Intelligent maintenance method and system for cutting bed equipment

Abstract

The invention belongs to the technical field of cutting bed maintenance, and provides an intelligent maintenance method and system for cutting bed equipment, and the method comprises the following steps: inputting spare part data into stock information, and uploading the stock information to the Internet of Things; a preset sensor is adopted to collect key component data of the cutting bed equipment in real time; uploading the key component data to a cloud by adopting a 5G communication protocol; performing data cleaning and data preprocessing on the key component data uploaded to the cloud; putting the key component data into the fault prediction model to obtain a fault prediction result, and outputting corresponding maintenance information according to the fault prediction result; pushing the maintenance information to maintenance personnel; and the maintainer dispatches the corresponding storehouse spare parts based on the maintenance information to carry out equipment maintenance, and updates the inventory information of the spare parts in real time after maintenance. According to the invention, the stability and reliability of the equipment can be obviously improved, the maintenance cost and the inventory cost are reduced, the production efficiency and the spare part utilization rate are improved, the resource configuration and supply chain management are optimized, and the decision-making efficiency is improved.

Description

Technical Field

[0001] The present invention belongs to the technical field of cutting table maintenance, and in particular relates to an intelligent maintenance method and system for cutting table equipment. Background Art

[0002] With the rapid development of the garment manufacturing industry, cutting equipment, as a key equipment in the production process, has a significant impact on production costs and product quality due to its operating efficiency and stability. However, the traditional maintenance methods of cutting equipment have the following defects:

[0003] Low maintenance efficiency: Traditional maintenance methods mainly rely on manual inspections and regular maintenance. However, this method often fails to detect equipment failures in a timely manner, resulting in low maintenance efficiency. At the same time, manual inspections are easily affected by human factors, resulting in unstable maintenance quality;

[0004] Disorganized spare parts management: Traditional spare parts management methods mainly rely on paper records and manual management. However, this method often leads to problems such as inaccurate spare parts inventory and unclear spare parts usage records. When equipment fails, it is often impossible to find the required spare parts in time, resulting in extended maintenance time;

[0005] Data island problem: Traditional cutting equipment often lacks data interfaces and communication protocols, which makes it impossible to upload equipment data to the cloud platform in real time. This leads to serious data island problems and makes it impossible to fully utilize data resources for fault prediction and maintenance optimization;

[0006] Lack of intelligent support: Traditional cutting machine equipment maintenance methods lack intelligent support and cannot achieve real-time monitoring, fault prediction and intelligent maintenance of equipment. This leads to low equipment maintenance efficiency and high maintenance costs.

[0007] To sum up, the traditional cutting equipment maintenance method has many shortcomings and cannot meet the modern clothing manufacturing industry's demand for efficient, stable and intelligent production. Summary of the invention

[0008] The present invention provides a cutting table equipment intelligent maintenance method and system, which are used to solve the problems existing in the prior art.

[0009] A method for intelligent maintenance of cutting bed equipment, characterized in that it comprises the following steps:

[0010] Step S1: Input spare parts data into inventory information and upload to the Internet of Things;

[0011] Step S2: using preset sensors to collect data of key components of the cutting bed equipment in real time, wherein the preset sensors include at least a position sensor, a pressure sensor, a speed sensor and a temperature sensor;

[0012] Step S3: using 5G communication protocol to upload the key component data to the cloud;

[0013] Step S4: performing data cleaning and data preprocessing on the key component data uploaded to the cloud;

[0014] Step S5: placing the key component data into a fault prediction model to obtain a fault prediction result, and outputting corresponding maintenance information according to the fault prediction result;

[0015] Step S6: Pushing the maintenance information to the maintenance personnel, wherein the push method includes one or more of SMS, email and APP push;

[0016] Step S7: The maintenance personnel dispatches the corresponding warehouse spare parts to perform equipment maintenance based on the maintenance information, and updates the inventory information of the spare parts in real time after the maintenance. The inventory information of the spare parts includes at least the type of spare parts, the quantity of spare parts and the location of spare parts.

[0017] In a preferred implementation, the position sensor is disposed at a key position of the cutting machine tool and the sharpening device to monitor the position information of the blade in real time;

[0018] The pressure sensor is arranged on the cutting contact surface of the cutting table to monitor the pressure change during the cutting process;

[0019] The speed sensor is arranged on the side of the cloth conveying track to monitor the cloth deployment speed;

[0020] The temperature sensor is arranged at the cutting head and is used to monitor the working temperature of the equipment.

[0021] In a preferred implementation, the data cleaning in step S4 specifically includes: using statistical methods and machine learning algorithms to identify and remove outliers in the data;

[0022] The data preprocessing specifically includes: smoothing the noise data.

[0023] In a preferred implementation, the fault prediction model includes at least a time series analysis model, a regression model and a machine learning algorithm model;

[0024] The time series analysis model uses the time series data to calculate and obtain the moving average data of the cutting table to predict the future operation status. The time series data at least includes the key component data of the cutting table equipment at each time node, and the moving average data at least includes the average cutting speed, average knife lowering frequency and average knife sharpening frequency of the cutting table.

[0025] The regression model includes a linear regression model and a nonlinear regression model established based on historical data and characteristic variables. The regression model obtains the negative pressure fluctuations of the cutting table under different states by analyzing the negative pressure values ​​and corresponding operating states of the historical data of the pressure sensor, and compares them with the negative pressure warning thresholds under different states in the regression model to predict the probability and time of failure.

[0026] The machine learning algorithm model performs big data analysis based on data statistics and daily, weekly, and monthly time periods, conducts a comprehensive assessment of the nodes and frequencies at which faults occur, and obtains fault prediction results.

[0027] In a preferred implementation, the step S5 of outputting corresponding maintenance information according to the fault prediction result specifically includes the following steps:

[0028] Step S501: According to the type of the fault prediction result, based on the built-in priority, the prediction result with a higher priority is arranged in front;

[0029] Step S502: Based on the type of fault prediction result and its maintenance history record, a spare parts loss model is used to predict the type and quantity of corresponding spare parts required in the future.

[0030] In a preferred implementation, the step S1 further includes: recording the shelf life information of the spare parts, and reminding management personnel to dispose of expired spare parts in a timely manner according to the shelf life information.

[0031] In a preferred implementation, step S7 further includes:

[0032] According to the production plan and equipment maintenance requirements, the minimum threshold and maximum threshold of the spare parts quantity are set. When the spare parts quantity is lower than the minimum threshold and higher than the maximum threshold, the early warning mechanism is automatically triggered to remind the maintenance personnel to take corresponding measures.

[0033] An intelligent maintenance system for cutting bed equipment, comprising:

[0034] Data input module, used to input spare parts data into inventory information;

[0035] A data acquisition module, used to acquire key component data of the cutting bed equipment collected in real time by preset sensors, wherein the preset sensors include at least a position sensor, a pressure sensor, a speed sensor and a temperature sensor;

[0036] A data transmission module, used to upload the key component data to the cloud using a 5G communication protocol;

[0037] Data processing module, used for data cleaning and data preprocessing of key component data uploaded to the cloud;

[0038] A data analysis module is used to analyze the key component data based on a fault prediction model preset in the cloud, obtain a fault prediction result, and output corresponding maintenance information according to the fault prediction result;

[0039] Data push module, used to push corresponding maintenance information to maintenance personnel;

[0040] The data management module is used to update the inventory information of spare parts in real time, record the shelf life information of spare parts to remind maintenance personnel to deal with expired spare parts, and set the minimum and maximum thresholds of the spare parts quantity. When the spare parts quantity is lower than the minimum threshold and higher than the maximum threshold, the early warning mechanism is automatically triggered to remind maintenance personnel to take corresponding measures.

[0041] Compared with the prior art, the present invention has at least the following beneficial effects:

[0042] The present invention can significantly improve the stability and reliability of equipment, reduce maintenance costs and inventory costs, improve production efficiency and spare parts utilization, optimize resource allocation and supply chain management, improve decision-making efficiency, and bring significant economic and social benefits to enterprises. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] Figure 1 The figure is a flow chart of the intelligent maintenance method for cutting table equipment in the first embodiment of the present invention. DETAILED DESCRIPTION

[0044] The technical solution of the present invention will be described clearly and completely below in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0045] The invention discloses an intelligent maintenance method and system for cutting table equipment, which are used to solve the problems existing in the prior art.

[0046] Embodiment 1:

[0047] like Figure 1 As shown, this embodiment discloses a method for intelligent maintenance of a cutting table device, the method comprising the following steps:

[0048] Step S1: Input spare parts data into inventory information and upload to the Internet of Things;

[0049] In this embodiment, the step S1 further includes: recording the shelf life information of the spare parts, and reminding the management personnel to dispose of expired spare parts in time according to the shelf life information;

[0050] Step S2: using preset sensors to collect data of key components of the cutting bed equipment in real time, wherein the preset sensors include at least a position sensor, a pressure sensor, a speed sensor and a temperature sensor;

[0051] Specifically, the position sensor is arranged at a key position of the cutting machine and the sharpening device, and is used to monitor the position information of the blade in real time to ensure the cutting accuracy; the pressure sensor is arranged on the cutting contact surface of the cutting table, and is used to monitor the pressure change during the cutting process to evaluate the degree of blade wear and the stress condition of the cloth; the speed sensor is arranged on the side of the cloth conveying track, and is used to detect the cloth deployment speed to ensure that the cutting process is synchronized with the cloth conveying to avoid misalignment and waste; the temperature sensor is arranged at the cutting head, and is used to monitor the working temperature of the equipment and prevent failures caused by overheating.

[0052] Step S3: using 5G communication protocol to upload the key component data to the cloud;

[0053] Step S4: performing data cleaning and data preprocessing on the key component data uploaded to the cloud;

[0054] In this embodiment, the data cleaning in step S3 specifically includes: using statistical methods and machine learning algorithms to identify and remove outliers in the data, and the data preprocessing specifically includes: smoothing the noise data.

[0055] Step S5: placing the key component data into a fault prediction model to obtain a fault prediction result, and outputting corresponding maintenance information according to the fault prediction result;

[0056] Specifically, the fault prediction model includes at least a time series analysis model, a regression model and a machine learning algorithm model;

[0057] The time series analysis model uses the time series data to calculate and obtain the moving average data of the cutting table to predict the future operation status. The time series data at least includes the key component data of the cutting table equipment at each time node, and the moving average data at least includes the average cutting speed, average knife lowering frequency and average knife sharpening frequency of the cutting table.

[0058] The regression model includes a linear regression model and a nonlinear regression model established based on historical data and characteristic variables. The regression model obtains the negative pressure fluctuations of the cutting table under different states by analyzing the negative pressure values ​​and corresponding operating states of the historical data of the pressure sensor, and compares them with the negative pressure warning thresholds under different states in the regression model to predict the probability and time of failure.

[0059] The machine learning algorithm model performs big data analysis based on data statistics and time periods such as days, weeks, and months, conducts a comprehensive assessment of the nodes and frequencies at which faults occur, and obtains fault prediction results.

[0060] In a preferred implementation, the step S5 of outputting corresponding maintenance information according to the fault prediction result specifically includes the following steps:

[0061] Step S501: According to the type of the fault prediction result, based on the built-in priority, the prediction result with a higher priority is arranged in front;

[0062] Step S502: Based on the type of fault prediction result and its maintenance history record, a spare parts loss model is used to predict the type and quantity of corresponding spare parts required in the future.

[0063] Step S6: Pushing the maintenance information to the maintenance personnel, wherein the push method includes one or more of SMS, email and APP push;

[0064] Step S7: The maintenance personnel dispatches the corresponding warehouse spare parts to perform equipment maintenance based on the maintenance information, and updates the inventory information of the spare parts in real time after the maintenance. The inventory information of the spare parts includes at least the type of spare parts, the quantity of spare parts and the location of spare parts.

[0065] It should be noted that for serious faults and maintenance tasks that are about to expire, urgent reminders need to be sent to maintenance personnel to ensure that the problems are resolved in a timely manner. After the maintenance personnel complete the task, they submit feedback through the system to ensure that the maintenance results are verified and recorded.

[0066] Specifically, the step S7 further includes:

[0067] According to the production plan and equipment maintenance requirements, the minimum threshold and maximum threshold of the spare parts quantity are set. When the spare parts quantity is lower than the minimum threshold and higher than the maximum threshold, the early warning mechanism is automatically triggered to remind the maintenance personnel to take corresponding measures.

[0068] Embodiment 2:

[0069] Based on the first embodiment, this embodiment discloses an intelligent maintenance system for cutting bed equipment, which includes:

[0070] Data input module, used to input spare parts data into inventory information;

[0071] A data acquisition module, used to acquire key component data of the cutting bed equipment collected in real time by preset sensors, wherein the preset sensors include at least a position sensor, a pressure sensor, a speed sensor and a temperature sensor;

[0072] A data transmission module, used to upload the key component data to the cloud using a 5G communication protocol;

[0073] Data processing module, used for data cleaning and data preprocessing of key component data uploaded to the cloud;

[0074] A data analysis module is used to analyze the key component data based on a fault prediction model preset in the cloud, obtain a fault prediction result, and output corresponding maintenance information according to the fault prediction result;

[0075] Data push module, used to push corresponding maintenance information to maintenance personnel;

[0076] The data management module is used to update the inventory information of spare parts in real time and record the shelf life information of spare parts to remind maintenance personnel to deal with expired spare parts. It is also used to set the minimum threshold and maximum threshold of the spare parts quantity. When the spare parts quantity is lower than the minimum threshold and higher than the maximum threshold, the early warning mechanism is automatically triggered to remind maintenance personnel to take corresponding measures.

[0077] Those skilled in the art can understand that all or part of the steps of implementing the above-mentioned embodiments can be completed by hardware related to program instructions, and the aforementioned program can be stored in a computer-readable storage medium. When the program is executed, it executes the steps of the above-mentioned method embodiment; and the aforementioned storage medium includes: ROM, RAM, magnetic disk or optical disk, etc. Various media that can store program codes.

[0078] For those skilled in the art, various other corresponding changes and deformations can be made according to the technical solutions and concepts described above, and all of these changes and deformations should fall within the protection scope of the claims of the present invention.

Claims

1. A method for intelligent maintenance of cutting bed equipment, characterized in that: The following steps are involved: Step S1: Input spare parts data into inventory information and upload to the Internet of Things; Step S2: using preset sensors to collect data of key components of the cutting bed equipment in real time, wherein the preset sensors include at least a position sensor, a pressure sensor, a speed sensor and a temperature sensor; Step S3: using 5G communication protocol to upload the key component data to the cloud; Step S4: performing data cleaning and data preprocessing on the key component data uploaded to the cloud; Step S5: placing the key component data into a fault prediction model to obtain a fault prediction result, and outputting corresponding maintenance information according to the fault prediction result; Step S6: Pushing the maintenance information to the maintenance personnel, wherein the push method includes one or more of SMS, email and APP push; Step S7: The maintenance personnel dispatches the corresponding warehouse spare parts to perform equipment maintenance based on the maintenance information, and updates the inventory information of the spare parts in real time after the maintenance. The inventory information of the spare parts includes at least the type of spare parts, the quantity of spare parts and the location of spare parts.

2. The intelligent maintenance method for cutting bed equipment according to claim 1 is characterized in that: The position sensor is arranged at a key position of the cutting machine tool and the sharpening device, and is used to monitor the position information of the blade in real time; The pressure sensor is arranged on the cutting contact surface of the cutting table to monitor the pressure change during the cutting process; The speed sensor is arranged on the side of the cloth conveying track to monitor the cloth deployment speed; The temperature sensor is arranged at the cutting head and is used to monitor the working temperature of the equipment.

3. The intelligent maintenance method for cutting bed equipment according to claim 1 is characterized in that: The data cleaning in step S4 specifically includes: using statistical methods and machine learning algorithms to identify and remove outliers in the data; The data preprocessing specifically includes: smoothing the noise data.

4. The intelligent maintenance method for cutting bed equipment according to claim 1, characterized in that: The fault prediction model at least includes a time series analysis model, a regression model and a machine learning algorithm model; The time series analysis model uses the time series data to calculate and obtain the moving average data of the cutting table to predict the future operation status. The time series data at least includes the key component data of the cutting table equipment at each time node, and the moving average data at least includes the average cutting speed, average knife lowering frequency and average knife sharpening frequency of the cutting table. The regression model includes a linear regression model and a nonlinear regression model established based on historical data and characteristic variables. The regression model obtains the negative pressure fluctuations of the cutting table under different states by analyzing the negative pressure values ​​and corresponding operating states of the historical data of the pressure sensor, and compares them with the negative pressure warning thresholds under different states in the regression model to predict the probability and time of failure. The machine learning algorithm model performs big data analysis based on data statistics and daily, weekly, and monthly time periods, conducts a comprehensive assessment of the nodes and frequencies at which faults occur, and obtains fault prediction results.

5. The intelligent maintenance method for cutting bed equipment according to claim 1, characterized in that: Outputting corresponding maintenance information according to the fault prediction result in step S5 specifically includes the following steps: Step S501: According to the type of the fault prediction result, based on the built-in priority, the prediction result with a higher priority is arranged in front; Step S502: Based on the type of fault prediction result and its maintenance history record, a spare parts loss model is used to predict the type and quantity of corresponding spare parts required in the future.

6. The intelligent maintenance method for cutting bed equipment according to claim 1, characterized in that: The step S1 further includes: recording the shelf life information of the spare parts, and reminding the management personnel to dispose of expired spare parts in time according to the shelf life information.

7. The intelligent maintenance method for cutting bed equipment according to claim 1, characterized in that: The step S7 further comprises: According to the production plan and equipment maintenance requirements, the minimum threshold and maximum threshold of the spare parts quantity are set. When the spare parts quantity is lower than the minimum threshold and higher than the maximum threshold, the early warning mechanism is automatically triggered to remind the maintenance personnel to take corresponding measures.

8. An intelligent maintenance system for cutting bed equipment, characterized in that: include: Data input module, used to input spare parts data into inventory information; A data acquisition module, used to acquire key component data of the cutting bed equipment collected in real time by preset sensors, wherein the preset sensors include at least a position sensor, a pressure sensor, a speed sensor and a temperature sensor; A data transmission module, used to upload the key component data to the cloud using a 5G communication protocol; Data processing module, used for data cleaning and data preprocessing of key component data uploaded to the cloud; A data analysis module is used to analyze the key component data based on a fault prediction model preset in the cloud, obtain a fault prediction result, and output corresponding maintenance information according to the fault prediction result; Data push module, used to push corresponding maintenance information to maintenance personnel; The data management module is used to update the inventory information of spare parts in real time and record the shelf life information of spare parts to remind maintenance personnel to deal with expired spare parts. It is also used to set the minimum threshold and maximum threshold of the spare parts quantity. When the spare parts quantity is lower than the minimum threshold and higher than the maximum threshold, the early warning mechanism is automatically triggered to remind maintenance personnel to take corresponding measures.

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