Factory equipment whole-process maintenance management system based on Internet of Things

Abstract

The invention is suitable for the technical field of equipment maintenance, and particularly relates to a factory equipment full-process maintenance management system based on the Internet of Things, and the system improves the response speed and efficiency of equipment maintenance through on-site code scanning reporting, inspection maintenance, abnormal data monitoring and spare part management. Through automatic maintenance assignment, maintenance recording and effect confirmation, the standardization and transparency of the maintenance process are ensured; through the functions of fault database statistics, maintenance log, equipment reliability analysis, cost accounting and the like, accurate maintenance cost control, equipment reliability evaluation and personnel performance analysis are provided, a maintenance strategy is optimized, the equipment downtime and maintenance cost are reduced, the intelligence and refinement level of equipment management is remarkably improved, and the maintenance efficiency is improved. The overall operation benefit of an enterprise is improved, and the production cost is reduced.

Description

Technical Field

[0001] The present invention belongs to the technical field of equipment maintenance and repair, and in particular relates to a full-process maintenance and repair management system for factory equipment based on the Internet of Things. Background Art

[0002] With the increasing variety and complexity of manufacturing equipment, the traditional equipment maintenance management model faces many challenges, including data silos, information lag, non-standard maintenance process, and chaotic spare parts management. These problems have led to slow maintenance response, extended equipment downtime, and increased maintenance costs, affecting the production efficiency and economic benefits of enterprises. Although the existing equipment management systems have been improved, most of them lack real-time monitoring, intelligent maintenance scheduling, and predictive maintenance functions, and it is difficult to meet the efficient and accurate needs of modern enterprises for equipment management. With the development of the Internet of Things, big data, and artificial intelligence technologies, intelligent and automated data collection and analysis platforms have emerged, which can realize real-time monitoring of equipment status, prediction of faults, and optimized maintenance scheduling, greatly improving the efficiency and accuracy of equipment management, and promoting equipment management to develop in the direction of intelligence and data-driven.

[0003] The existing technology obtains equipment status parameters and matches them with pre-stored status data sets, automatically outputs maintenance suggestions, and corrects the suggestions based on deep learning models. The adaptability of the existing technology to the diversity and complexity of equipment is unclear, and it cannot effectively cover all equipment types and failure modes in practical applications. Summary of the invention

[0004] The purpose of the present invention is to provide a full-process maintenance and management system for factory equipment based on the Internet of Things, aiming to solve the problem that the existing technology is unclear in adaptability to the diversity and complexity of equipment, and cannot effectively cover all equipment types and failure modes in practical applications.

[0005] The present invention is implemented as follows: a full-process maintenance management system for factory equipment based on the Internet of Things, the system comprising:

[0006] Maintenance data collection subsystem, which is used to collect equipment information, complete maintenance order reporting, perform abnormal data monitoring and spare parts management;

[0007] A maintenance process management subsystem, which is used to assign maintenance tasks, record maintenance processes, and record the use of spare parts;

[0008] The maintenance data analysis subsystem is used to build a fault knowledge base, optimize maintenance strategies, analyze equipment operation data, and perform cost accounting and performance analysis.

[0009] Preferably, the maintenance process management subsystem includes a maintenance assignment module, which is used to group and manage the devices according to the hierarchical structure of the IoT devices, set maintenance positions and bind them to the devices, ensure that a one-to-many relationship is established between maintenance personnel and equipment, and when an abnormality occurs in the equipment, assign designated maintenance personnel to the maintenance order based on the equipment type and job responsibilities, and push the task to the corresponding personnel.

[0010] Preferably, the maintenance process management subsystem also includes a maintenance record and effect confirmation module, which is used to record maintenance records. The maintenance records include the cause of the failure, solutions, effect photos and the use of spare parts. After the maintenance is completed, the reporting personnel or management personnel confirm the maintenance effect and evaluate the maintenance effect based on the confirmation result. After confirmation, a maintenance completion report is generated and all maintenance records are archived.

[0011] Preferably, the maintenance process management subsystem also includes a spare parts usage record module, which is used to record the entry and exit of spare parts, record the use of spare parts, including the spare part name, use organization and use quantity, track the consumption of spare parts used in each maintenance process, automatically update the inventory and generate spare parts usage records.

[0012] Preferably, the maintenance data collection subsystem includes an on-site reporting module and an inspection and maintenance module, which are used to automatically collect instrument information of the equipment through a real-time data connection with the equipment through the Internet of Things. The inspection and maintenance module is used to regularly generate inspection tasks according to a predetermined inspection and maintenance plan, and push the tasks to the inspection person in charge. During the inspection process, the inspection personnel directly feedback abnormal problems through the task list, and the system automatically generates a maintenance reporting form based on the feedback information. Combined with the abnormal data recorded during the inspection process, the system automatically generates a maintenance order and pushes it to the maintenance team for processing.

[0013] Preferably, the maintenance data collection subsystem also includes an abnormal data monitoring module and a spare parts management module. The abnormal data monitoring module is used to collect equipment operation data in real time, and automatically monitor abnormal conditions of the equipment by setting abnormal monitoring points and identification labels. When an abnormality is detected, the module automatically identifies the abnormality and generates a maintenance order based on preset fault conditions.

[0014] Preferably, the maintenance data analysis subsystem includes a fault analysis and intelligent knowledge base construction module, which is used to store fault records and maintenance data, analyze historical fault data, identify the relationship between different fault types and equipment operating environment, configuration and operating conditions, and generate effective rules.

[0015] Preferably, the maintenance data analysis subsystem also includes a maintenance log module, which is used to systematically record and track the repair, maintenance and inspection operations of the equipment, provide detailed time, content and results, identify potential equipment problems and predict failure trends through maintenance logs, and optimize maintenance strategies.

[0016] Preferably, the maintenance data analysis subsystem further includes an equipment reliability analysis module, which is used to analyze the equipment operation data and calculate the mean trouble-free operation time MTBF and the mean repair time MTTR of the equipment.

[0017] The calculation method of mean trouble-free operation time is: MTBF = total operation time / total number of failures;

[0018] Mean repair time MTTR calculation method: MTTR = total fault repair time / number of faults.

[0019] Preferably, the maintenance data analysis subsystem also includes a cost accounting module and a performance analysis module. The cost accounting module is used to record the procurement cost of spare parts, inventory consumption, and maintenance hours and labor costs. The performance analysis module is used to perform weighted analysis based on maintenance complexity and fault type, and conduct a comprehensive performance evaluation of maintenance personnel from multiple dimensions.

[0020] The full-process maintenance and management system for factory equipment based on the Internet of Things provided by the present invention realizes multi-dimensional collection, full-process management and intelligent data analysis of maintenance data. The system improves the response speed and efficiency of equipment maintenance through on-site code scanning and reporting, inspection and maintenance, abnormal data monitoring and spare parts management; through automated maintenance assignment, maintenance records and effect confirmation, it ensures the standardization and transparency of the maintenance process; through functions such as fault library statistics, maintenance logs, equipment reliability analysis and cost accounting, it provides accurate maintenance cost control, equipment reliability evaluation and personnel performance analysis, optimizes maintenance strategies, reduces equipment downtime and maintenance costs, significantly improves the intelligence and refinement of equipment management, helps enterprises improve overall operational benefits, reduces production costs, and provides strong support for the transformation to intelligent manufacturing. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] Figure 1 An architecture diagram of a factory equipment full-process maintenance and management system based on the Internet of Things provided by an embodiment of the present invention;

[0022] Figure 2 A workflow diagram of a full-process factory equipment maintenance and management system based on the Internet of Things provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0023] 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.

[0024] It is understood that the terms "first", "second", etc. used in this application may be used herein to describe various elements, but unless otherwise specified, these elements are not limited by these terms. These terms are only used to distinguish a first element from another element. For example, without departing from the scope of this application, a first xx script may be referred to as a second xx script, and similarly, a second xx script may be referred to as a first xx script.

[0025] like Figure 1 As shown, it is an architecture diagram of a factory equipment full-process maintenance management system based on the Internet of Things provided by an embodiment of the present invention, and the system includes:

[0026] The maintenance data collection subsystem is used to collect equipment information, complete maintenance order reporting, perform abnormal data monitoring and spare parts management.

[0027] In this system, the maintenance data collection subsystem is used to achieve high efficiency and automation of equipment maintenance management. The maintenance data collection subsystem includes:

[0028] On-site reporting module:

[0029] Scan code to report: On-site personnel scan the QR code of the device through the mobile app. The QR code is generated by the unique identification of the device number. Each device is assigned a unique QR code. By scanning or searching the device number / name on site, the specific device can be quickly located.

[0030] Automatically fill in instrument information: Through the real-time data connection with the equipment through the Internet of Things technology, the system automatically collects the instrument information of the equipment (such as temperature, pressure, current, etc.). On-site personnel only need to supplement the fault description and take photos to quickly complete the reporting of the maintenance order.

[0031] Multi-dimensional feedback: Supports multiple types of on-site feedback, such as taking photos, videos, and voice input, to ensure that equipment fault information is comprehensive and accurate.

[0032] Inspection and maintenance module:

[0033] The system generates inspection tasks regularly according to the scheduled inspection and maintenance plan, and pushes the tasks to the inspection person in charge. During the inspection process, the inspectors can directly feedback abnormal problems through the task list, and the system will automatically generate a maintenance report based on the feedback information.

[0034] Automatically generate maintenance orders based on abnormal data: Based on the abnormal data recorded during the inspection process, the system automatically generates maintenance orders and pushes them to the maintenance team for processing.

[0035] Abnormal data monitoring module:

[0036] Based on IoT device management, the system collects equipment operation data (such as online status, emergency stop, abnormal shutdown, etc.) in real time, and automatically monitors abnormal conditions of equipment by setting abnormal monitoring points and identification tags.

[0037] Automatically generate maintenance orders: When an abnormality is detected, the system can automatically identify the abnormality and generate a maintenance order based on the preset fault conditions, thereby improving the response speed of equipment fault handling.

[0038] Spare parts management module:

[0039] This module is used to manage spare parts during equipment maintenance, including the entry and exit, use and inventory status of spare parts. The system integrates factory spare parts information and provides spare parts details for maintenance personnel to select the required spare parts during maintenance.

[0040] Intelligent inventory management: The system dynamically predicts the demand for spare parts based on historical usage data and current maintenance needs, optimizes inventory management, and avoids shortages or backlogs.

[0041] The maintenance process management subsystem is used to assign maintenance tasks, record maintenance processes and record the use of spare parts.

[0042] In this system, if Figure 2 As shown in the figure, it mainly involves the whole process management of the maintenance process, covering all links from maintenance reporting to maintenance effect confirmation. The maintenance process management subsystem includes:

[0043] Maintenance assignment module:

[0044] Device grouping and management: The system automatically groups and manages devices according to the hierarchical structure of IoT devices to ensure systematic and hierarchical device management.

[0045] Position management and binding: The system supports position management by department, equipment type, team, etc., sets maintenance positions and binds them to equipment to ensure a one-to-many relationship between maintenance personnel and equipment.

[0046] Automatically assign maintenance personnel: When an abnormality occurs in the equipment, the system automatically assigns designated maintenance personnel to the maintenance order based on information such as equipment type and job responsibilities, and pushes the task to relevant personnel to ensure the accuracy and timeliness of maintenance assignments.

[0047] Maintenance record and effect confirmation module:

[0048] Automatically generate maintenance records: After receiving the maintenance task, the maintenance personnel will perform on-site maintenance according to the contents of the maintenance order and fill in detailed maintenance records, including the cause of the failure, solution measures, effect photos and the use of spare parts.

[0049] Effect confirmation mechanism: After the maintenance is completed, the reporting personnel or management personnel will confirm the maintenance effect and evaluate the maintenance effect based on the confirmation results. After confirmation, the system will generate a maintenance completion report and archive all maintenance records.

[0050] Spare parts usage record module:

[0051] The system integrates the spare parts in and out of the warehouse, and records the spare parts issuance in detail, including spare parts name, issuance organization, issuance quantity and other information.

[0052] Spare parts consumption tracking: The system tracks the consumption of spare parts used in each maintenance process, automatically updates inventory and generates spare parts usage records.

[0053] The maintenance data analysis subsystem is used to build a fault knowledge base, optimize maintenance strategies, analyze equipment operation data, and perform cost accounting and performance analysis.

[0054] In this system, the maintenance data analysis subsystem uses the data analysis module to collect and analyze various types of data during the maintenance process, and provides functions such as equipment failure prediction, maintenance decision support, cost control and performance management. Specifically, it includes:

[0055] Fault analysis and intelligent knowledge base building module

[0056] During the equipment maintenance process, a large amount of fault records and maintenance data are generated, which contain key information about fault types, causes, maintenance measures, etc. The system uses Association Rule Learning (ARL) to deeply mine these historical data and automatically discover the potential relationship between fault types, causes and solutions.

[0057] Discovering fault patterns: By mining a large amount of historical fault data, the system can identify the relationship between different fault types and the equipment operating environment, configuration, and operating conditions. For example, certain types of faults may occur frequently in high temperature environments, or the fault rate may be higher under specific equipment configurations.

[0058] Generate effective rules: The system can generate a series of association rules for fault occurrence through "if-then" rules (such as "if the temperature exceeds 80°C, the motor may have an overload fault"). These rules can help maintenance personnel locate the problem more quickly when a fault occurs.

[0059] To evaluate the effectiveness and importance of association rules, the following three main indicators are usually used:

[0060] Support: Indicates the frequency of occurrence of a rule. Support measures the frequency of occurrence of a certain item or item set in the entire data set.

[0061] formula:

[0062] Among them, freq(A) represents the number of times item set A appears, and N represents the total number of transactions.

[0063] Confidence: It indicates the probability that conclusion B will occur when premise A occurs. Confidence reflects the reliability of the rule.

[0064] formula:

[0065] This means that among the transactions that include A, what percentage also includes B.

[0066] Lift: Measures the strength of the association between A and B. The higher the lift, the stronger the relationship between A and B. If the lift is 1, it means there is no relationship between A and B; greater than 1 indicates a positive correlation, equal to 1 indicates no correlation, and less than 1 indicates a negative correlation.

[0067] formula:

[0068] Knowledge base establishment: The system generates a fault knowledge base by statistically analyzing the fault types, causes and solutions that occur during the maintenance process. Through continuous learning and optimization of association rule learning, the system can automatically update and improve the fault knowledge base, and continuously improve fault classification and solution recommendations based on new fault cases. When the system identifies a new type of fault, the system will automatically learn its cause and solution, update the knowledge base, and respond quickly to similar faults in the future.

[0069] Maintenance log module:

[0070] Log Recording and Analysis: The system systematically records and tracks equipment repair, maintenance and inspection operations, providing detailed information such as time, content, and results, which helps with equipment failure analysis, maintenance decisions, and performance optimization.

[0071] Long-term data accumulation: By accumulating long-term maintenance logs, the system can identify potential equipment problems, predict failure trends, and optimize maintenance strategies.

[0072] Equipment reliability analysis module:

[0073] MTBF and MTTR calculation: The system analyzes the equipment operation data and calculates the equipment's mean time between failures (MTBF) and mean time to repair (MTTR), providing a scientific basis for equipment reliability assessment.

[0074] The calculation method of MTBF is:

[0075] MTBF = total operating time / total number of failures

[0076] Mean time to repair (MTTR) calculation method:

[0077] MTTR = Total time to repair a fault / Number of faults

[0078] Failure trends and maintenance efficiency: Combining MTBF and MTTR indicators, the system can predict equipment failure frequency, evaluate maintenance efficiency, and provide decision support for enterprises to optimize equipment operation and maintenance management.

[0079] Cost accounting module:

[0080] Maintenance cost tracking: The system accurately calculates the cost of each maintenance activity by recording the purchase cost of spare parts, inventory consumption, and maintenance hours and labor costs.

[0081] Cost optimization and budget management: Integrated data supports enterprises in reasonable budget planning and cost optimization, helping enterprises to better allocate resources and reduce maintenance costs.

[0082] Performance analysis module:

[0083] Maintenance personnel performance evaluation: The system automatically records and analyzes key indicators of each maintenance, such as maintenance time, troubleshooting success rate, maintenance frequency, etc., to quantify the maintenance personnel's work efficiency, skill level and problem-solving ability.

[0084] Comprehensive performance evaluation: We conduct weighted analysis based on factors such as maintenance complexity and fault type, and conduct a comprehensive performance evaluation of maintenance personnel from multiple dimensions, providing a basis for enterprises to improve the overall quality of their maintenance teams.

[0085] Each module of the present invention adopts an architecture that combines the Internet of Things technology with a cloud computing platform. The system collects data in real time through Internet of Things devices and uploads the data to the cloud platform for centralized management and analysis. The maintenance management system supports multi-platform access, including PC, mobile phone and mobile devices, ensuring efficient linkage of data collection, task assignment, maintenance execution and data analysis.

[0086] The technical features of the above-described embodiments may be arbitrarily combined. To make the description concise, not all possible combinations of the technical features in the above-described embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0087] The above-mentioned embodiments only express several implementation methods of the present invention, and the description thereof is relatively specific and detailed, but it cannot be understood as limiting the scope of the patent of the present invention. It should be pointed out that, for ordinary technicians in this field, several variations and improvements can be made without departing from the concept of the present invention, which all belong to the protection scope of the present invention. Therefore, the protection scope of the patent of the present invention shall be subject to the attached claims.

[0088] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

Claims

1. A full-process maintenance management system for factory equipment based on the Internet of Things, characterized in that: The system comprises: Maintenance data collection subsystem, which is used to collect equipment information, complete maintenance order reporting, perform abnormal data monitoring and spare parts management; A maintenance process management subsystem, which is used to assign maintenance tasks, record maintenance processes, and record the use of spare parts; The maintenance data analysis subsystem is used to build a fault knowledge base, optimize maintenance strategies, analyze equipment operation data, and perform cost accounting and performance analysis.

2. According to the Internet of Things-based factory equipment full-process maintenance management system according to claim 1, it is characterized in that: The maintenance process management subsystem includes a maintenance assignment module, which is used to group and manage devices according to the hierarchical structure of IoT devices, set maintenance positions and bind them to devices to ensure a one-to-many relationship between maintenance personnel and equipment. When an abnormality occurs in the equipment, a designated maintenance personnel is assigned to the maintenance order based on the equipment type and job responsibilities, and the task is pushed to the corresponding personnel.

3. The whole process maintenance management system of factory equipment based on the Internet of Things according to claim 2 is characterized in that: The maintenance process management subsystem also includes a maintenance record and effect confirmation module, which is used to record maintenance records. The maintenance records include the cause of the failure, solutions, effect photos and the use of spare parts. After the maintenance is completed, the reporting personnel or management personnel confirm the maintenance effect and evaluate the maintenance effect based on the confirmation result. After confirmation, a maintenance completion report is generated and all maintenance records are archived.

4. The whole process maintenance management system of factory equipment based on the Internet of Things according to claim 3 is characterized in that: The maintenance process management subsystem also includes a spare parts usage record module, which is used to record the entry and exit of spare parts, record the use of spare parts, including spare part name, use organization and use quantity, track the consumption of spare parts used in each maintenance process, automatically update inventory and generate spare parts usage records.

5. The whole process maintenance management system of factory equipment based on the Internet of Things according to claim 1 is characterized in that: The maintenance data collection subsystem includes an on-site reporting module and an inspection and maintenance module, which are used to automatically collect instrument information of the equipment through a real-time data connection with the equipment through the Internet of Things. The inspection and maintenance module is used to regularly generate inspection tasks according to a predetermined inspection and maintenance plan, and push the tasks to the inspection person in charge. During the inspection process, the inspection personnel directly feedback abnormal problems through the task list. The system automatically generates a maintenance reporting form based on the feedback information. Combined with the abnormal data recorded during the inspection process, the system automatically generates a maintenance order and pushes it to the maintenance team for processing.

6. The whole process maintenance management system of factory equipment based on the Internet of Things according to claim 5 is characterized in that: The maintenance data collection subsystem also includes an abnormal data monitoring module and a spare parts management module. The abnormal data monitoring module is used to collect equipment operation data in real time, and automatically monitor the abnormal conditions of the equipment by setting abnormal monitoring points and identification tags. When an abnormality is detected, it automatically identifies the abnormality and generates a maintenance order based on preset fault conditions.

7. The whole process maintenance management system of factory equipment based on the Internet of Things according to claim 1 is characterized in that: The maintenance data analysis subsystem includes a fault analysis and intelligent knowledge base construction module, which is used to store fault records and maintenance data. By analyzing historical fault data, it identifies the relationship between different fault types and the equipment operating environment, configuration and operating conditions, and generates effective rules.

8. The whole process maintenance management system of factory equipment based on the Internet of Things according to claim 7 is characterized in that: The maintenance data analysis subsystem also includes a maintenance log module, which is used to systematically record and track the repair, maintenance and inspection operations of the equipment, provide detailed time, content and results, identify potential equipment problems and predict failure trends through maintenance logs, and optimize maintenance strategies.

9. The whole process maintenance management system of factory equipment based on the Internet of Things according to claim 8 is characterized in that: The maintenance data analysis subsystem also includes an equipment reliability analysis module for analyzing equipment operation data and calculating the mean trouble-free operation time MTBF and mean repair time MTTR of the equipment. The calculation method of mean trouble-free operation time is: MTBF = total operation time / total number of failures; Mean repair time MTTR calculation method: MTTR = total fault repair time / number of faults.

10. The whole process maintenance management system of factory equipment based on Internet of Things according to claim 9 is characterized in that: The maintenance data analysis subsystem also includes a cost accounting module and a performance analysis module. The cost accounting module is used to record the purchase cost of spare parts, inventory consumption, and maintenance hours and labor costs. The performance analysis module is used to perform weighted analysis based on maintenance complexity and fault type, and conduct a comprehensive performance evaluation of maintenance personnel from multiple dimensions.

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