the degree of damage and deterioration of all chain links, the chain link with the highest degree of damage or the chain link corresponding to the highest degree of damage is selected from the chain links of each damage type as the chain breakage risk point for that damage type. If the equipment is more sensitive to the damage development trend and focuses more on the rapid deterioration of chain link damage, then the chain link corresponding to the highest degree of damage is selected as the chain breakage risk point. For example, in some high-load, continuously operating scraper conveyors, even if the current degree of damage to the chain link is relatively minor, if the rate of damage deterioration is rapid, it may still cause a chain breakage failure in a short period of time. If the equipment focuses more on assessing the current severity of damage to the chain link, then the chain link corresponding to the highest degree of damage is selected as the chain breakage risk point. For some scraper conveyors with slower operating speeds and relatively stable loads, chain links with a higher degree of damage are more likely to break. By identifying the chain breakage risk points, these high-risk chain links can be monitored in a targeted manner, replaced in advance, or other maintenance measures can be taken, effectively reducing the probability of chain breakage failures in scraper conveyors.

[0053] Furthermore, as an optional embodiment of the present invention, after the controller selects the maximum damage deterioration degree or the maximum damage level from each damage type based on the first magnetic characteristic signal and the second magnetic characteristic signal, and designates the chain link corresponding to the maximum damage deterioration degree as the chain breakage failure risk point of the corresponding damage type, or designates the chain link corresponding to the maximum damage level as the chain breakage failure risk point of the corresponding damage type, the controller is further configured to control the scraper conveyor to stop and send the position of the chain link corresponding to the maximum damage deterioration degree or the chain link corresponding to the maximum damage level to the host computer when the maximum damage deterioration degree exceeds the first threshold or the maximum damage level exceeds the second threshold, so as to remind maintenance personnel to maintain the chain link corresponding to the maximum damage deterioration degree or the chain link corresponding to the maximum damage level.

[0054] Specifically, the values ​​of the first and second thresholds can be determined according to actual conditions, and this embodiment of the invention does not limit them. When the maximum damage deterioration exceeds the first threshold or the maximum damage exceeds the second threshold, it indicates that the chain link may break. Therefore, the scraper conveyor is stopped, and the position of the chain link corresponding to the maximum damage deterioration, or the position of the chain link corresponding to the maximum damage, is sent to the host computer. This allows maintenance personnel to perform maintenance and troubleshooting on the chain link, preventing losses caused by sudden chain breakage of the scraper conveyor. The position of the chain link can be the pre-marked position of the scraper closest to the chain link, and the scraper position is pre-marked.

[0055] This invention, by installing a first insert plate and a first magnetic sensor at the first opening of the scraper conveyor trough, enables continuous real-time acquisition of the first magnetic characteristic signal of the chain links during chain operation. This ensures timely acquisition of chain link status information at every moment of chain operation, greatly improving the timeliness and effectiveness of monitoring. The controller analyzes the chain link damage state based on the first magnetic characteristic signal and predicts potential chain failure points. Through in-depth analysis of the magnetic characteristic signal, it identifies early fatigue cracks, wear, and other potential damage states of the chain links, predicting chain links at risk of breakage in advance with high accuracy. This prevents chain breakage failures before they occur, effectively avoiding sudden accidents caused by chain breakage. Because it can accurately predict chain failure risk points, maintenance plans can be formulated in advance based on the prediction results, and downtime for maintenance can be reasonably arranged, avoiding unplanned downtime caused by sudden chain breakage. This reduces the number of production interruptions and downtime maintenance time, ensuring continuous and stable operation of the scraper conveyor, significantly improving production efficiency, and reducing economic losses caused by equipment failure. Furthermore, early detection of chain link damage and prediction of chain failure risk points allow for timely measures such as chain link replacement, reducing the probability of chain breakage accidents and preventing safety hazards such as material splashing and equipment part detachment caused by chain breakage. This creates a safer working environment for operators and protects their lives. In this way, early prediction of chain damage in scraper conveyors enables early warning and preventative maintenance of chain breakage, improving the reliability and safety of scraper conveyor operation.

[0056] Based on the same inventive concept, embodiments of the present invention also provide a method for predicting chain breakage faults in scraper conveyors, such as... Figure 4 As shown, Figure 4 This is a schematic flowchart of a chain breakage prediction method for a scraper conveyor according to an embodiment of the present invention. The present invention provides a chain breakage prediction method for a scraper conveyor, which can be executed by the controller of the scraper conveyor, and includes the following steps:

[0057] Step S401: Obtain the first magnetic feature signal of the chain link that has passed through the first magnetic sensor, with the first magnetic sensor facing one side of the chain link.

[0058] Step S402: Analyze the damage state of the chain link based on the first magnetic characteristic signal, and predict whether the chain link is a risk point of chain breakage based on the damage state.

[0059] Optionally, analyzing the damage state of the chain link based on the first magnetic characteristic signal and predicting whether the chain link is a potential point of chain breakage based on the damage state includes: comparing the first magnetic characteristic signal with the normal magnetic characteristic signal of the chain link under normal conditions; if the first magnetic characteristic signal is inconsistent with the normal magnetic characteristic signal, then the chain link is determined to be damaged; comparing the first magnetic characteristic signal with the damaged magnetic characteristic signals of chain links with different damage types to determine the damage type corresponding to the first magnetic characteristic signal; calculating the first difference between the first magnetic characteristic signals of the same chain link for each damage type in adjacent monitoring cycles, using the first difference as the degree of damage degradation of the chain link for each damage type, and using the signal intensity of the first magnetic characteristic signal of the chain link for each damage type monitored in a single cycle as the degree of damage of the chain link; selecting the maximum degree of damage degradation or the maximum degree of damage from each damage type, and using the chain link corresponding to the maximum degree of damage degradation as the potential point of chain breakage for the corresponding damage type, or using the chain link corresponding to the maximum degree of damage as the potential point of chain breakage for the corresponding damage type.

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

[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 marked in advance.

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

[0063] Optionally, analyzing the damage state of the chain links based on the first and second magnetic characteristic signals, and predicting whether the chain is at risk of breakage based on the damage state, includes: comparing the first and second magnetic characteristic signals with the normal magnetic characteristic signals of the chain links under normal conditions; if the first and / or second magnetic characteristic signals are inconsistent with the normal magnetic characteristic signals, then the chain links are determined to be damaged; comparing the first and second magnetic characteristic signals with the damaged magnetic characteristic signals of chain links with different damage types to determine the damage type corresponding to the first and second magnetic characteristic signals; and calculating the damage type of the same chain link when the damage type corresponding to the first and second magnetic characteristic signals is the same. The second difference between the first magnetic characteristic signal and the third difference between the second magnetic characteristic signal in adjacent monitoring periods are weighted and superimposed to obtain a first superimposed value, which is used as the degree of damage and deterioration of the chain link. The signal intensity of the first magnetic characteristic signal and the signal intensity of the second magnetic characteristic signal of the chain link are weighted and superimposed to obtain a second superimposed value, which is used as the degree of damage of the chain link in a single monitoring period. The chain link with the maximum degree of damage or the maximum degree of damage is selected from the chain links of each damage type, and the chain link corresponding to the maximum degree of damage or the chain link corresponding to the maximum degree of damage is used as the chain breakage failure risk point of the corresponding damage type.

[0064] It should be noted that the chain breakage prediction method for scraper conveyors provided in this embodiment of the 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. Repeated parts will not be described again.

[0065] Furthermore, based on the same inventive concept, embodiments of the present invention also provide a chain breakage prediction device for scraper conveyors, as described above. Figure 3 This is a schematic diagram of a chain breakage prediction device provided in an embodiment of the present invention. The scraper conveyor includes a sprocket, a chain, and scrapers. The chain is composed of multiple chain links connected together, and scrapers are distributed at intervals on the chain. When the sprocket rotates, it drives the chain and scrapers to move. The device is characterized in that it includes a controller, a first insert plate, and a first magnetic sensor fixed to the first insert plate. The conveying trough of the scraper conveyor has a first opening. The first insert plate is inserted into the first opening and is located on the side of the chain near the chain track. The first magnetic sensor is fixed to the first insert 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 based on the first magnetic characteristic signal, and predict whether the chain link is a chain breakage risk point based on the damage state.

[0066] Optionally, the chain breakage prediction device further includes: a second insert plate and a second magnetic sensor fixed to the second insert plate; the conveyor trough has a second opening, the second insert plate is inserted into the second opening and located on the other side of the chain away from the chain path, and the second magnetic sensor is fixed to the second insert plate and opposite to the other side of the chain; during the operation of the scraper conveyor chain, 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 based on the first magnetic characteristic signal and the second magnetic characteristic signal of the same chain link, and predict whether the chain link is a chain breakage risk point based on the damage state.

[0067] It should be noted that the chain breakage prediction device for the scraper conveyor provided in this embodiment of the 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. Repeated parts will not be described again.

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

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

Claims

1. A method for predicting chain breakage faults in a scraper conveyor, characterized in that, A chain breakage prediction device based on a scraper conveyor, wherein the scraper conveyor includes a sprocket, a chain and scrapers, the chain is composed of multiple chain links connected together, and the scrapers are distributed at intervals on the chain. When the sprocket rotates, it drives the chain and scrapers to move. The chain breakage prediction device includes: a controller, a first insert plate and a first magnetic sensor fixed to the first insert plate. The scraper conveyor has a first opening in its conveying trough. The first insert plate is inserted into the first opening and is located on the side of the chain near the chain track. The first magnetic sensor is fixed to the first insert plate and is opposite to one side of the chain. During the operation of the scraper conveyor chain, 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 based on the first magnetic feature signal, and predict whether the chain link is a risk point of chain breakage based on the damage state. The chain breakage fault prediction method includes: Acquire the first magnetic characteristic signal of the chain link that has passed through the first magnetic sensor, wherein the first magnetic sensor is opposite to one side of the chain link; The damage state of the chain link is analyzed based on the first magnetic feature signal, and the chain link is predicted to be a potential point of chain breakage based on the damage state. The step of analyzing the damage state of the chain link based on the first magnetic feature signal and predicting whether the chain link is at risk of breakage based on the damage state includes: The first magnetic feature signal is compared with the normal magnetic feature signal of the chain link under normal conditions. If the first magnetic feature signal is inconsistent with the normal magnetic feature signal, it is determined that the chain link is damaged. The first magnetic feature signal is compared with the damage magnetic feature signals of chain links with different damage types to determine the damage type corresponding to the first magnetic feature signal. Calculate the first difference between the first magnetic feature signals of the same chain link for each damage type in adjacent monitoring periods, and use the first difference as the degree of damage degradation of the chain link for each damage type. The maximum degree of damage degradation is selected from each damage type, and the link corresponding to the maximum degree of damage degradation is taken as the chain breakage risk point of the corresponding damage type.

2. The method for predicting chain breakage faults in a scraper conveyor according to claim 1, characterized in that, After selecting the maximum damage degradation degree from each damage type and taking the link corresponding to the maximum damage degradation degree as the chain breakage failure risk point for the corresponding damage type, the method further includes: If the maximum damage and deterioration level exceeds the first threshold, the scraper conveyor is stopped and the position of the chain link corresponding to the maximum damage and deterioration level is sent to the host computer to remind maintenance personnel to maintain the chain link corresponding to the maximum damage and deterioration level.

3. The method for predicting chain breakage faults in a scraper conveyor according to claim 2, characterized in that, The position of the chain link is the position of the scraper closest to the chain link, and the position of the scraper is marked in advance.

4. The method for predicting chain breakage faults in a scraper conveyor according to claim 1, characterized in that, The chain breakage fault prediction method also includes: Acquire the second magnetic feature signal of the chain link that has passed through the second magnetic sensor, the second magnetic sensor being opposite to the other side of the chain link; The step of analyzing the damage state of the chain link based on the first magnetic feature signal and predicting whether the chain link is at risk of breakage based on the damage state includes: The damage state of the chain link is analyzed based on the first and second magnetic characteristic signals of the same chain link, and the chain link is predicted to be at risk of breakage based on the damage state.

5. The method for predicting chain breakage faults in a scraper conveyor according to claim 4, characterized in that, The step of analyzing the damage state of the chain links based on the first magnetic feature signal and the second magnetic feature signal, and predicting whether the chain is at risk of breakage based on the damage state, includes: The first magnetic feature signal and the second magnetic feature signal are compared with the normal magnetic feature signal of the chain link under normal conditions. If the first magnetic feature signal and / or the second magnetic feature signal are inconsistent with the normal magnetic feature signal, it is determined that the chain link is damaged. The first magnetic feature signal and the second magnetic feature signal are compared with the damage magnetic feature signals of chain links with different damage types to determine the damage type corresponding to the first magnetic feature signal and the second magnetic feature signal. When the damage types of the same chain link corresponding to the first magnetic feature signal and the second magnetic feature signal are the same, the second difference between the first magnetic feature signal and the third difference between the second magnetic feature signal of the same chain link in adjacent monitoring periods are calculated respectively. The second difference and the third difference are weighted and superimposed to obtain the first superimposed value. The first superimposed value is used as the degree of damage deterioration of the chain link. The chain link with the maximum degree of damage is selected from the chain links of each damage type, and the chain link corresponding to the maximum degree of damage is taken as the chain breakage risk point of the corresponding damage type.

6. The method for predicting chain breakage faults in a scraper conveyor according to claim 1, characterized in that, The chain breakage fault prediction device further includes: a second insert plate and a second magnetic sensor fixed to the second insert plate; The conveying groove has a second opening, the second insert plate is inserted into the second opening and is located on the other side of the chain away from the chain track, and the second magnetic sensor is fixed to the second insert plate and is opposite to the other side of the chain; During the operation of the scraper conveyor chain, 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 based on the first magnetic feature signal and the second magnetic feature signal of the same chain link, and to predict whether the chain link is a risk point of chain breakage based on the damage state.

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