Scraper chain fault detection method for scraper conveyor

Abstract

The invention relates to the technical field of image processing, and particularly discloses a scraper chain fault detection method for a scraper conveyor, which comprises the following steps of: acquiring scraper chain images at each moment in a rotating state; calculating the abnormal degree of the scraper chain image at each moment in a preset time length, and when the abnormal degrees of the scraper chain image at all moments in the preset time length are greater than a threshold value, judging that the scraper chain has a fault; the abnormal degree is inversely correlated with the similarity of the transverse features and the similarity of the longitudinal features of the scraper chain image at the current moment and the scraper chain image at the previous set moment; the transverse feature is a row mean value sequence formed by mean values of the tendency degrees of all rows of pixel points in the scraper chain image, and the longitudinal feature is a column mean value sequence formed by mean values of the tendency degrees of all columns of pixel points in the scraper chain image. According to the scheme, the scraper chain and the chain wheel in the scraper chain image can be accurately distinguished, and the fault detection result is more accurate.

Description

Technical Field

[0001] The invention relates to the technical field of image processing, and in particular to a method for detecting a scraper chain fault of a scraper conveyor. Background Art

[0002] Scraper conveyor is an important coal mine machinery, which plays an important role in the material transportation process of coal mines. Scraper conveyor is a kind of equipment that drives the scraper chain to move through the rotation of the sprocket to realize material transportation. Therefore, the normal operation of the scraper chain is very important for conveying materials. If the scraper chain fails, such as the chain is stuck, the chain is off, or even the scraper chain is broken, it will not only affect the material transportation and reduce the transportation efficiency, but also may cause safety accidents and cause property losses. Therefore, it is necessary to perform fault detection on the scraper chain in order to detect the fault of the scraper chain in time and perform manual intervention.

[0003] In the prior art, manual methods are usually used to detect faults in scraper chains. However, this method relies on human observation, resulting in low accuracy and efficiency of fault detection. With the development of image processing technology, it is becoming more and more common to apply image processing technology to fault detection of scraper chains, which can improve the accuracy and efficiency of fault detection. The use of image processing technology to detect faults in scraper chains includes the following steps: collecting scraper chain images at multiple times; separating the scraper chain from the sprocket in the image by an image segmentation method; comparing the position information of the scraper chain at the current moment with that at the previous moment. If the position change of the scraper chain at the current moment is greater than the set value, it is determined that the scraper chain has a fault.

[0004] However, in the scraper chain images collected, the scraper chain and the sprocket are similar. When segmenting the image, it is difficult to accurately separate the scraper chain and the sprocket, and the sprocket may be mistakenly classified as the scraper chain. Therefore, when comparing the position information of the scraper chain, the image of the segmented scraper chain is inaccurate, making the comparison result inaccurate, resulting in low accuracy of scraper chain fault detection. Summary of the invention

[0005] The present invention provides a scraper chain fault detection method for a scraper conveyor, aiming to solve the technical problem in the prior art that it is difficult to accurately distinguish the scraper chain and the sprocket in the scraper chain image, resulting in low accuracy of scraper chain fault detection.

[0006] The present invention provides a method for detecting a scraper chain fault of a scraper conveyor, comprising the following steps: Collect the scraper chain images at each moment in the rotating state; Calculate the abnormality of the scraper chain image at each moment within the preset time length, and when the abnormality of the scraper chain image at all moments within the preset time length is greater than a threshold, it is determined that the scraper chain has a fault; Among them, the degree of abnormality is negatively correlated with the similarity of the lateral features and the similarity of the longitudinal features of the scraper chain image at the current moment and the scraper chain image at the set moment; the lateral feature is a row mean sequence composed of the mean of the tendency degree of each row of pixel points in the scraper chain image, and the longitudinal feature is a column mean sequence composed of the mean of the tendency degree of each column of pixel points in the scraper chain image; the tendency degree is positively correlated with the normalized value of the difference between the pixel values ​​of the corresponding pixel point in the first color channel and the second color channel, and is positively correlated with the normalized value of the difference between the pixel values ​​of each reference pixel point in the first color channel and the second color channel; the reference pixel point is the first pixel point after multiple rays radiating outward from the corresponding pixel point pass through the nearest edge; the first color channel is the color channel with the largest pixel value of the pixel point in the color of the scraper chain, and the second color channel is the color channel with the largest pixel value of the pixel point in the color of the sprocket.

[0007] In the above scheme, by calculating the inclination degree of each pixel point, the scraper chain and the sprocket in the scraper chain image can be accurately distinguished. And by comparing the similarity of the horizontal features and the similarity of the vertical features in the scraper chain image, the abnormality of the scraper chain image is obtained, making the calculation result more accurate.

[0008] Preferably, Abnormality of the scraper chain image at a certain moment for: ; In the formula, For the The moment and The similarity of the lateral features of the scraper chain image at each moment, For the The moment and The similarity of the longitudinal features of the scraper chain image at the moment The first moment is the set moment, and the The moment is The moment when the maximum value of the similarity of the lateral features of the scraper chain image at the moment is closest, The natural constant The exponential function of base .

[0009] In the above scheme, the degree of abnormality of the scraper chain image is comprehensively and accurately reflected through the similarity of the horizontal features and the similarity of the vertical features of the scraper chain image. And the inverse correlation between the degree of abnormality and the similarity can be reflected through the exponential function.

[0010] Preferably, The scraper chain image at the moment The tendency of the pixel for: ; In the formula, For the The scraper chain image at the moment The pixel value of the first color channel, For the The scraper chain image at the moment The pixel value of the second color channel, For the The scraper chain image at the moment The pixel The pixel value of the reference pixel in the first color channel, For the The scraper chain image at the moment The pixel The pixel value of the reference pixel in the second color channel, is the normalization function, To adjust the parameters, , For the The scraper chain image at the moment The total number of reference pixels of pixels, The natural constant The exponential function of base .

[0011] In the above scheme, the degree of inclination is corrected by the difference between the pixel values ​​of each reference pixel point in the first color channel and the second color channel, so as to prevent the calculated degree of inclination from being too small due to the wear of the protective paint on the scraper chain, resulting in inaccurate calculation results.

[0012] Preferably, the similarity is calculated using cosine similarity or Pearson correlation coefficient.

[0013] In the above schemes, cosine similarity has the advantages of being simple, easy to implement, and fast in calculation, while Pearson correlation coefficient has the advantages of being simple, intuitive, and easy to explain.

[0014] Preferably, the edge of the scraper chain image is obtained by using a Canny edge detection algorithm.

[0015] In the above scheme, the Canny edge detection algorithm can effectively suppress image noise and reduce the impact of noise on edge detection.

[0016] Preferably, the scraper chain and the sprocket are in any one of red, blue and green colors, and the scraper chain and the sprocket are in different colors.

[0017] Preferably, each reference pixel is an eight-neighborhood pixel or a four-neighborhood pixel.

[0018] Preferably, the scraper chain image is acquired by an image acquisition device disposed at the feed end of the scraper conveyor.

[0019] In the above scheme, the image acquisition device is set in this way to prevent the material from blocking the scraper chain, resulting in the inability to accurately obtain the scraper chain image, and does not affect the normal operation of the scraper conveyor.

[0020] Preferably, the preset duration ranges from 8 to 12 seconds.

[0021] Preferably, the threshold value ranges from [0.5, 0.7].

[0022] The beneficial effects are: The solution of the present invention can accurately distinguish the scraper chain and the sprocket in the scraper chain image by introducing the tendency degree of pixel points, thereby facilitating the subsequent calculation of the abnormality degree of the scraper chain image. Moreover, by comparing the similarity of the horizontal features and the similarity of the vertical features in the scraper chain image, the abnormality degree of the scraper chain image can be fully and accurately reflected, thereby making the fault detection result more accurate. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] Figure 1 A flowchart of the steps of a method for detecting a scraper chain fault of a scraper conveyor according to an embodiment of the present invention; Figure 2 A flowchart of the steps of obtaining the abnormality degree of the scraper chain image according to an embodiment of the present invention; Figure 3 It is a distribution diagram of any pixel point and its reference pixel point in the scraper chain image of an embodiment of the present invention. DETAILED DESCRIPTION

[0024] The embodiments described below with reference to the accompanying drawings are exemplary and are intended to be used to explain the present invention, but should not be construed as limiting the present invention.

[0025] like Figure 1 As shown, the present invention provides a method for detecting a scraper chain fault of a scraper conveyor, comprising the following steps: S1. Collect the scraper chain images at each moment in the rotating state.

[0026] The scraper conveyor includes a sprocket, a scraper chain and a scraper. When the scraper conveyor is working, the sprocket rotates and drives the scraper chain to move, and the scraper chain drives the scraper to move to realize the conveyance of materials. When collecting the scraper chain image, the image acquisition device is fixed relative to the scraper conveyor, and the scraper chain image at each moment is collected through the movement of the scraper chain. Therefore, in the present invention, the scraper conveyor can realize the collection of the scraper chain image without stopping, which does not affect the normal operation of the scraper conveyor and avoids the economic loss caused by stopping.

[0027] Since the top of the scraper chain will be covered by materials during the material conveying process and the scraper chain image cannot be obtained, the image acquisition device can be set at the feed end of the scraper conveyor. The scraper chain here is in the state of unloading materials but not loading materials. Since there is no material obstruction, a good image acquisition effect can be achieved.

[0028] The image acquisition device can be a camera, and by setting the camera's photographing frequency, the scraper chain images at different times can be collected. Of course, the image acquisition device can also be a video recorder, and the scraper chain images at different times can be intercepted by the acquired video recording.

[0029] Since the scraper chain is driven by a sprocket, the collected scraper chain image includes the scraper chain and the sprocket.

[0030] S2. Calculate the abnormality degree of the scraper chain image at each moment within a preset time length. When the abnormality degree of the scraper chain image at all moments within the preset time length is greater than a threshold, determine that the scraper chain is faulty.

[0031] When the scraper conveyor is working, the scraper chain is constantly moving, and the scraper chain corresponding to the image acquisition device has different states at different times, that is, the angles of the scraper chain are different, so the scraper chain images collected at each time are also different. Since the scraper chain has structural repeatability, two scraper chain images separated by a period of time have a high similarity, that is, the change of the scraper chain image is periodic. Under normal circumstances, the scraper chain at two moments separated by a cycle has the same state, so the similarity of the corresponding two scraper chain images is high. However, when the scraper chain fails at a certain moment, for example, the scraper chain deviates left and right on the sprocket or the scraper chain breaks along its length, the corresponding scraper chain image will have a lower similarity with the scraper chain image separated by a cycle, that is, the corresponding scraper chain image has a higher degree of abnormality. Therefore, it is possible to determine whether the scraper chain has a fault by calculating the degree of abnormality of the scraper chain image at each moment. Specifically, step S2 includes the following steps: S21. Obtain the abnormality degree of the scraper chain image at each moment.

[0032] The degree of abnormality is inversely correlated with the similarity of the transverse features and the similarity of the longitudinal features of the scraper chain image at the current moment and the scraper chain image at the set moment. Among them, the transverse feature characterizes the degree of inclination of the pixels in each row of the scraper chain image. By comparing the similarity of the transverse features of the scraper chain images at two moments, it can be reflected whether the scraper chain has cracks or even breaks in its length direction. The longitudinal feature characterizes the degree of inclination of the pixels in each column of the scraper chain image. By comparing the similarity of the longitudinal features of the scraper chain images at two moments, it can be reflected whether the scraper chain has offset in the left and right directions. The transverse feature is a row mean sequence composed of the mean of the inclination of the pixels in each row of the scraper chain image, and the longitudinal feature is a column mean sequence composed of the mean of the inclination of the pixels in each column of the scraper chain image. Therefore, if Figure 2 As shown, step S21 also includes the following steps: S211, obtaining the tendency degree of each pixel point.

[0033] The tendency degree represents the degree to which the corresponding pixel point tends to be located at the scraper chain in the scraper chain image. The tendency degree is positively correlated with the normalized value of the difference between the pixel values ​​of the first color channel and the second color channel of the corresponding pixel point. The first color channel is the color channel with the largest pixel value of the pixel point in the color of the scraper chain, and the second color channel is the color channel with the largest pixel value of the pixel point in the color of the sprocket.

[0034] Those skilled in the art will know that color images usually adopt the RGB color mode, which means that the color of any pixel in the color image is formed by the superposition of different pixel values ​​of the red channel, the green channel and the blue channel. For example, if the color of the scraper chain tends to be blue, then the color channel with the largest pixel value of the pixel in the color of the scraper chain is the blue channel, that is, the first color channel is the blue channel. If the color of the sprocket tends to be red, then the color channel with the largest pixel value of the pixel in the color of the sprocket is the red channel, that is, the second color channel is the red channel. In the present invention, the colors of the scraper chain and the sprocket are any one of red, blue and green, and the scraper chain and the sprocket are different colors.

[0035] Since the first color channel is the color channel with the largest pixel value in the color of the scraper chain, the pixel value of the scraper chain in the first color channel in the scraper chain image is greater than the pixel value in the second color channel. And the greater the difference between the pixel value of the pixel point in the first color channel and the pixel value of the second color channel, the more likely the pixel point is to be located at the scraper chain in the scraper chain image, that is, the greater the tendency of the pixel point, and the smaller the difference, the less likely the pixel point is to be located at the scraper chain in the scraper chain image, that is, the smaller the tendency of the pixel point.

[0036] The degree of the tendency is also positively correlated with the normalized value of the difference between the pixel values ​​of the first color channel and the second color channel of each reference pixel. The reference pixel is the first pixel after multiple rays radiating outward from the corresponding pixel pass through the nearest edge.

[0037] In one embodiment, Figure 3 As shown, each reference pixel is an eight-neighborhood pixel, that is, the eight-neighborhood pixel is located on eight rays radiating outward from the corresponding pixel as the center. The angle between the ray where the first reference pixel is located and the horizontal right vector is 0 degrees, the angle between the ray where the second reference pixel is located and the horizontal right vector is 45 degrees, the angle between the ray where the third reference pixel is located and the horizontal right vector is 90 degrees, the angle between the ray where the fourth reference pixel is located and the horizontal right vector is 135 degrees, the angle between the ray where the fifth reference pixel is located and the horizontal right vector is 180 degrees, the angle between the ray where the sixth reference pixel is located and the horizontal right vector is 225 degrees, the angle between the ray where the seventh reference pixel is located and the horizontal right vector is 270 degrees, and the angle between the ray where the eighth reference pixel is located and the horizontal right vector is 315 degrees. Of course, each reference pixel can also be a four-neighborhood pixel.

[0038] The edge of the scraper chain image is obtained by using the Canny edge detection algorithm. The Canny edge detection algorithm can effectively suppress image noise and reduce the impact of noise on edge detection. Of course, edge detection can also use the Laplace algorithm.

[0039] The present invention also uses reference pixels to characterize the degree of inclination of pixels because: when the scraper conveyor is actually working, protective paints of different colors are usually applied to the scraper chain and sprocket, and the color of the protective paint is usually any one of red, blue and green. With the use of the scraper conveyor, the protective paint on the scraper chain will wear and even partially disappear, exposing the metal color of the scraper chain. This part of the scraper chain is called the missing part. In order to avoid the calculated pixel point of the missing part having a low inclination, it is necessary to correct the inclination of the pixel point of the missing part.

[0040] The reference pixel point is the part adjacent to the outside of the missing part. The larger the difference between the pixel values ​​of the reference pixel point in the first color channel and the second color channel, the greater the possibility that the reference pixel point is located at the scraper chain, and the greater the possibility that the corresponding pixel point is located in the missing part. It is necessary to correct the tendency degree of the pixel point to increase the tendency degree of the pixel point.

[0041] Therefore, the The scraper chain image at the moment The tendency of the pixel for: ; In the formula, For the The scraper chain image at the moment The pixel value of the first color channel, For the The scraper chain image at the moment The pixel value of the second color channel, For the The scraper chain image at the moment The pixel The pixel value of the reference pixel in the first color channel, For the The scraper chain image at the moment The pixel The pixel value of the reference pixel in the second color channel, is the normalization function, To adjust the parameters, , For the The scraper chain image at the moment The total number of reference pixels of pixels, The natural constant The exponential function of base .

[0042] In the above formula, the difference between the pixel value of the pixel point and the reference pixel point in the first color channel and the second color channel is divided by 255 to prevent the difference from being too large or too small. The normalized value is close to 0 or -1, which cannot well reflect and correct the tendency. Set the adjustment parameters In order to make the correction stronger, preferably, is 2.

[0043] is a normalization function that can map the calculation result to a range from 0 to 1. Of course, a minimum-maximum normalization function can also be used for normalization.

[0044] In the present invention, the tendency degree of a pixel point is not only characterized by the difference between the pixel values ​​of the pixel point in the first color channel and the second color channel, but is also corrected by the difference between the pixel values ​​of each reference pixel point in the first color channel and the second color channel, so as to prevent the calculated tendency degree from being too small due to the wear of the protective paint on the scraper chain, resulting in inaccurate calculation results.

[0045] S212. According to the inclination degree of each pixel point, the horizontal and vertical features of the scraper chain image at each moment are obtained.

[0046] The row mean sequence formed by the mean of the inclination degree of the pixels in each row in the scraper chain image is used as the horizontal feature of the corresponding scraper chain image, and the column mean sequence formed by the mean of the inclination degree of the pixels in each column in the scraper chain image is used as the vertical feature of the corresponding scraper chain image.

[0047] S213. Obtain the abnormality degree of the scraper chain image at each moment according to the similarity of the transverse features and the similarity of the longitudinal features of the scraper chain image.

[0048] Since the degree of abnormality is inversely correlated with the similarity of the transverse characteristics and the longitudinal characteristics of the scraper chain image at the current moment and the scraper chain image at the previous set moment, it is necessary to obtain the set moment of the scraper chain image, which is the moment closest to the maximum value of the similarity of the transverse characteristics of the scraper chain image at the current moment. The time interval between the set moment and the current moment can also be called the cycle of the scraper chain image change.

[0049] In one embodiment, the setting time of the scraper chain image is recalculated every 2 to 5 minutes to adapt to the change of the conveying speed of the scraper conveyor.

[0050] In this step, when the similarity of the lateral features of the scraper chain images at the current time and the set time is the maximum value, the scraper chains at the two times have the same state and can therefore be compared. Moreover, the set time can be automatically calculated, which reduces the dependence on manual adjustment, improves the adaptability of the fault detection method, and makes the scraper chain fault detection adaptable to different conveying speeds, scene changes, and equipment changes.

[0051] Therefore, the Abnormality of the scraper chain image at a certain moment for: ; In the formula, For the The moment and The similarity of the lateral features of the scraper chain image at each moment, For the The moment and The similarity of the longitudinal features of the scraper chain image at the moment The first moment is the set moment, and the The moment is The moment when the maximum value of the similarity of the lateral features of the scraper chain image at the moment is closest, The natural constant The exponential function of base .

[0052] In this formula, and The purpose of adding 1 and then dividing by 2 is to perform normalization to facilitate subsequent calculations.

[0053] In one embodiment, the similarity of the transverse features and the similarity of the longitudinal features of the scraper chain image are both calculated using cosine similarity, which has the advantages of being simple to implement and having a fast calculation speed.

[0054] In another embodiment, the similarity may also be represented by the Pearson correlation coefficient, which has the advantages of being simple, intuitive, and easy to explain.

[0055] In step S21, the abnormality degree of the scraper chain image is characterized by the similarity of the transverse features of the scraper chain image, which can reflect whether the scraper chain has cracks or breaks along its length. The similarity of the longitudinal features of the scraper chain image can reflect whether the scraper chain has left and right deviations. Therefore, the abnormality degree of the scraper chain image can be comprehensively and accurately reflected.

[0056] S22. Compare the abnormality degree of the scraper chain image at each moment within the preset time with the threshold value. When the abnormality degree of all scraper chain images within the preset time is greater than the threshold value, it is determined that the scraper chain has a fault.

[0057] In step S22, by setting a preset time length and calculating the abnormality of the scraper chain image within a period of time, accidental errors can be reduced, making the calculation result more accurate. The preset time length ranges from 8 to 12 seconds, preferably, the preset time length is 10 seconds, and of course the preset time length can be adjusted as needed.

[0058] The threshold value range is [0.5, 0.7]. Preferably, the threshold value is 0.6. Of course, the threshold value can be adjusted as needed.

[0059] When it is determined that the scraper chain is faulty, the scraper conveyor can be controlled to stop running, and then the staff can check or repair the scraper chain to prevent safety accidents.

[0060] In the scraper chain fault detection method for a scraper conveyor of the present invention, by calculating the difference between the pixel values ​​of each pixel point in the scraper chain image in the first color channel and the second color channel, and the difference between the pixel values ​​of each corresponding reference pixel point in the first color channel and the second color channel, the tendency degree of each pixel point is obtained, and the scraper chain and the sprocket in the scraper chain image can be accurately distinguished, thereby facilitating the fault detection of the scraper chain. And by comparing the similarity of the horizontal features and the similarity of the vertical features in the scraper chain image, the abnormality of the scraper chain image at each moment is obtained, and then according to the abnormality of all scraper chain images within a preset time length, it is determined whether the scraper chain has a fault, which can not only comprehensively and accurately reflect the abnormality of the scraper chain image, but also avoid misjudgment caused by accidental errors, making the fault detection result more reliable.

[0061] Although this specification has shown and described a number of embodiments of the present invention, it will be apparent to those skilled in the art that such embodiments are provided by way of example only. Those skilled in the art will conceive of many modifications, changes and alternatives without departing from the ideas and spirit of the present invention. It should be understood that in the practice of the present invention, various alternatives to the embodiments of the present invention described herein may be employed.

Claims

1. A method for detecting a scraper chain fault of a scraper conveyor, characterized in that: The method comprises the following steps: collecting images of the scraper chain at each moment in a rotating state; Calculate the abnormality of the scraper chain image at each moment within the preset time length, and when the abnormality of the scraper chain image at all moments within the preset time length is greater than a threshold, it is determined that the scraper chain has a fault; Among them, the degree of abnormality is anti-correlated with the similarity of the lateral features and the similarity of the longitudinal features of the scraper chain image at the current moment and the scraper chain image at the previously set moment; the lateral feature is a row mean sequence composed of the mean of the tendency degree of each row of pixel points in the scraper chain image, and the longitudinal feature is a column mean sequence composed of the mean of the tendency degree of each column of pixel points in the scraper chain image; the tendency degree is positively correlated with the normalized value of the difference between the pixel values ​​of the corresponding pixel point in the first color channel and the second color channel, and is positively correlated with the normalized value of the difference between the pixel values ​​of each reference pixel point in the first color channel and the second color channel; the reference pixel point is the first pixel point after multiple rays radiating outward from the corresponding pixel point pass through the nearest edge; the first color channel is the color channel with the largest pixel value of the pixel point in the color of the scraper chain, and the second color channel is the color channel with the largest pixel value of the pixel point in the color of the sprocket.

2. The scraper chain fault detection method for a scraper conveyor according to claim 1, characterized in that: No. Abnormality of the scraper chain image at a certain moment for: ； In the formula, For the The moment and The similarity of the lateral features of the scraper chain image at each moment, For the The moment and The similarity of the longitudinal features of the scraper chain image at the moment The first moment is the set moment, and the The moment is The moment when the maximum value of the similarity of the lateral features of the scraper chain image at the moment is closest, The natural constant The exponential function of base .

3. The scraper chain fault detection method for a scraper conveyor according to claim 1, characterized in that: No. The scraper chain image at the moment The tendency of the pixel for: ； In the formula, For the The scraper chain image at the moment The pixel value of the first color channel, For the The scraper chain image at the moment The pixel value of the second color channel, For the The scraper chain image at the moment The pixel The pixel value of the reference pixel in the first color channel, For the The scraper chain image at the moment The pixel The pixel value of the reference pixel in the second color channel, is the normalization function, To adjust the parameters, , For the The scraper chain image at the moment The total number of reference pixels of pixels, The natural constant The exponential function of base .

4. The scraper chain fault detection method for a scraper conveyor according to claim 1, characterized in that: The similarity is calculated using cosine similarity or Pearson correlation coefficient.

5. The method for detecting a scraper chain fault of a scraper conveyor according to claim 1, characterized in that: The edge of the scraper chain image is obtained by using the Canny edge detection algorithm.

6. The method for detecting a scraper chain fault of a scraper conveyor according to claim 1, characterized in that: The colors of the scraper chain and the sprocket are any one of red, blue and green, and the scraper chain and the sprocket are different colors.

7. The method for detecting a scraper chain fault of a scraper conveyor according to claim 1, characterized in that: Each reference pixel is an eight-neighborhood pixel or a four-neighborhood pixel.

8. The method for detecting a scraper chain fault of a scraper conveyor according to claim 1, characterized in that: The scraper chain image is acquired by an image acquisition device disposed at the feeding end of the scraper conveyor.

9. The method for detecting a scraper chain fault of a scraper conveyor according to claim 1, characterized in that: The preset duration ranges from 8 to 12 seconds.

10. The method for detecting a scraper chain fault of a scraper conveyor according to claim 1, characterized in that: The threshold value ranges from [0.5, 0.7].

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