Algorithm for evaluating health state of air compressor

Abstract

The invention discloses an air compressor health state evaluation algorithm, which comprises the following steps of: data collection: by installing an electricity meter, a flow meter, a power meter and a pressure meter on an air compressor, collecting operation data related to the health state of the air compressor, including pressure, temperature and current, and analyzing the data; the method comprises the following steps: collecting original data, comparing and analyzing the collected original data with a set normal value, extracting characteristics related to the health state of the air compressor, evaluating the state, establishing an air compressor health state evaluation model according to a comparison result of the air compressor data, and comparing real-time data with the model to realize the evaluation of the health state of the air compressor. According to the air compressor health state evaluation algorithm, by monitoring the operation data of the air compressor in real time, the abnormal condition of equipment can be found in time, production loss caused by faults is avoided, the health algorithm based on data analysis has high accuracy, and a reliable basis can be provided for maintenance and management of the air compressor.

Description

Technical Field

[0001] The present invention relates to the technical field of air compressors, and in particular to an air compressor health status assessment algorithm. Background Art

[0002] Air compressors are widely used in industrial production, and their operating status has a significant impact on the production process. However, traditional air compressor management methods often rely on manual inspections and regular maintenance, which cannot achieve real-time monitoring and prediction of air compressor operating status. Summary of the invention

[0003] In view of the above-mentioned deficiencies in the prior art, the present invention provides an air compressor health status assessment algorithm, the purpose of which is to provide a health algorithm that can monitor the operating status of the air compressor in real time and give optimization suggestions.

[0004] In order to achieve the above-mentioned object of the invention, the technical solution adopted by the present invention is:

[0005] An air compressor health status assessment algorithm includes the following steps:

[0006] Step 1: Data collection: by installing electric meters, flow meters, power meters and pressure gauges on the air compressor, collect operating data related to the health status of the air compressor, including pressure, temperature and current;

[0007] Step 2: Data analysis: compare and analyze the collected raw data with the set normal values ​​to extract features related to the health status of the air compressor;

[0008] Step 3: Status assessment: through the comparison results of air compressor data, an air compressor health status assessment model is established, and the real-time data is compared with the model to evaluate the health status of the air compressor;

[0009] Step 4: Event notification: If the health data of the air compressor is abnormal or maintenance is about to be required, the event will be pushed to the user to immediately remind the user to check, repair or maintain the air compressor;

[0010] Step 5: Maintenance recommendations: Provide reasonable recommendations for air compressor maintenance based on the air compressor's health status and performance prediction results.

[0011] Furthermore, the air compressor health status indicators include air compressor main indicators and air compressor maintenance indicators.

[0012] Furthermore, the main indicators of the air compressor include energy efficiency detection, main engine detection, motor detection and oil circuit detection.

[0013] Furthermore, the air compressor maintenance index includes an air filter pressure difference parameter, an oil filter pressure difference parameter and an oil separator pressure difference parameter.

[0014] Furthermore, the air compressor health status indicator indicates the health level of the air compressor.

[0015] Furthermore, the air compressor health level includes healthy state, relatively healthy state, sub-healthy state and slightly pathological state.

[0016] The beneficial effects of the present invention are:

[0017] 1. By real-time monitoring of the operating data of the air compressor, abnormal conditions of the equipment can be discovered in time to avoid production losses caused by failures;

[0018] 2. The health algorithm based on data analysis has high accuracy and can provide a reliable basis for the maintenance and management of air compressors;

[0019] 3. The optimization suggestions proposed are highly targeted and can effectively improve the service life and production efficiency of the air compressor.

[0020] 4. The promotion and application of this algorithm will improve the efficiency of industrial production and reduce production costs. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] Figure 1 The overall flow chart of the air compressor health status assessment of the present invention;

[0022] Figure 2 It is a schematic diagram of the air compressor health status assessment model of the present invention. DETAILED DESCRIPTION

[0023] The technical solutions in the embodiments of the present invention are clearly and completely described below in conjunction with the accompanying drawings.

[0024] like Figure 1 to Figure 2 As shown, an air compressor health status assessment algorithm collects the operating data of the air compressor for real-time analysis, evaluates the health status of the air compressor based on the analysis results, and provides optimization suggestions for the health status; the algorithm includes the following steps:

[0025] Data collection: By installing sensors such as electricity meters, flow meters, power meters, and pressure gauges on the air compressor, operating data related to the health status of the air compressor, including pressure, temperature, and current, is collected.

[0026] Data analysis: The collected raw data is compared with normal values ​​to extract features related to the health status of the air compressor.

[0027] Status assessment: By comparing the data of the air compressor, an air compressor health status assessment model is established, and the real-time data is compared with the model to evaluate the health status of the air compressor.

[0028] Event notification: If the health data of the air compressor is abnormal or maintenance is about to be required, the event will be pushed to the user to immediately remind the user to check, repair or maintain the air compressor.

[0029] Maintenance recommendations: Provide reasonable recommendations for air compressor maintenance based on the health status and performance prediction results of the air compressor.

[0030] An air compressor health status assessment algorithm of the present invention extracts and integrates suitable indicator data from air compressor data, and evaluates the health status of the air compressor in combination with an algorithm model. The air compressor health status indicators mainly include two dimensions: air compressor main indicators and air compressor maintenance indicators.

[0031] The main indicators of air compressors include energy efficiency testing, main engine testing, motor testing and oil circuit testing.

[0032] Energy efficiency detection: If the specific power is in the range of 1-6.4, it is displayed as normal, otherwise it is displayed as abnormal.

[0033] Host detection: If all the following indicators of the host are within the normal range, it is normal.

[0034] Host vibration value: Use vibration sensors to monitor the host vibration value in real time, compare it with the standard value, infer the use of the host, scientifically predict the operation of the host, and infer the use of the host bearings.

[0035] Main engine first-stage injection temperature and pressure, second-stage injection temperature and pressure: Use the injection temperature sensor to detect the injection pressure and injection temperature of each level of the main engine in real time, compare them with the standard values, infer the use of the main engine, and predict the operating conditions of the oil circuit and cooling system.

[0036] Motor detection: If all the following indicators of the motor are within the normal range, it will be displayed as normal; if one of them is abnormal, it will be displayed as abnormal.

[0037] Motor vibration value: Use the motor vibration sensor to detect the motor's vibration value in real time, compare it with the standard value, infer the motor's usage, predict the motor's operating status, and infer the motor bearing's usage.

[0038] Motor stator coil temperature: Use the motor coil PT100 temperature sensor to detect the motor stator coil temperature in real time. When the temperature reaches the set value, the machine will shut down and alarm.

[0039] Motor bearing temperature: Use the motor bearing PT100 temperature sensor to detect the motor bearing temperature in real time. When the temperature reaches the set value, the motor will shut down and alarm.

[0040] Oil circuit detection: If all the following indicators of the oil circuit are within the normal range, it will be normal.

[0041] Oil-gas separation tank temperature: Use the oil-gas separation tank temperature sensor to detect the oil-gas separation tank temperature in real time. When the temperature reaches the set value, the machine will shut down and provide an oil-gas separation tank high temperature alarm to prevent the oil temperature from being too high and causing oil carbonization.

[0042] Oil-gas separation tank pressure: Use the oil-gas separator pressure sensor to detect the oil-gas separation tank pressure in real time. When the unit gas supply pressure - oil-gas separation tank pressure is greater than the preset value, an oil separator core blockage warning is provided.

[0043] Oil filter inlet pressure and oil outlet pressure: Use pressure sensor to detect pressure, and issue an alarm when the value of oil filter inlet pressure minus oil filter outlet pressure is greater than the preset value.

[0044] The air compressor maintenance indicators include parameters such as air filter pressure difference, oil filter pressure difference, oil separator pressure difference, etc., which are used to calculate the time until the next maintenance of the air compressor.

[0045] Pressure difference before and after the air filter: Use the air filter pressure difference transmitter to detect the pressure difference before and after the air filter. When the air filter pressure difference reaches the set value, an early warning will be issued to prompt the user to replace the air filter. If an event occurs, the system will display the data abnormality.

[0046] Pressure difference before and after the oil filter: Use the oil filter pressure difference transmitter to detect the pressure difference before and after the oil filter. When the pressure difference before and after the oil filter reaches the set value, an early warning will be issued to prompt the oil filter to be replaced. If an event occurs, the system will display the data as abnormal.

[0047] Pressure difference before and after the oil separator core: Use the oil separator core pressure difference transmitter to detect the pressure difference before and after the oil separator core filter. When the pressure difference before and after the oil separator core reaches the set value, an early warning will be issued to prompt the oil separator core to be replaced. If an event occurs, the system will display the data abnormality.

[0048] Maintenance time: calculated by subtracting the usage time from the maximum maintenance time set according to the protection parameters of the equipment.

[0049] Based on the pre-maintenance events, maintenance events, warning events and alarm events generated by the above indicators, determine the health level of the air compressor:

[0050] A pre-maintenance event occurs or no event occurs: the air compressor is determined to be in a healthy state.

[0051] A maintenance event occurs: the air compressor is judged to be in a relatively healthy state.

[0052] A warning event is generated: the air compressor is judged to be in a sub-healthy state.

[0053] An alarm event is generated: the air compressor is judged to be in a slightly pathological state.

[0054] Provide corresponding optimization suggestions based on the health level:

[0055] Healthier / sub-healthy optimization suggestions:

[0056] Clean the radiator (air compressor and air dryer) and air filter element weekly to avoid high temperature and air filter blockage.

[0057] Observe the operating temperature every day to see if it is normal, maintain it at 70℃-95℃, strengthen ventilation, and lower the ambient temperature. If the temperature is lower than 0℃ (causing oil emulsification) or higher than 95℃ (oil deterioration due to high temperature) for a long time during operation, contact maintenance personnel.

[0058] The gas tank and filter are drained every day, the automatic drainer is checked every day to see if it is operating normally, and the filter and valve core are cleaned every month to avoid blockage. The condensed water in the oil and gas barrel is drained before starting the machine every day.

[0059] Check the operating voltage, current and oil level every day to see if they are normal, and check whether there is oil leakage in the internal joints and oil pipes of the machine.

[0060] The fine filter needs to be replaced every 2500 hours.

[0061] Establish a complete equipment maintenance and care system. For example, regularly perform equipment maintenance and repairs, replace aging or damaged parts, and avoid equipment failures or abnormalities. This can effectively improve the stability and reliability of the equipment, extend the service life of the equipment, and reduce equipment failures and repair costs.

[0062] Slightly morbid optimization suggestion:

[0063] Check and resolve equipment abnormalities as quickly as possible to avoid further impact on equipment performance and safety.

[0064] Establish a complete equipment maintenance and repair system, regularly perform equipment maintenance and repair, replace aging or damaged parts, and avoid equipment failure or abnormal conditions. This can effectively improve the stability and reliability of the equipment, extend the service life of the equipment, and reduce equipment failure and repair costs.

[0065] An air compressor health status assessment algorithm of the present invention can timely discover abnormal conditions of the equipment by real-time monitoring of the operating data of the air compressor, thereby avoiding production losses caused by failures;

[0066] The health algorithm based on data analysis has high accuracy and can provide a reliable basis for the maintenance and management of air compressors;

[0067] The optimization suggestions put forward are highly targeted and can effectively improve the service life and production efficiency of the air compressor.

[0068] 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 shall be included in the protection scope of the present invention.

Claims

1. An air compressor health status assessment algorithm, characterized in that: The following steps are involved: Step 1: Data collection: by installing electric meters, flow meters, power meters and pressure gauges on the air compressor, collect operating data related to the health status of the air compressor, including pressure, temperature and current; Step 2: Data analysis: compare and analyze the collected raw data with the set normal values ​​to extract features related to the health status of the air compressor; Step 3: Status assessment: through the comparison results of air compressor data, an air compressor health status assessment model is established, and the real-time data is compared with the model to evaluate the health status of the air compressor; Step 4: Event notification: If the health data of the air compressor is abnormal or maintenance is about to be required, the event will be pushed to the user to immediately remind the user to check, repair or maintain the air compressor; Step 5: Maintenance recommendations: Provide reasonable recommendations for air compressor maintenance based on the air compressor's health status and performance prediction results.

2. The air compressor health status assessment algorithm according to claim 1 is characterized in that: The air compressor health status indicators include air compressor main indicators and air compressor maintenance indicators.

3. The air compressor health status assessment algorithm according to claim 2 is characterized in that: The main indicators of the air compressor include energy efficiency detection, main engine detection, motor detection and oil circuit detection.

4. The air compressor health status assessment algorithm according to claim 1, characterized in that: The air compressor maintenance index includes air filter pressure difference parameter, oil filter pressure difference parameter and oil separator pressure difference parameter.

5. The air compressor health status assessment algorithm according to claim 2 is characterized in that: The air compressor health status indicator indicates the health level of the air compressor.

6. The air compressor health status assessment algorithm according to claim 5, characterized in that: The air compressor health level includes healthy state, relatively healthy state, sub-healthy state and slightly sick state.

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