A gas pump assembly fault management system

Abstract

The application discloses a kind of air pump unit fault management systems, it is related to air pump fault management technical field, real-time acquisition and air pump safety operation related multi-source data are obtained by sensing equipment, processing module is preprocessed to the multi-source data collected, including data cleaning, denoising, etc. Operation is to ensure data quality and accuracy. Feature data is extracted from the data after preprocessing, analysis module will be obtained into air pump prediction model and comprehensively analyzed after feature data, air pump prediction model is air pump and outputs an operation assignment, regional division module will air pump be divided into corresponding area according to the comparison result of operation assignment and gradient threshold value, and according to the regional division result of air pump, corresponding management strategy is generated, and management strategy is sent to administrator. The management system can effectively predict the fault of air pump in the operation process, so as to carry out early warning and management before air pump failure, guarantee the safety and stability of air pump operation.

Description

Technical Field

[0001] This invention relates to the field of air pump fault management technology, and more specifically to an air pump unit fault management system. Background Technology

[0002] With the rapid development of industrial technology and the continuous expansion of equipment unit scale, the troubleshooting and maintenance management of equipment units are facing increasingly greater challenges. Traditional troubleshooting methods often rely on manual experience and on-site operation, resulting in low efficiency and difficulty in dealing with complex and ever-changing fault situations.

[0003] Existing systems typically issue alerts when equipment malfunctions during operation. However, this delay in timely repairs can lead to the spread of the malfunction (especially in high-risk environments), increasing maintenance costs and posing safety hazards.

[0004] Based on this, the present invention proposes a fault management system for air pump units, which can effectively predict air pump failures during operation, thereby providing early warning and management before air pump failures occur, and ensuring the safety and stability of air pump operation. Summary of the Invention

[0005] The purpose of this invention is to provide a fault management system for air pump units to address the shortcomings in the prior art.

[0006] To achieve the above objectives, the present invention provides the following technical solution: a fault management system for air pump units, comprising a numbering module, a data acquisition module, a processing module, an analysis module, a region division module, and a maintenance module;

[0007] Numbering module: Obtain the number of air pumps in the factory, number each air pump, and send the air pump number information and identification information to the database for storage;

[0008] Data acquisition module: During the operation of the air pump, it acquires multi-source data related to the safe operation of the air pump in real time through sensing devices;

[0009] Processing module: preprocesses the collected multi-source data and extracts feature data from the preprocessed data;

[0010] Analysis module: After the acquired feature data is substituted into the air pump prediction model for comprehensive analysis, the air pump prediction model outputs an operating value for the air pump.

[0011] Region division module: Based on the comparison results between the running assignment and the gradient threshold, the air pump is assigned to the corresponding region, and a corresponding management policy is generated based on the region division results of the air pump. The management policy is then sent to the administrator.

[0012] Maintenance module: When performing regular maintenance on the air pumps in the factory, the module retrieves the historical operating values ​​of the air pumps from the database, combines the historical operating values ​​with the air pump operating frequency to generate maintenance values ​​for the air pumps, retrieves the number and identification information from the database, sorts all the air pumps according to their maintenance values ​​from largest to smallest, generates a maintenance list, and sends the maintenance list to the administrator.

[0013] Preferably, the analysis module: after substituting the acquired feature data into the air pump prediction model for comprehensive analysis, the air pump prediction model outputs an operating value for the air pump;

[0014] Acquire characteristic data of the air pump, including inlet and outlet flow deviation coefficients, multi-point air pressure deviations, and current consumption rates.

[0015] By substituting the inlet and outlet flow deviation coefficients, multi-point air pressure deviations, power factor, and current consumption rate into the air pump prediction model for comprehensive analysis, the output operating values ​​are calculated. The function expression is as follows:

[0016] In the formula, Assign values ​​for execution. The parameters are power factor, inlet and outlet flow deviation coefficient, multi-point gas pressure deviation, and current consumption rate. , , , These are the power factor, inlet / outlet flow deviation coefficient, multi-point gas pressure deviation, and proportional coefficient of current consumption rate, respectively. , , , All are greater than 0.

[0017] Preferably, the region division module classifies the air pump into the corresponding region based on the comparison result of the operation assignment and the gradient threshold, and generates the corresponding management strategy based on the region division result of the air pump.

[0018] The gradient threshold includes a first threshold and a second threshold. After obtaining the running assignment, the running assignment is compared with the first threshold and the second threshold. If the running assignment is greater than or equal to the second threshold, it is predicted that there is no fault in the operation of the air pump, the air pump is classified into the operating area, and no management strategy is generated.

[0019] If the first threshold ≤ the operation assignment < the second threshold, it is predicted that there is a minor fault in the operation of the air pump. The air pump is then included in the monitoring area, and a management strategy for continuous monitoring of the air pump operation is generated.

[0020] If the running value is less than the first threshold, it is predicted that there is a serious malfunction in the air pump. The air pump will be classified into the maintenance area, and a maintenance management strategy for the air pump will be generated.

[0021] Preferably, the logic for obtaining the power factor is as follows: obtain the active power and apparent power of the air pump. The active power is the power that the air pump actually uses to do work, and the apparent power is the total power that the air pump actually consumes, including active power and reactive power. The power factor is obtained by dividing the active power by the apparent power.

[0022] The logic for obtaining the current consumption rate is as follows: obtain the current consumption of the air pump at the previous moment, obtain the current consumption of the air pump at the current moment, subtract the current consumption of the air pump at the previous moment from the current consumption of the air pump at the current moment to obtain the consumption increase, obtain the monitoring duration by subtracting the previous moment from the current moment, and obtain the current consumption rate by dividing the consumption increase by the monitoring duration.

[0023] Preferably, the numbering module obtains the number of air pumps in the factory through the device interface, and generates a unique number for each air pump, using a number, letter, or combination of numbers.

[0024] Collect identification information for each air pump, including air pump model, installation location, and manufacturer information. Send the serial number and identification information of each air pump to the database for storage, using either an SQL database or a NoSQL database.

[0025] Establish the corresponding table structure in the database, including fields such as air pump number and identification information, and back up the database regularly.

[0026] Preferably, the logic for obtaining the inlet and outlet flow deviation coefficient is as follows: during the operation of the air pump, the difference between the actual flow rate and the standard flow rate at the air pump inlet is obtained and marked as the first difference; the difference between the actual flow rate and the standard flow rate at the air pump outlet is obtained and marked as the second difference; the first difference is added to the second difference to obtain the inlet and outlet flow deviation coefficient.

[0027] The logic for obtaining the multi-point air pressure deviation is as follows: multiple sampling points are obtained on the surface of the air pump. The air pressure difference between the inside and outside of the air pump is calculated at each sampling point through an air pressure sensor, and the standard deviation of the air pressure difference is calculated as the multi-point air pressure deviation.

[0028] Preferably, the processing module performs preliminary processing on the collected data, including removing outliers, filling in missing values, filtering the raw data, and eliminating noise caused by sensor errors or environmental interference.

[0029] From the preprocessed data, key features describing the operating status of the air pump are extracted, including frequency domain, time domain and statistical features. Based on the importance and correlation of each feature, a feature set is selected.

[0030] The extracted feature data is normalized so that all feature values ​​are within the same numerical range.

[0031] A method for intelligent processing and utilization of equipment unit fault data, the method comprising the following steps:

[0032] The number of air pumps in the factory is obtained, and each air pump is numbered. The number and identification information of the air pumps are then sent to the database for storage.

[0033] During the operation of the air pump, multi-source data related to the safe operation of the air pump are acquired in real time through sensing devices. The collected multi-source data is preprocessed, and feature data is extracted from the preprocessed data.

[0034] After the acquired feature data is substituted into the air pump prediction model for comprehensive analysis, the air pump prediction model outputs an operating value for the air pump.

[0035] Based on the comparison between the operational assignment and the gradient threshold, the air pump is assigned to the corresponding area, and a corresponding management policy is generated based on the area division of the air pump. The management policy is then sent to the administrator.

[0036] When performing regular maintenance on the air pumps in the factory, the historical operating values ​​of the air pumps are obtained from the database. The historical operating values ​​and the operating frequency of the air pumps are combined to generate maintenance values ​​for the air pumps. All air pumps are sorted from largest to smallest according to their maintenance values ​​to generate a maintenance list, which is then sent to the administrator.

[0037] The technical effects and advantages provided by the present invention in the above technical solution are as follows:

[0038] 1. This application utilizes a data acquisition module to acquire multi-source data related to the safe operation of the air pump in real time through sensing devices during the air pump's operation. A processing module preprocesses the acquired multi-source data, including data cleaning and noise reduction, to ensure data quality and accuracy. Feature data is extracted from the preprocessed data. The analysis module then inputs the acquired feature data into the air pump prediction model for comprehensive analysis. The air pump prediction model outputs an operational assignment for the air pump. The region division module classifies the air pump into corresponding regions based on the comparison between the operational assignment and gradient thresholds, and generates corresponding management strategies based on the region division results. These management strategies are then sent to the administrator. This management system can effectively predict air pump failures during operation, providing early warnings and management before air pump failures occur, thus ensuring the safety and stability of air pump operation.

[0039] 2. When this application performs periodic maintenance on the air pumps in the factory through the maintenance module, it retrieves the historical operating values ​​of the air pumps from the database. Combining these historical operating values ​​with the air pump operating frequency, it generates maintenance values ​​for each air pump. After retrieving the pump number and identification information from the database, it sorts all air pumps according to their maintenance values ​​from highest to lowest, generating a maintenance list. This maintenance list is then sent to the administrator, who selects the maintenance order for the air pumps based on the ascending order of the list. This system effectively utilizes the operating values ​​of the air pumps to generate maintenance values ​​for all air pumps, thereby significantly improving the efficiency of air pump maintenance. Attached Figure Description

[0040] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in this invention. For those skilled in the art, other drawings can be obtained based on these drawings.

[0041] Figure 1 This is a system module diagram of the present invention.

[0042] Figure 2 This is a flowchart of the method of the present invention. Detailed Implementation

[0043] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0044] Example: Please refer to Figure 1 and Figure 2 As shown in this embodiment, a fault management system for an air pump unit includes a numbering module, a data acquisition module, a processing module, an analysis module, a region division module, and a maintenance module.

[0045] Numbering module: Obtain the number of air pumps in the factory, number each air pump, and send the air pump number information and identification information to the database for storage;

[0046] Data acquisition module: During the operation of the air pump, it acquires multi-source data related to the safe operation of the air pump in real time through sensing devices;

[0047] Processing module: Preprocesses the collected multi-source data, including data cleaning, noise reduction, and other operations, to ensure data quality and accuracy. Extracts feature data, such as frequency domain features, time domain features, and statistical features, from the preprocessed data to describe the operating status of the air pump;

[0048] Analysis module: After the acquired feature data is substituted into the air pump prediction model for comprehensive analysis, the air pump prediction model outputs an operating value for the air pump.

[0049] Region division module: Based on the comparison results between the running assignment and the gradient threshold, the air pump is assigned to the corresponding region, and a corresponding management policy is generated based on the region division results of the air pump. The management policy is then sent to the administrator.

[0050] Maintenance Module: When performing regular maintenance on the air pumps in the factory, the module retrieves the historical operating values ​​of the air pumps from the database. Combining the historical operating values ​​with the air pump operating frequency, it generates maintenance values ​​for the air pumps. After retrieving the number and identification information from the database, it sorts all the air pumps according to their maintenance values ​​from largest to smallest, generates a maintenance list, and sends the maintenance list to the administrator. The administrator selects the maintenance order of the air pumps according to the ascending order of the maintenance list.

[0051] This application utilizes a data acquisition module to acquire multi-source data related to the safe operation of the air pump in real time through sensors during operation. A processing module preprocesses the acquired multi-source data, including data cleaning and noise reduction, to ensure data quality and accuracy. Feature data is extracted from the preprocessed data. An analysis module then inputs this feature data into an air pump prediction model for comprehensive analysis. The prediction model outputs an operational assignment for the air pump. A region division module, based on a comparison between the operational assignment and a gradient threshold, classifies the air pump into a corresponding region and generates a corresponding management strategy based on the region division results. This management strategy is then sent to the administrator. This management system can effectively predict air pump failures during operation, providing early warnings and management before failures occur, thus ensuring the safety and stability of air pump operation.

[0052] This application utilizes a maintenance module to periodically maintain air pumps within the factory. It retrieves historical operating values ​​from a database, combines these values ​​with the pump's operating frequency to generate maintenance values ​​for each pump. After obtaining the pump's serial number and identification information from the database, all pumps are sorted according to their maintenance values ​​from highest to lowest, generating a maintenance list. This list is then sent to the administrator, who selects the appropriate maintenance order for each pump based on the list. This system effectively utilizes the pump's operating values ​​to generate maintenance values ​​for all pumps, thereby significantly improving maintenance efficiency.

[0053] The specific workflow is as follows:

[0054] The system obtains the number of air pumps in the factory and assigns a number to each pump. The pump number and identification information are then sent to the database for storage. During the operation of the air pumps, multi-source data related to the safe operation of the air pumps are acquired in real time through sensing devices. The collected multi-source data is preprocessed, including data cleaning, noise reduction, and other operations, to ensure data quality and accuracy. Feature data, such as frequency domain features, time domain features, and statistical features, are extracted from the preprocessed data to describe the operating status of the air pump. After comprehensive analysis, the acquired feature data is substituted into the air pump prediction model, which outputs an operating value for the air pump. Based on the comparison between the operating value and the gradient threshold, the air pump is classified into the corresponding region, and a corresponding management strategy is generated based on the region classification result. The management strategy is sent to the administrator. When the air pumps in the factory are regularly maintained, the historical operating values ​​of the air pumps are obtained from the database. The historical operating values ​​and the air pump operating frequency are combined to generate maintenance values ​​for the air pumps. All air pumps are sorted from largest to smallest according to their maintenance values ​​to generate a maintenance list. The maintenance list is sent to the administrator, who selects the maintenance order of the air pumps according to the ascending order of the maintenance list.

[0055] Numbering module: Obtain the number of air pumps in the factory, number each air pump, and send the air pump number information and identification information to the database for storage;

[0056] Obtain the number of air pumps: Obtain the number of air pumps in the factory through the system or equipment interface, and ensure that all air pumps are numbered.

[0057] Number each air pump: Generate a unique number for each air pump, which can be a number, letter, or a combination of numbers, for subsequent identification and management.

[0058] Identification information collection: Collect identification information for each air pump, including air pump model, installation location, manufacturer and other relevant information, for subsequent maintenance and management.

[0059] Data is sent to a database for storage: The serial number and identification information of each air pump are sent to a database for storage to ensure the security and reliability of the information. SQL or NoSQL databases can be used for storage.

[0060] Database Management: Establish corresponding table structures in the database, including fields such as air pump number and identification information, for subsequent querying and management. Regularly back up the database to ensure data integrity and recoverability.

[0061] To ensure effective management of all air pumps within the factory, it is necessary to accurately obtain the quantity of air pumps through system or equipment interfaces and assign a unique number to each pump. A scientifically rigorous numbering method, such as numbers, letters, or combinations thereof, should be used to assign a unique identification number to each air pump, ensuring accuracy for subsequent identification and management. Detailed identification information should be collected for each air pump, covering key information such as pump model, installation location, and manufacturer, providing comprehensive support for subsequent maintenance and management. To ensure the long-term security and reliability of the information, all air pump numbers and identification information will be sent to a database for storage via an efficient data transmission mechanism. This process is compatible with various storage methods, including SQL and NoSQL databases. Structured information tables should be built in the database, containing core fields such as air pump number and identification information, facilitating subsequent data retrieval and management. Simultaneously, a regular database backup strategy should be implemented to ensure data integrity and recoverability.

[0062] Data acquisition module: During the operation of the air pump, it acquires multi-source data related to the safe operation of the air pump in real time through sensing devices;

[0063] Sensor deployment: Deploy sensor devices, including vibration sensors, temperature sensors, and pressure sensors, on the air pump equipment to monitor the operating status of the air pump in real time.

[0064] Data acquisition parameter settings: Set the acquisition parameters of the sensing device, including sampling frequency, data storage format, data transmission method, etc., to ensure the accuracy and integrity of the acquired data.

[0065] Real-time data acquisition: The sensing device collects data from multiple sources during the operation of the air pump in real time, including vibration data, temperature data, pressure data, etc., to gain a comprehensive understanding of the air pump's operating status.

[0066] Data transmission and storage: The collected data is transmitted to the data acquisition system or cloud platform through communication protocols to achieve real-time data transmission and storage, ensuring data security and reliability.

[0067] Data quality monitoring: Monitor the data quality during the data collection process, including data loss, abnormal data, etc., and process and correct them in a timely manner to ensure the accuracy and reliability of the data.

[0068] To ensure real-time monitoring of the air pump's operation, we need to deploy various sensors on the air pump, including vibration sensors, temperature sensors, and pressure sensors, to comprehensively capture the air pump's operating status information. To ensure the accuracy and completeness of the collected data, we will set reasonable acquisition parameters for the sensors, including key parameters such as sampling frequency, data storage format, and data transmission method. The sensors will collect multi-source data during the air pump's operation in real time, including vibration data, temperature data, and pressure data, to provide a comprehensive understanding of the air pump's operating status. We will transmit the collected data to the data acquisition system or cloud platform in real time through efficient communication protocols to ensure data timeliness and security. Simultaneously, we will establish a reliable data storage mechanism to guarantee data security and reliability. To ensure data accuracy and credibility, we will establish a strict data quality monitoring system to monitor data loss and abnormal data during the data acquisition process in real time, and promptly process and correct them.

[0069] Processing module: Preprocesses the collected multi-source data, including data cleaning, noise reduction, and other operations, to ensure data quality and accuracy. Extracts feature data, such as frequency domain features, time domain features, and statistical features, from the preprocessed data to describe the operating status of the air pump;

[0070] Data preprocessing: The collected data is initially processed, including removing outliers and filling in missing values, to ensure the integrity and accuracy of the data.

[0071] Data filtering: To improve data quality, the raw data is filtered to eliminate noise caused by sensor errors or environmental interference.

[0072] Feature extraction: Extract key features describing the operating status of the air pump from the preprocessed data, including frequency domain, time domain and statistical features.

[0073] Feature selection: Select the most representative feature set based on the importance and relevance of each feature.

[0074] Data normalization: Normalize the extracted feature data so that the feature values ​​are within the same range, which facilitates subsequent data analysis and processing.

[0075] Analysis module: After the acquired feature data is substituted into the air pump prediction model for comprehensive analysis, the air pump prediction model outputs an operating value for the air pump.

[0076] Acquire characteristic data of the air pump, including inlet and outlet flow deviation coefficients, multi-point air pressure deviations, and current consumption rates.

[0077] By substituting the inlet and outlet flow deviation coefficients, multi-point air pressure deviations, power factor, and current consumption rate into the air pump prediction model for comprehensive analysis, the output operating values ​​are calculated. The function expression is as follows:

[0078] In the formula, Assign values ​​for execution. The parameters are power factor, inlet and outlet flow deviation coefficient, multi-point gas pressure deviation, and current consumption rate. , , , These are the power factor, inlet / outlet flow deviation coefficient, multi-point gas pressure deviation, and proportional coefficient of current consumption rate, respectively. , , , All are greater than 0;

[0079] The logic for obtaining the inlet and outlet flow deviation coefficient is as follows: During the operation of the air pump, the difference between the actual flow rate and the standard flow rate at the air pump inlet is obtained and marked as the first difference. The difference between the actual flow rate and the standard flow rate at the air pump outlet is obtained and marked as the second difference. The first difference is added to the second difference to obtain the inlet and outlet flow deviation coefficient. The larger the inlet and outlet flow deviation coefficient, the greater the flow deviation at the air pump inlet and outlet, which may indicate the following abnormalities in the air pump:

[0080] Leakage: Increased flow deviation may indicate an internal leak in the air pump, causing gas to be unable to be fully delivered to the outlet, resulting in an imbalance of flow at the inlet and outlet.

[0081] Blockage: Increased flow deviation may also be due to blockage in components such as internal pipes or filters of the air pump, which obstructs gas flow and causes an imbalance in inlet and outlet flow.

[0082] Abnormal wear: Abnormal wear of internal components of the air pump, such as blades and seals, may also lead to an increase in the flow deviation between the inlet and outlet, affecting the normal operation of the air pump.

[0083] Unbalanced operation: Increased flow deviation at the inlet and outlet of the air pump may also be due to unbalanced operation of the air pump, such as impeller asymmetry, bearing wear, etc., which leads to unstable output flow of the air pump.

[0084] The logic for obtaining multi-point air pressure deviation is as follows: Multiple sampling points are obtained on the surface of the air pump. The air pressure difference between the inside and outside of the air pump is calculated at each sampling point using an air pressure sensor. The standard deviation of the air pressure difference is then calculated as the multi-point air pressure deviation. The larger the multi-point air pressure deviation, the greater the fluctuation in air pressure deviation at the multiple sampling points on the air pump surface, which may indicate a leakage problem. Specifically:

[0085] Poor sealing: Damaged or aged seals of the air pump, such as sealing rings and gaskets, prevent the air pump from sealing completely during operation, resulting in gas leakage and increased fluctuations in air pressure at different sampling points.

[0086] Loose connections: Loose connections or loose threads at the internal pipe connections of the air pump can cause gas leakage during transport, resulting in increased fluctuations in air pressure at different sampling points.

[0087] Leakage points: Cracks, leaks, or other leaks exist in the internal pipes or connections of the air pump, preventing the gas from being completely delivered to the outlet and causing increased fluctuations in air pressure.

[0088] Valve issues: The inlet and outlet valves of the air pump are faulty or not fully closed, causing gas to leak at the valves, resulting in increased fluctuations in air pressure at different sampling points.

[0089] Wear and tear: Wear or damage to internal components of the air pump, such as blades and seals, reduces the air pump's sealing performance, leading to gas leakage and increased fluctuations in air pressure.

[0090] The logic for obtaining the power factor is as follows: Obtain the active power and apparent power of the air pump. Active power is the power actually used by the air pump to do work, and apparent power is the total power actually consumed by the air pump, including active power and reactive power. The power factor is obtained by dividing the active power by the apparent power. The smaller the power factor of the air pump, the more likely the air pump has the following abnormalities:

[0091] High reactive power loss: When the power factor of the air pump is low, it indicates that there is a large reactive power loss. The electrical energy in the system is not effectively converted into useful power. This may be due to circuit design problems, capacitor failure, or circuit overload.

[0092] Low energy efficiency: A low power factor means that the proportion of power actually used to do work in the total power is low, which leads to inefficient use of electrical energy, reduces the operating efficiency of the air pump, and causes serious energy waste.

[0093] Increased grid load: Air pumps with a low power factor can bring a large reactive power load to the grid, thereby affecting the stability and operating efficiency of the entire grid, and may cause abnormal operation of other equipment or grid load imbalance.

[0094] The logic for obtaining the current consumption rate is as follows: Obtain the current consumption of the air pump at the previous moment, obtain the current consumption of the air pump at the current moment, subtract the current consumption of the air pump at the previous moment from the current current consumption to obtain the consumption increase, obtain the monitoring duration by subtracting the previous moment from the current moment, and divide the consumption increase by the monitoring duration to obtain the current consumption rate. The higher the current consumption rate of the air pump, the more likely the air pump has the following abnormalities:

[0095] Motor overload: When the pump's current consumption rate increases, it may mean that the motor is under excessive load, leading to increased current consumption during operation. This could be caused by poor pump operating conditions, excessive resistance, abnormal liquid flow, or other reasons.

[0096] Wear of mechanical parts: An increase in the pump's current consumption rate may also be due to severe wear of the pump's mechanical parts, which leads to increased pump operating resistance, requiring the motor to consume more current to maintain normal operation.

[0097] Circuit fault: In addition, an increased current consumption rate of the air pump may also be due to circuit faults, such as poor cable contact or short circuit in the motor windings, which can lead to an abnormal increase in current.

[0098] Region division module: Based on the comparison results between the running assignment and the gradient threshold, the air pump is assigned to the corresponding region, and a corresponding management policy is generated based on the region division results of the air pump. The management policy is then sent to the administrator.

[0099] The gradient threshold includes a first threshold and a second threshold. After obtaining the running assignment, the running assignment is compared with the first threshold and the second threshold. If the running assignment is greater than or equal to the second threshold, it is predicted that there is no fault in the operation of the air pump, the air pump is classified into the operating area, and no management strategy is generated.

[0100] If the first threshold ≤ the operation assignment < the second threshold, it is predicted that there is a minor fault in the operation of the air pump. The air pump is then included in the monitoring area, and a management strategy for continuous monitoring of the air pump operation is generated.

[0101] If the running value is less than the first threshold, it is predicted that there is a serious malfunction in the air pump. The air pump will be classified into the maintenance area, and a maintenance management strategy for the air pump will be generated.

[0102] Maintenance module: When performing regular maintenance on the air pumps in the factory, the module retrieves the historical operating values ​​of the air pumps from the database, combines the historical operating values ​​with the air pump operating frequency to generate maintenance values ​​for the air pumps, retrieves the number and identification information from the database, sorts all the air pumps according to the maintenance values ​​from largest to smallest, generates a maintenance list, and sends the maintenance list to the administrator. The administrator selects the maintenance order of the air pumps according to the ascending order of the maintenance list.

[0103] After obtaining the historical operating values ​​for each air pump, since there are multiple historical operating values, the average operating value for each air pump is calculated. Then, the historical operating frequency of the air pump is obtained. The maintenance value is obtained by weighting the average operating value and the historical operating frequency. The expression is as follows:

[0104] In the formula, To maintain the value, Assign a value to the average running rate. Historical operating frequency;

[0105] Sort all air pumps according to their maintenance values ​​from highest to lowest to generate a maintenance list. Send the maintenance list to the administrator, who will then select the maintenance order for the air pumps based on the ascending order of the list.

[0106] The above formulas are all dimensionless calculations. The formulas are derived from software simulations based on a large amount of collected data to obtain the most recent real-world results. The preset parameters in the formulas are set by those skilled in the art according to the actual situation.

[0107] In the description of this specification, references to terms such as "an embodiment," "example," "specific example," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of the invention. In this specification, illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples.

[0108] The preferred embodiments of the present invention disclosed above are merely illustrative of the invention. These preferred embodiments do not exhaustively describe all details, nor do they limit the invention to any specific implementation. Clearly, many modifications and variations can be made based on the content of this specification. This specification selects and specifically describes these embodiments to better explain the principles and practical applications of the invention, thereby enabling those skilled in the art to better understand and utilize the invention. The invention is limited only by the claims and their full scope and equivalents.

Claims

1. A fault management system for an air pump unit, characterized in that: It includes a numbering module, a data acquisition module, a processing module, an analysis module, a region division module, and a maintenance module; Numbering module: Obtain the number of air pumps in the factory, number each air pump, and send the air pump number information and identification information to the database for storage; Data acquisition module: During the operation of the air pump, it acquires multi-source data related to the safe operation of the air pump in real time through sensing devices; Processing module: preprocesses the collected multi-source data and extracts feature data from the preprocessed data; Analysis module: After the acquired feature data is substituted into the air pump prediction model for comprehensive analysis, the air pump prediction model outputs an operating value for the air pump. Region division module: Based on the comparison results between the running assignment and the gradient threshold, the air pump is assigned to the corresponding region, and a corresponding management policy is generated based on the region division results of the air pump. The management policy is then sent to the administrator. Maintenance module: When performing regular maintenance on the air pumps in the factory, the module retrieves the historical operating values ​​of the air pumps from the database, combines the historical operating values ​​with the air pump operating frequency to generate maintenance values ​​for the air pumps, retrieves the number information and identification information from the database, sorts all the air pumps according to the maintenance values ​​from largest to smallest, generates a maintenance list, and sends the maintenance list to the administrator. The analysis module inputs the acquired feature data into the air pump prediction model for comprehensive analysis, and then the air pump prediction model outputs an operating value for the air pump. Acquire characteristic data of the air pump, including inlet and outlet flow deviation coefficients, multi-point air pressure deviations, and current consumption rates. By substituting the inlet and outlet flow deviation coefficients, multi-point air pressure deviations, power factor, and current consumption rate into the air pump prediction model for comprehensive analysis, the output operating values ​​are calculated. The function expression is as follows: In the formula, Assign values ​​for execution. For power factor, The inlet and outlet flow deviation coefficient. For multi-point air pressure deviation, For current consumption rate, , , , These are the power factor, inlet / outlet flow deviation coefficient, multi-point gas pressure deviation, and proportional coefficient of current consumption rate, respectively. , , , All are greater than 0.

2. The air pump unit fault management system according to claim 1, characterized in that: The region division module assigns the air pump to the corresponding region based on the comparison between the operation assignment and the gradient threshold, and generates corresponding management strategies based on the region division results of the air pump. The gradient threshold includes a first threshold and a second threshold. After obtaining the running assignment, the running assignment is compared with the first threshold and the second threshold. If the running assignment is greater than or equal to the second threshold, it is predicted that there is no fault in the operation of the air pump, the air pump is classified into the operating area, and no management strategy is generated. If the first threshold ≤ the operation assignment < the second threshold, it is predicted that there is a minor fault in the operation of the air pump. The air pump is then included in the monitoring area, and a management strategy for continuous monitoring of the air pump operation is generated. If the running value is less than the first threshold, it is predicted that there is a serious malfunction in the air pump. The air pump will be classified into the maintenance area, and a maintenance management strategy for the air pump will be generated.

3. The air pump unit fault management system according to claim 2, characterized in that: The processing module performs preliminary processing on the collected data, including removing outliers, filling in missing values, filtering the raw data, and eliminating noise caused by sensor errors or environmental interference. From the preprocessed data, key features describing the operating status of the air pump are extracted, including frequency domain, time domain and statistical features. Based on the importance and correlation of each feature, a feature set is selected. The extracted feature data is normalized so that all feature values ​​are within the same numerical range.

4. The air pump unit fault management system according to claim 3, characterized in that: The numbering module obtains the number of air pumps in the factory through the device interface, and generates a unique number for each air pump, using a number, letter, or combination of numbers. Collect identification information for each air pump, including air pump model, installation location, and manufacturer information. Send the serial number and identification information of each air pump to the database for storage, using either an SQL database or a NoSQL database. Establish the corresponding table structure in the database, including fields for air pump number and identification information, and back up the database regularly.

5. The air pump unit fault management system according to claim 4, characterized in that: The logic for obtaining the inlet and outlet flow deviation coefficient is as follows: during the operation of the air pump, the difference between the actual flow rate and the standard flow rate at the air pump inlet is obtained and marked as the first difference; the difference between the actual flow rate and the standard flow rate at the air pump outlet is obtained and marked as the second difference; the first difference is added to the second difference to obtain the inlet and outlet flow deviation coefficient. The logic for obtaining the multi-point air pressure deviation is as follows: multiple sampling points are obtained on the surface of the air pump. The air pressure difference between the inside and outside of the air pump is calculated at each sampling point through an air pressure sensor, and the standard deviation of the air pressure difference is calculated as the multi-point air pressure deviation.

6. The air pump unit fault management system according to claim 5, characterized in that: The logic for obtaining the power factor is as follows: obtain the active power and apparent power of the air pump. The active power is the power that the air pump actually uses to do work, and the apparent power is the total power that the air pump actually consumes, including active power and reactive power. The power factor is obtained by dividing the active power by the apparent power. The logic for obtaining the current consumption rate is as follows: obtain the current consumption of the air pump at the previous moment, obtain the current consumption of the air pump at the current moment, subtract the current consumption of the air pump at the previous moment from the current consumption of the air pump at the current moment to obtain the consumption increase, obtain the monitoring duration by subtracting the previous moment from the current moment, and obtain the current consumption rate by dividing the consumption increase by the monitoring duration.

7. A method for intelligent processing and utilization of equipment unit fault data, implemented through the management system described in any one of claims 1-6, characterized in that: The utilization method includes the following steps: The number of air pumps in the factory is obtained, and each air pump is numbered. The number and identification information of the air pumps are then sent to the database for storage. During the operation of the air pump, multi-source data related to the safe operation of the air pump are acquired in real time through sensing devices. The collected multi-source data is preprocessed, and feature data is extracted from the preprocessed data. After substituting the acquired feature data into the air pump prediction model for comprehensive analysis, the air pump prediction model outputs an operational value for the air pump. The function expression is as follows: In the formula, Assign values ​​for execution. For power factor, The inlet and outlet flow deviation coefficient. For multi-point air pressure deviation, For current consumption rate, , , , These are the power factor, inlet / outlet flow deviation coefficient, multi-point gas pressure deviation, and proportional coefficient of current consumption rate, respectively. , , , All are greater than 0; Based on the comparison between the operational assignment and the gradient threshold, the air pump is assigned to the corresponding area, and a corresponding management policy is generated based on the area division of the air pump. The management policy is then sent to the administrator. When performing regular maintenance on the air pumps in the factory, the historical operating values ​​of the air pumps are obtained from the database. The historical operating values ​​and the operating frequency of the air pumps are combined to generate maintenance values ​​for the air pumps. All air pumps are sorted from largest to smallest according to their maintenance values ​​to generate a maintenance list, which is then sent to the administrator.

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