A conveyor belt misalignment detection system

Abstract

The application discloses a kind of conveyer belt deviation detection system, including mobile detection system and fixed detection system, mobile detection system is along the belt conveyor using track inspection robot and is inspected, and the camera of robot is used to the shooting of belt and carrier roller, and using detection algorithm judges whether there is belt deviation and the severity of deviation, fixed detection system is used to supplement detection to mobile detection system, and using the semantic segmentation area of carrier roller and belt judges the severity of belt deviation.The beneficial effects of the present application are: by setting mobile detection system and fixed detection system, and making fixed detection system supplement detection to mobile detection system, compared with full fixed point mode, when the transport distance is long to realize full coverage, the cost can be reduced, and mobile detection system and fixed detection system can not only make binary judgment whether deviation, but also can make graded alarm to the severity of deviation.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of belt conveyors, in particular to a conveyor belt deviation detection system. BACKGROUND

[0002] Belt conveyors are widely used in mining, coal, steel, power, chemical and other industries. As a key material conveying equipment, it undertakes continuous and stable transportation tasks. However, due to various factors, the belt may deviate during long-term operation. Deviation failure not only leads to decreased transportation efficiency and increased maintenance costs, but also may cause equipment downtime, material loss, and even safety accidents. Therefore, real-time and accurate monitoring of the deviation state of the belt is the key to ensuring production safety and improving equipment reliability.

[0003] Traditional belt deviation detection methods rely on manual inspection and fixed camera detection-based monitoring systems. Manual inspection can detect obvious deviation, but due to the time-consuming nature of the inspection process and the risk of missed detection, it cannot meet the needs of efficient, all-weather, and uninterrupted monitoring. Fixed camera detection systems usually require manual setting of fault modes and alarm thresholds for the detection position of the monitoring camera, i.e., by detecting whether the number of rollers on both sides of the belt is consistent to determine whether the belt is deviating. This method cannot distinguish between mild, moderate, and severe deviation, and has many limitations on installation location, poor flexibility, and difficulty in adapting to complex changes in different working environments or operating states, making it difficult to cover the entire belt of the conveyor. SUMMARY

[0004] The present application aims to overcome the shortcomings of the prior art and provide a conveyor belt deviation detection system.

[0005] The present application achieves the above-mentioned purpose by the following technical solution: a conveyor belt deviation detection system, comprising a mobile detection system and a fixed detection system. The mobile detection system uses a track inspection robot to inspect the belt conveyor. The robot camera captures images of the belt and rollers, and uses detection algorithms to determine whether there is belt deviation and the severity of the deviation. The fixed detection system is used to supplement the detection of the mobile detection system and determine the severity of the belt deviation using semantic segmentation area of the rollers and the belt.

[0006] Preferably, the steps of the mobile detection system when working are as follows:

[0007] S1: Capture the belt and roller images I(x, y) through the inspection robot camera;

[0008] S2: Identify the belt edge and roller edge in the image;

[0009] S3: Calculate the distance between the left and right belt edge points and the roller edge points,

[0010]

[0011]

[0012] S4: comparing the distance obtained in step S3 with a normal distance to obtain a deviation,

[0013]

[0014] S5: judging the deviation obtained in step S4 against a set deviation to determine the extent of deviation,

[0015]

[0016] S6: the robot issuing a belt deviation alarm.

[0017] Preferably, step S2 further comprises the following steps:

[0018] S21: obtaining the pixel area of the belt and the pixel area of the carrier roller by using an image segmentation algorithm,

[0019] M(x, y) = S(I(x, y));

[0020] wherein I(x, y) is the image before filtering, S represents the image segmentation algorithm, and M(x, y) is the mask of the segmented area;

[0021] S22: extracting the belt area and the carrier roller area according to the ID of the segmentation mask,

[0022] M(x, y) = M0(x, y) + M1(x, y) + M2(x, y);

[0023] wherein M0(x, y) is the mask of the background area, M1(x, y) is the mask of the belt area, and M2(x, y) is the mask of the carrier roller area;

[0024] S23: extracting the mask edges of the belt area and the carrier roller area by using an edge detection algorithm,

[0025] B i (x, y) = D(M i (x, y)), i = 1, 2;

[0026] wherein B i (x, y) is the obtained mask edge, and D is the edge detection algorithm;

[0027] S24: selecting the left and right edges of the belt and the left and right edges of the carrier roller by using a logical filtering condition,

[0028] B k(x, y) = U(F1(B1), F2(B2));

[0029] Wherein, F is the junction filtering condition,

[0030] S25: find out the belt edge target point and the roller end point from the edge point set,

[0031] P(x, y) = L(B k );

[0032] Wherein, L is the traversal screening logic of the edge point, and P is the target point.

[0033] Preferably, the steps of the fixed detection system when working are as follows:

[0034] A1: the camera of the edge computing device shoots the image I(x, y) of the belt and the roller;

[0035] A2: instance segmentation is performed on the image to obtain the pixel area of the roller and the belt,

[0036] M(x, y) = S(I(x, y));

[0037] Wherein, I(x, y) is the image before filtering, S represents the image segmentation algorithm, and M(x, y) is the mask of the roller area;

[0038] A3: the area ratio of the pixel area on the left and right sides of the image of the roller is calculated,

[0039]

[0040] A4: the area ratio obtained in step A3 is compared with the preset normal value to obtain a difference value,

[0041] diff = |ratio-ratio normal |;

[0042] A5: the difference value obtained in step A4 is compared with the set difference value to judge the deviation degree,

[0043]

[0044] A6: the edge computing device issues a belt deviation alarm.

[0045] The present application has the following advantages: the present application sets the mobile detection system and the fixed detection system, and the fixed detection system supplements the detection of the mobile detection system, compared with the full fixed point distribution mode, when the transportation distance is long and full coverage is realized, the cost can be reduced, and the mobile detection system and the fixed detection system can not only make binary judgment of whether to deviate, but also can make graded alarm of the severity of deviation. BRIEF DESCRIPTION OF DRAWINGS

[0046] Figure 1 is a schematic view of a mobile detection system;

[0047] Figure 2 is a schematic view of a fixed detection system. DETAILED DESCRIPTION

[0048] In order to make the objects, technical solutions and advantages of the embodiments of the present application clearer, the technical solutions in the embodiments of the present application will be described clearly and completely below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only some but not all of the embodiments of the present application. Generally, the components of the embodiments of the present application described and shown in the drawings herein can be arranged and designed in various different configurations.

[0049] Therefore, the following detailed description of the embodiments of the present application provided in the drawings is not intended to limit the scope of the claimed application, but merely represents selected embodiments of the application. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative work fall within the scope of protection of the present application.

[0050] It should be noted that the embodiments in the present application and the features in the embodiments can be combined with each other without conflict.

[0051] It should be noted that: similar reference numerals and letters indicate similar items in the following drawings, and therefore, once an item is defined in one drawing, it does not need to be further defined and explained in subsequent drawings.

[0052] In the description of the present application, it should be noted that the terms "center", "upper", "lower", "left", "right", "vertical", "horizontal", "inner", "outer" and the like indicate the orientation or positional relationship based on the orientation or positional relationship shown in the drawings, or the orientation or positional relationship commonly understood by those skilled in the art, and are only for the convenience of describing the present application and simplifying the description, and therefore cannot be understood as indicating or implying that the indicated device or element must have a particular orientation, be constructed and operated in a particular orientation, and therefore cannot be understood as limiting the present application. In addition, the terms "first", "second" and the like are only used to distinguish description, and cannot be understood as indicating or implying relative importance.

[0053] In the description of the present application, it is also necessary to point out that, unless otherwise explicitly specified and limited, the terms "arrange", "install", "connect", "join" should be understood broadly, for example, can be fixedly connected, can also be detachably connected, or integrally connected; can be mechanically connected, can also be electrically connected; can be directly connected, can also be indirectly connected through an intermediate medium, can be the communication inside two elements. For those skilled in the art, the specific meanings of the above terms in the present application can be understood according to the specific circumstances.

[0054] In the embodiment, a conveyor belt deviation detection system includes a mobile detection system and a fixed detection system. The mobile detection system uses a track inspection robot to inspect the belt conveyor. The robot camera shoots the belt and the carrier roller, and uses a detection algorithm to determine whether there is belt deviation and the severity of the deviation. The fixed detection system is used to supplement the detection of the mobile detection system, and uses the semantic segmentation area of the carrier roller and the belt to determine the severity of the belt deviation. By setting the mobile detection system and the fixed detection system, and making the fixed detection system supplement the detection of the mobile detection system, compared with the full fixed point arrangement mode, when the transportation distance is long and full coverage is achieved, the cost can be reduced. At the same time, the mobile detection system and the fixed detection system can not only make binary judgment of whether to deviate, but also make graded alarm of the severity of the deviation.

[0055] Further, as shown in Figure 1 , the steps of the mobile detection system when working are as follows:

[0056] S 1: shoot the belt and carrier roller image I(x, y) through the inspection robot camera;

[0057] S2: identify the belt edge and the carrier roller edge in the image; specifically, the belt edge is the inner dotted line in Figure 1 , and the carrier roller edge is the outer dotted line in Figure 1 , and further comprising the following steps:

[0058] S21: use an image segmentation algorithm to obtain the pixel area of the belt and the pixel area of the carrier roller,

[0059] M(x, y) = S(I(x, y));

[0060] Wherein, I(x, y) is the image before filtering, S represents the image segmentation algorithm, and M(x, y) is the mask of the segmentation area;

[0061] S22: extract the belt area and the carrier roller area according to the ID of the segmentation mask,

[0062] M(x, y) = M0(x, y) + M1(x, y) + M2(x, y);

[0063] Where M0(x,y) is the background area mask, M1(x,y) is the belt area mask, and M2(x,y) is the idler roller area mask;

[0064] S23: Use an edge detection algorithm to extract the mask edges of the belt area and idler roller area.

[0065] B i (x, y) = D(M) i (x, y)), i = 1, 2;

[0066] Among them, B i (x, y) represents the obtained mask edge, and D represents the edge detection algorithm;

[0067] S24: Using logical filtering conditions, select the left and right edges of the belt and the idler roller.

[0068] B k (x, y)=U(F1(B1), F2(B2));

[0069] Where F represents the boundary filtering condition.

[0070] S25: Identify the target points on the belt edge and the endpoints of the idler rollers from the set of edge points.

[0071] P(x, y) = L(B) k );

[0072] Where L represents the traversal and filtering logic of edge points, and P represents the target point.

[0073] S3: Calculate the distance between the edge points of the left and right belts and the edge points of the idler rollers.

[0074]

[0075]

[0076] S4: Compare the distance obtained in step S3 with the normal distance to determine the offset.

[0077]

[0078] S5: Determine the degree of deviation by comparing the offset obtained in step S4 with the set offset.

[0079]

[0080] S6: The robot issues an alarm for belt misalignment.

[0081] In this embodiment, as Figure 2 As shown, the steps of the fixed detection system during operation are as follows:

[0082] A1: The camera of the edge computing device takes an image I(x, y) of the belt and the idler;

[0083] A2: Instance segmentation is performed on the image to obtain the pixel area of the idler and the belt,

[0084] M(x, y) = S(I(x, y));

[0085] where I(x, y) is the image before filtering, S represents the image segmentation algorithm, and M(x, y) is the mask of the idler area;

[0086] A3: Calculate the area ratio of the pixel areas on the left and right sides of the idler in the image,

[0087]

[0088] A4: Compare the area ratio obtained in step A3 with the preset normal value to obtain a difference value,

[0089] diff = |ratio-ratio normal |;

[0090] A5: Determine the degree of deviation of the difference value obtained in step A4 from the set difference value,

[0091]

[0092] A6: The edge computing device issues a belt deviation alarm.

[0093] Although the present application has been described in detail with reference to the foregoing embodiments, those skilled in the art can modify the technical solutions described in the foregoing embodiments or replace some of the technical features with equivalent ones, and any modification, equivalent replacement, improvement, etc. within the spirit and principles of the present application shall be included in the protection scope of the present application.

Claims

1. A conveyor belt misalignment detection system, characterized by: The mobile detection system is used for track inspection by a track inspection robot, and the track inspection robot camera is used to take pictures of the belt and the carrier roller, and a detection algorithm is used to determine whether there is belt deviation and the severity of the deviation, and the fixed detection system is used to supplement the detection of the mobile detection system, and the semantic segmentation area of the carrier roller and the belt is used to determine the severity of the belt deviation. The steps of the mobile detection system when working are as follows: S1 : Take images of the belt and idler by the inspection robot camera ; S2: identify the belt edge and the carrier roller edge in the image; S3: calculate the distance between the left and right belt edge points and the carrier roller edge points, ； ； S4: compare the distance obtained in step S3 with the normal distance to obtain the offset, ； S5: compare the offset obtained in step S4 with the set offset to determine the degree of deviation, ； S6: the robot issues a belt deviation alarm; In step S2, the following steps are further included: S21: obtain the pixel area of the belt and the pixel area of the carrier roller by using an image segmentation algorithm, ； wherein, is the image before filtering, S represents an image segmentation algorithm, is a mask of the segmented region; S22: extract the belt area and the carrier roller area according to the ID of the segmentation mask, ； wherein, is a background area mask, is a belt area mask, is a roller area mask; S23: use an edge detection algorithm to extract the mask edge of the belt area and the carrier roller area, ； wherein, D is an edge detection algorithm to obtain the mask edges. S24: use a logical filtering condition to select the left and right edges of the belt and the carrier roller, ； Wherein, F is the boundary filtering condition, S25: find the belt edge target point and the carrier roller endpoint from the edge point set, ； wherein, edge point traversal filtering logic, is a target point.

2. The conveyor belt misalignment detection system of claim 1, wherein: The steps of the fixed detection system when working are as follows: A1 : A camera of the edge computing device takes images of the belt and the idler ; A2: instance segmentation is performed on the image to obtain the pixel area of the carrier roller and the belt, ； wherein, is the image before filtering, represents an image segmentation algorithm, is a mask of the roller region; A3: calculate the area ratio of the pixel area on the left and right sides of the carrier roller in the image, ； A4: compare the area ratio obtained in step A3 with the preset normal value to obtain a difference value, ； A5: compare the difference value obtained in step A4 with the set difference value to determine the degree of deviation, ； A6: the edge calculation device issues a belt deviation alarm.

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