CN119001373B - Automatic detection system for online insulation monitoring of marine cable - Google Patents
Automatic detection system for online insulation monitoring of marine cable Download PDFInfo
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- G01R31/1263—Testing dielectric strength or breakdown voltage ; Testing or monitoring effectiveness or level of insulation, e.g. of a cable or of an apparatus, for example using partial discharge measurements; Electrostatic testing of components, parts or materials of solid or fluid materials, e.g. insulation films, bulk material; of semiconductors or LV electronic components or parts; of cable, line or wire insulation
- G01R31/1272—Testing dielectric strength or breakdown voltage ; Testing or monitoring effectiveness or level of insulation, e.g. of a cable or of an apparatus, for example using partial discharge measurements; Electrostatic testing of components, parts or materials of solid or fluid materials, e.g. insulation films, bulk material; of semiconductors or LV electronic components or parts; of cable, line or wire insulation of cable, line or wire insulation, e.g. using partial discharge measurements
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Abstract
本发明公开了一种对船用电缆在线绝缘监控的自动检测系统，涉及船用电缆绝缘检测技术领域，包括船体模型模块、电缆模型模块、船体受力监测模块、船体形变监测模块、船体形变分析模块、电缆形变分析模块、环境监测模块、电缆硬度预测模块、电缆安全数据分析模块；该对船用电缆在线绝缘监控的自动检测系统，通过对电缆硬度与环境数据变化情况的关系进行分析，从而可通过环境数据的变化曲线预测电缆的硬度，降低电缆硬度的检测频率降低维护成本，并通过定期检测的电缆硬度数据更新电缆硬度预测模型，保证模型预测的精度，将基于回归分析预测绝缘强度与设置的绝缘强度标准值对比，判断电缆是否异常并在异常时进行提醒对电缆进行更换维护。
The invention discloses an automatic detection system for online insulation monitoring of ship cables, relates to the technical field of ship cable insulation detection, and comprises a hull model module, a cable model module, a hull force monitoring module, a hull deformation monitoring module, a hull deformation analysis module, a cable deformation analysis module, an environmental monitoring module, a cable hardness prediction module, and a cable safety data analysis module; the automatic detection system for online insulation monitoring of ship cables analyzes the relationship between cable hardness and environmental data changes, thereby predicting the hardness of the cable through the change curve of environmental data, reducing the detection frequency of the cable hardness and reducing the maintenance cost, and updating the cable hardness prediction model through regularly detected cable hardness data to ensure the accuracy of model prediction, comparing the insulation strength predicted based on regression analysis with a set insulation strength standard value, judging whether the cable is abnormal, and reminding the cable to be replaced and maintained when abnormal.
Description
Technical Field
The invention relates to the technical field of marine cable insulation detection, in particular to an automatic detection system for online insulation monitoring of a marine cable.
Background
The performance of the electrical equipment is constantly changed under the influence of various external factors during operation, the external factors mainly comprise an external electric field, environment, high voltage, corrosion degree, machinery and the like, the electrical equipment is easy to have unpredictable faults under the influence of the adverse factors, and the electrical operation is seriously interrupted, so that the insulation of equipment cables and cables needs to be ensured, particularly for cables on ships, the cables on ships sailing on the water surface are in the environment with high humidity for a long time, the cables on ships are more easily corroded by seawater, river water and the like, and the insulation performance of the cables on the ships needs to be ensured particularly so as to ensure the operation safety of the electrical equipment on the ships.
The prior art with the publication number of CN117782957A discloses a method and a system for testing the aging performance of a marine cable, and relates to the technical field of data processing, wherein the method comprises the following steps: carrying out vibration test on the cable for the ship by using vibration test equipment, simulating the actual working condition of the cable on the ship, recording the vibration frequency, the vibration amplitude and the vibration duration, and calculating a dynamic factor according to the vibration frequency, the vibration amplitude and the vibration duration; and substituting the dielectric loss tangent value and the dynamic factor into a thermal life equation together, and calculating to obtain an ageing performance evaluation result of the marine cable. The invention can simulate the thermal aging and mechanical aging processes of the cable under the actual working condition, thereby reflecting the aging performance of the cable more truly.
However, when the ship sails on the sea, the environment is more severe and is more easily influenced by factors such as weather, sea waves and the like, the service life of the cable can be shortened due to corrosion caused by the severe environment, the ship body is slightly deformed possibly due to factors such as strong wind, sea waves and the like, the stress of the cable is changed, and the mechanical loss of the cable is increased, so that the service life of the cable is reduced.
Disclosure of Invention
The invention aims to provide an automatic detection system for on-line insulation monitoring of a marine cable, which aims to solve the defects in the prior art.
In order to achieve the aim, the invention provides the technical scheme that the automatic detection system for online insulation monitoring of the marine cable comprises a ship model module, a cable model module, a ship stress monitoring module, a ship deformation analysis module, a cable deformation analysis module, an environment monitoring module, a cable hardness prediction module and a cable safety data analysis module;
The ship body model module is used for building a ship body assembly model according to the actual structure and the material of the ship body, wherein the ship body assembly model truly restores all the structures, parts, materials and the like of the ship body;
The cable model module is used for establishing a cable model and is assembled into the ship body assembly model according to the actual installation position of the cable;
The ship body stress monitoring module is used for monitoring external force suffered by the ship body to obtain ship body stress data, for example, a strain gauge is attached to the surface of the ship body, and the stress magnitude is calculated by measuring the strain of a material;
The ship deformation monitoring module is used for monitoring deformation of a ship set position to obtain ship deformation data, and deformation monitoring of the set position, such as bending, elongation, displacement and the like, can be realized by installing strain gauges, optical fiber sensors, laser range finders and the like at the set position.
The ship body deformation analysis module is used for carrying out stress analysis based on the ship body assembly model to obtain a ship body stress analysis model, and is used for analyzing ship body deformation data of each ship body assembly model corresponding to each ship body stress data under each load;
the ship body deformation analysis module is also used for correcting the ship body stress model based on the ship body stress data and the ship body deformation data.
The cable deformation analysis module is used for extracting deformation data of the ship body position corresponding to the cable from the ship body deformation data obtained through analysis of the ship body deformation analysis module, and obtaining cable deformation data;
The environment monitoring module is used for monitoring the temperature and the humidity respectively by adopting a temperature sensor, a humidity sensor and the like based on the acquired environment data on the ship body.
The cable hardness prediction module is used for detecting the hardness of a cable to obtain cable hardness data, is also used for obtaining environment data, drawing an environment data change curve, training a deep learning model based on the environment data change curve and the cable hardness data to obtain a cable hardness prediction model, and outputting a corresponding cable hardness data prediction value based on the input environment data change curve, wherein in the training process, the environment data change curve and the cable hardness data are taken as samples, the environment data change curve is taken as input, and the cable hardness data are taken as output.
The cable safety data analysis module is also used for testing the safety elongation and the insulation strength corresponding to the cables when the hardness data of the cables are tested, wherein the safety elongation is the maximum elongation of the cables when the surfaces of the cables are not damaged, regression analysis is respectively carried out based on the hardness data, the safety elongation and the insulation strength of the cables, a safety elongation regression model and an insulation strength regression model are respectively obtained, and the safety elongation regression model and the insulation strength regression model are respectively used for calculating the corresponding safety elongation predicted value and the corresponding insulation strength predicted value based on the hardness data of the cables;
the cable safety data analysis module is also used for judging whether the cable deformation data are larger than a safety elongation predicted value of corresponding time, if so, carrying out early warning, and meanwhile, judging whether the insulation strength predicted value is smaller than a set insulation strength standard value, and if so, carrying out early warning.
Further, the cable hardness prediction module is further configured to obtain the detected cable hardness data, and perform training update on the cable hardness prediction model, and the method includes the following steps:
a1, performing hardness detection on the cable according to a set period, storing the obtained cable hardness data in a detection sample set, dividing the cable on the ship into a plurality of sections during detection, taking part of the cable as a detection sample in each section, and performing hardness detection;
a2, judging whether the number of the cable hardness data in the detection sample set is larger than or equal to the set sample number;
And A3, if so, training and updating the cable hardness prediction model by using the detection sample set, so that the cable hardness prediction model is corrected by using the detected cable hardness data at regular intervals, and the prediction precision of the cable prediction model is ensured.
Further, the cable safety data analysis module obtains a safety elongation regression model, which comprises the following steps:
b1, acquiring cables with different hardness data;
b2, respectively carrying out tensile test on the cables with the hardness data, gradually increasing the tensile length during the tensile test, and carrying out ultrasonic flaw detection on the cables at the same time to judge whether mechanical damage exists on the surfaces of the cables;
If not, returning to the step B2, if so, acquiring the elongation of the cable at the moment to obtain the safe elongation of the cable corresponding to the hardness data, wherein when the tensile test is carried out, the tensile test can be carried out on the cable of each hardness data for a plurality of times to obtain a plurality of safe elongations corresponding to the hardness data, and finally taking the average value of the plurality of safe elongations as the safe elongation corresponding to the hardness data of the cable;
and B4, carrying out regression analysis based on the hardness data of the cable and the safe elongation corresponding to the hardness data to obtain a safe elongation regression model, and outputting a corresponding safe elongation predicted value based on the input hardness data.
Further, the cable safety data analysis module obtains an insulation strength regression model, which comprises the following steps:
C1, acquiring cables with different hardness data;
C2, respectively carrying out insulation strength test on the cables with the hardness data to obtain insulation strength data corresponding to the cables under the condition of the hardness data;
and C3, carrying out regression analysis based on the hardness data of the cable and the insulation strength data corresponding to the hardness data to obtain an insulation strength regression model, and outputting an insulation strength predicted value based on the input hardness data.
Further, the cable safety data analysis module is further configured to determine whether the cable deformation data is greater than a predicted value of a safety elongation of a corresponding time, and if yes, perform early warning, including the following steps:
acquiring monitoring data of a ship body stress monitoring module to obtain historical ship body stress data of the ship body;
Analyzing the historical ship body stress data based on a box line diagram, screening abnormal historical ship body stress data, and obtaining a ship body normal stress data interval of the ship body;
Analyzing the normal stress data interval of the ship body based on a ship body stress analysis model to obtain a normal deformation data interval of the ship body, extracting deformation data of the ship body position corresponding to the cable, and obtaining a normal deformation data interval of the cable;
Inputting a current environmental data change curve to a cable hardness prediction model to obtain a current cable hardness data prediction value, and inputting the current cable hardness data prediction value to a safe elongation regression model to obtain a current safe elongation prediction value;
And judging whether the current safe elongation predicted value is larger than a normal deformation data interval of the cable, and if not, sending out a first type of early warning.
Further, the cable safety data analysis module is further configured to determine whether the cable deformation data is greater than a predicted value of a safety elongation of the corresponding time, and if yes, perform early warning, and further includes the following steps:
acquiring real-time ship stress data;
Analyzing the real-time ship stress data based on a ship stress analysis model to obtain ship real-time deformation data, and extracting deformation data of a ship position corresponding to the cable to obtain the cable real-time deformation data;
And judging whether the real-time deformation data of the cable is larger than the current safe elongation, and if so, sending out a second type of early warning.
Further, the step of judging whether the predicted value of the insulation strength is smaller than the set standard value of the insulation strength, if yes, performing early warning, and comprises the following steps:
Setting an insulation strength standard value, wherein the insulation strength standard value can be set according to industry standards, for example, the insulation strength standard of a ship cable generally requires that insulation is ensured when a test voltage applied within 1 minute is 2500 volts;
inputting a current environmental data change curve to a cable hardness prediction model to obtain a current cable hardness data prediction value, and inputting the current cable hardness data prediction value to an insulation strength regression model to obtain a current insulation strength prediction value;
Judging whether the current predicted value of the insulation strength is larger than or equal to the standard value of the insulation strength, and if not, sending out a second type early warning.
1. Compared with the prior art, the automatic detection system for online insulation monitoring of the ship cable establishes the three-dimensional assembly model of the ship body by arranging the ship body model module, the cable model module, the ship body stress monitoring module, the ship body deformation analysis module and the cable deformation analysis module, accurately reflects the structure and the material of the ship body, facilitates stress analysis, can simulate and analyze deformation conditions of the ship body according to the external force received by the monitored ship body, thereby obtaining the cable deformation condition reflected by the deformation condition of the ship body cable, and can correct the analysis result of the model according to the deformation condition monitored by the ship body deformation monitoring module arranged in a part of areas, thereby improving the precision of ship body deformation analysis and cable deformation analysis.
2. Compared with the prior art, the automatic detection system for online insulation monitoring of the marine cable provided by the invention has the advantages that the environment monitoring module and the cable hardness prediction module are arranged, the cable hardness prediction model can be obtained by analyzing the relation between the cable hardness and the change condition of environment data such as temperature and humidity, the hardness of the cable is predicted through the change curve of the environment data, the hardness of the cable is detected regularly, the cable hardness model is updated by using the cable hardness data obtained through detection, the hardness of the cable can be predicted through the change curve of the environment data, the hardness of the cable is known in real time, the detection frequency of the cable hardness is reduced, the damage to the cable during hardness detection is reduced, the maintenance cost is reduced, and the cable hardness prediction model is updated through the cable hardness data detected regularly, so that the model prediction precision is ensured.
3. Compared with the prior art, the automatic detection system for online insulation monitoring of the marine cable provided by the invention has the advantages that the body model module, the cable model module, the ship body stress monitoring module, the ship body deformation analysis module, the cable deformation analysis module and the cable safety data analysis module are arranged, the safety elongation and the insulation strength of each hardness of the cable are tested, the relation between the hardness and the safety elongation and the insulation strength of the cable are respectively analyzed through regression analysis, the normal deformation data interval of the cable is obtained by acquiring the external force range born in the ship body navigation process, the safety elongation predicted based on the regression analysis is compared with the normal deformation data interval of the cable, the insulation strength predicted based on the regression analysis is compared with the set insulation strength standard value, whether the cable is abnormal or not is judged, the replacement maintenance of the cable is reminded when the cable is abnormal, and the safety of the cable is further improved.
Drawings
In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for the embodiments will be briefly described below, and it is apparent that the drawings in the following description are only some embodiments described in the present application, and other drawings may be obtained according to these drawings for a person having ordinary skill in the art.
FIG. 1 is a block diagram of a system architecture according to an embodiment of the present invention;
FIG. 2 is a diagram of a step of training and updating a cable hardness prediction model according to an embodiment of the present invention;
FIG. 3 is a step chart of obtaining a safe elongation regression model by the cable safety data analysis module according to the embodiment of the invention;
Fig. 4 is a step chart of obtaining an insulation strength regression model by the cable safety data analysis module according to the embodiment of the invention;
fig. 5 is a diagram of a step of judging a safe elongation of a cable safety data analysis module according to an embodiment of the present invention;
FIG. 6 is a diagram showing another step of determining the elongation of the cable safety data analysis module according to the embodiment of the present invention;
fig. 7 is a diagram of a step of judging insulation strength of a cable safety data analysis module according to an embodiment of the present invention.
Detailed Description
In order to make the technical scheme of the present invention better understood by those skilled in the art, the present invention will be further described in detail with reference to the accompanying drawings.
In the description of the present invention, the terms "first," "second," and the like are used for descriptive purposes only and are not to be construed as indicating or implying relative importance or implicitly indicating the number of technical features indicated. Thus, a feature defining "a first" or "a second" may explicitly or implicitly include one or more of the described features. In the description of the present invention, the meaning of "a plurality" is two or more, unless explicitly defined otherwise. The terms "mounted," "connected," "coupled," and "connected" are used in a broad sense, and may be, for example, fixedly connected, detachably connected, or integrally connected, mechanically connected, electrically connected, directly connected, or indirectly connected via an intermediate medium, or may be in communication with the interior of two elements. The specific meaning of the above terms in the present invention will be understood in specific cases by those of ordinary skill in the art.
Example embodiments will be described more fully hereinafter with reference to the accompanying drawings, but may be embodied in various forms and should not be construed as limited to the embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the scope of the disclosure to those skilled in the art.
Embodiments of the disclosure and features of embodiments may be combined with each other without conflict.
As used herein, the term "and/or" includes any and all combinations of one or more of the associated listed items.
The terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the disclosure. As used herein, the singular forms "a," "an," and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise. It will be further understood that the terms "comprises" and/or "comprising," when used in this specification, specify the presence of stated features, integers, steps, operations, elements, and/or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and/or groups thereof.
Embodiments described herein may be described with reference to plan and/or cross-sectional views with the aid of idealized schematic diagrams of the present disclosure. Accordingly, the example illustrations may be modified in accordance with manufacturing techniques and/or tolerances. Thus, the embodiments are not limited to the embodiments shown in the drawings, but include modifications of the configuration formed based on the manufacturing process. Thus, the regions illustrated in the figures have schematic properties and the shapes of the regions illustrated in the figures illustrate the particular shapes of the regions of the elements, but are not intended to be limiting.
Unless otherwise defined, all terms (including technical and scientific terms) used herein have the same meaning as commonly understood by one of ordinary skill in the art. It will be further understood that terms, such as those defined in commonly used dictionaries, should be interpreted as having a meaning that is consistent with their meaning in the context of the relevant art and the present disclosure, and will not be interpreted in an idealized or overly formal sense unless expressly so defined herein.
Referring to fig. 1, an automatic detection system for online insulation monitoring of a marine cable includes a hull model module, a cable model module, a hull stress monitoring module, a hull deformation analysis module, a cable deformation analysis module, an environment monitoring module, a cable hardness prediction module, and a cable safety data analysis module;
the ship body model module is used for building a ship body assembly model according to the actual structure and the material of the ship body, wherein the ship body assembly model truly restores all the structures, parts, materials and the like of the ship body;
the cable model module is used for establishing a cable model and is assembled into the ship body assembly model according to the actual installation position of the cable;
The ship body stress monitoring module is used for monitoring external force suffered by the ship body to obtain ship body stress data, for example, stress magnitude can be calculated by attaching a strain gauge on the surface of the ship body and measuring strain of a material;
The ship deformation monitoring module is used for monitoring deformation of a ship set position to obtain ship deformation data, for example, deformation monitoring of the set position, such as bending, elongation, displacement and the like, can be realized by installing strain gauges, optical fiber sensors, laser range finders and the like at the set position, and the ship deformation monitoring module can be installed at a part of positions to monitor the deformation and is used for verifying and correcting the deformation data analyzed by the ship deformation analysis module.
The ship body deformation analysis module is used for carrying out stress analysis based on the ship body assembly model to obtain a ship body stress analysis model, and is used for analyzing ship body deformation data of each ship body assembly model corresponding to each ship body stress data under each load;
the ship deformation analysis module is also used for correcting the ship stress model based on the ship stress data and the ship deformation data, and further, the ship stress model can be analyzed by a finite element method during stress analysis, for example, the ship stress model can be obtained by the following steps:
a. analysis targets and ranges are defined to determine the structural parts and operating conditions (e.g., hull, deck, cargo hold, etc.) that need to be evaluated. Explicit targets such as evaluating the strength and stability of the hull under different loads and sea conditions.
B. and (3) collecting ship data, namely acquiring ship design drawings, including ship body structures, material specifications, dimensions and the like. Actual operational data such as speed of travel, load distribution, sea state information, etc. are collected.
C. Analysis tools, finite Element Analysis (FEA) software, such as ANSYS, ABAQUS, are selected and configured to simulate the stresses and deformations of the hull under different loads. Computational Fluid Dynamics (CFD) software, such as OpenFOAM, is used to simulate the effects of the marine environment on the hull.
D. And establishing a model, namely establishing a three-dimensional geometric model of the ship body according to the design drawing. Grid division-dividing the hull model into a finite number of cells to facilitate finite element analysis.
E. material properties and boundary conditions are defined, namely mechanical property data of hull materials, such as elastic modulus, poisson ratio, yield strength and the like are input. Boundary conditions and loads are set, including hydrostatic pressure, wave load, cargo load, etc.
F. And (3) carrying out structural analysis, namely carrying out static analysis, and evaluating stress distribution and deformation conditions under static load. And (3) dynamically analyzing by considering the influence of dynamic loads such as waves, wind power and the like on the hull structure. Fatigue analysis, namely evaluating possible fatigue damage of the ship under repeated load.
G. and (3) verifying results, namely comparing the simulation results with actual test data, and performing structural test on a laboratory or an actual ship to verify the accuracy of analysis results.
The cable deformation analysis module is used for extracting deformation data of the ship body position corresponding to the cable from the ship body deformation data obtained through analysis of the ship body deformation analysis module, and obtaining the cable deformation data;
The environment monitoring module is used for acquiring environment data on the ship body, wherein the environment data comprise temperature and humidity, the environment monitoring module can adopt a temperature sensor, a humidity sensor and the like to monitor the temperature and the humidity respectively, and preferably, the environment monitoring module can be uniformly arranged along the trend of the cable, and the temperature and the humidity of each area are obtained by the temperature average value and the humidity average value obtained by monitoring by the environment monitoring module of the area, so that the accuracy of the environment data of the monitored area can be improved.
The method comprises the steps of obtaining cable hardness data by detecting the hardness of a cable, drawing an environment data change curve based on the environment data change curve and the cable hardness data, training a deep learning model to obtain a cable hardness prediction model, outputting a corresponding cable hardness data prediction value based on the input environment data change curve, selecting a proper model according to actual conditions in a training process without limiting the specific deep learning model, training to ensure the accuracy of the trained model, such as a feedforward neural network model, a long-short-period memory network model, a convolutional neural network model, and the like, taking the environment data change curve and the cable hardness data as samples in the training process, taking the environment data change curve as input, taking the cable hardness data as output, dividing the samples into two parts of a training set and a verification set according to the ratio of 6:4, training by using the deep learning model of the training set to obtain parameters of the model, substituting the deep learning model into the parameters, verifying the accuracy of the output of the deep learning model by the verification set, determining model parameters if the accuracy meets the set requirements, obtaining the model parameters if the cable hardness prediction model does not meet the set requirements, and resetting the set requirements, or resetting the model until the accuracy of the training function is required to be set, and the model is reset after the training function is set.
Referring to fig. 2, the cable hardness prediction module is further configured to obtain detected cable hardness data, and perform training update on the cable hardness prediction model, where the method includes the following steps:
a1, performing hardness detection on the cable according to a set period, storing the obtained cable hardness data in a detection sample set, dividing the cable on the ship into a plurality of sections during detection, taking part of the cable as a detection sample in each section, and performing hardness detection;
a2, judging whether the number of the cable hardness data in the detection sample set is larger than or equal to the set sample number;
And A3, if so, training and updating the cable hardness prediction model by using the detection sample set, so that the cable hardness prediction model is corrected by using the detected cable hardness data at regular intervals, and the prediction precision of the cable prediction model is ensured.
The cable safety data analysis module is also used for testing the safety elongation and the insulation strength corresponding to the cables when the hardness data of the cables are tested, wherein the safety elongation is the maximum elongation of the cables when the surface of the cables is not damaged, regression analysis is respectively carried out based on the hardness data of the cables, the safety elongation and the insulation strength, a safety elongation regression model and an insulation strength regression model are respectively obtained, and the safety elongation regression model and the insulation strength regression model are respectively used for calculating the corresponding safety elongation predicted value and the insulation strength predicted value based on the hardness data of the cables;
Wherein, referring to fig. 3, the cable safety data analysis module obtains a safe elongation regression model, including the following steps:
b1, acquiring cables with different hardness data;
B2, respectively carrying out tensile test on the cables with the hardness data, gradually increasing the tensile length during the tensile test, and simultaneously carrying out ultrasonic flaw detection on the cables to judge whether mechanical damage exists on the surfaces of the cables;
If not, returning to the step B2, if so, acquiring the elongation of the cable at the moment to obtain the safe elongation of the cable corresponding to the hardness data, wherein when a tensile test is carried out, the cable of each hardness data can be subjected to multiple tensile tests to obtain multiple safe elongations corresponding to the hardness data, and finally, taking the average value of the multiple safe elongations as the safe elongation corresponding to the hardness data of the cable;
and B4, carrying out regression analysis based on the hardness data of the cable and the safe elongation corresponding to the hardness data to obtain a safe elongation regression model, and outputting a corresponding safe elongation predicted value based on the input hardness data.
Referring to fig. 4, the cable safety data analysis module obtains an insulation strength regression model, which includes the steps of:
C1, acquiring cables with different hardness data;
C2, respectively carrying out insulation strength test on the cables with the hardness data to obtain insulation strength data corresponding to the cables under the condition of the hardness data;
and C3, carrying out regression analysis based on the hardness data of the cable and the insulation strength data corresponding to the hardness data to obtain an insulation strength regression model, and outputting an insulation strength predicted value based on the input hardness data.
Referring to fig. 5-6, the cable safety data analysis module is further configured to determine whether cable deformation data is greater than a predicted value of a safe elongation corresponding to time, and if yes, perform early warning, including the following steps:
D1a, acquiring monitoring data of a ship body stress monitoring module, and acquiring historical ship body stress data of the ship body;
D2a, analyzing the historical ship stress data based on the box line diagram, screening out abnormal historical ship stress data, and obtaining a ship normal stress data interval of the ship;
D3a, analyzing a normal stress data interval of the ship body based on the ship body stress analysis model to obtain a normal deformation data interval of the ship body, extracting deformation data of the ship body position corresponding to the cable, and obtaining a normal deformation data interval of the cable;
d4a, inputting a current environmental data change curve to the cable hardness prediction model to obtain a current cable hardness data predicted value, and inputting the current cable hardness data predicted value to the safe elongation regression model to obtain a current safe elongation predicted value;
D5a, judging whether the current safe elongation predicted value is larger than a normal deformation data interval of the cable, and if not, sending out a first type of early warning;
D1b, acquiring real-time ship stress data;
d2b, analyzing real-time ship stress data based on a ship stress analysis model to obtain ship real-time deformation data, and extracting deformation data of a ship position corresponding to the cable to obtain the cable real-time deformation data;
And D3b, judging whether the real-time deformation data of the cable is larger than the current safe elongation, and if so, sending out a second type of early warning.
Referring to fig. 7, the cable safety data analysis module is further configured to determine whether the predicted value of the insulation strength is smaller than the set standard value of the insulation strength, and if yes, perform early warning, including the following steps:
E1, setting an insulation strength standard value, wherein the insulation strength standard value can be set according to industry standards, for example, the insulation strength standard of a ship cable generally requires that insulation is ensured when a test voltage applied within 1 minute is 2500 volts;
E2, inputting a current environmental data change curve to the cable hardness prediction model to obtain a current cable hardness data predicted value, and inputting the current cable hardness data predicted value to the insulation strength regression model to obtain a current insulation strength predicted value;
And E3, judging whether the current predicted value of the insulation strength is larger than or equal to the standard value of the insulation strength, and if not, sending out a second type of early warning.
While certain exemplary embodiments of the present invention have been described above by way of illustration only, it will be apparent to those of ordinary skill in the art that modifications may be made to the described embodiments in various different ways without departing from the spirit and scope of the invention. Accordingly, the drawings and description are to be regarded as illustrative in nature and not as restrictive of the scope of the invention, which is defined by the appended claims.
Claims (7)
1. The automatic detection system for online insulation monitoring of the marine cable is characterized by comprising a ship body model module, a cable model module, a ship body stress monitoring module, a ship body deformation analysis module, a cable deformation analysis module, an environment monitoring module, a cable hardness prediction module and a cable safety data analysis module;
the ship body model module is used for building a ship body assembly model according to the actual structure and the material of the ship body;
The cable model module is used for establishing a cable model and is assembled into the ship body assembly model according to the actual installation position of the cable;
the ship body stress monitoring module is used for monitoring external force suffered by the ship body to obtain ship body stress data;
The ship deformation monitoring module is used for monitoring deformation of the ship at the set position to obtain ship deformation data;
The ship body deformation analysis module is used for carrying out stress analysis based on the ship body assembly model to obtain a ship body stress analysis model, and is used for analyzing ship body deformation data of each ship body assembly model corresponding to each ship body stress data under each load;
the ship body deformation analysis module is also used for correcting the ship body stress model based on the ship body stress data and the ship body deformation data;
The cable deformation analysis module is used for extracting deformation data of the ship body position corresponding to the cable from the ship body deformation data obtained through analysis of the ship body deformation analysis module, and obtaining cable deformation data;
The environment monitoring module is used for acquiring environment data on the ship body, wherein the environment data comprise temperature and humidity;
The cable hardness prediction module is used for detecting the hardness of the cable to obtain cable hardness data, also used for obtaining environment data, drawing an environment data change curve, training a deep learning model based on the environment data change curve and the cable hardness data to obtain a cable hardness prediction model, and outputting a corresponding cable hardness data prediction value based on the input environment data change curve;
The cable safety data analysis module is also used for testing the safety elongation and the insulation strength corresponding to the cables when the hardness data of the cables are tested, wherein the safety elongation is the maximum elongation of the cables when the surfaces of the cables are not damaged, regression analysis is respectively carried out based on the hardness data, the safety elongation and the insulation strength of the cables, a safety elongation regression model and an insulation strength regression model are respectively obtained, and the safety elongation regression model and the insulation strength regression model are respectively used for calculating the corresponding safety elongation predicted value and the corresponding insulation strength predicted value based on the hardness data of the cables;
the cable safety data analysis module is also used for judging whether the cable deformation data are larger than a safety elongation predicted value of corresponding time, if so, carrying out early warning, and meanwhile, judging whether the insulation strength predicted value is smaller than a set insulation strength standard value, and if so, carrying out early warning.
2. The automatic detection system for monitoring on-line insulation of a marine cable according to claim 1, wherein the cable hardness prediction module is further configured to obtain detected cable hardness data, and perform training update on a cable hardness prediction model, and the automatic detection system comprises the following steps:
a1, performing hardness detection on the cable according to a set period, and storing the obtained cable hardness data in a detection sample set;
a2, judging whether the number of the cable hardness data in the detection sample set is larger than or equal to the set sample number;
and A3, if so, training and updating the cable hardness prediction model by using the detection sample set.
3. The automatic detection system for online insulation monitoring of a marine cable according to claim 1, wherein the cable safety data analysis module obtains a safe elongation regression model, and the automatic detection system comprises the following steps:
b1, acquiring cables with different hardness data;
b2, respectively carrying out tensile test on the cables with the hardness data, gradually increasing the tensile length during the tensile test, and carrying out ultrasonic flaw detection on the cables at the same time to judge whether mechanical damage exists on the surfaces of the cables;
b3, if not, returning to the step B2, and if so, acquiring the elongation of the cable at the moment to obtain the safe elongation of the cable corresponding to the hardness data;
and B4, carrying out regression analysis based on the hardness data of the cable and the safe elongation corresponding to the hardness data to obtain a safe elongation regression model, and outputting a corresponding safe elongation predicted value based on the input hardness data.
4. The automatic detection system for online insulation monitoring of a marine cable according to claim 1, wherein the cable safety data analysis module obtains an insulation strength regression model, and the automatic detection system comprises the following steps:
C1, acquiring cables with different hardness data;
C2, respectively carrying out insulation strength test on the cables with the hardness data to obtain insulation strength data corresponding to the cables under the condition of the hardness data;
and C3, carrying out regression analysis based on the hardness data of the cable and the insulation strength data corresponding to the hardness data to obtain an insulation strength regression model, and outputting an insulation strength predicted value based on the input hardness data.
5. The automatic detection system for monitoring on-line insulation of a marine cable according to claim 1, wherein the cable safety data analysis module is further configured to determine whether the cable deformation data is greater than a predicted value of a safe elongation corresponding to time, and if yes, perform early warning, and comprises the following steps:
acquiring monitoring data of a ship body stress monitoring module to obtain historical ship body stress data of the ship body;
Analyzing the historical ship body stress data based on a box line diagram, screening abnormal historical ship body stress data, and obtaining a ship body normal stress data interval of the ship body;
Analyzing the normal stress data interval of the ship body based on a ship body stress analysis model to obtain a normal deformation data interval of the ship body, extracting deformation data of the ship body position corresponding to the cable, and obtaining a normal deformation data interval of the cable;
Inputting a current environmental data change curve to a cable hardness prediction model to obtain a current cable hardness data prediction value, and inputting the current cable hardness data prediction value to a safe elongation regression model to obtain a current safe elongation prediction value;
And judging whether the current safe elongation predicted value is larger than a normal deformation data interval of the cable, and if not, sending out a first type of early warning.
6. The automatic detection system for monitoring on-line insulation of a marine cable according to claim 5, wherein the cable safety data analysis module is further configured to determine whether the cable deformation data is greater than a predicted value of a safe elongation corresponding to time, and if yes, perform early warning, and further comprises the following steps:
acquiring real-time ship stress data;
Analyzing the real-time ship stress data based on a ship stress analysis model to obtain ship real-time deformation data, and extracting deformation data of a ship position corresponding to the cable to obtain the cable real-time deformation data;
And judging whether the real-time deformation data of the cable is larger than the current safe elongation, and if so, sending out a second type of early warning.
7. The automatic detection system for monitoring on-line insulation of a marine cable according to claim 6, wherein the method is characterized in that whether the predicted value of the insulation strength is smaller than the set standard value of the insulation strength is judged, and if yes, early warning is carried out, and the method comprises the following steps:
setting an insulation strength standard value;
inputting a current environmental data change curve to a cable hardness prediction model to obtain a current cable hardness data prediction value, and inputting the current cable hardness data prediction value to an insulation strength regression model to obtain a current insulation strength prediction value;
Judging whether the current predicted value of the insulation strength is larger than or equal to the standard value of the insulation strength, and if not, sending out a second type early warning.
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| CN102353870A (en) * | 2011-06-30 | 2012-02-15 | 大连大工安道船舶技术有限责任公司 | An automatic detection and analysis system for online fault judgment of marine cables |
| CN110297147A (en) * | 2019-07-30 | 2019-10-01 | 南京荣港电气技术有限公司 | A kind of test method of cable for ship ageing properties |
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| FR2807842B1 (en) * | 2000-04-13 | 2002-06-14 | Cgg Marine | STEAMER POSITIONING SIMULATION AND NAVIGATION AID METHOD |
| EP2098877B1 (en) * | 2008-03-07 | 2018-06-06 | RTE Réseau de Transport d'Electricité | Method, device and installation for locating a fault in an electrical link |
| CN117782957B (en) * | 2024-02-28 | 2024-05-28 | 山东中船线缆股份有限公司 | Marine cable aging performance testing method and system |
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| CN102353870A (en) * | 2011-06-30 | 2012-02-15 | 大连大工安道船舶技术有限责任公司 | An automatic detection and analysis system for online fault judgment of marine cables |
| CN110297147A (en) * | 2019-07-30 | 2019-10-01 | 南京荣港电气技术有限公司 | A kind of test method of cable for ship ageing properties |
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