Composite material cable performance test and analysis system

Abstract

The invention discloses a composite material cable performance test and analysis system, and relates to the technical field of composite material cable performance test.The composite material cable performance test and analysis system comprises a data circulation module, an automatic process module and an intelligent terminal; the data circulation module comprises a data management and analysis module, a control module and a wireless transmission module, the automatic process module comprises a feeding module, a multi-dimensional detection module and a discharging module, and the detection module comprises an appearance detection module, an electrical detection module, a structure detection module and a strength detection module. According to the composite material cable performance test analysis system, through the data management and analysis module, large-batch detection data are classified and charted, and through collaborative analysis of the large-batch data, analysis of data such as defect trend and qualified rate change is realized, and the production yield is improved.

Description

Technical Field

[0001] This invention relates to the field of composite cable performance testing technology, and in particular to a composite cable performance testing and analysis system. Background Technology

[0002] The cable performance testing and analysis system is a professional system that integrates hardware equipment and software platform to comprehensively test the electrical, mechanical, and appearance performance of cables and automatically analyze data to generate reports. The core value of this system lies in replacing manual operation, achieving standardized testing processes, automated data acquisition, and accurate analysis results. It is widely used in cable production quality inspection, power operation and maintenance, and scientific research.

[0003] The existing technology has the following problems: Existing analysis systems analyze the test data of individual cables to obtain cable quality reports. However, during mass production, there are differences in the quality of cables within the same batch and between different batches. Analyzing the test results of individual cables makes it difficult to conduct collaborative analysis on a large number of different batches of cables. Summary of the Invention

[0004] The main objective of this invention is to provide a composite material cable performance testing and analysis system that can effectively solve the problem of difficulty in conducting collaborative analysis on a large number of different batches of cables.

[0005] To solve the above-mentioned technical problems, the technical solution adopted by the present invention is as follows: A composite material cable performance testing and analysis system includes a data transfer module, an automated process module, and an intelligent terminal. The data transfer module includes a data management and analysis module, a control module, and a wireless transmission module. The automated process module includes a feeding module, a multi-dimensional detection module, and a unloading module. The detection module includes an appearance detection module, an electrical detection module, a structural detection module, and a strength detection module.

[0006] Preferably, the control module is used to control the automated process module and store the detection results in the data management and analysis module. The control module transmits the detection results to the smart terminal via the wireless transmission module. Control module: It adopts a PLC (Programmable Logic Controller) + Industrial Ethernet architecture, supports multi-module timing linkage (such as triggering appearance inspection after material loading is completed, and starting electrical inspection after the appearance is qualified), and has a built-in fault self-diagnosis function (such as automatically pausing material loading and pushing maintenance reminder when camera abnormality is detected). Wireless transmission module: Based on 5G / Wi-Fi 6 protocol, it achieves low-latency data transmission (transmission rate ≥100Mbps, latency ≤20ms), supports encrypted transmission (using AES-256 encryption algorithm), and ensures the security of detection data (especially sensitive information such as electrical parameters and defect images); it also has an offline caching function, automatically storing data when the network is interrupted and automatically retransmitting it after the connection is restored.

[0007] Preferably, the appearance inspection module uses an industrial camera in conjunction with a ring light source / strip light source to acquire 360° surface images of the cable, identifies defects through deep learning algorithms (such as YOLOv8, Faster R-CNN), and outputs the location, type and size of the defects; In addition to industrial cameras (resolution ≥ 5 million pixels) and ring / strip light sources, a stain cleaning mechanism is added (the surface dust is removed by a lint-free cloth and compressed air before inspection) to avoid misjudgment; the deep learning algorithm supports self-updating of the defect database (after the manager marks new defect types, the system automatically optimizes the model, with an accuracy rate of ≥ 99%). The identifiable defect types include: scratches (depth ≥ 0.1 mm), bulges (diameter ≥ 0.5 mm), color difference (ΔE ≥ 3), and damage (area ≥ 1 mm²).

[0008] Preferably, the electrical detection module uses a multi-channel resistance tester to detect the resistance value between core wires to determine continuity, uses an insulation resistance meter (megohmmeter) to apply a DC high voltage to measure the resistance between the core wire and the shield / ground, and uses a withstand voltage tester to apply a specified voltage to monitor the leakage current and determine whether the insulation layer has broken down. The multi-channel resistance tester supports simultaneous testing of 8 channels (test range 0.01Ω-10MΩ, accuracy ±0.1%), quickly determining core wire short circuits / open circuits; the insulation resistance meter can apply adjustable DC high voltage of 500V / 1000V / 2500V (test range 1MΩ-10TΩ) to meet the requirements of cables with different insulation levels; the withstand voltage tester adopts AC / DC dual mode (voltage range 0-10kV), the leakage current threshold can be set (0.1mA-10mA), and automatically cuts off the voltage and records the breakdown voltage value when breakdown occurs.

[0009] Preferably, the structure detection module measures the outer diameter by laser scanning and outputs dimensional data in real time, and measures the cable length in conjunction with the conveying mechanism by recognizing the number and arrangement of the core wires through image recognition. Laser scanning employs triangulation (scanning frequency ≥10kHz, accuracy ±0.01mm) to generate outer diameter curves in real time (sampling interval 0.1mm) and automatically identifies ellipticity (marked when deviation ≥5%). Core wire arrangement identification is achieved through a high-resolution line scan camera (with backlight source), which can identify circular, fan-shaped, twisted, and other arrangement methods, and supports counting and sorting verification of up to 12 core cables. Length measurement is calculated through encoder linkage with conveying speed (accuracy ±0.1%), and supports fixed-length cutting triggering (such as automatic marking of 100m / roll as required by the customer).

[0010] Preferably, the strength testing module tests the tensile strength of the cable using a tensile device and the torsional strength of the cable using a torsion device. This testing module is a sampling module, which selects only a portion of the cables for testing. The tensile testing equipment is driven by a servo motor (maximum tensile force 50kN, accuracy ±1%), and can test fracture strength and elongation (test range 0-500%), and record stress-strain curves; the torsion testing equipment supports bidirectional torsion (angle range 0-3600°, speed 0-60r / min), and tests the number of torsional fractures or the change in insulation / conductivity performance after a specified torsion; the sampling logic can be customized (e.g., 5% of each batch, with no less than 3 pieces), and the sampling results are linked to the calculation of the overall batch pass rate.

[0011] Preferably, the data management and analysis module stores, analyzes, and provides early warnings for the test results. Data storage: records the test ID, time, batch, defect details, performance parameters, etc. of each cable, and supports data export (Excel / PDF). Statistical analysis: generates pass rate trend charts, defect type distribution charts, and equipment utilization reports, and supports filtering by batch / date. Early warning function: when the occurrence rate of a certain type of defect exceeds the threshold, the system automatically pushes early warning information to the management personnel. Data storage: A distributed database is used (supporting data storage of millions of cables). In addition to basic information, the system also records the testing equipment number (for easy tracking of equipment errors) and environmental parameters (temperature 20±5℃, humidity 40-60%, which affect the accuracy of electrical performance testing); it also supports historical data backtracking (such as querying specific defect images of a batch of cables from 3 months ago). Statistical Analysis: Built-in visualization reporting tools can generate multi-dimensional charts (such as daily pass rate line charts, defect type percentage pie charts, and bar charts of time consumption for each inspection module), support data drill-down (such as clicking "Appearance Defects" to drill down to trend analysis of specific defect types); provide SPC (Statistical Process Control) analysis, and identify abnormal fluctuations in inspection data through control charts (such as XR charts) (such as 5 consecutive data points biased towards the upper tolerance limit); Early warning function: Set multiple early warning thresholds (e.g., a yellow warning for a defect occurrence rate exceeding 5%, and a red warning for exceeding 10%). Early warning information is delivered through multiple channels such as smart terminal APP push, SMS, and device audible and visual alarms; Supports early warning source tracing (clicking on the early warning information allows direct viewing of the relevant batch's test data, equipment status, and operator records), and assists in quickly locating problems (e.g., a recent decrease in the defect recognition rate of a certain device may be due to lens contamination).

[0012] Compared with the prior art, the present invention has the following beneficial effects: 1. This invention provides a composite material cable performance testing and analysis system. Through a data management and analysis module, it classifies and graphs large volumes of test data. By collaboratively analyzing this large amount of data, it enables the analysis of defect trends, yield rate changes, and other data, thereby improving the production yield rate. 2. This invention provides a composite material cable performance testing and analysis system that uses stimulation learning and deep learning to improve detection accuracy. The accuracy of defect identification and parameter measurement far exceeds that of manual inspection, meeting high industry standards.

[0013] 3. This invention provides a composite material cable performance testing and analysis system, covering cable appearance (such as damage, bulges, scratches), electrical performance (such as conductivity, insulation resistance, withstand voltage), structural parameters (such as diameter, length, number of cores), and the cable's own strength, etc., reducing manual intervention, improving testing efficiency, reducing human error, and uploading test results and data synchronously without manual recording. Managers can view the testing progress in real time and avoid production delays caused by data lag. Attached Figure Description

[0014] Figure 1 This is a schematic diagram of the system of the present invention; Figure 2 This is a schematic diagram of the system flow of the present invention. Detailed Implementation

[0015] To make the technical means, creative features, objectives and effects of this invention easier to understand, the invention will be further described below in conjunction with specific embodiments.

[0016] like Figure 1 , Figure 2 As shown, a composite material cable performance testing and analysis system includes a data transfer module, an automated process module, and an intelligent terminal. The data transfer module includes a data management and analysis module, a control module, and a wireless transmission module. The automated process module includes a feeding module, a multi-dimensional detection module, and a unloading module. The detection module includes an appearance detection module, an electrical detection module, a structural detection module, and a strength detection module.

[0017] Preferably, the control module is used to control the automated process module and store the detection results in the data management and analysis module. The control module transmits the detection results to the smart terminal via the wireless transmission module. Control module: It adopts a PLC (Programmable Logic Controller) + Industrial Ethernet architecture, supports multi-module timing linkage (such as triggering appearance inspection after material loading is completed, and starting electrical inspection after the appearance is qualified), and has a built-in fault self-diagnosis function (such as automatically pausing material loading and pushing maintenance reminder when camera abnormality is detected). Wireless transmission module: Based on 5G / Wi-Fi 6 protocol, it achieves low-latency data transmission (transmission rate ≥100Mbps, latency ≤20ms), supports encrypted transmission (using AES-256 encryption algorithm), and ensures the security of detection data (especially sensitive information such as electrical parameters and defect images); it also has an offline caching function, automatically storing data when the network is interrupted and automatically retransmitting it after the connection is restored.

[0018] Preferably, the appearance inspection module uses an industrial camera in conjunction with a ring light source / strip light source to acquire 360° surface images of the cable, identifies defects through deep learning algorithms (such as YOLOv8, Faster R-CNN), and outputs the location, type and size of the defects; In addition to industrial cameras (resolution ≥ 5 million pixels) and ring / strip light sources, a stain cleaning mechanism is added (the surface dust is removed by a lint-free cloth and compressed air before inspection) to avoid misjudgment; the deep learning algorithm supports self-updating of the defect database (after the manager marks new defect types, the system automatically optimizes the model, with an accuracy rate of ≥ 99%). The identifiable defect types include: scratches (depth ≥ 0.1 mm), bulges (diameter ≥ 0.5 mm), color difference (ΔE ≥ 3), and damage (area ≥ 1 mm²).

[0019] Preferably, the electrical detection module uses a multi-channel resistance tester to detect the resistance value between core wires to determine continuity, uses an insulation resistance meter (megohmmeter) to apply a DC high voltage to measure the resistance between the core wire and the shield / ground, and uses a withstand voltage tester to apply a specified voltage to monitor the leakage current and determine whether the insulation layer has broken down. The multi-channel resistance tester supports simultaneous testing of 8 channels (test range 0.01Ω-10MΩ, accuracy ±0.1%), quickly determining core wire short circuits / open circuits; the insulation resistance meter can apply adjustable DC high voltage of 500V / 1000V / 2500V (test range 1MΩ-10TΩ) to meet the requirements of cables with different insulation levels; the withstand voltage tester adopts AC / DC dual mode (voltage range 0-10kV), the leakage current threshold can be set (0.1mA-10mA), and automatically cuts off the voltage and records the breakdown voltage value when breakdown occurs.

[0020] Preferably, the structure detection module measures the outer diameter by laser scanning and outputs dimensional data in real time, and measures the cable length in conjunction with the conveying mechanism by recognizing the number and arrangement of the core wires through image recognition. Laser scanning employs triangulation (scanning frequency ≥10kHz, accuracy ±0.01mm) to generate outer diameter curves in real time (sampling interval 0.1mm) and automatically identifies ellipticity (marked when deviation ≥5%). Core wire arrangement identification is achieved through a high-resolution line scan camera (with backlight source), which can identify circular, fan-shaped, twisted, and other arrangement methods, and supports counting and sorting verification of up to 12 core cables. Length measurement is calculated through encoder linkage with conveying speed (accuracy ±0.1%), and supports fixed-length cutting triggering (such as automatic marking of 100m / roll as required by the customer).

[0021] Preferably, the strength testing module tests the tensile strength of the cable using a tensile device and the torsional strength of the cable using a torsion device. This testing module is a sampling module, which selects only a portion of the cables for testing. The tensile testing equipment is driven by a servo motor (maximum tensile force 50kN, accuracy ±1%), and can test fracture strength and elongation (test range 0-500%), and record stress-strain curves; the torsion testing equipment supports bidirectional torsion (angle range 0-3600°, speed 0-60r / min), and tests the number of torsional fractures or the change in insulation / conductivity performance after a specified torsion; the sampling logic can be customized (e.g., 5% of each batch, with no less than 3 pieces), and the sampling results are linked to the calculation of the overall batch pass rate.

[0022] Preferably, the data management and analysis module stores, analyzes, and provides early warnings for the test results. Data storage: records the test ID, time, batch, defect details, performance parameters, etc. of each cable, and supports data export (Excel / PDF). Statistical analysis: generates pass rate trend charts, defect type distribution charts, and equipment utilization reports, and supports filtering by batch / date. Early warning function: when the occurrence rate of a certain type of defect exceeds the threshold, the system automatically pushes early warning information to the management personnel. Data storage: A distributed database is used (supporting data storage of millions of cables). In addition to basic information, the system also records the testing equipment number (for easy tracking of equipment errors) and environmental parameters (temperature 20±5℃, humidity 40-60%, which affect the accuracy of electrical performance testing); it also supports historical data backtracking (such as querying specific defect images of a batch of cables from 3 months ago). Statistical Analysis: Built-in visualization reporting tools can generate multi-dimensional charts (such as daily pass rate line charts, defect type percentage pie charts, and bar charts of time consumption for each inspection module), support data drill-down (such as clicking "Appearance Defects" to drill down to trend analysis of specific defect types); provide SPC (Statistical Process Control) analysis, and identify abnormal fluctuations in inspection data through control charts (such as XR charts) (such as 5 consecutive data points biased towards the upper tolerance limit); Early warning function: Set multiple early warning thresholds (e.g., a yellow warning for a defect occurrence rate exceeding 5%, and a red warning for exceeding 10%). Early warning information is delivered through multiple channels such as smart terminal APP push, SMS, and device audible and visual alarms; Supports early warning source tracing (clicking on the early warning information allows direct viewing of the relevant batch's test data, equipment status, and operator records), and assists in quickly locating problems (e.g., a recent decrease in the defect recognition rate of a certain device may be due to lens contamination).

[0023] The working principle of this invention is as follows: First, through the data management and analysis module, a large amount of inspection data is classified and plotted. Through collaborative analysis of a large amount of data, the analysis of defect trends, pass rate changes, and other data is realized, thereby improving the production yield. Stimulated learning and deep learning are used to improve the detection accuracy. The accuracy of defect identification and parameter measurement far exceeds that of manual inspection, meeting high industry standards. Finally, it covers cable appearance (such as damage, bulges, scratches), electrical performance (such as conductivity, insulation resistance, withstand voltage), structural parameters (such as diameter, length, number of core wires), and the cable's own strength, reducing manual intervention, improving inspection efficiency, and reducing human error. The inspection results and data are uploaded synchronously, eliminating the need for manual recording. Managers can view the inspection progress in real time, avoiding production delays caused by data lag.

[0024] The foregoing has shown and described the basic principles, main features, and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited to the above embodiments. The embodiments and descriptions in the specification are merely illustrative of the principles of the invention. Various changes and modifications can be made to the invention without departing from its spirit and scope, and all such changes and modifications fall within the scope of the present invention as claimed. The scope of protection of this invention is defined by the appended claims and their equivalents.

Claims

1. A composite material cable performance testing and analysis system, characterized in that: The composite material cable performance testing and analysis system includes a data transfer module, an automated process module, and an intelligent terminal. The data transfer module includes a data management and analysis module, a control module, and a wireless transmission module. The automated process module includes a feeding module, a multi-dimensional detection module, and a unloading module. The detection module includes an appearance detection module, an electrical detection module, a structural detection module, and a strength detection module.

2. The composite material cable performance testing and analysis system according to claim 1, characterized in that: The control module is used to control the automated process module and store the detection results in the data management and analysis module. The control module transmits the detection results to the smart terminal through the wireless transmission module. Control module: It adopts a PLC (Programmable Logic Controller) + Industrial Ethernet architecture, supports multi-module timing linkage (such as triggering appearance inspection after material loading is completed, and starting electrical inspection after the appearance is qualified), and has a built-in fault self-diagnosis function (such as automatically pausing material loading and pushing maintenance reminder when camera abnormality is detected). Wireless transmission module: Based on 5G / Wi-Fi 6 protocol, it achieves low-latency data transmission (transmission rate ≥100Mbps, latency ≤20ms), supports encrypted transmission (using AES-256 encryption algorithm), and ensures the security of detection data (especially sensitive information such as electrical parameters and defect images); it also has an offline caching function, automatically storing data when the network is interrupted and automatically retransmitting it after the connection is restored.

3. The composite material cable performance testing and analysis system according to claim 1, characterized in that: The appearance inspection module uses an industrial camera with a ring light source / strip light source to collect 360° surface images of the cable, and identifies defects through deep learning algorithms (such as YOLOv8, Faster R-CNN), and outputs the location, type and size of the defects. In addition to an industrial camera (resolution ≥ 5 million pixels) and a ring / strip light source, a stain cleaning mechanism is added (to remove surface dust with a lint-free cloth and compressed air before inspection) to avoid misjudgment; The deep learning algorithm supports self-updating of the defect database (after administrators label new defect types, the system automatically optimizes the model, with an accuracy rate of ≥99%). The identifiable defect types include: scratches (depth ≥0.1mm), bulges (diameter ≥0.5mm), color difference (ΔE ≥3), and damage (area ≥1mm²).

4. The composite material cable performance testing and analysis system according to claim 1, characterized in that: The electrical testing module uses a multi-channel resistance tester to detect the resistance value between core wires to determine continuity, uses an insulation resistance meter (megohmmeter) to apply a DC high voltage to measure the resistance between the core wire and the shield / ground, and uses a withstand voltage tester to apply a specified voltage to monitor leakage current and determine whether the insulation layer has broken down. The multi-channel resistance tester supports simultaneous testing of 8 channels (test range 0.01Ω-10MΩ, accuracy ±0.1%), quickly determining core wire short circuits / open circuits; the insulation resistance meter can apply adjustable DC high voltage of 500V / 1000V / 2500V (test range 1MΩ-10TΩ) to meet the requirements of cables with different insulation levels; the withstand voltage tester adopts AC / DC dual mode (voltage range 0-10kV), the leakage current threshold can be set (0.1mA-10mA), and automatically cuts off the voltage and records the breakdown voltage value when breakdown occurs.

5. The composite material cable performance testing and analysis system according to claim 1, characterized in that: The structural detection module measures the outer diameter using laser scanning and outputs dimensional data in real time. It also uses image recognition to determine the number and arrangement of core wires and, in conjunction with the conveying mechanism, measures the cable length. Laser scanning employs triangulation (scanning frequency ≥10kHz, accuracy ±0.01mm) to generate outer diameter curves in real time (sampling interval 0.1mm) and automatically identifies ellipticity (marked when deviation ≥5%). Core wire arrangement identification is achieved through a high-resolution line scan camera (with backlight source), which can identify circular, fan-shaped, twisted, and other arrangement methods, and supports counting and sorting verification of up to 12 core cables. Length measurement is calculated through encoder linkage with conveying speed (accuracy ±0.1%), and supports fixed-length cutting triggering (such as automatic marking of 100m / roll as required by the customer).

6. The composite material cable performance testing and analysis system according to claim 1, characterized in that: The strength testing module tests the tensile strength of the cable using a tensile device and the torsional strength of the cable using a torsion device. This testing module is a sampling module, which selects only a portion of the cables for testing. The tensile testing equipment is driven by a servo motor (maximum tensile force 50kN, accuracy ±1%), and can test fracture strength and elongation (test range 0-500%), and record stress-strain curves; the torsion testing equipment supports bidirectional torsion (angle range 0-3600°, speed 0-60r / min), and tests the number of torsional fractures or the change in insulation / conductivity performance after a specified torsion; the sampling logic can be customized (e.g., 5% of each batch, with no less than 3 pieces), and the sampling results are linked to the calculation of the overall batch pass rate.

7. The composite material cable performance testing and analysis system according to claim 1, characterized in that: The data management and analysis module stores, analyzes, and issues early warnings for the test results. Data storage: records the test ID, time, batch, defect details, performance parameters, etc. of each cable, and supports data export (Excel / PDF). Statistical analysis: generates pass rate trend charts, defect type distribution charts, and equipment utilization reports, and supports filtering by batch / date. Early warning function: when the occurrence rate of a certain type of defect exceeds the threshold, the system automatically pushes early warning information to the management personnel. Data storage: A distributed database is used (supporting data storage of millions of cables). In addition to basic information, the system also records the testing equipment number (for easy tracking of equipment errors) and environmental parameters (temperature 20±5℃, humidity 40-60%, which affect the accuracy of electrical performance testing); it also supports historical data backtracking (such as querying specific defect images of a batch of cables from 3 months ago). Statistical Analysis: Built-in visualization reporting tools can generate multi-dimensional charts (such as daily pass rate line charts, defect type percentage pie charts, and bar charts of time consumption for each inspection module), support data drill-down (such as clicking "Appearance Defects" to drill down to trend analysis of specific defect types); provide SPC (Statistical Process Control) analysis, and identify abnormal fluctuations in inspection data through control charts (such as XR charts) (such as 5 consecutive data points biased towards the upper tolerance limit); Early warning function: Set multiple early warning thresholds (e.g., a yellow warning if the defect occurrence rate exceeds 5%, and a red warning if it exceeds 10%). Early warning information is delivered through multiple channels such as smart terminal APP push, SMS, and device sound and light alarms. Supports early warning and source tracing (clicking on the early warning information allows direct viewing of the relevant batch's test data, equipment status, and operator records), assisting in quickly locating problems (e.g., a recent decrease in the defect recognition rate of a certain device may be due to lens contamination).

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