An online monitoring system for silver composite contact forming equipment

Abstract

The present invention relates to the technical field of silver composite contact production monitoring, and in particular to an online monitoring system for silver composite contact molding equipment. The present invention collects and analyzes high-definition images of raw material feed ports and discharge ports and X-ray images of discharge ports through a visual detection module, and can timely discover material problems and surface defects of raw materials, as well as appearance defects and internal defects of silver composite contacts; physical parameter detection modules and electrical parameter detection modules monitor key parameters of molding equipment in real time to ensure that the equipment operates in the best state, thereby improving the quality stability of the product, and multi-dimensional detection ensures stable product quality, covering the appearance and internal defect detection of raw materials and finished products, as well as key parameter monitoring of equipment, which can timely adjust the process to improve quality, and real-time monitoring and early warning reduce the expansion of equipment failures and downtime, and optimize the production process through parameter analysis, thereby improving production efficiency, reducing scrap rate and reducing production costs.

Description

Technical Field

[0001] The invention relates to the technical field of silver composite contact production monitoring, in particular to an online monitoring system for silver composite contact molding equipment. Background Art

[0002] In modern industrial production, silver composite contacts are a key electronic component and are widely used in various electrical equipment. The stability and reliability of their quality directly affect the performance and service life of electrical equipment.

[0003] In the production process, traditional silver composite contact forming equipment mainly relies on manual inspection and regular sampling inspection to monitor product quality and equipment operation status. This method has the following obvious shortcomings:

[0004] 1. Untimely testing

[0005] Manual inspections and regular sampling tests cannot monitor every link in the production process in real time, which can easily lead to problems not being discovered in time. For example, if material problems or surface defects of raw materials cannot be discovered in time during the raw material feeding stage, the subsequent molding process may be affected, resulting in a decline in product quality. During the operation of the molding equipment, if abnormal changes in parameters such as pressure, temperature, and displacement cannot be monitored in time, problems such as product size deviation and internal defects may occur, and may even cause equipment failure.

[0006] 2. Limited detection accuracy

[0007] Manual inspection and sampling inspection are limited by human senses and experience, and the detection accuracy is relatively low. Some minor defects, such as pores and slag inclusions inside silver composite contacts, are often difficult to detect through manual inspection. At the same time, sampling inspection can only represent the quality of a part of the products, and cannot fully reflect the quality level of the entire production batch.

[0008] Therefore, in view of the above problems, an online monitoring system for silver composite contact forming equipment is proposed. Summary of the invention

[0009] The object of the present invention is to provide an online monitoring system for a silver composite contact forming device to solve the problems raised in the above background technology.

[0010] To achieve the above object, the present invention provides the following technical solutions:

[0011] An online monitoring system for a silver composite contact forming device, comprising:

[0012] Data acquisition end: The data acquisition end includes a visual inspection module, a physical parameter inspection module and an electrical parameter inspection module; the visual inspection module collects high-definition images of the raw material inlet and outlet of the molding equipment and X-ray images of the outlet; the physical parameter inspection module collects the pressure, temperature and displacement parameters of the molding equipment during spot welding; the electrical parameter inspection module monitors the current and voltage data of the molding equipment in real time during operation;

[0013] Database; the database includes a data storage unit and a data management unit; the data storage unit stores the specification parameters of silver composite contacts of different specifications; the data management unit is responsible for the daily management, maintenance, backup, and data entry, update, query and cleaning of the database;

[0014] Supervision platform: The supervision platform includes data sorting module, data analysis module and abnormal data interaction module;

[0015] The data sorting module establishes a data management time axis, receives the detection data of the visual detection module, the physical parameter detection module and the electrical parameter detection module in real time, and synchronizes it to each time node of the data management time axis;

[0016] The data analysis module uses the visual inspection analysis model, physical parameter analysis model and electrical parameter analysis model to analyze the quality, processing parameters and equipment operation of the silver composite contact, and generates an analysis report based on the analysis results;

[0017] The abnormal data interaction module receives the analysis report, extracts the abnormal data in the analysis report, traces the abnormality based on the abnormal data, and synchronizes the abnormality tracing information to the maintenance end.

[0018] Preferably, the visual inspection module includes high-definition cameras arranged at the raw material feed port and discharge port, and X-ray flaw detection equipment arranged at the discharge port; the physical parameter detection module includes a pressure sensor installed on the pressure applying device, a temperature sensor distributed in the heating area, and a displacement sensor installed on the moving parts of the molding equipment; the electrical parameter detection module includes a current sensor and a voltage sensor connected to the electrical system of the molding equipment.

[0019] Preferably, the specification parameters of the silver composite contact include the size of the silver composite contact, the thickness of the silver layer, the uniformity of the silver layer, the type of base material, the size of the base material, the molding pressure range, the molding temperature range, the displacement accuracy requirements, the internal defect standards and the surface quality requirements.

[0020] Preferably, when performing data sorting, the data sorting module establishes a unified time axis, divides the time axis into set time intervals, and continuously receives detection data uploaded by the visual inspection module, the physical parameter detection module, and the electrical parameter detection module, wherein the detection data include high-definition images at the raw material feed port and the X-ray image at the discharge port, the pressure, temperature and displacement parameters of the molding equipment during spot welding, and the current and voltage data of the molding equipment during operation; the high-definition images at the raw material feed port and the X-ray image at the discharge port are serially numbered in the order of shooting, and synchronized to the time node corresponding to the time axis; the pressure, temperature and displacement parameters of the molding equipment during spot welding are extracted with the processing of a silver composite contact as a cycle, and the pressure curve, temperature curve and displacement distance curve corresponding to the time axis are established; and the current and voltage curves corresponding to the time axis are established.

[0021] Preferably, when the visual inspection and analysis model analyzes the high-definition images at the raw material feed port and the X-ray images at the discharge port, color features are extracted from the high-definition pictures of the raw material feed port to determine the material quality and surface defects of the raw materials; the high-definition images at the discharge port are processed, and the shape features, size features, and surface texture features in the image are extracted using an image analysis algorithm, and the shape features, size features, and surface texture features are quantified to obtain the appearance inspection data of the silver composite contacts; the X-ray images at the discharge port are analyzed to extract the features of pores, slag inclusions, and cracks, and the features of pores, slag inclusions, and cracks are quantified to obtain the internal inspection data of the silver composite contacts, and the appearance inspection data and the internal inspection data of the silver composite contacts are compared with the specification parameters of the silver composite contacts stored in the database to generate visual analysis data.

[0022] Preferably, the physical parameter analysis model receives the pressure curve, temperature curve and displacement distance curve when performing physical parameter analysis. When the values ​​in the pressure curve, temperature curve and displacement distance curve exceed the molding pressure range, molding temperature range and displacement accuracy requirements, the pressure curve, temperature curve and displacement distance curve segments corresponding to the time node are marked as abnormal, and data statistics are performed on the marked pressure curve, temperature curve and displacement distance curve to generate physical parameter analysis data; and the time series analysis method is used to analyze the changing trends of the pressure curve, temperature curve and displacement distance curve.

[0023] Preferably, when performing electrical parameter analysis, the electrical parameter analysis model receives current and voltage curves, uses an electrical parameter analysis algorithm to diagnose electrical faults that appear in the current and voltage curves, determines the type of fault, marks the abnormality, and generates electrical parameter analysis data.

[0024] Preferably, after generating the visual analysis data, physical parameter analysis data and electrical parameter analysis data, the data analysis module performs multi-parameter management analysis on the visual analysis data, physical parameter analysis data and electrical parameter analysis data to determine the impact of raw material material and surface defects, physical parameter analysis data and electrical parameter analysis data abnormalities on the visual analysis data.

[0025] Preferably, after receiving the analysis report, the abnormal data interaction module retrieves the abnormal data in the visual analysis data, the physical parameter analysis data and the electrical parameter analysis data, and determines the root cause of the abnormality based on the influence of the raw material material and surface defects, the physical parameter analysis data and the electrical parameter analysis data when abnormalities occur on the visual analysis data, performs a source tracing analysis on the root cause of the abnormality, determines the cause and location of the abnormality, and generates abnormal traceability information.

[0026] Compared with the prior art, the invention has the following beneficial effects: multi-dimensional detection ensures stable product quality, covers the appearance and internal defect detection of raw materials and finished products, and the monitoring of key equipment parameters, can timely adjust the process to improve quality, real-time monitoring and early warning reduce the expansion of equipment failures and downtime, optimize the production process through parameter analysis, thereby improving production efficiency, reducing scrap rate and reducing production costs, while extending equipment life and reducing update and maintenance costs. Specifically:

[0027] The visual inspection module collects and analyzes high-definition images of the raw material inlet and outlet, as well as X-ray images of the outlet, to promptly detect material problems and surface defects of the raw materials, as well as appearance defects and internal defects of the silver composite contacts. At the same time, the physical parameter detection module and the electrical parameter detection module monitor the key parameters of the molding equipment in real time to ensure that the equipment operates in the best condition, thereby improving the quality stability of the product;

[0028] The data analysis module performs multi-parameter management and analysis on visual analysis data, physical parameter analysis data, and electrical parameter analysis data to determine the impact of raw material material and surface defects, physical parameters, and electrical parameters abnormalities on visual analysis data.

[0029] The online monitoring system can monitor the operating status of the molding equipment in real time. Once an abnormal situation occurs, it can issue an early warning signal in time to remind the operator to take corresponding measures;

[0030] By analyzing the physical and electrical parameters, we can understand the operating rules and performance characteristics of the equipment, and provide a basis for optimizing the production process. BRIEF DESCRIPTION OF THE DRAWINGS

[0031] Figure 1 The present invention is a schematic diagram of the overall structure of an online monitoring system for silver composite contact forming equipment. DETAILED DESCRIPTION

[0032] In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0033] In order to better understand the above technical solution, the above technical solution will be described in detail below in conjunction with the accompanying drawings and specific implementation methods.

[0034] Example

[0035] See also Figure 1 , this embodiment provides a technical solution:

[0036] An online monitoring system for silver composite contact forming equipment comprises a data collection terminal, a database and a supervision platform. The data collection terminal and the database are respectively interoperable with the supervision platform.

[0037] In this embodiment, the data acquisition end includes a visual detection module, a physical parameter detection module and an electrical parameter detection module;

[0038] The visual inspection module collects high-definition images of the raw material inlet and outlet of the molding equipment and X-ray images of the outlet;

[0039] Furthermore, the visual inspection module includes a high-definition camera arranged at the raw material feed port and the discharge port, and an X-ray flaw detection device arranged at the discharge port;

[0040] The physical parameter detection module collects the pressure, temperature and displacement parameters of the molding equipment during spot welding;

[0041] Further, the physical parameter detection module includes a pressure sensor installed on the pressure applying device, a temperature sensor distributed in the heating area, and a displacement sensor installed on the moving part of the molding equipment;

[0042] The electrical parameter detection module monitors the current and voltage data of the molding equipment in real time during operation;

[0043] Further, the electrical parameter detection module includes a current sensor and a voltage sensor connected to the electrical system of the molding device;

[0044] Among them, high-definition cameras, X-ray flaw detection equipment, pressure sensors, temperature sensors, displacement sensors, current sensors and voltage sensors are all marked with positions.

[0045] In this embodiment, the data collection end includes a data storage unit and a data management unit;

[0046] The data storage unit stores specification parameters of silver composite contacts of different specifications, and the specification parameters of the silver composite contacts include silver composite contact size, silver layer thickness, silver layer uniformity, base material type, base material size, molding pressure range, molding temperature range, displacement accuracy requirements, internal defect standards and surface quality requirements;

[0047] The data management unit is responsible for the daily management, maintenance, backup, data entry, update, query and cleanup of the database, including:

[0048] Regularly check the operating status of the database to ensure the stability and reliability of the database system, optimize the database, and improve the efficiency of data storage and retrieval;

[0049] Formulate data backup strategies, regularly back up data in the database, and establish backup storage management mechanisms to ensure the security and accessibility of backup data;

[0050] Implement data access control strategies, limit access rights to the database, conduct regular data security audits, check database security vulnerabilities and potential risks, and take timely measures to repair and prevent them;

[0051] Responsible for entering the specification parameters of silver composite contacts of different specifications into the database, and updating the data in the database in a timely manner as the production process improves and product specifications are adjusted;

[0052] Regularly clean up the data in the database, delete expired or useless data, establish rules and processes for data cleaning, and ensure the security and rationality of data cleaning.

[0053] In this embodiment, the supervision platform includes a data sorting module, a data analysis module and an abnormal data interaction module;

[0054] The data sorting module establishes a data management time axis, receives the detection data of the visual detection module, the physical parameter detection module and the electrical parameter detection module in real time, and synchronizes it to each time node of the data management time axis, specifically including the following steps;

[0055] 1. Establish a unified timeline

[0056] Determine the time reference point:

[0057] Select an appropriate starting time, such as system startup time, first device operation time, or specific clock time, as the reference point of the time axis;

[0058] Ensure that all data acquisition modules and analysis modules use this time reference point as a reference so that the data can be accurately synchronized to the time axis;

[0059] Divide time intervals:

[0060] Divide the time axis into set time intervals, such as hours, shifts, days, etc. This makes it easier to count and analyze data from different time periods;

[0061] Each time interval has a unique identifier so that data for a specific time period can be quickly located during data collation and analysis;

[0062] 2. Receive test data

[0063] Continuous monitoring data collection end:

[0064] The data sorting module continuously receives the detection data uploaded by the visual detection module, the physical parameter detection module and the electrical parameter detection module;

[0065] Ensure the stability and timeliness of data reception to avoid data loss or delay;

[0066] Data classification and storage:

[0067] Classify the received data and organize them according to different data types and sources; for example, classify the visual inspection data into categories such as raw material inlet HD images, outlet HD images, and outlet X-ray images; classify the physical parameter data into categories such as pressure, temperature, and displacement parameters; and classify the electrical parameter data into categories such as current and voltage data;

[0068] Store the classified data in a temporary buffer or database pending further processing;

[0069] 3. Image Data Processing

[0070] Serial number mark:

[0071] For the high-definition images at the raw material inlet and outlet and the X-ray images at the outlet, serial numbers are marked in the order in which they were taken; this makes it easy to quickly locate specific image data on the time axis;

[0072] The serial number tag can use a combination of numbers, letters or other symbols to ensure that each image has a unique identification;

[0073] Sync to timeline:

[0074] Synchronize the image data with serial number marks to the time node corresponding to the time axis; accurately map the image to the corresponding position on the time axis according to the shooting time of the image;

[0075] Ensure the synchronization of image data and other detection data on the time axis to facilitate multi-parameter analysis and anomaly detection;

[0076] 4. Physical parameter data processing

[0077] Data extraction:

[0078] Taking the processing of a silver composite contact as a cycle, the pressure, temperature and displacement parameters of the molding equipment during spot welding are extracted; in this way, the detailed physical parameter changes during the processing of each contact can be obtained;

[0079] Curve generation:

[0080] According to the extracted pressure, temperature and displacement parameters, the pressure curve, temperature curve and displacement distance curve corresponding to the time axis are established; these curves can intuitively show the changing trend of physical parameters on the time axis;

[0081] 5. Electrical parameter data processing

[0082] Curve generation:

[0083] Establish current and voltage curves corresponding to the time axis; according to the current and voltage data monitored in real time by the electrical parameter detection module, draw them on the time axis to form current and voltage curves.

[0084] The data analysis module uses the visual inspection analysis model, physical parameter analysis model and electrical parameter analysis model to analyze the quality, processing parameters and equipment operation of the silver composite contact, and generates an analysis report based on the analysis results;

[0085] The visual inspection analysis model analyzes the high-definition images at the raw material inlet and outlet and the X-ray images at the outlet, including the following steps:

[0086] 1. High-definition image analysis of raw material feed port

[0087] Image acquisition and preprocessing:

[0088] The image is collected by a high-definition camera installed at the raw material feeding port;

[0089] Perform pre-processing operations such as denoising and enhancement on the collected images to improve the quality and clarity of the images;

[0090] Color feature extraction:

[0091] Use color space conversion and feature extraction algorithms to extract color features in images; convert images from RGB color space to HSV color space, and then extract features such as hue, saturation, and brightness;

[0092] Determine the material of the raw material based on the extracted color features; raw materials of different materials may have different color features, and the material of the raw material can be determined by comparing it with the color features of known materials in the database;

[0093] Surface defect detection:

[0094] Use image analysis algorithms to detect surface defects on raw materials, such as scratches, pits, stains, etc. Edge detection, morphological processing and other methods can be used to highlight defective areas;

[0095] Mark and count the detected defects and evaluate the severity of surface defects;

[0096] 2. High-definition image analysis at the discharge port

[0097] Image acquisition and preprocessing:

[0098] The image is collected by using a high-definition camera arranged at the discharge port;

[0099] Also perform pre-processing operations such as denoising and enhancement to provide high-quality image data for subsequent analysis;

[0100] Shape feature extraction:

[0101] Using shape detection algorithms, the shape features of the silver composite contacts in the image, such as circle, square, ellipse, etc., can be extracted; edge detection, contour extraction and other methods can be used to determine the shape of the contacts;

[0102] Quantify shape features, such as calculating the perimeter, area, aspect ratio, and other parameters of the contact points;

[0103] Size feature extraction:

[0104] Through image analysis, the size characteristics of the silver composite contact, such as diameter, length, width, etc., can be determined; measurement tools or image segmentation algorithms can be used to obtain the size information of the contact;

[0105] Quantify size characteristics for comparison with specification parameters;

[0106] Surface texture feature extraction:

[0107] Using texture analysis algorithms, the surface texture features of the silver composite contacts, such as roughness, granularity, and directionality, can be extracted; gray-level co-occurrence matrix, wavelet transform, and other methods can be used to describe the texture features;

[0108] Quantify surface texture features to provide a basis for quality assessment;

[0109] 3. X-ray image analysis at the discharge port

[0110] Image acquisition and preprocessing:

[0111] The image is collected by using the X-ray flaw detection equipment arranged at the discharge port;

[0112] Perform pre-processing operations such as denoising and enhancement on X-ray images to improve image contrast and clarity for better detection of internal defects;

[0113] Porosity, slag inclusion and crack feature extraction:

[0114] Use image analysis algorithms to detect defects such as pores, slag inclusions, and cracks inside silver composite contacts; use threshold segmentation, morphological processing, and other methods to highlight defect areas;

[0115] Quantify the extracted defect features, such as calculating the size and number of pores, the area of ​​slag inclusions, the length of cracks and other parameters;

[0116] 4. Data comparison and generation of visual analysis data

[0117] Specifications acquisition:

[0118] Obtain specification parameters of silver composite contacts of different specifications from the database, including size of silver composite contacts, thickness of silver layer, uniformity of silver layer, type of base material, size of base material, internal defect standard and surface quality requirements, etc.;

[0119] Appearance inspection data generation:

[0120] The shape, size and surface texture features obtained by high-definition image analysis at the discharge port are quantified and compared with the specification parameters to determine whether the appearance quality of the silver composite contact meets the requirements;

[0121] Based on the comparison results, generate the appearance inspection data of the silver composite contact, including appearance quality assessment, deviation from specification parameters and other information;

[0122] Internal test data generation:

[0123] The quantified data of pores, slag inclusions and crack features obtained by X-ray image analysis at the discharge port are compared with the specification parameters to determine whether the internal quality of the silver composite contact meets the requirements;

[0124] Generate internal inspection data of silver composite contacts based on the comparison results, including internal quality assessment, defect type and severity information;

[0125] Visual analysis data generation:

[0126] Integrate the appearance inspection data and internal inspection data of the silver composite contacts to generate visual analysis data; the visual analysis data includes the overall quality assessment of the silver composite contacts, detailed information on appearance and internal defects, and comparison with specification parameters;

[0127] The physical parameter analysis model specifically includes the following steps when performing physical parameter analysis:

[0128] 1. Data Reception

[0129] Receive pressure curve, temperature curve and displacement distance curve data:

[0130] Obtaining pressure curve, temperature curve and displacement distance curve data generated by pressure, temperature and displacement parameters of the molding equipment during spot welding from the data sorting module;

[0131] Ensure the integrity and accuracy of data and provide a reliable basis for subsequent analysis;

[0132] 2. Parameter range check

[0133] Compared with the specifications:

[0134] Extract specification parameters such as molding pressure range, molding temperature range and displacement accuracy requirements of silver composite contacts of different specifications from the database;

[0135] Compare the received values ​​of the pressure curve, temperature curve and displacement distance curve with the corresponding specification parameters respectively;

[0136] Exception markers:

[0137] When the values ​​in the pressure curve, temperature curve and displacement distance curve exceed the molding pressure range, molding temperature range and displacement accuracy requirements, the pressure curve, temperature curve and displacement distance curve segment corresponding to the time node are marked as abnormal;

[0138] 3. Statistics

[0139] Abnormal data extraction:

[0140] Extract specific abnormal data points from the curve segments marked as abnormal, including pressure values, temperature values, displacement values, etc.;

[0141] Statistical analysis:

[0142] Perform statistical analysis on the extracted abnormal data, such as calculating the number, mean, standard deviation and other statistics of abnormal data;

[0143] Analyze the distribution of abnormal data to understand the severity and trend of the anomalies;

[0144] IV. Time Series Analysis

[0145] Trend Analysis:

[0146] Use time series analysis method to conduct trend analysis on pressure curve, temperature curve and displacement distance curve;

[0147] Observe the changing trend of the curve on the time axis to determine whether there is a long-term upward, downward or fluctuating trend;

[0148] Periodic analysis:

[0149] Analyze the curve to see if there are periodic changes, such as periodic fluctuations caused by the working cycle of the equipment or external factors;

[0150] Determine the period and amplitude of periodic changes to better understand the variation patterns of physical parameters;

[0151] 5. Generate physical parameter analysis data

[0152] Summary of abnormal situations:

[0153] Based on the results of data statistics and time series analysis, summarize the abnormal conditions of physical parameters, including the type, occurrence time, severity, etc. of the abnormality;

[0154] Analysis report generation:

[0155] Integrate the abnormal situation summary with other relevant information to generate physical parameter analysis data;

[0156] The analysis data includes detailed abnormal descriptions, statistical analysis results, trend analysis charts, periodic analysis conclusions, etc.

[0157] The electrical parameter analysis model includes the following steps when performing electrical parameter analysis:

[0158] 1. Data Reception

[0159] Receive current and voltage curve data:

[0160] Obtain the current and voltage curve data of the molding equipment during operation from the data sorting module;

[0161] Ensure the integrity and accuracy of data and provide a reliable basis for subsequent analysis;

[0162] 2. Electrical Fault Detection

[0163] Set normal range:

[0164] Determine the normal current and voltage ranges based on the equipment specifications and operating requirements; this can be determined by consulting the equipment manual, referring to historical data, or performing experimental tests;

[0165] For example, for a specific model of molding equipment, the current during normal operation fluctuates within a certain range, and the voltage should also remain relatively stable;

[0166] Anomaly Detection:

[0167] Compare the received current and voltage curve data with the set normal range; if the current or voltage exceeds the normal range, or there is abnormal fluctuation or mutation, it is considered that there may be an electrical fault;

[0168] For example, if the current suddenly increases or decreases, or if there are large fluctuations in voltage, these could be signs of an electrical fault;

[0169] 3. Fault diagnosis

[0170] Using electrical parameter analysis algorithms:

[0171] Use specific electrical parameter analysis algorithms to analyze abnormal current and voltage data to determine the type of fault;

[0172] Common electrical parameter analysis algorithms include time domain analysis, frequency domain analysis, harmonic analysis, etc.

[0173] For example, through time domain analysis, we can observe the changing trends of current and voltage and determine whether there are faults such as short circuit and open circuit; through frequency domain analysis, we can detect whether there are problems such as harmonic interference;

[0174] Determine the type of fault:

[0175] Determine the specific type of electrical fault based on the results of the analysis algorithm; common electrical fault types include short circuit, open circuit, overload, ground fault, harmonic distortion, etc.;

[0176] For example, if the current suddenly increases and the voltage decreases, it may be a short circuit fault; if the current is zero and the voltage is normal, it may be an open circuit fault;

[0177] 4. Abnormal marking

[0178] Marking abnormal data:

[0179] Mark the detected abnormal current and voltage data for subsequent analysis and processing;

[0180] Specific marking symbols or colors can be used to distinguish normal data from abnormal data;

[0181] Record fault information:

[0182] Record relevant information about electrical faults, including the time when the fault occurred, the type of fault, the specific value of abnormal data, etc. This information will be used to generate electrical parameter analysis data and trace the source of abnormalities;

[0183] 5. Generate electrical parameter analysis data

[0184] Analysis report generation:

[0185] Integrate the fault diagnosis results with other relevant information to generate electrical parameter analysis data;

[0186] The analysis data may include detailed fault description, fault type, chart display of abnormal data, possible cause analysis, etc.

[0187] Providing decision support:

[0188] Provide decision support for equipment maintenance and troubleshooting based on electrical parameter analysis data; for example, make specific repair suggestions, suggestions for replacing parts, or suggestions for adjusting equipment operating parameters;

[0189] After generating visual analysis data, physical parameter analysis data and electrical parameter analysis data, the data analysis module performs multi-parameter management analysis on the visual analysis data, physical parameter analysis data and electrical parameter analysis data to determine the impact of raw material material and surface defects, physical parameter analysis data and electrical parameter analysis data abnormalities on the visual analysis data. The specific operation steps are as follows:

[0190] 1. Data preparation and organization

[0191] Data Collection:

[0192] Obtain visual analysis data from the visual inspection analysis model, including the material and surface defect judgment results of the raw material feed port, the appearance inspection data (shape, size, surface texture feature quantification value) and internal inspection data (pores, slag inclusions, crack feature quantification value) of the silver composite contact at the discharge port;

[0193] Obtain physical parameter analysis data from the physical parameter analysis model, including abnormal markings of pressure curve, temperature curve, displacement distance curve, and statistical analysis and time series analysis results;

[0194] Acquire electrical parameter analysis data, namely, electrical fault diagnosis results and determined fault types, from the electrical parameter analysis model;

[0195] Data classification storage:

[0196] The visual analysis data is classified and stored according to the raw material inlet and outlet, which facilitates the subsequent analysis of data at different locations;

[0197] Physical parameter analysis data is stored by category according to pressure, temperature, and displacement, and abnormal time nodes are marked;

[0198] The electrical parameter analysis data is stored by category according to current and voltage, and the time of fault occurrence is also marked;

[0199] 2. Analysis of the impact of raw material materials and surface defects

[0200] Material feature analysis:

[0201] Compare the characteristics of raw materials of different materials in terms of color, texture, etc., and determine their typical performance in visual analysis;

[0202] For example, a certain material may have a specific color range. By analyzing the color characteristics of the high-definition picture of the feed port, the raw material material can be judged more accurately;

[0203] Analyze the effects of different materials on the silver composite contact forming process, such as hardness, melting point and other characteristics may affect the parameter settings of the forming equipment and the quality of the contacts;

[0204] Surface defect detection and analysis:

[0205] High-definition images of raw material feed ports are used for detailed surface defect inspection, including scratches, pits, stains, etc.

[0206] Count the number and severity of different types of defects and assess their potential impact on subsequent processing;

[0207] For example, severe surface defects may cause uneven pressure distribution during the molding process, thus affecting the shape and internal structure of the silver composite contact;

[0208] Impact Orientation Determination:

[0209] Determine the impact on visual analysis data based on the analysis results of material characteristics and surface defects;

[0210] If a material is prone to certain types of surface defects, and these defects affect the appearance or internal quality of the silver composite contact during subsequent processing, then the impact relationship between this material and surface defects and visual analysis data can be established;

[0211] 3. Analysis of the impact of abnormal data on physical parameter analysis

[0212] Abnormal pressure analysis:

[0213] Analyze abnormal data points in the pressure curve to determine the time intervals when the pressure is too high or too low;

[0214] Study the effects of these abnormal pressures on the forming process of silver composite contacts, such as possible deformation of the contact shape and loose bonding between the silver layer and the substrate;

[0215] Observe the high-definition images and X-ray images at the discharge port, and analyze the specific impact of abnormal pressure on appearance inspection data (shape, surface texture) and internal inspection data (pores, slag inclusions);

[0216] Temperature anomaly analysis:

[0217] Determine the abnormal temperature range and time nodes in the temperature curve;

[0218] Analyze the effects of abnormal temperature on silver composite contacts, including possible color changes, increased internal defects, and other issues;

[0219] Compare visual analysis data under normal temperature and abnormal temperature to determine the impact of temperature anomaly on shape features, size features, surface texture features and internal inspection data;

[0220] Displacement anomaly analysis:

[0221] Analyze the abnormal displacement in the displacement distance curve and determine the time point when the displacement accuracy exceeds the requirement;

[0222] Study the effect of abnormal displacement on the dimensional consistency of silver composite contacts and the impact on the contact molding position in the mold cavity;

[0223] By observing the visual analysis data of the discharge port, determine the specific impact of displacement abnormality on the appearance inspection data and internal inspection data;

[0224] Comprehensive impact orientation determination:

[0225] Comprehensively consider the abnormal conditions of three physical parameters, pressure, temperature and displacement, and analyze their joint impact on visual analysis data;

[0226] For example, abnormal pressure and temperature at the same time may lead to specific appearance and internal quality problems of silver composite contacts, so the impact orientation in this combined situation can be determined;

[0227] 4. Analysis of the impact of abnormal electrical parameter analysis data

[0228] Current abnormality analysis:

[0229] Analyze abnormal fluctuations in the current curve to determine the time interval when the current is too high or too low;

[0230] Study the effect of abnormal current on the surface ablation of silver composite contacts and its potential impact on the conductive properties and internal structure;

[0231] Observe the visual analysis data at the discharge port and analyze the specific impact of current abnormality on the appearance inspection data and internal inspection data;

[0232] Voltage abnormality analysis:

[0233] Determine the abnormal voltage range and time nodes in the voltage curve;

[0234] Analyze the influence of abnormal voltage on the working effect of X-ray flaw detection equipment and the molding quality of silver composite contacts;

[0235] Compare the visual analysis data under normal voltage and abnormal voltage to determine the impact of voltage anomaly on shape features, size features, surface texture features and internal detection data;

[0236] Comprehensive impact analysis of electrical faults:

[0237] Consider the abnormal conditions of two electrical parameters, current and voltage, and analyze their joint impact on visual analysis data;

[0238] For example, abnormal current and voltage at the same time may cause specific appearance and internal quality problems of silver composite contacts, so as to determine the impact orientation in this combined situation;

[0239] 5. Multi-parameter comprehensive analysis and impact-oriented summary

[0240] Multi-parameter association analysis:

[0241] Conduct a comprehensive correlation analysis of raw material materials and surface defects, physical parameter analysis data, and electrical parameter analysis data;

[0242] Find the interactions and influences between different parameters. For example, the raw material may interact with specific physical parameters abnormally, resulting in specific visual analysis results.

[0243] Impact-oriented summary:

[0244] Based on the results of multi-parameter correlation analysis, summarize the impact of different situations on visual analysis data;

[0245] Identify the possible change trends and problem types of the appearance inspection data and internal inspection data of the silver composite contacts when there are abnormalities in raw material materials and surface defects, physical parameter analysis data, and electrical parameter analysis data;

[0246] Providing decision support:

[0247] Providing impact-oriented analysis results to maintenance and production management personnel to provide guidance for abnormality tracing and problem solving;

[0248] For example, the possible cause and location of the fault can be determined based on the impact orientation so as to take targeted maintenance measures and adjust the production process parameters;

[0249] The abnormal data interaction module receives the analysis report, extracts the abnormal data in the analysis report, traces the abnormality based on the abnormal data, and synchronizes the abnormality tracing information to the maintenance end;

[0250] Furthermore, after receiving the analysis report, the abnormal data interaction module retrieves abnormal data from the visual analysis data, the physical parameter analysis data, and the electrical parameter analysis data, and determines the root cause of the abnormality based on the influence of the raw material material and surface defects, the physical parameter analysis data, and the electrical parameter analysis data on the visual analysis data when the abnormality occurs, performs a source tracing analysis on the root cause of the abnormality, determines the cause and location of the abnormality, and generates abnormality tracing information;

[0251] The specific steps include:

[0252] 1. Receiving analysis reports and abnormal data retrieval

[0253] Receive analysis report:

[0254] Continuously monitor the data analysis module and receive new analysis reports as soon as they are generated;

[0255] Ensure the completeness and accuracy of the report, and provide a reliable basis for subsequent abnormal data retrieval and traceability analysis;

[0256] Retrieve exception data:

[0257] Comprehensive retrieval of visual analysis data, physical parameter analysis data, and electrical parameter analysis data;

[0258] In the visual analysis data, find the abnormal judgment results of the material and surface defects of the raw material feed port, as well as the abnormal conditions in the appearance and internal inspection data of the silver composite contact at the discharge port, such as the abnormal quantitative values ​​of the shape, size, surface texture characteristics and internal pores, slag inclusions, and crack characteristics;

[0259] In the physical parameter analysis data, determine the values ​​in the pressure, temperature and displacement curves that are beyond the normal range, and the curve segments marked as abnormal;

[0260] In the electrical parameter analysis data, find out the electrical faults and abnormal fluctuation data in the current and voltage curves;

[0261] 2. Determine the root cause of the anomaly based on impact orientation

[0262] Consider raw material and surface defects:

[0263] Analyze the relationship between raw material quality and abnormal data; if the raw material of a certain material frequently has problems in the visual analysis data and is related to the abnormality of physical parameters or electrical parameters, then the raw material quality may be one of the root causes of the abnormality;

[0264] Evaluate the impact of raw material surface defects on subsequent processing; if surface defects at the feed inlet are reflected in subsequent visual, physical and electrical parameters, then surface defects may have caused the anomaly;

[0265] Analysis of abnormal physical parameters:

[0266] Abnormal conditions in the physical parameter analysis data are combined with their impact on the visual analysis data; for example, if the pressure abnormality causes the shape of the silver composite contact to deform, and this deformation is detected in the visual analysis data, then the pressure abnormality may be one of the root causes of the abnormality;

[0267] Similar analyses are performed on the anomaly effects of temperature and displacement parameters to determine whether they are the source of the anomaly;

[0268] Consider the impact of abnormal electrical parameters:

[0269] Analyze the impact of electrical faults in electrical parameter analysis data on visual analysis data; for example, if current anomalies cause ablation of the silver composite contact surface, and this ablation phenomenon can be observed in visual analysis, then the electrical fault may be one of the root causes of the anomaly;

[0270] Evaluate the impact of voltage anomalies on silver composite contact molding quality and visual analysis data to determine if it is the root cause of the anomaly;

[0271] Comprehensively determine the root cause of the abnormality:

[0272] Comprehensively consider the raw material material and surface defects, the abnormal conditions of the physical parameter analysis data and the electrical parameter analysis data and their impact on the visual analysis data to determine the root cause of the abnormality;

[0273] The abnormality may be caused by a single factor or it may be the result of multiple factors working together;

[0274] 3. Conduct source tracing analysis to determine cause and location

[0275] Single factor anomaly tracing:

[0276] If the root cause of the abnormality is determined to be a single factor, such as a specific physical parameter abnormality, further trace the cause and location of the parameter abnormality;

[0277] For abnormal pressure, check the working status of the pressure sensor, the mechanical structure of the pressure applying device, and the records of process parameters to determine the specific cause and location of the abnormality;

[0278] For temperature abnormalities, check the operation of temperature sensors, heating elements, and control systems to determine the cause and location of the temperature abnormality;

[0279] For abnormal electrical parameters, check sensors, electrical circuits and electrical components of equipment to determine the cause and location of electrical faults;

[0280] Multi-factor anomaly tracing:

[0281] If the anomaly is caused by multiple factors, conduct a traceability analysis on each factor separately;

[0282] For example, if the raw material, pressure abnormality and electrical failure jointly lead to the quality problem of silver composite contacts, trace the source and quality control process of the raw materials, the cause and location of the pressure abnormality and the specific circumstances of the electrical failure respectively;

[0283] Analyze the interaction between multiple factors and determine the specific mechanism by which they jointly lead to abnormalities;

[0284] Determine the cause and location of the anomaly:

[0285] Determine the specific cause and location of the anomaly through source tracing analysis;

[0286] The reasons may include equipment failure, improper process parameter settings, raw material quality issues, operator errors, etc.

[0287] The location may be a specific sensor, equipment component, process step, or raw material batch, etc.;

[0288] 4. Generate abnormal traceability information

[0289] Information collation and recording:

[0290] Organize and record the results of the abnormality tracing analysis, including the root cause, reason, and location of the abnormality;

[0291] Describe in detail the role and impact of each factor in the occurrence of the anomaly, as well as the process and basis of source tracing analysis;

[0292] Generate anomaly tracing information report:

[0293] Generate an abnormal tracing information report based on the collated information;

[0294] The report should include a description of the abnormal data, the process of determining the root cause of the abnormality, the analysis results of the cause and location, and possible solutions and suggestions;

[0295] The report can be in the form of text, charts, data tables, etc., so as to clearly show the results of abnormality tracing;

[0296] Information synchronization and feedback:

[0297] Synchronize the generated abnormal tracing information to the maintenance end and relevant management personnel so that timely measures can be taken to solve the abnormal problems;

[0298] Receive feedback from maintenance and management personnel to further improve exception tracing information and solutions.

[0299] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. An online monitoring system for silver composite contact forming equipment, characterized in that: include: Data collection terminal; The data acquisition end includes a visual detection module, a physical parameter detection module and an electrical parameter detection module; The visual inspection module collects high-definition images of the raw material inlet and outlet of the molding equipment and X-ray images of the outlet; the physical parameter detection module collects the pressure, temperature and displacement parameters of the molding equipment during spot welding; the electrical parameter detection module monitors the current and voltage data of the molding equipment in real time during operation; Database; the database includes a data storage unit and a data management unit; the data storage unit stores the specification parameters of silver composite contacts of different specifications; the data management unit is responsible for the daily management, maintenance, backup of the database, and data entry, update, query and cleaning; Supervision platform; the supervision platform includes a data sorting module, a data analysis module and an abnormal data interaction module; The data sorting module establishes a data management time axis, receives the detection data of the visual detection module, the physical parameter detection module and the electrical parameter detection module in real time, and synchronizes the detection data to each time node of the data management time axis; The data analysis module analyzes the quality, processing parameters and equipment operation of the silver composite contact using a visual inspection analysis model, a physical parameter analysis model and an electrical parameter analysis model, and generates an analysis report based on the analysis results; The abnormal data interaction module receives the analysis report, extracts the abnormal data in the analysis report, performs abnormal source tracing based on the abnormal data, and synchronizes the abnormal source tracing information to the maintenance end; The visual inspection module includes a high-definition camera installed at the raw material feed port and the discharge port, and an X-ray flaw detection device installed at the discharge port; the physical parameter detection module includes a pressure sensor installed on the pressure application device, a temperature sensor distributed in the heating area, and a displacement sensor installed on the moving parts of the molding equipment; the electrical parameter detection module includes a current sensor and a voltage sensor connected to the electrical system of the molding equipment; The specification parameters of the silver composite contact include the size of the silver composite contact, the thickness of the silver layer, the uniformity of the silver layer, the type of the base material, the size of the base material, the molding pressure range, the molding temperature range, the displacement accuracy requirements, the internal defect standards and the surface quality requirements; When performing data sorting, the data sorting module establishes a unified time axis, divides the time axis into set time intervals, and continuously receives detection data uploaded by the visual detection module, the physical parameter detection module, and the electrical parameter detection module, wherein the detection data includes high-definition images at the raw material feed port and the discharge port, X-ray images at the discharge port, pressure, temperature, and displacement parameters of the molding equipment during spot welding, and current and voltage data when the molding equipment is running; the high-definition images at the raw material feed port and the discharge port, and the X-ray images at the discharge port are serially marked in the order of shooting, and synchronized to the time nodes corresponding to the time axis; The pressure, temperature and displacement parameters of the molding equipment during spot welding are extracted based on the processing of a silver composite contact as a cycle, and the pressure curve, temperature curve and displacement distance curve corresponding to the time axis are established; Establish the current and voltage curves corresponding to the time axis; When the visual inspection and analysis model analyzes the high-definition images at the raw material feed port and the X-ray images at the discharge port, the color features of the high-definition images of the raw material feed port are extracted to determine the material quality and surface defects of the raw materials; the high-definition images at the discharge port are processed, and the shape features, size features, and surface texture features in the image are extracted by using an image analysis algorithm, and the shape features, size features, and surface texture features are quantified to obtain the appearance inspection data of the silver composite contact; the X-ray images at the discharge port are analyzed to extract the features of pores, slag inclusions, and cracks, and the features of pores, slag inclusions, and cracks are quantified to obtain the internal inspection data of the silver composite contact, and the appearance inspection data and the internal inspection data of the silver composite contact are compared with the specification parameters of the silver composite contact stored in the database to generate visual analysis data; When performing physical parameter analysis, the physical parameter analysis model receives a pressure curve, a temperature curve and a displacement distance curve. When the values ​​in the pressure curve, the temperature curve and the displacement distance curve exceed the molding pressure range, the molding temperature range and the displacement accuracy requirements, the pressure curve, the temperature curve and the displacement distance curve segments corresponding to the time node are marked as abnormal. Data statistics are performed on the marked pressure curve, the temperature curve and the displacement distance curve to generate physical parameter analysis data. The change trends of the pressure curve, the temperature curve and the displacement distance curve are analyzed using a time series analysis method. When performing electrical parameter analysis, the electrical parameter analysis model receives current and voltage curves, uses an electrical parameter analysis algorithm to diagnose electrical faults appearing in the current and voltage curves, determines the type of fault, performs abnormal marking, and generates electrical parameter analysis data; After generating the visual analysis data, the physical parameter analysis data and the electrical parameter analysis data, the data analysis module performs multi-parameter management analysis on the visual analysis data, the physical parameter analysis data and the electrical parameter analysis data to determine the impact of the raw material material and surface defects, the physical parameter analysis data and the electrical parameter analysis data on the visual analysis data when abnormalities occur; After receiving the analysis report, the abnormal data interaction module retrieves the abnormal data in the visual analysis data, the physical parameter analysis data and the electrical parameter analysis data, and determines the root cause of the abnormality based on the influence of the raw material material and surface defects, the physical parameter analysis data and the electrical parameter analysis data on the visual analysis data when the abnormalities occur, performs a source tracing analysis on the root cause of the abnormality, determines the cause and location of the abnormality, and generates abnormal tracing information.

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