CN111126196A - Equipment oil leakage detection method - Google Patents
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- CN111126196A CN111126196A CN201911260943.7A CN201911260943A CN111126196A CN 111126196 A CN111126196 A CN 111126196A CN 201911260943 A CN201911260943 A CN 201911260943A CN 111126196 A CN111126196 A CN 111126196A
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- 238000001514 detection method Methods 0.000 title claims abstract description 27
- 238000012549 training Methods 0.000 claims abstract description 19
- 238000000034 method Methods 0.000 claims description 12
- 238000012544 monitoring process Methods 0.000 claims description 6
- 238000005520 cutting process Methods 0.000 claims description 4
- 238000013135 deep learning Methods 0.000 claims description 3
- 230000001537 neural effect Effects 0.000 claims description 3
- 238000004519 manufacturing process Methods 0.000 abstract description 5
- 238000012423 maintenance Methods 0.000 description 3
- 239000002828 fuel tank Substances 0.000 description 2
- 238000007689 inspection Methods 0.000 description 2
- 238000011897 real-time detection Methods 0.000 description 2
- 230000002159 abnormal effect Effects 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 238000010586 diagram Methods 0.000 description 1
- 238000002372 labelling Methods 0.000 description 1
- 238000005461 lubrication Methods 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
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Abstract
The invention provides an equipment oil leakage detection method, which comprises the following steps: collecting oil leakage block picture samples in various shapes to perform target recognition training, and generating an oil leakage block recognition model; installing a camera at the front upper part of an equipment oil tank, and setting an oil tank and a ground area below the oil tank in a video image of the camera; acquiring a video image of the camera in real time through a network and an RTSP (real time streaming protocol); and detecting and analyzing the acquired video image by using the oil leakage block identification model, and judging whether the equipment leaks oil and the severity of the oil leakage. According to the invention, the target recognition training is carried out on the picture samples of the oil leakage blocks in various shapes to generate the oil leakage block recognition model, and the video images acquired by the camera are detected in real time by using the oil leakage block recognition model, so that whether the equipment leaks oil or not is judged, the real-time intelligent detection of the oil leakage condition of the equipment is realized, the detection efficiency and the accuracy are high, and the production safety is improved.
Description
Technical Field
The invention relates to the technical field of equipment oil leakage detection, in particular to an equipment oil leakage detection method.
Background
The treatment of oil leakage of equipment is one of the main tasks in equipment management and maintenance work. The oil leakage of the equipment wastes a large amount of oil, pollutes the environment, increases the workload of lubrication and maintenance, and even causes equipment accidents to influence the production in serious cases. Therefore, the treatment of oil leakage is one of the important measures for improving the technical state of equipment.
In the prior art, the oil leakage condition of the equipment is mainly detected in a manual inspection mode, and after an inspector finds that the equipment leaks oil, the inspection personnel immediately operate the equipment to stop and inform related personnel of maintenance. However, the detection mode cannot monitor the oil leakage condition of the equipment in real time, is difficult to find abnormal conditions in time, has certain hysteresis, and brings potential safety hazards to the equipment. In addition, the oil stain ground also can bring the potential safety hazard for the personnel of patrolling and examining.
Disclosure of Invention
The invention aims to provide an equipment oil leakage detection method, which is used for realizing real-time intelligent detection on the oil leakage condition of equipment and improving the detection efficiency and safety.
In order to solve the above technical problem, an embodiment of the present invention provides an apparatus oil leakage detection method, including the following steps:
s1, collecting oil leakage block picture samples in various shapes to perform target recognition training, and generating an oil leakage block recognition model;
s2, installing a camera at the front upper part of the oil tank of the equipment, and setting the oil tank and the ground area under the oil tank in the video image of the camera;
s3, acquiring the video image of the camera in real time through a network and an RTSP protocol;
and S4, detecting and analyzing the acquired video image by using the oil leakage block identification model, and judging whether the equipment leaks oil and the severity of the oil leakage.
Preferably, the step S1 includes:
collecting oil leakage oil block picture samples in different time periods, and classifying the oil leakage oil block picture samples according to shapes;
and respectively carrying out target recognition training on the oil leakage block picture samples of each shape based on a deep learning convolution neural algorithm to generate an oil leakage block recognition model.
Preferably, the step of collecting oil leakage lump oil picture samples of different time periods and classifying according to shape specifically includes:
cutting the size of the oil leakage block picture sample according to actual requirements;
marking the outline of the oil block in the oil block leakage picture sample by using a marking tool;
and classifying the collected oil leakage block picture samples according to the marked outline shape.
Preferably, in the step S2, the monitoring area of the camera covers the whole oil tank and the ground below the oil tank, and the camera supports the RTSP and ONVIF protocols.
Preferably, the step S4 includes:
loading the obtained video image into the generated oil leakage block recognition model for image detection;
when the oil leakage block cannot be detected in the video image, judging that the oil leakage does not occur in the equipment;
and when the oil leakage block is detected in the video image, judging that the oil leakage occurs in the equipment.
Preferably, the step S4 further includes:
when an oil leakage block is detected in the video image, comparing the outline of the oil leakage block with the outline of an oil leakage block picture sample in the oil leakage block identification model;
and judging the severity of the oil leakage of the equipment according to the comparison result.
Preferably, the detection method further includes, after the step S4:
when the oil leakage of the equipment is judged, an alarm is given to a worker, and the alarm mode comprises an acousto-optic alarm, a monitoring screen display alarm and a mobile terminal information pushing alarm.
Preferably, the detection method further includes, after the step S4:
and when the oil leakage of the equipment is judged, sending an instruction to the equipment controller to control the equipment to stop or adjust the operation parameters.
The technical scheme of the invention has the following beneficial effects:
in the scheme, the method and the device perform target recognition training on the picture samples of the oil leakage blocks in various shapes to generate an oil leakage block recognition model, and perform real-time detection on the video images acquired by the camera by using the oil leakage block recognition model, so as to judge whether the oil leakage of the equipment occurs and the severity of the oil leakage. The invention can realize real-time intelligent detection of the oil leakage condition of the equipment, has high detection efficiency and high accuracy, and can alarm and link the equipment in time when judging the oil leakage of the equipment, thereby improving the production safety.
Drawings
FIG. 1 is a flow chart of an apparatus oil leakage detection method according to an embodiment of the present invention;
FIGS. 2a to 2c are schematic diagrams illustrating outline labeling of the oil leakage block in the embodiment of the invention.
Detailed Description
In order to make the technical problems, technical solutions and advantages of the present invention more apparent, the following detailed description is given with reference to the accompanying drawings and specific embodiments.
An embodiment of the present invention provides an apparatus oil leakage detection method, as shown in fig. 1, the method includes the following steps:
s1, collecting oil leakage block picture samples in various shapes to perform target recognition training, and generating an oil leakage block recognition model;
s2, installing a camera at the front upper part of the oil tank of the equipment, and setting the oil tank and the ground area under the oil tank in the video image of the camera;
s3, acquiring the video image of the camera in real time through a network and an RTSP protocol;
and S4, detecting and analyzing the acquired video image by using the oil leakage block identification model, and judging whether the equipment leaks oil and the severity of the oil leakage.
In the scheme, the target recognition training is carried out on the picture samples of the oil leakage blocks in various shapes to generate the oil leakage block recognition model, and the video images acquired by the camera are detected in real time by using the oil leakage block recognition model, so that whether the equipment leaks oil and the severity of the oil leakage are judged, the real-time intelligent detection of the oil leakage condition of the equipment is realized, the detection efficiency and the accuracy are high, and the production safety is improved.
Further, step S1 includes:
collecting oil leakage oil block picture samples in different time periods, and classifying the oil leakage oil block picture samples according to shapes; in the step, the shape of the oil leakage block picture sample is required to be as much as possible, and a data sample can be obtained through an oil leakage simulation experiment;
and respectively carrying out target recognition training on the oil leakage block picture samples of each shape based on a deep learning convolution neural algorithm to generate an oil leakage block recognition model.
The steps of collecting oil leakage oil lump picture samples in different time periods and classifying according to shapes specifically comprise:
cutting the size of the oil leakage block picture sample according to actual requirements;
marking the outline of the oil block in the oil block leakage picture sample by using a marking tool;
and classifying the collected oil leakage block picture samples according to the marked outline shape.
The specific algorithm of model training is as follows:
1) data set preprocessing
The method comprises the steps of cutting the size of the oil leakage block picture sample according to actual requirements, and marking the collected oil leakage block picture sample by using a marking tool labelme. For example, as shown in fig. 2 a-2 c, outlines of leaked oil masses of various shapes are labeled.
2) Model training
The method comprises the following steps: configuring training parameters → training head part → training all layers
The training parameters are configured as follows:
loading a pre-training model:
training a model:
model prediction:
configuring a test class InferenceConfig class:
further, in step S2, the monitoring area of the camera covers the entire fuel tank and the ground under the fuel tank, and the camera supports the RTSP and ONVIF protocols.
The algorithm for acquiring the real-time video image of the camera is as follows:
wherein cv2 is an opencv library,
rtsp/admin: admin @192.168.1.45:554// Streaming/Channels/1 is the rtsp Streaming media address of the camera, and is acquired according to the actual camera, and the frame is the video image acquired in real time.
Further, step S4 includes:
loading the obtained video image into the generated oil leakage block recognition model for image detection;
when the oil leakage block cannot be detected in the video image, judging that the oil leakage does not occur in the equipment;
and when the oil leakage block is detected in the video image, judging that the oil leakage occurs in the equipment.
Further, step S4 further includes:
when an oil leakage block is detected in the video image, comparing the outline of the oil leakage block with the outline of an oil leakage block picture sample in the oil leakage block identification model;
and judging the severity of the oil leakage of the equipment according to the comparison result.
The specific implementation algorithm is as follows:
according to the result returned by results, if the oil leakage image in any shape is detected, the oil leakage of the equipment is judged, and on the contrary, the oil leakage of the equipment does not occur.
Further, the detection method further includes, after step S4:
when the oil leakage of the equipment is judged, an alarm is given to a worker, and the alarm mode comprises an acousto-optic alarm, a monitoring screen display alarm and a mobile terminal information pushing alarm.
And when the oil leakage of the equipment is judged, sending an instruction to the equipment controller to control the equipment to stop or adjust the operation parameters.
In summary, the method of the present invention performs target recognition training on the image samples of the leaked oil cake in various shapes to generate a leaked oil cake recognition model, and performs real-time detection on the video image acquired by the camera by using the leaked oil cake recognition model, so as to determine whether the oil leaks from the device and the severity of the leaked oil. The invention can realize real-time intelligent detection of the oil leakage condition of the equipment, has high detection efficiency and high accuracy, and can alarm and link the equipment in time when judging the oil leakage of the equipment, thereby improving the production safety.
While the foregoing is directed to the preferred embodiment of the present invention, it will be understood by those skilled in the art that various changes and modifications may be made without departing from the spirit and scope of the invention as defined in the appended claims.
Claims (8)
1. An equipment oil leakage detection method is characterized by comprising the following steps:
s1, collecting oil leakage block picture samples in various shapes to perform target recognition training, and generating an oil leakage block recognition model;
s2, installing a camera at the front upper part of the oil tank of the equipment, and setting the oil tank and the ground area under the oil tank in the video image of the camera;
s3, acquiring the video image of the camera in real time through a network and an RTSP protocol;
and S4, detecting and analyzing the acquired video image by using the oil leakage block identification model, and judging whether the equipment leaks oil and the severity of the oil leakage.
2. The method for detecting oil leakage from equipment according to claim 1, wherein said step S1 includes:
collecting oil leakage oil block picture samples in different time periods, and classifying the oil leakage oil block picture samples according to shapes;
and respectively carrying out target recognition training on the oil leakage block picture samples of each shape based on a deep learning convolution neural algorithm to generate an oil leakage block recognition model.
3. The method for detecting oil leakage of equipment according to claim 2, wherein the step of collecting picture samples of oil blocks with oil leakage in different time periods and classifying the picture samples according to shapes specifically comprises:
cutting the size of the oil leakage block picture sample according to actual requirements;
marking the outline of the oil block in the oil block leakage picture sample by using a marking tool;
and classifying the collected oil leakage block picture samples according to the marked outline shape.
4. The method for detecting oil leakage from equipment according to claim 1, wherein in step S2, the monitoring area of the camera covers the entire oil tank and the ground under the oil tank, and the camera supports RTSP and ONVIF protocols.
5. The method for detecting oil leakage from equipment according to claim 1, wherein said step S4 includes:
loading the obtained video image into the generated oil leakage block recognition model for image detection;
when the oil leakage block cannot be detected in the video image, judging that the oil leakage does not occur in the equipment;
and when the oil leakage block is detected in the video image, judging that the oil leakage occurs in the equipment.
6. The method for detecting oil leakage from equipment according to claim 5, wherein said step S4 further includes:
when an oil leakage block is detected in the video image, comparing the outline of the oil leakage block with the outline of an oil leakage block picture sample in the oil leakage block identification model;
and judging the severity of the oil leakage of the equipment according to the comparison result.
7. The method for detecting oil leakage from an apparatus according to any one of claims 1-6, further comprising, after said step S4:
when the oil leakage of the equipment is judged, an alarm is given to a worker, and the alarm mode comprises an acousto-optic alarm, a monitoring screen display alarm and a mobile terminal information pushing alarm.
8. The method for detecting oil leakage from an apparatus according to any one of claims 1-6, further comprising, after said step S4:
and when the oil leakage of the equipment is judged, sending an instruction to the equipment controller to control the equipment to stop or adjust the operation parameters.
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Cited By (5)
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| CN112349488A (en) * | 2020-10-22 | 2021-02-09 | 四川盛鑫源电器设备制造有限公司 | Polygonal oil tank of transformer and online oil leakage monitoring method thereof |
| CN113514193A (en) * | 2021-07-13 | 2021-10-19 | 江铃汽车股份有限公司 | Automobile collision fuel leakage detection method based on image recognition |
| CN113670524A (en) * | 2021-07-13 | 2021-11-19 | 江铃汽车股份有限公司 | Detection method and detection system for fuel leakage in automobile collision |
| CN113982808A (en) * | 2021-10-25 | 2022-01-28 | 安康水力发电厂 | System and method for monitoring running state of mixed-flow hydraulic generator |
| CN114320709A (en) * | 2021-12-30 | 2022-04-12 | 中国长江电力股份有限公司 | Deep learning-based classification detection method for oil leakage inside power station generator |
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| CN113670524A (en) * | 2021-07-13 | 2021-11-19 | 江铃汽车股份有限公司 | Detection method and detection system for fuel leakage in automobile collision |
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| CN113982808A (en) * | 2021-10-25 | 2022-01-28 | 安康水力发电厂 | System and method for monitoring running state of mixed-flow hydraulic generator |
| CN114320709A (en) * | 2021-12-30 | 2022-04-12 | 中国长江电力股份有限公司 | Deep learning-based classification detection method for oil leakage inside power station generator |
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