METHOD OF DUST SUPPRESSION FOR CRUSHERS WITH SPRAYING DEVICES

Abstract

A method of dust suppression for crushers (2) with spraying devices (3) is described. To facilitate a resource-sparing dust suppression independently of the operator and even in the case of heterogeneous bulk material, the deviation between an image representation recorded by a first sensor (4) of a pattern arranged in its detection region as an actual value and a specified target value is determined, whereupon the spraying devices (3) assigned to the pattern are activated if the deviation exceeds a specified threshold.

Claims

1. A method of dust suppression for a crusher with spraying devices, said method comprising: recording an image with a first sensor of a pattern arranged in a detection region such that said image serves as an actual value, and determining a deviation between the actual value and a specified target value; and when the deviation exceeds a specified threshold value, activating the spraying devices associated with the pattern.

2. The method according to claim 1, wherein the method further comprises detecting images of several patterns simultaneously with the first sensor.

3. The method according to claim 1, wherein an image of the pattern recorded by a second sensor is the target value, and the deviation is determined as a number of non-corresponding pattern points in the target value and the actual value.

4. The method according to claim 3, wherein the first sensor and the second sensor form a stereo camera.

5. The method according to claim 4, wherein the method further comprises generating with the stereo camera a two-dimensional depth image of bulk material conveyed past the stereo camera and feeding the two-dimensional depth image to a previously trained convolutional neural network that has at least three convolution layers arranged one behind the other and, for each class of a particle size distribution, a downstream quantity classifier, output values thereof being output as a particle size distribution.

6. The method according to claim 5, wherein the depth image comprises pixels each having a respective value, and the method comprises removing from the depth image the values of the pixels that have a depth that corresponds to, or exceeds, a previously detected distance between the stereo camera and a background for the pixel.

7. The method according to claim 5, wherein a volume classifier is arranged downstream of the convolution layers, and an output value of the volume classifier is output as a volume of the bulk material present in the detection region.

8. A training method for training a neural network for the method according to claim 5, said training method comprising: first acquiring example depth images of a respective example grain with a known volume and storing said depth images together with the known volume thereof; combining a plurality of example depth images randomly so as to form a training depth image to which a sum of the known volumes of the composite example depth images is assigned as bulk material volume or a class-wise distribution of bulk material volumes of the composite example depth images is assigned as the particle size distribution; and feeding the training depth image to the neural network on an input side thereof and feeding the assigned bulk material volume or the assigned particle size distribution is fed to the neural network on an output side thereof; and adapting weights of individual network nodes of the neural network in a learning step.

9. The method according to claim 2, wherein an image of the pattern recorded by a second sensor is the target value, and the deviation is determined as a number of non-corresponding pattern points in the target value and the actual value.

10. The method according to claim 9, wherein the first sensor and the second sensor form a stereo camera.

11. The method according to claim 10, wherein the method further comprises generating with the stereo camera a two-dimensional depth image of bulk material conveyed past the stereo camera and feeding the two-dimensional depth image to a previously trained convolutional neural network that has at least three convolution layers arranged one behind the other and, for each class of a particle size distribution, a downstream quantity classifier, output values thereof being output as a particle size distribution.

12. The method according to claim 11, wherein the depth image comprises pixels each having a respective value, and the method comprises removing from the depth image the values of the pixels that have a depth that corresponds to, or exceeds, a previously detected distance between the stereo camera and a background for the pixel.

13. The method according to claim 11, wherein a volume classifier is arranged downstream of the convolution layers, and an output value of the volume classifier is output as a volume of the bulk material present in the detection region.

14. The method according to claim 12, wherein a volume classifier is arranged downstream of the convolution layers, and an output value of the volume classifier is output as a volume of the bulk material present in the detection region.

15. The method according to claim 6, wherein a volume classifier is arranged downstream of the convolution layers, and an output value of the volume classifier is output as a volume of the bulk material present in the detection region.

Description

BRIEF DESCRIPTION OF THE INVENTION

[0016] In the drawing, the subject matter of the invention is shown by way of example, wherein:

[0017] FIG. 1 shows a side view of a mobile crusher on which the method according to the invention is applied, and

[0018] FIG. 2 shows a schematic representation of an alignment of corresponding pattern points of bulk material by a stereo camera.

DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0019] A method according to the invention can be used, for example, for dust suppression of dust particles produced during crushing of bulk material 1. For this purpose, a mobile crusher 2 used for this purpose and shown in FIG. 1 has spraying devices 3. In order to enable a resource-saving dust suppression, the spraying devices 3 are activated only in case of an increased dust load. The assessment of whether an increased dust load is present is made by a first sensor 4, which detects a pattern arranged in its detection region 5 and compares the image of the pattern as an actual value with a target value stored on a control unit 6. If a deviation exceeding a certain threshold value is detected between the actual value and the target value of the pattern, only the spraying devices 3 assigned to this pattern are activated. The spraying devices 3 subsequently emit a binder to bind the dust particles until they are deactivated. To enable the spraying devices 3 to be activated in as differentiated a manner as possible, several patterns can be arranged at representative locations in the crusher. Such locations may not be provided in the crushing chamber 7, but may be provided at the beginning or end of the conveyor belt 8 or at locations which are necessary for the visual assessment of the condition of the bulk material 1. The assignment of the spraying devices 3 to the patterns can be made in dependence on the distance of the spraying devices 3 to the patterns, so that a deviation of a certain pattern exceeding a threshold value activates the closest spraying device 3. However, it is also conceivable that the spraying devices 3 and the first sensor 4 are connected to a control device 6, which can continuously optimize the control of the dust suppression by a parameter variation, for example a varying assignment of the spraying devices 3 to a certain pattern or varying binder quantities delivered by the spraying devices 3. In principle, the spraying devices 3 are arranged on the crusher 2 in such a way that they enable comprehensive dust suppression, especially in the relevant areas of increased dust loading.

[0020] In order to reduce the measurement and maintenance effort, several patterns can be arranged in the detection region 5 of the first sensor 4. This means that a large number of patterns can be detected with just one optical sensor 4, enabling differentiated and thus efficient activation of the spraying devices 2 arranged at different positions. A wide-angle or 360° camera, for example, is suitable as the first sensor 4 for this purpose.

[0021] FIG. 2 shows the possibility that the bulk material 1 itself can also be used as a sample for assessing the prevailing dust load. This is made possible in that the image of the bulk material 1 recorded by a second sensor 9 serves as a target value. The target value is therefore not permanently stored on a control unit 6, but is continuously re-recorded. The deviation between the actual value recorded by the first sensor 4 and the target value recorded by the second sensor 9 corresponds to the number of non-corresponding pattern points between the actual and target values.

[0022] The first sensor 4 and the second sensor 9 can form a stereo camera 10, whereby, in addition to detecting the dust load in the area of the stereo camera 10, information about the depth data can also be acquired, which can subsequently be used to assess the condition of the bulk material 1.

[0023] As disclosed in FIG. 1, both an optical camera 4 and a stereo camera 10 may be provided as sensors.