An intelligent monitoring system for air compressor operation status

Abstract

The present invention discloses an intelligent monitoring system for the operation status of an air compressor, which belongs to the technical field of air compressor control, and specifically comprises the following steps: interval monitoring of exhaust volume data of an air compressor group, and obtaining corresponding pipeline network air pressure data; generating a fitting model of exhaust volume changing with pipeline network air pressure according to the exhaust volume data and pipeline network air pressure data; obtaining pipeline network air pressure in real time, and inputting the pipeline network air pressure into the fitting model in real time to obtain the corresponding optimal exhaust volume; collecting operation indicators of all air compressors in the air compressor group, and calculating the operation status score of each air compressor based on a standardized matrix and Euclidean distance; sorting the air compressors from large to small according to the operation status scores, selecting a number of air compressors with top rankings, and prompting the above-mentioned air compressors to be overhauled; the present invention realizes the accuracy and stability of pipeline network exhaust volume.

Description

Technical Field

[0001] The invention relates to the technical field of air compressor control, and in particular to an intelligent monitoring system for the operating status of an air compressor. Background Art

[0002] In modern industrial environments, air compressors are key energy equipment, and their operating status directly affects the stability and efficiency of the production process. Traditional air compressor monitoring and management methods usually have many problems, such as easy equipment consumption, difficult maintenance, and potential safety hazards. In order to meet these challenges, modern air compressor monitoring systems use advanced Internet of Things technology and big data analysis to achieve real-time monitoring of equipment, fault warning, and intelligent maintenance.

[0003] Existing online monitoring systems for air compressors mainly monitor the operating parameters of air compressors, such as temperature, pressure, and current, in real time through sensor equipment and data acquisition devices, and store and analyze data through cloud platforms to achieve remote monitoring and management. These systems can automatically diagnose faults and issue alarms to help operation and maintenance personnel quickly locate and solve problems. However, existing systems often only focus on a single or a few operating parameters, ignoring the impact of other important parameters on the operating status of the air compressor, and have certain limitations in data processing and analysis.

[0004] Due to changes in the factory's use environment and operating conditions, traditional detection methods may not be able to capture the changing trends of equipment performance in a timely manner. And for some air compressors that have not yet failed, if the abnormal conditions can be discovered in time and preventive inspections can be carried out, the troubles caused by subsequent failures can be avoided, and the objective reasons for the abnormalities can be determined by analyzing the status of the air compressor. Summary of the invention

[0005] The purpose of the present invention is to provide an intelligent monitoring system for the operation status of an air compressor to solve the following technical problems:

[0006] Traditional detection methods are unable to capture changing trends in equipment performance in a timely manner.

[0007] The purpose of the present invention can be achieved through the following technical solutions:

[0008] An intelligent monitoring system for the operation status of an air compressor, comprising:

[0009] The data acquisition module is used to monitor the exhaust volume of the pipeline network at intervals, obtain the exhaust volume data of the air compressor unit when the pipeline network air pressure is in the preset air pressure range [M, N] within a few days, and obtain the corresponding pipeline network air pressure data;

[0010] A data processing module, used to generate a fitting model of the exhaust volume changing with the pipe network air pressure according to the exhaust volume data and the pipe network air pressure data, and obtain the optimal exhaust volume corresponding to different pipe network air pressures;

[0011] The data output module is used to obtain the pipeline network air pressure in real time, input the pipeline network air pressure into the fitting model in real time, obtain the corresponding optimal exhaust volume, and adjust the speed of the air compressor unit accordingly;

[0012] The data optimization module, when the pipeline network air pressure is monitored to be higher than N, reduces the speed of the current air compressor unit, reduces the exhaust volume to reduce the air pressure to the set pressure range; when the pipeline network air pressure is monitored to be lower than M, increases the speed of the current air compressor unit, increases the exhaust volume to raise the air pressure to the set pressure range;

[0013] The equipment evaluation module is used to collect the operating indicators of all air compressors in the air compressor group, including wind speed, exhaust volume and rotation speed, and calculate the difference between each operating indicator and the corresponding rated value; mark the absolute value of the difference as the verification indicator of the air compressor operation, and calculate the operating status score of each air compressor based on the standardized matrix and Euclidean distance;

[0014] The equipment screening module is used to sort the air compressors from large to small according to the operating status scores, screen out several air compressors with the highest ranking, and prompt the above several air compressors to be repaired.

[0015] As a further solution of the present invention: the data optimization module also includes:

[0016] Step 1: Evenly divide the preset air pressure range into several air pressure nodes T1, T2, ..., T i , i is a positive integer, and the network pressure is obtained from T i Increase to T i+1 , the decrease in exhaust volume is X; the network pressure is obtained by T i Reduce to T i-1 , the increase in exhaust volume Y;

[0017] Step 2: If there is a curve segment in which the exhaust volume of the pipe network decreases by X within a time period in the fitting model, the starting time coordinate t1 and the ending time coordinate t2 of the curve segment X are selected;

[0018] Step 3: If there is a curve segment in the fitting model in which the total air output of the pipe network increases by Y within a time period, the starting time coordinate t3 and the ending time coordinate t4 of the curve segment Y are selected.

[0019] As a further solution of the present invention: the data optimization module also includes:

[0020] When the time coordinate is at t1, the current exhaust volume is obtained and marked as W1, and the current exhaust volume is kept unchanged until the pipe network pressure is at T i and T i+1 When the midpoint of t2 is reached, the time coordinate at this time is obtained and marked as ta, the air conditioning exhaust volume is reduced to W3, W3=W1-X / 2(t2-ta), and the exhaust volume W3 is maintained to the time coordinate t2;

[0021] When the time coordinate is at t3, the current exhaust volume is obtained and marked as W2, and the current exhaust volume is kept unchanged until the pipe network pressure is at T i and T i-1 At the midpoint, obtain the time coordinate at this time and mark it as tb, control the air conditioning exhaust volume to W4, W4=W2+Y / 2(t4-tb), and maintain the exhaust volume W3 to the time coordinate t4.

[0022] As a further solution of the present invention: the air pressure of the pipeline network is measured by an air pressure sensor, and the air pressure sensor includes an internal air pressure sensor and an external air pressure sensor. The external air pressure sensor is arranged on one side of the pipeline network wall, and the internal air pressure sensor is evenly arranged in various places in the room.

[0023] As a further solution of the present invention: the calculation process of the standardized matrix is:

[0024] Mark the jth verification index of the i-th air compressor in the air compressor group as x ij , mark the number of air compressors in the air compressor group as n, and standardize the forward matrix. The formula for standardization is:

[0025] ;

[0026] Then the standardized matrix is:

[0027] .

[0028] As a further solution of the present invention: the calculation process of the Euclidean distance is:

[0029] The maximum value set in the normalized matrix is ​​labeled as Z + , and sort them according to the column they are in, Z + =(Z + 1. Z + 2. Z + 3);

[0030] Z + Each element in the matrix represents the maximum value of the element column in which the element is located, that is, Z + =(max{z 11 , z 21 , zn1},max{z 12 , z 22 , z n2},max{z 13 , z 23 , z n3});

[0031] The minimum value set in the normalized matrix is ​​marked as Z - , and sort them according to the column they are in, Z - =(Z - 1. Z - 2. Z - 3);

[0032] Z - Each element in represents the minimum value of the element column in the matrix, that is, Z - =(min{z 11 , z 21 , z n1},min{z 12 , z 22 , z n2},min{z 13 , z 23 , z n3});

[0033] Calculate the standardized element and maximum value Z of the i-th air compressor + The Euclidean distance D i + , and to the minimum value Z - The Euclidean distance D i - , the calculation formula is:

[0034] ; .

[0035] As a further solution of the present invention: the calculation process of the running status score is:

[0036] The unnormalized running status score is calculated according to the Euclidean distance, and the formula is:

[0037] ;

[0038] Then the calculation formula for the normalized operating status score of any air compressor in the air compressor group is:

[0039] .

[0040] Beneficial effects of the present invention:

[0041] The present invention monitors and analyzes the pipeline network air pressure and exhaust volume data in real time, dynamically adjusts the air compressor unit speed to keep the pipeline network air pressure within a preset range. The system uses the data processing module to generate a fitting model, optimizes the exhaust volume control strategy, and evaluates the operating status of each air compressor through the equipment evaluation module to achieve preventive maintenance; in addition, the system uses standardized matrices and Euclidean distance calculation methods to quantitatively evaluate the performance of the air compressor, ensuring the objectivity and accuracy of the evaluation results. The intelligent monitoring system not only improves the operating efficiency and stability of the air compressor, but also reduces energy consumption and maintenance costs, providing users with an efficient and reliable air compressor management solution. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] The present invention will be further described below in conjunction with the accompanying drawings.

[0043] Figure 1 It is a schematic diagram of the process of the present invention. DETAILED DESCRIPTION

[0044] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. 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] See also Figure 1 As shown, the present invention is an intelligent monitoring system for the operation status of an air compressor, comprising:

[0046] The data acquisition module is used to monitor the exhaust volume of the pipeline network at intervals, obtain the exhaust volume data of the air compressor unit when the pipeline network air pressure is in the preset air pressure range [M, N] within a few days, and obtain the corresponding pipeline network air pressure data;

[0047] A data processing module, used to generate a fitting model of the exhaust volume changing with the pipe network air pressure according to the exhaust volume data and the pipe network air pressure data, and obtain the optimal exhaust volume corresponding to different pipe network air pressures;

[0048] The data output module is used to obtain the pipeline network air pressure in real time, input the pipeline network air pressure into the fitting model in real time, obtain the corresponding optimal exhaust volume, and adjust the speed of the air compressor unit accordingly;

[0049] The data optimization module, when the pipeline network air pressure is monitored to be higher than N, reduces the speed of the current air compressor unit, reduces the exhaust volume to reduce the air pressure to the set pressure range; when the pipeline network air pressure is monitored to be lower than M, increases the speed of the current air compressor unit, increases the exhaust volume to raise the air pressure to the set pressure range;

[0050] The equipment evaluation module is used to collect the operating indicators of all air compressors in the air compressor group, including wind speed, exhaust volume and rotation speed, and calculate the difference between each operating indicator and the corresponding rated value; mark the absolute value of the difference as the verification indicator of the air compressor operation, and calculate the operating status score of each air compressor based on the standardized matrix and Euclidean distance;

[0051] The equipment screening module is used to sort the air compressors from large to small according to the operating status scores, screen out several air compressors with the highest ranking, and prompt the above several air compressors to be repaired.

[0052] By real-time monitoring and analysis of pipeline network air pressure and exhaust volume data, the air compressor unit speed is dynamically adjusted to keep the pipeline network air pressure within the preset range. The system uses the data processing module to generate a fitting model, optimize the exhaust volume control strategy, and evaluate the operating status of each air compressor through the equipment evaluation module to achieve preventive maintenance; in addition, the system uses standardized matrices and Euclidean distance calculation methods to quantitatively evaluate the performance of the air compressor, ensuring the objectivity and accuracy of the evaluation results. The intelligent monitoring system not only improves the operating efficiency and stability of the air compressor, but also reduces energy consumption and maintenance costs, providing users with an efficient and reliable air compressor management solution.

[0053] In another preferred embodiment of the present invention, the data optimization module further includes:

[0054] Step 1: Evenly divide the preset air pressure range into several air pressure nodes T1, T2, ..., T i , i is a positive integer, and the network pressure is obtained from T i Increase to T i+1 , the decrease in exhaust volume is X; the network pressure is obtained by T i Reduce to T i-1 , the increase in exhaust volume Y;

[0055] Step 2: If there is a curve segment in which the exhaust volume of the pipe network decreases by X within a time period in the fitting model, the starting time coordinate t1 and the ending time coordinate t2 of the curve segment X are selected;

[0056] Step 3: If there is a curve segment in the fitting model in which the total air output of the pipe network increases by Y within a time period, the starting time coordinate t3 and the ending time coordinate t4 of the curve segment Y are selected.

[0057] In another preferred embodiment of the present invention, the data optimization module further includes:

[0058] When the time coordinate is at t1, the current exhaust volume is obtained and marked as W1, and the current exhaust volume is kept unchanged until the pipe network pressure is at T i and T i+1When the midpoint of t2 is reached, the time coordinate at this time is obtained and marked as ta, the air conditioning exhaust volume is reduced to W3, W3=W1-X / 2(t2-ta), and the exhaust volume W3 is maintained to the time coordinate t2;

[0059] When the time coordinate is at t3, the current exhaust volume is obtained and marked as W2, and the current exhaust volume is kept unchanged until the pipe network pressure is at T i and T i-1 At the midpoint, obtain the time coordinate at this time and mark it as tb, control the air conditioning exhaust volume to W4, W4=W2+Y / 2(t4-tb), and maintain the exhaust volume W3 to the time coordinate t4.

[0060] In another preferred embodiment of the present invention, the pipe network air pressure is measured by an air pressure sensor, which includes an internal air pressure sensor and an external air pressure sensor. The external air pressure sensor is arranged on one side of the pipe network wall, and the internal air pressure sensor is evenly arranged in various places in the room.

[0061] In another preferred embodiment of the present invention, the calculation process of the standardized matrix is:

[0062] Mark the jth verification index of the i-th air compressor in the air compressor group as x ij , mark the number of air compressors in the air compressor group as n, and standardize the forward matrix. The formula for standardization is:

[0063] ;

[0064] Then the standardized matrix is:

[0065] .

[0066] By introducing a standardized matrix, the system can convert operating indicators of different dimensions and units into dimensionless standardized data. This process eliminates the order of magnitude differences between different indicators, making the contribution of each indicator to the final score more balanced. Standardization not only improves the accuracy of data analysis, but also enhances the generalization ability of the model, allowing the system to more comprehensively evaluate the operating status of the air compressor and provide a reliable basis for subsequent optimization and decision-making.

[0067] In another preferred embodiment of the present invention, the calculation process of the Euclidean distance is:

[0068] The maximum value set in the normalized matrix is ​​labeled as Z + , and sort them according to the column they are in, Z + =(Z + 1. Z + 2. Z + 3);

[0069] Z + Each element in the matrix represents the maximum value of the element column in which the element is located, that is, Z + =(max{z 11 , z 21 , z n1},max{z 12 , z 22 , z n2},max{z 13 , z 23 , z n3});

[0070] The minimum value set in the normalized matrix is ​​marked as Z - , and sort them according to the column they are in, Z - =(Z - 1. Z - 2. Z - 3);

[0071] Z - Each element in represents the minimum value of the element column in the matrix, that is, Z - =(min{z 11 , z 21 , z n1},min{z 12 , z 22 , z n2},min{z 13 , z 23 , z n3});

[0072] Calculate the standardized element and maximum value Z of the i-th air compressor + The Euclidean distance D i + , and to the minimum value Z - The Euclidean distance D i - , the calculation formula is:

[0073] ; .

[0074] By using Euclidean distance as the standard for evaluating the operating status of air compressors, the system can intuitively quantify the gap between each air compressor and the optimal operating status. By calculating the distance to the maximum value set and the minimum value set, the system can accurately identify equipment with extremely good or poor performance, thereby providing a clear direction for maintenance and optimization. The introduction of this method not only improves the objectivity of the evaluation, but also makes problem diagnosis and resolution more efficient.

[0075] In another preferred embodiment of the present invention, the calculation process of the running status score is:

[0076] The unnormalized running status score is calculated according to the Euclidean distance, and the formula is:

[0077] ;

[0078] The calculation formula for the normalized operating status score of any air compressor in the air compressor group is:

[0079] .

[0080] By calculating the unnormalized operating status score and converting it into a normalized score, the system enables a fair comparison of the performance of different air compressors. The normalized score takes into account the relative performance of all equipment rather than the absolute value, which helps to reveal potential performance bottlenecks and advantages. This mechanism provides users with a clear and intuitive performance ranking, making it easy to quickly identify equipment that needs attention, optimize resource allocation, and improve overall operational efficiency.

[0081] The above is a detailed description of an embodiment of the present invention, but the content is only a preferred embodiment of the present invention and cannot be considered to limit the scope of implementation of the present invention. All equivalent changes and improvements made within the scope of the present invention should still fall within the scope of the patent coverage of the present invention.

Claims

1. An intelligent monitoring system for the operation status of an air compressor, characterized in that: include: The data acquisition module is used to monitor the exhaust volume of the pipeline network at intervals, obtain the exhaust volume data of the air compressor unit when the pipeline network air pressure is in the preset air pressure range [M, N] within a few days, and obtain the corresponding pipeline network air pressure data; A data processing module, used to generate a fitting model of the exhaust volume changing with the pipe network air pressure according to the exhaust volume data and the pipe network air pressure data, and obtain the optimal exhaust volume corresponding to different pipe network air pressures; The data output module is used to obtain the pipeline network air pressure in real time, input the pipeline network air pressure into the fitting model in real time, obtain the corresponding optimal exhaust volume, and adjust the speed of the air compressor unit accordingly; The data optimization module, when the pipeline network air pressure is monitored to be higher than N, reduces the speed of the current air compressor unit, reduces the exhaust volume to reduce the air pressure to the set pressure range; when the pipeline network air pressure is monitored to be lower than M, increases the speed of the current air compressor unit, increases the exhaust volume to raise the air pressure to the set pressure range; The equipment evaluation module is used to collect the operating indicators of all air compressors in the air compressor group, including wind speed, exhaust volume and rotation speed, and calculate the difference between each operating indicator and the corresponding rated value; The absolute value of the difference is marked as the verification index of the air compressor operation, and the operating status score of each air compressor is calculated based on the standardized matrix and Euclidean distance; An equipment screening module is used to sort the air compressors from large to small according to the operation status scores, screen out a number of air compressors with the highest ranking, and prompt the air compressors to be repaired; The data optimization module also includes: Step 1: Evenly divide the preset air pressure range into several air pressure nodes T1, T2, ..., T i , i is a positive integer, and the network pressure is obtained from T i Increase to T i+1 , the decrease in exhaust volume is X; the network pressure is obtained by T i Reduce to T i-1 , the increase in exhaust volume Y; Step 2: If there is a curve segment in which the exhaust volume of the pipe network decreases by X within a time period in the fitting model, the starting time coordinate t1 and the ending time coordinate t2 of the curve segment X are selected; Step 3: If there is a curve segment in the fitting model in which the total amount of air output of the pipe network increases by Y within a time period, the starting time coordinate t3 and the ending time coordinate t4 of the curve segment Y are selected; The data optimization module also includes: When the time coordinate is at t1, the current exhaust volume is obtained and marked as W1, and the current exhaust volume is kept unchanged until the pipe network pressure is at T i and T i+1 At the midpoint of t2, obtain the time coordinate at this time and mark it as ta, reduce the exhaust volume of the air compressor unit to W3, W3=W1-X / 2(t2-ta), and maintain the exhaust volume W3 to the time coordinate t2; When the time coordinate is at t3, the current exhaust volume is obtained and marked as W2, and the current exhaust volume is kept unchanged until the pipe network pressure is at T i and T i-1 At the midpoint, obtain the time coordinate at this time and mark it as tb, control the exhaust volume of the air compressor unit to rise to W4, W4=W2+Y / 2(t4-tb), and maintain the exhaust volume W3 to the time coordinate t4.

2. The intelligent monitoring system for the operation status of an air compressor according to claim 1 is characterized in that: The air pressure of the pipe network is measured by an air pressure sensor, which includes an internal air pressure sensor and an external air pressure sensor. The external air pressure sensor is arranged on one side of the pipe network wall, and the internal air pressure sensors are evenly arranged at various locations in the pipe network.

3. The intelligent monitoring system for the operation status of an air compressor according to claim 1 is characterized in that: The calculation process of the standardized matrix is: Mark the jth verification index of the i-th air compressor in the air compressor group as x ij , mark the number of air compressors in the air compressor group as n, and standardize the forward matrix. The formula for standardization is: ； Then the standardized matrix is: 。 4. The intelligent monitoring system for the operation status of an air compressor according to claim 3 is characterized in that: The calculation process of the Euclidean distance is: The maximum value set in the normalized matrix is ​​labeled as Z + , and sort them according to the column they are in, Z + =(Z + 1. Z + 2. Z + 3); Z + Each element in the matrix represents the maximum value of the element column in which the element is located, that is, Z + =(max{z 11 , z 21 , z n1 },max{z 12 , z 22 , z n2 },max{z 13 , z 23 , z n3 }); The minimum value set in the normalized matrix is ​​marked as Z - , and sort them according to the column they are in, Z - =(Z - 1. Z - 2. Z - 3); Z - Each element in represents the minimum value of the element column in the matrix, that is, Z - =(min{z 11 , z 21 , z n1 },min{z 12 , z 22 , z n2 },min{z 13 , z 23 , z n3 }); Calculate the standardized element and maximum value Z of the i-th air compressor + The Euclidean distance D i + , and to the minimum value Z - The Euclidean distance D i - , the calculation formula is: ； 。 5. The intelligent monitoring system for the operation status of an air compressor according to claim 4 is characterized in that: The calculation process of the running status score is: The unnormalized running status score is calculated according to the Euclidean distance, and the formula is: ; Then the calculation formula for the normalized operating status score of any air compressor in the air compressor group is: .

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