Detection system and detection method

Abstract

The detection system (10) according to the invention comprises: a camera (11) designed to take an image of a fluid flowing through a pipeline; an image processing unit (13) configured to execute image processing including binarization processing of a captured image captured by the camera (11); and a detection unit (14) designed to detect dirt mixed into the fluid on the basis of the image that has been image-processed by the image processing unit (13).

Description

Technical Field

[0001] The present invention relates to a detection system and a detection method for detecting contaminants (hereinafter appropriately referred to as "dirt") mixed in a fluid, such as oil. Background Art

[0002] Figure 1 FIG. is an example showing a schematic configuration of a general extruder 900.

[0003] Figure 1 The illustrated extruder 900 includes a cylinder 901, a screw 902, a speed reducer 903, and a motor 904. The screw 902 is rotatably accommodated in the cylinder 901. The speed reducer 903 is connected to the screw 902 and the motor 904 in the cylinder 901. The speed reducer 903 rotates the screw 902 by transmitting the rotational force of the motor 904 to the screw 902 at a reduced rotational speed. The extruder 900 extrudes the raw material supplied through a raw material supply section (not shown) forward (in the leftward direction in the figure) while pressing the raw material with the screw 902.

[0004] Note that a plurality of gears and bearings are used in the speed reducer 903. Therefore, generally, oil is supplied to the speed reducer 903 as lubricating oil for lubricating the gears and bearings.

[0005] Figure 2 FIG. is an example showing a schematic configuration of a general oil circulation mechanism for supplying oil to the speed reducer 903.

[0006] As Figure 2 shown, a pump 911 supplies oil to the speed reducer 903 while circulating the oil in a pipe 912.

[0007] However, in the speed reducer 903, dirt may be generated due to, for example, the surface of components (such as gears, bearings, etc.) peeling off due to fatigue caused by the use of these components, and the generated dirt may be mixed into the oil. The dirt generated inside the speed reducer 903 is mainly iron powder generated from the surface of the components. In addition, when these components bite the dirt mixed in the oil, the generation of dirt can be further promoted. As a result, these components may be damaged, causing the speed reducer 903 to stop or malfunction.

[0008] Figure 3 FIG. is a flowchart for explaining an example of the mechanism by which components are damaged due to dirt in the speed reducer 903.

[0009] As Figure 3As shown, when the speed reducer 903 operates (step S11), a load is applied to the component (step S12). This load causes the surface of the component to flake due to fatigue, thus generating dirt (step S13), and then the generated dirt mixes into the oil (step S14). In addition, when the component catches the dirt mixed in the oil (step S15), a high load is locally applied to the component (step S16), and the generation of dirt is promoted (step S13). Further, in step S16, depending on the load applied to the component, the component may be damaged, resulting in the stop of the speed reducer 903 (step S17).

[0010] Figure 4 FIG. is an example showing a state in which the gears in the speed reducer 903 are damaged due to dirt. Specifically, Figure 4 shows a state in which, at the meshing portion of the gears 921 and 922, the surface of one of the two gears 921 and 922 flakes, thus generating dirt, and the dirt mixes into the oil and then flows away, thereby damaging other parts of the respective surfaces of the gears 921 and 922.

[0011] Therefore, in recent years, in order to avoid premature damage of components, dirt mixed in the oil flowing through the pipeline is detected by a sensor. Typical examples of such sensors include the laser type sensor and the magnetic field type sensor disclosed in Patent Document 1.

[0012] Figure 5 FIG. is a diagram for explaining the principle of the laser type sensor.

[0013] As Figure 5 shown, in the laser type sensor, the light emitting unit 931 makes the laser enter the light receiving unit 932 as incident light, such that the laser passes through the oil flowing through the pipeline 933. Then, the number of dirt particles blocking the incident light and their sizes are monitored.

[0014] Figure 6 FIG. is a diagram for explaining the principle of the magnetic field type sensor.

[0015] As Figure 6 shown, in the magnetic field type sensor, the detection coil L2 is provided on one side of the coaxial side of the exciting coil L1, and the compensation coil L3 is provided on the other side. Then, in a state where the exciting coil L1 is excited by an exciting current from the power supply device 942, the container (such as a pipeline) 941 is inserted into the exciting coil L1 from the detection coil L2 side, and a sample (such as oil) S mixed with iron powder as dirt is contained in the container 941. At this time, the difference in the induced voltages of the detection coil L2 and the compensation coil L3 is detected by the differential amplifier 943.

[0016] Citation List

[0017] Patent Document

[0018] [Patent Document 1] Japanese Unexamined Patent Application Publication JP2015-184096.

[0019] Technical Problem

[0020] However, each of the above-mentioned laser-type sensors and magnetic field-type sensors has the following problems respectively.

[0021] First of all, the laser-type sensor has the problem that when the oil itself deteriorates, the detection sensitivity of dirt decreases.

[0022] In addition, the problem of the magnetic field-type sensor is that the magnetic field-type sensor cannot detect rust and thus dirt that is no longer magnetic, so it can only detect iron powder dirt.

[0023] In addition, each of the laser-type sensor and the magnetic field-type sensor has the problem of being unable to detect the deterioration of the oil itself. Summary of the Invention

[0024] Therefore, an object of the present invention is to provide a detection system and a detection method capable of satisfactorily detecting dirt mixed in oil or detecting the deterioration of the oil itself.

[0025] Solution to the Problem

[0026] The detection system according to one aspect includes: a camera designed to capture an image of a fluid flowing through a pipeline; an image processing unit designed to perform image processing, which includes a process of binarizing the captured image captured by the camera; and a detection unit designed to detect dirt mixed in the fluid based on the image on which the image processing unit has performed image processing.

[0027] The detection system according to another aspect includes: a camera designed to capture an image of a fluid flowing through a pipeline; and a detection unit designed to determine the color of the fluid based on the captured image captured by the camera, and determine the degree of deterioration of the fluid itself based on the determined color of the fluid.

[0028] The detection method according to one aspect is a detection method executed by a detection system, and the detection method includes the following steps: capturing an image of a fluid flowing through a pipeline by a camera; performing image processing, which includes a process of binarizing the captured fluid image; and detecting dirt mixed in the fluid based on the image on which the image processing has been performed.

[0029] Advantageous Effects of the Invention

[0030] According to the above aspects, a detection system and a detection method capable of satisfactorily detecting dirt mixed in oil or detecting the deterioration of the oil itself can be provided. Description of the Drawings

[0031] Figure 1 is a diagram showing an example of a schematic configuration of a general extruder;

[0032] Figure 2 is a diagram showing an example of a schematic configuration of a general oil circulation mechanism;

[0033] Figure 3 is a flowchart showing an example of a mechanism for explaining the damage of components due to dirt in a speed reducer;

[0034] Figure 4 is a diagram showing an example of a state in which a gear is damaged due to dirt in a speed reducer;

[0035] Figure 5 is a diagram for explaining the principle of a laser type sensor;

[0036] Figure 6 is a diagram for explaining the principle of a magnetic field type sensor;

[0037] Figure 7 is a diagram showing an example of a schematic configuration of a functional block of a detection system according to a first embodiment.

[0038] Figure 8 is a diagram showing an example of a schematic configuration of an oil circulation mechanism according to a first embodiment;

[0039] Figure 9 is a diagram showing an example of an imaging unit according to a first embodiment.

[0040] Figure 10 is a diagram for explaining an example of a process of converting a captured image captured by a camera into a grayscale image in an image processing unit according to a first embodiment.

[0041] Figure 11 is a diagram for explaining an example of a process of performing a trimming process for extracting necessary parts from a grayscale image in an image processing unit according to a first embodiment;

[0042] Figure 12 is a diagram for explaining an example of a process of binarizing an image for which a trimming process has been performed in an image processing unit according to a first embodiment;

[0043] Figure 13 is a diagram for explaining an example of a process of performing black and white inversion processing on a binarized image in an image processing unit according to a first embodiment;

[0044] Figure 14This is a diagram for explaining an example of a process in which, in the image processing unit according to the first embodiment, a dirt region in an image that has been black-and-white inverted is subjected to region recognition, and then the image is superimposed on the original image;

[0045] Figure 15 This is a diagram for explaining an example of an operation of calculating the dirt concentration in oil in the detection unit according to the first embodiment;

[0046] Figure 16 This is a diagram for explaining an example of an operation of determining the color of oil in the detection unit according to the first embodiment;

[0047] Figure 17 This is a flowchart for explaining an example of a schematic process performed by the detection system according to the first embodiment;

[0048] Figure 18 This is a diagram showing an example of a comparison table of the characteristics of a laser type sensor, a magnetic field type sensor, and a camera type sensor;

[0049] Figure 19 This is a diagram showing an example of an image capturing unit according to another embodiment;

[0050] Figure 20 This is a diagram showing an example of an image capturing unit according to another embodiment;

[0051] Figure 21 This is a diagram showing an example of an image capturing unit according to another embodiment; and

[0052] Figure 22 This is a diagram showing an example of an image, in which an image of oil flowing through a pipe is captured by a camera having a focus adjustment function, and in this pipe, the thickness of the flow path is large. Detailed Embodiments

[0053] Embodiments of the present invention will be described below with reference to the accompanying drawings. Note that, for clarity of explanation, parts of the following description and drawings are appropriately omitted and simplified. In addition, in all the drawings, the same elements are denoted by the same reference numerals, and redundant explanations are omitted as necessary. Further, the specific numerical values and the like shown below are merely examples for facilitating the understanding of the present invention, and they are not limited to those shown below. In addition, in the following description and drawings, the fluid is taken as an example of oil. However, the fluid is not limited to oil.

[0054] <First Embodiment>

[0055] Figure 7 This is a diagram showing an example of a schematic configuration of the functional blocks of the detection system 10 according to the first embodiment. Note that the detection system 10 according to the first embodiment detects the inflow of a supply to be provided inFigure 1 Dirt in the oil of the speed reducer 903 of the extruder 900 shown.

[0056] As Figure 7 shown, the detection system 10 according to the first embodiment is a camera-type sensor that uses a camera 11 to detect dirt mixed in the oil flowing through the pipeline, and includes a camera 11 and a detection device 12.

[0057] The detection device 12 includes an image processing unit 13 and a detection unit 14. The detection device 12 is implemented by a computer, such as a general-purpose personal computer. In addition, in Figure 7 it, the camera 11 is provided outside the detection device 12. However, the present disclosure is not limited thereto. For example, when the camera is installed in the computer that implements the detection device 12, the camera can be used as the camera 11.

[0058] The camera 11 captures an image of the oil supplied to the speed reducer 903.

[0059] Figure 8 is a diagram showing an example of the schematic configuration of the oil circulation mechanism for supplying oil to the speed reducer 903 according to the first embodiment. Note that in Figure 8 it, the speed reducer 903 and the pump 911 are the same as the speed reducer 903 and the pump 911 shown in Figure 2 it.

[0060] In Figure 8 the oil circulation mechanism shown, in the normal state, the two valves 103 and 104 are closed, and the oil circulates in the pipeline 101. On the other hand, when dirt is detected, the two valves 103 and 104 are opened, and the oil flows through the pipeline 102. An image capturing unit (i.e., the part where the camera captures an image) 105 is provided in the middle of the pipeline 102.

[0061] Figure 9 shows an example of the image capturing unit 105 according to the first embodiment.

[0062] Figure 9 The image capturing unit 105 shown includes a sight glass 107 and a screen 108. The sight glass 107 is inserted into the middle of the pipeline 102. In addition, a window 109 serving as a transparent part is formed on the sight glass 107, and the oil flowing inside the sight glass 107 can be visually observed through the window 109. The screen 108 serves as a background image and is arranged behind the sight glass 107 when observed from the camera 11. However, if an image of the oil can be captured by the camera 11 such that dirt flowing into the oil can be detected even without the screen 108, the screen 108 may not be provided.

[0063] In addition, although the passage in Figure 9Not shown in the figure, the image capturing unit 105 preferably includes a light source for illumination to illuminate the interior of the window of the sight glass 107. However, since the appropriate type of light source varies depending on the installation environment of the pipe 102, a light source of a type suitable for this environment can be used.

[0064] The camera 11 captures an image of the oil flowing through the pipe 102 in the above-mentioned image capturing unit 105 through the window 109, which is the oil supplied to the speed reducer 903.

[0065] The image processing unit 13 performs image processing on the captured image captured by the camera 11. Note that the details of the image processing performed by the image processing unit 13 will be described later.

[0066] The detection unit 14 detects contaminants mixed in the oil based on the image on which the image processing unit 13 has performed image processing.

[0067] In addition, the detection unit 14 can count the number of contaminants mixed in the oil based on the image on which the image processing unit 13 has performed image processing. In addition, the detection unit 14 can calculate the contaminant concentration in the oil based on the number of contaminants. In addition, the detection unit 14 can determine the degree of deterioration of components such as gears and bearings used in the speed reducer 903 based on the number of contaminants or the contaminant concentration in the oil. At this time, the detection unit 14 can determine the degree of deterioration of the components by comparing the contaminant concentration in the oil with a threshold value. In addition, if multiple threshold values are set as the threshold, the degree of deterioration of the components can be classified into a greater number of levels.

[0068] In addition, the detection unit 14 can determine the color of the oil based on the captured image captured by the camera 11 or the image on which the image processing unit 13 has performed image processing, and determine the degree of deterioration of the oil itself based on the determined color of the oil. At this time, the detection unit 14 can pre-learn various combinations of the color of the oil and the degree of deterioration of the oil under that color. In addition, the detection unit 14 can determine the degree of deterioration of the oil itself based on the determined color of the oil and the result of the above learning.

[0069] In addition, the detection unit 14 can have an output function. For example, the detection unit 14 can generate an output signal indicating at least one of the number of contaminants mixed in the oil, the contaminant concentration in the oil, the degree of deterioration of the components, and the degree of deterioration of the oil itself. In addition, the detection unit 14 can display the output signal on a display unit (not shown) provided in the detection device 12, or output the output signal to any device (not shown) provided outside the detection device 12. However, an output unit (not shown) having such an output function can be provided separately from the detection unit 14 in the detection device 12.

[0070] Next, the operations performed by the image processing unit 13 will be described in detail.

[0071] As described above, the image processing unit 13 performs image processing on the captured image of the oil captured by the camera 11.

[0072] Figures 10 to 14 It is a diagram for explaining an example of the steps included in the image processing performed by the image processing unit 13.

[0073] Step 1( Figure 10 ):

[0074] First, the image processing unit 13 obtains an image Im0, which is the captured image of the oil captured by the camera 11. The image Im0 is a color image. Therefore, the image processing unit 13 transforms the image Im0 into a grayscale image to obtain an image Im1.

[0075] Step 2( Figure 11 ):

[0076] Subsequently, the image processing unit 13 performs a trimming process to extract a necessary part from the image Im1, thereby obtaining an image Im2. At this time, in order to prevent a change in detection accuracy, the part extracted from the image Im1 is a predetermined part.

[0077] Step 3( Figure 12 ):

[0078] Next, the image processing unit 13 binarizes the image Im2 to obtain an image Im3. In the image Im3, the area presented in black corresponds to dirt.

[0079] Step 4( Figure 13 ):

[0080] Next, the image processing unit 13 reverses the black and white of the image Im3 to obtain an image Im4. Therefore, in the image Im4, the area presented in white corresponds to dirt.

[0081] Step 5( Figure 14 ):

[0082] Thereafter, the image processing unit 13 performs region recognition on the dirt region presented in white in the image Im4. Region recognition is a process of transforming the dirt region into a predetermined color. In Figure 14 , the dirt region is transformed into green. Then, the image processing unit 13 superimposes the image on which region recognition has been performed on the original image Im0 captured by the camera 11 to obtain an image Im5.

[0083] However, the image processing unit 13 does not need to perform all of the above steps 1 to 5 as image processing. Depending on the resolution of the image obtained by binarization, etc., the detection unit 14 provided in the downstream stage can detect the dirt mixed in the oil based on the image obtained by binarization.

[0084] Therefore, the image processing unit 13 only needs to perform at least the process of binarizing the captured image as image processing, and optionally perform other steps among steps 1 to 5 according to the resolution of the image obtained by binarizing the captured image and the like.

[0085] Note that when the image processing unit 13 does not perform steps 4 and 5, the detection unit 14 cannot determine the color of the oil from the above-mentioned image Im3. Therefore, in this case, the detection unit 14 can determine the color of the oil based on the image Im0 (color image) captured by the camera 11, and determine the degree of deterioration of the oil itself based on the color of the oil. Alternatively, the detection unit 14 can determine the color of the oil based on the image Im1 or the image Im2 obtained by transforming the image Im0 into a grayscale image by the image processing unit 13, and determine the degree of deterioration of the oil itself based on the color of the oil.

[0086] Next, the operations performed by the detection unit 14 will be described in detail.

[0087] Figure 15 FIG. is a diagram for explaining an example of an operation of calculating the concentration of contaminants in oil in the detection unit 14.

[0088] As Figure 15 shown, the detection unit 14 counts the number of contaminants mixed in the oil, and calculates the concentration of contaminants [ppm] in the oil based on the number of contaminants. For example, the contaminant concentration can be calculated by dividing the number of contaminants by the flow path volume of the oil. In addition, the detection unit 14 sets two thresholds (30 [ppm] and 100 [ppm]) for the contaminant concentration, and classifies the contaminant concentration into three levels: normal value, caution value, and abnormal value according to the two set thresholds.

[0089] Note that the detection unit 14 can perform further processing using the above classification results. For example, the detection unit 14 can determine the degree of deterioration of the components used in the speed reducer 903 by using the above classification results. For example, when the contaminant concentration is an abnormal value, a caution value, and a normal value, the detection unit 14 can determine that the degree of deterioration of the components is high, medium, and low, respectively. In addition, when the contaminant concentration is an abnormal value, the detection unit 14 can output an alarm recommending oil replacement.

[0090] Figure 16 FIG. is a diagram for explaining an example of an operation of determining the color of the oil in the detection unit 14.

[0091] As Figure 16 shown, when the oil itself deteriorates, the color of the oil changes. Therefore, the detection unit 14 determines the color of the oil in order to determine the degree of deterioration of the oil itself. Note that as described above, when determining the degree of deterioration of the oil itself, the determination can be made based on the color of the oil and the result of learning the color of the oil according to the degree of deterioration of the oil.

[0092] Figure 17 It is a flowchart showing an example of a schematic process for explaining the operations performed by the detection system 10 according to the first embodiment.

[0093] As Figure 17 shown, first, the camera 11 captures an image of the oil flowing through the pipeline 102 in the imaging unit 105 (step S21).

[0094] Subsequently, the image processing unit 13 performs image processing on the captured image captured by the camera 11 in step S21 (step S22).

[0095] At this time, the image processing unit 13 performs at least the process of binarizing the captured image as image processing.

[0096] In addition, the image processing unit 13 may optionally perform process 1 of converting the captured image into a grayscale image, process 2 of performing a trimming process for extracting necessary parts from the grayscale image, process 3 of binarizing the image on which the trimming process has been performed, process 4 of inverting the black and white of the binarized image, and process 5 of performing region recognition on the dirt region in the image whose black and white have been inverted and then superimposing the image on the original image.

[0097] Thereafter, the detection unit 14 detects dirt mixed in the oil based on the image on which the image processing has been performed by the image processing unit 13 in step S22 (step S23).

[0098] At this time, the detection unit 14 can count the number of dirt mixed in the oil based on the image on which the image processing has been performed by the image processing unit 13 in step S22. In addition, the detection unit 14 can calculate the dirt concentration in the oil based on the number of dirt.

[0099] In addition, the detection unit 14 can determine the degree of deterioration of components used in the speed reducer 903, such as gears and bearings, based on the number of dirt or the dirt concentration in the oil. In addition, the detection unit 14 can determine the color of the oil based on the captured image captured by the camera 11 in step S21 or the image on which the image processing has been performed by the image processing unit 13 in step S22, and can determine the degree of deterioration of the oil itself based on the color of the oil.

[0100] As described above, the detection system 10 according to the first embodiment is a camera-type sensor that detects dirt mixed in the oil flowing through the pipeline by using the camera 11 and determines the degree of deterioration of the oil itself.

[0101] The advantages of the camera-type sensor will be described below.

[0102] Figure 18It is a diagram showing an example of a characteristic comparison table of a laser type sensor, a magnetic field type sensor, and a camera type sensor.

[0103] As Figure 18 shown, the laser type sensor has a problem that when the oil itself deteriorates, the detection sensitivity of dirt decreases.

[0104] In contrast, the advantage of the camera type sensor is that the deterioration of the oil does not affect its ability to detect dirt. Another advantage of the camera type sensor is that it can detect the deterioration of the oil itself.

[0105] In addition, the magnetic field type sensor has a problem that the magnetic field type sensor can only detect dirt of iron powder because it cannot detect dirt that has rusted and thus is no longer magnetic.

[0106] In contrast, the camera type sensor has the advantage that it can detect dirt that has rusted and thus is no longer magnetic, and thus can detect all dirt regardless of the composition of the dirt.

[0107] As described above, since the detection system 10 according to the first embodiment is implemented as a camera type sensor, the effects of obtaining the advantages of the camera type sensor can be achieved, and thus dirt mixed in the oil can be satisfactorily detected and the deterioration of the oil itself can be detected.

[0108] <Other Embodiments>

[0109] In Figure 9 the example, the image capturing unit 105 has a structure using the sight glass 107 inserted in the middle of the insertion pipe 102. However, the present disclosure is not limited thereto. The image capturing unit 105 may have a structure in which a transparent part is provided in the pipe 102 itself, and the camera 11 can capture an image inside the pipe 102 through the transparent part, or another member provided with such a transparent part may be inserted in the middle of the pipe 102.

[0110] Figures 19 to 21 It is a diagram showing an example of the image capturing unit 105 according to another embodiment.

[0111] In Figure 19 the example, the window 110 as the transparent part is provided on the pipe 102 itself, and only the part of the window 110 is flat.

[0112] In Figure 20 the example, the window 112 as the transparent part is provided on the pipe 102 itself, and the part of the pipe 102 other than the window 112 is white. Therefore, the white part of the pipe 102 can be used as a screen.

[0113] In Figure 21In the example, the pipeline 102 has a structure in which the camera 11 and the screen 115 are integrated. The screen 115 is white. In addition, a window 114 as a transparent part is provided on the pipeline 102 itself, and the camera 11 is fixed to the outside of the pipeline 102 (window 114) so that it focuses on the surface 116.

[0114] In addition, when the thickness of the oil flow path in the pipeline 102 is large and the depth of field of the camera 11 (the range in which the camera 11 can focus) is shallow, dirt that the camera 11 cannot focus on cannot be detected. As a result, when the dirt concentration in the flow path is uneven, the accuracy of dirt counting decreases. Therefore, a camera with a focus adjustment function can be used as the camera 11.

[0115] Figure 22 It is a diagram showing an image example, in which an image of oil flowing in the pipeline 102 with a very large thickness of the flow path is captured by the camera 11 having a focus adjustment function.

[0116] As Figure 22 shown, the camera 11 is a camera that can adjust the focus position by moving the lens 118 in the front-rear direction (the y direction in the figure). The camera 11 captures images while adjusting the focus position, thereby obtaining a plurality of images. The image processing unit 13 performs image processing after superimposing the plurality of images. Therefore, it is expected to improve the accuracy of dirt counting.

[0117] The inventors' invention of the present application has been specifically described above based on the embodiments. However, it goes without saying that the present invention is not limited to the above embodiments, and various changes can be made without departing from the spirit and scope of the present invention. In addition, the designs in each embodiment can be appropriately exchanged between the embodiments.

[0118] For example, the above detection device 12 may include a processor (such as a central processing unit (CPU, Central Processing Unit)), a memory, etc., and the processor can read a computer program stored in the memory and execute it, thereby realizing any processing of the detection device 12.

[0119] The above program can be stored on a non-transitory computer-readable medium or a tangible storage medium. By way of example and not limitation, the non-transitory computer-readable medium or the tangible storage medium may include RAM, ROM, flash memory, SSD, or other types of memory technologies, compact disc (CD)-ROM, digital versatile disc (DVD), Blu-ray (registered trademark) disc, or other types of optical disc memories, cassette tapes, magnetic tapes, and magnetic disk memories, or other types of magnetic devices. The program can be transmitted on a transitory computer-readable medium or a communication medium. By way of example and not limitation, the transitory computer-readable medium or the communication medium may include electrical, optical, acoustic, or other forms of propagated signals.

[0120] This application is based on and claims the priority of Japanese Patent Application JP 2022-193312 filed on December 2, 2022, the entire disclosure of which is incorporated herein by reference.

[0121] List of Reference Numerals

[0122] 10 Detection system

[0123] 11 Camera

[0124] 12 Detection device

[0125] 13 Image processing unit

[0126] 14 Detection unit

[0127] 101, 102 Pipelines

[0128] 103, 104 Valves

[0129] 105 Image capturing unit

[0130] 107 Sight glass

[0131] 108 Screen

[0132] 109 Window

[0133] 903 Reducer

[0134] 911 Pump

Claims

1. Detection system, comprising: A camera designed to capture an image of a fluid flowing through a pipeline; An image processing unit designed to perform image processing, which includes performing binarization processing on the captured image captured by the camera; And A detection unit designed to detect contaminants mixed in the fluid based on the image on which the image processing has been performed by the image processing unit.

2. The detection system according to claim 1, wherein The image processing further includes processing of transforming the captured image into a grayscale image and performing binarization on the grayscale image.

3. The detection system according to claim 1, wherein, The image processing further includes performing region recognition processing on the contaminant region in the binarized image.

4. The detection system according to claim 3, wherein, The image processing further includes processing of superimposing the image on which the region recognition has been performed on the captured image.

5. The detection system according to claim 1, wherein, The detection unit counts the number of contaminants mixed in the fluid based on the image on which the image processing has been performed by the image processing unit.

6. The detection system according to claim 5, wherein, The detection unit calculates the contaminant concentration in the fluid based on the number of contaminants mixed in the fluid.

7. The detection system according to claim 6, wherein The fluid is oil, and the pipeline is designed to circulate the oil supplied to the reducer.

8. The detection system according to claim 7, wherein, The reducer is a reducer provided on an extruder, and the detection unit determines the degree of deterioration of the components constituting the reducer based on the number or concentration of contaminants in the oil.

9. The detection system according to claim 8, wherein, The detection unit determines the degree of deterioration of the components by comparing the contaminant concentration in the oil with a threshold value.

10. Detection system, comprising: A camera designed to capture an image of a fluid flowing through a pipeline; And A detection unit designed to determine the color of the fluid based on the captured image captured by the camera, and determine the degree of deterioration of the fluid itself based on the determined color of the fluid.

11. The detection system according to claim 10, wherein, It further includes an image processing unit designed to transform the captured image into a grayscale image, wherein the detection unit determines the color of the fluid based on the image obtained by the image processing unit transforming the captured image into a grayscale image, and determines the degree of deterioration of the fluid itself based on the determined color of the fluid.

12. The detection system according to claim 10 or 11, wherein, The detection unit pre-learns the color of the fluid corresponding to the corresponding degree of deterioration of the fluid, and the detection unit determines the degree of deterioration of the fluid itself based on the determined color of the fluid and the learning result.

13. The detection system according to claim 1, wherein, The pipeline is configured such that a transparent part is provided on the pipeline, or is configured such that a member with a transparent part is inserted in the middle of the pipeline, and the camera captures an image of the fluid flowing through the pipeline through the transparent part.

14. Detection method performed by a detection system, the detection method comprising the following steps: Capturing an image of a fluid flowing through a pipeline by a camera; Performing image processing, which includes performing binarization processing on the captured fluid image; And Detecting contaminants mixed in the fluid based on the image on which the image processing has been performed.

Images