Intelligent detection method and system for hydrogen compressor performance

Abstract

The application belongs to the technical field of compressors and provides an intelligent detection method and system for the performance of a hydrogen compressor, comprising: obtaining real-time performance data of multiple operating performances of a hydrogen compressor to be detected by using a hydrogen compressor sensor; obtaining historical data of the hydrogen compressor to be detected; determining reference data of each operating performance according to the historical data; and judging whether the performance of the hydrogen compressor to be detected is normal according to the real-time performance data and the reference data. The application comprehensively detects the performance of the hydrogen compressor by using the sensor, realizes efficient and accurate real-time performance monitoring, provides accurate and reliable data support for the operation of the hydrogen compressor, optimizes the performance of the hydrogen compressor to ensure its long-term stable operation, and provides a safer and more efficient hydrogen compressor application solution for the related industry through this innovative technology.

Description

Technical Field

[0001] This application belongs to the technical field of compressors, and particularly relates to an intelligent detection method and system for the performance of a hydrogen compressor. Background Technology

[0002] A hydrogen compressor is a device specifically designed to compress hydrogen gas. Its purpose is to compress hydrogen to a high pressure to meet the demands of hydrogen energy applications. Ensuring the proper functioning of the hydrogen compressor is crucial for its efficient utilization during storage and transportation. It is a device used to process hydrogen by increasing its pressure, thus reducing its volume for convenient storage and transport. Therefore, testing the performance of hydrogen compressors is essential.

[0003] Currently, it is difficult to monitor hydrogen compressor data in real time during operation, resulting in the lack of a dedicated method for real-time online monitoring of hydrogen compressor performance. Therefore, a new method for monitoring hydrogen compressor performance is needed. Summary of the Invention

[0004] The purpose of this invention is to provide an intelligent detection method for the performance of a hydrogen compressor, aiming to solve the technical problem that there is no specific focus on real-time online detection of hydrogen compressor performance in the prior art.

[0005] To achieve the above objectives, the present invention provides an intelligent detection method for the performance of a hydrogen compressor, comprising:

[0006] Using hydrogen compressor sensors, real-time performance data of multiple operating performance parameters of the hydrogen compressor under test are obtained;

[0007] Obtain historical data of the hydrogen compressor to be tested;

[0008] Based on the historical data, baseline data for each operating performance level are determined;

[0009] Based on the real-time performance data and the baseline data, determine whether the performance of the hydrogen compressor under test is normal.

[0010] The beneficial effects of this invention are as follows: First, sensors pre-installed on the hydrogen compressor are used to detect multiple operating performance parameters of the hydrogen compressor under test and obtain real-time performance data of these parameters. Then, historical data of these parameters are obtained, and the historical performance data is analyzed to determine baseline data for each parameter. Based on the real-time performance data and baseline data, the system determines whether the performance of the hydrogen compressor is normal. Thus, the sensors can be used to comprehensively detect the performance of the hydrogen compressor, achieving efficient and accurate real-time performance monitoring and providing precise and reliable data support for the operation of the hydrogen compressor.

[0011] Optionally, the real-time performance data of the multiple operating performance parameters include: voltage data, current data, temperature data, pressure data, and vibration data.

[0012] Optionally, a hydrogen compressor sensor can be used to acquire voltage amplitude data, voltage amplitude change curve data, current amplitude data, current amplitude change curve data, first temperature data, and second temperature data of the hydrogen compressor under test.

[0013] The voltage data and current data are determined based on the voltage amplitude data, the voltage amplitude change curve data, the current amplitude data, and the current amplitude change curve data.

[0014] Generate thermoelectric potential difference data based on the first temperature data and the second temperature data;

[0015] The temperature data is determined based on the thermoelectric potential difference data;

[0016] Acquire the first pressure data, the second pressure data, and the pressure change curve data collected by the hydrogen compressor sensor;

[0017] The pressure data is generated based on the first pressure data and the second pressure data;

[0018] The vibration data is obtained based on the pressure change curve data;

[0019] Based on the voltage data, current data, temperature data, pressure data, and vibration data, the performance data of the multiple operating performance parameters are determined.

[0020] Optionally, acquire analog data collected by the sensor of the hydrogen compressor to be tested;

[0021] Acquire digital data from the simulation data, classify the digital data, and calculate the actual data of the hydrogen compressor to be tested;

[0022] Based on the actual data, obtain the fluctuation curve within a preset time range.

[0023] Optionally, based on the detection data and the preset detection state, the first state data of the hydrogen compressor to be tested is determined, wherein the first state data is the state data of the historical data starting point;

[0024] Based on the historical data and the preset historical state, the second state data of the hydrogen compressor to be tested is determined, wherein the second state data is the state data at the end point of the historical data.

[0025] Based on the first state data and the second state data, the reference data of the hydrogen compressor to be tested is determined.

[0026] Optionally, obtain the error tolerance data of the reference data;

[0027] The benchmark judgment range is determined based on the benchmark data and the error allowable data;

[0028] Determine whether the real-time performance data is within the benchmark judgment range;

[0029] If the real-time performance data is within the benchmark judgment range, the performance of the hydrogen compressor under test is determined to be normal; otherwise, the performance of the hydrogen compressor under test is abnormal.

[0030] Optionally, if the performance of the hydrogen compressor under test is normal, the predicted lifespan data of the hydrogen compressor under test is obtained.

[0031] If the performance of the hydrogen compressor under test is abnormal, abnormal data is sent to the maintenance terminal, and maintenance personnel information is determined.

[0032] This invention also provides an intelligent detection system for the performance of a hydrogen compressor, comprising:

[0033] Performance acquisition module: Used to acquire real-time performance data of multiple operating performance characteristics of the hydrogen compressor under test using hydrogen compressor sensors;

[0034] Historical data acquisition module: used to acquire historical data of the hydrogen compressor under test;

[0035] Benchmark determination module: used to determine the benchmark data for each operating performance based on the historical data;

[0036] Performance judgment module: used to determine whether the performance of the hydrogen compressor under test is normal based on the real-time performance data and the benchmark data. Attached Figure Description

[0037] To more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0038] Figure 1 A flowchart illustrating an intelligent detection method for the performance of a hydrogen compressor provided in an embodiment of the present invention;

[0039] Figure 2 A block diagram of an intelligent detection system for the performance of a hydrogen compressor provided in an embodiment of the present invention;

[0040] Figure 3 This is a block diagram of a performance judgment module for an intelligent detection system for hydrogen compressor performance, provided in an embodiment of the present invention. Detailed Implementation

[0041] To make the objectives, features, and advantages of this invention more apparent and understandable, the technical solutions of the embodiments of this invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the embodiments described below are only some embodiments of this invention, and not all embodiments. Based on the embodiments of this invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this invention.

[0042] Please see Figure 1 One embodiment of the intelligent detection method for the performance of a hydrogen compressor according to the present invention may include:

[0043] The S100 uses a hydrogen compressor sensor to acquire real-time performance data of multiple operating performance parameters of the hydrogen compressor under test.

[0044] Specifically, sensors installed in the hydrogen compressor under test are used to monitor the compressor in real time and obtain real-time performance data of multiple operating parameters of the compressor, including voltage data, current data, temperature data, pressure data, and vibration data.

[0045] In one embodiment, step S100 includes:

[0046] S110 uses a hydrogen compressor sensor to acquire voltage amplitude data, voltage amplitude change curve data, current amplitude data, current amplitude change curve data, first temperature data, and second temperature data of the hydrogen compressor under test.

[0047] Specifically, voltage and current data of the hydrogen compressor are continuously collected to generate voltage amplitude change curves and current amplitude change curves. Voltage amplitude data is obtained from the voltage amplitude change curves, and current amplitude data is obtained from the current amplitude change curves. At the same time, first and second temperature data are collected at two temperature measurement points on the hydrogen compressor.

[0048] Specifically, the voltage peak value can be obtained by dividing the voltage peak value by the square root of two based on the voltage amplitude change curve data. Similarly, the maximum and minimum current values ​​can be obtained by dividing the difference between the maximum and minimum current values ​​by two based on the current amplitude change curve data.

[0049] S120 determines the voltage and current data based on the voltage amplitude data, voltage amplitude change curve data, current amplitude data, and current amplitude change curve data.

[0050] Specifically, based on the voltage amplitude variation curve data, the phase difference of the voltage phase and the effective value of the voltage are determined. Based on the voltage amplitude data, the phase difference of the voltage phase and the effective value of the voltage, the voltage data of the hydrogen compressor are determined. Based on the current amplitude variation curve data, the phase difference of the current phase and the effective value of the current are determined. Based on the voltage amplitude data, the phase difference of the current phase and the effective value of the current, the current data of the hydrogen compressor are determined.

[0051] S130 generates thermoelectric potential difference data based on the first temperature data and the second temperature data.

[0052] Specifically, first, obtain the first temperature data of the first temperature test point, then obtain the second temperature data of the second temperature test point, obtain the temperature difference between the two test points, generate the thermoelectric potential difference, and obtain the thermoelectric potential difference data.

[0053] S140, determine the temperature data based on the thermoelectric potential difference data.

[0054] Specifically, the first temperature data is set as E1, the second temperature data is set as E2, and the calculated thermoelectric potential difference data is E1-E2. Then, the thermoelectric potential difference data is calculated to determine the temperature data of the hydrogen compressor. The formula for calculating the temperature data is T=k×(E1-E2), where T is the temperature data, k is the sensitivity, E1 is the first temperature data, and E2 is the second temperature data. The potential difference of the thermocouple is used to convert the temperature data of the hydrogen compressor into calculation data.

[0055] S150: Acquire the first pressure data, second pressure data, and pressure change curve data collected by the hydrogen compressor sensor.

[0056] Specifically, first, acquire the first pressure data detected by the first pressure sensor of the hydrogen compressor, the second pressure data detected by the second pressure sensor, and the vibration signal detected by the acceleration sensor. Record the vibration signal over a period of time and generate a pressure change curve. When using the acceleration sensor, the acquisition frequency of the vibration signal should not be lower than 10kHz.

[0057] S160, generate pressure data based on the first pressure data and the second pressure data.

[0058] Specifically, based on the first pressure data and the second pressure data, the pressure difference between the two pressure data is obtained, and it is determined whether the pressure difference is within the preset pressure difference range. If the pressure difference is within the preset pressure difference range, the pressure data closest to the standard pressure value is determined. If the pressure difference is outside the preset pressure difference range, the first pressure data and the second pressure data detected by the sensor need to be obtained again.

[0059] S170: Obtain vibration data based on pressure change curve data.

[0060] Specifically, based on the pressure change curve data of the hydrogen compressor, sample values ​​of multiple vibration signals are obtained, and then vibration data is calculated based on these sample values. The formula for calculating the vibration data is as follows: In the formula, RMS represents vibration data, n represents the number of samples, and x1, x2, ..., x n The value is the sampled value of the signal.

[0061] S180 determines multiple operational performance data based on voltage, current, temperature, pressure, and vibration data.

[0062] Specifically, the average values ​​of multiple voltage, current, temperature, pressure, and vibration data from multiple hydrogen compressors were determined, and these five average values ​​were used as the performance data for each function.

[0063] S200: Obtain historical data of the hydrogen compressor under test.

[0064] Specifically, the hydrogen compressor under test is monitored in real time to obtain detection data and historical data from the multi-stage pressure sensor of the hydrogen compressor under test. In particular, the hydrogen compressor is monitored in real time to obtain continuous detection data, and historical data can be determined based on the continuous detection data.

[0065] In one embodiment, the process after step S200 includes:

[0066] S210: Acquire analog data collected by the sensor of the hydrogen compressor to be tested.

[0067] Specifically, multiple analog data points are acquired from the sensor of the hydrogen compressor under test, where the analog data are analog signals acquired by the sensor.

[0068] S220: Acquire digital data from the analog data, classify and process the digital data, and calculate the actual data of the hydrogen compressor to be tested.

[0069] Specifically, the simulated data is sampled, quantized, and encoded to convert it into digital data. The digital data is then filtered and noise-reduced, and the actual data of the hydrogen compressor under test is calculated.

[0070] S230: Based on actual data, obtain the fluctuation curve within a preset time range.

[0071] Specifically, based on the actual data of the hydrogen compressor to be tested, the fluctuation curve for each preset time range is determined. The sensor collects data continuously for time period T1 and then stops collecting, and then resumes collecting after time period T2. The fluctuation curve is the curve of how the actual data changes over time within time period T1.

[0072] S300 uses historical data to determine baseline data for various operating performance parameters.

[0073] Specifically, based on the performance relationship curves, performance formulas, and tested performance data of the hydrogen compressor under test, benchmark data for multiple operating performance parameters of the hydrogen compressor are determined.

[0074] In one embodiment, step S300 includes:

[0075] S310, based on the detection data and preset detection status, determines the first state data of the hydrogen compressor to be tested.

[0076] Specifically, based on the detection data of the hydrogen compressor to be tested, it is determined whether the detection data is within the preset detection state requirement. If the detection data meets the preset detection state requirement, the detection data is determined to be the first state data. Otherwise, the monitoring data of the hydrogen compressor to be tested is reacquired. The first state data is the state data at the starting point of the historical data.

[0077] S320 determines the second state data of the hydrogen compressor to be tested based on historical data and preset historical states.

[0078] Specifically, historical data for at least three historical time periods (T1) are acquired. The historical data for each historical time period (T1) is compared with preset historical state data to determine if the historical data for each historical time period meets the requirements of the preset historical state data. If the satisfaction rate reaches 80%, the second state data is determined to be the average of the selected historical data. Otherwise, the acquired historical time periods are removed, and new time period combinations are acquired to calculate the satisfaction rate. The second state data is the state data at the end point of the historical data.

[0079] S330, determine the reference data of the hydrogen compressor to be tested based on the first state data and the second state data.

[0080] Specifically, based on the first state data and the second state data, the average value of the first state data and the second state data is calculated and determined as the state data of the hydrogen compressor to be tested.

[0081] S400 determines whether the performance of the hydrogen compressor under test is normal based on real-time performance data and baseline data.

[0082] Specifically, the status data is compared with the baseline data. Based on the comparison results, it is determined whether the performance of the hydrogen compressor under test is normal. If it is normal, it meets the performance requirements for normal use of the hydrogen compressor. Otherwise, the performance of the hydrogen compressor does not meet the usage requirements.

[0083] In one embodiment, step S400 includes:

[0084] S410, obtain the allowable error data for the reference data.

[0085] Specifically, based on the preset error values ​​of multiple operating performance parameters of the hydrogen compressor to be tested, the allowable error data of multiple operating performance benchmark data are determined.

[0086] S420, determine the benchmark judgment range based on the benchmark data and the error allowable data.

[0087] Specifically, based on the baseline data and the allowable error data, the allowable error data is increased or decreased on the basis of the baseline data, and this is determined as the baseline judgment range.

[0088] S430 determines whether real-time performance data is within the baseline judgment range.

[0089] Specifically, the status data of the hydrogen compressor is compared with the benchmark judgment range to determine whether the status data is within the benchmark judgment range.

[0090] S440: If the real-time performance data is within the baseline judgment range, the performance of the hydrogen compressor under test is determined to be normal; otherwise, the performance of the hydrogen compressor under test is abnormal.

[0091] Specifically, if the status data of the hydrogen compressor is within the baseline judgment range, the performance of the hydrogen compressor under test is determined to be normal; otherwise, the performance of the hydrogen compressor under test is determined to be abnormal.

[0092] In one embodiment, the following is included after step S440:

[0093] S441, if the performance of the hydrogen compressor under test is normal, then obtain the predicted lifespan data of the hydrogen compressor under test.

[0094] Specifically, if the performance of the hydrogen compressor under test is normal, the service life of the hydrogen compressor under test can be estimated, and the predicted service life data of the hydrogen compressor under test can be obtained.

[0095] S442, if the performance of the hydrogen compressor to be tested is abnormal, send abnormal data to the maintenance terminal and confirm the maintenance personnel information.

[0096] Specifically, if the performance of the hydrogen compressor under test is abnormal, then the performance of the hydrogen compressor under test does not meet the usage requirements. The detected abnormal data needs to be sent to the maintenance terminal, and the information of the corresponding maintenance personnel should be arranged to carry out performance maintenance on the hydrogen compressor under test.

[0097] The implementation principle of the intelligent detection method for hydrogen compressor performance in this application embodiment is as follows: First, voltage and current data of multiple hydrogen compressors are acquired. Then, first and second temperature data are acquired. Based on the first and second temperature data, thermoelectric potential difference data is generated, and temperature data is determined. Pressure data is then generated, and vibration data is generated based on the pressure change curve data. Next, the voltage, current, temperature, pressure, and vibration data of multiple hydrogen compressors are determined as performance data for multiple operating performances. Then, historical data of the hydrogen compressor under test is acquired. Based on the historical data, the benchmark data for each operating performance of the hydrogen compressor under test is determined. Finally, based on the benchmark data and real-time performance data, it is determined whether the performance of the hydrogen compressor under test is normal. If the performance of the hydrogen compressor under test is normal, the predicted lifespan data of the hydrogen compressor under test is acquired. If the performance of the hydrogen compressor under test is abnormal, abnormal data is sent to the maintenance terminal. In this way, the performance of the hydrogen compressor can be comprehensively detected by sensors, and efficient and accurate real-time performance monitoring can be achieved, providing accurate and reliable data support for the operation of the hydrogen compressor and optimizing the performance of the hydrogen compressor to ensure its long-term stable operation.

[0098] Please see Figure 2 One embodiment of the intelligent detection system for the performance of a hydrogen compressor according to the present invention may include:

[0099] Benchmark data determination module: used to acquire performance data of multiple operating performances collected by multiple hydrogen compressor sensors, and determine the benchmark data of multiple operating performances;

[0100] Detection data acquisition module: used to acquire detection data and historical data collected by the sensor of the hydrogen compressor under test;

[0101] Status data determination module: used to determine the status data of the hydrogen compressor to be tested based on the detection data and the historical data;

[0102] Performance judgment module: used to determine whether the performance of the hydrogen compressor under test is normal based on the status data and the benchmark data.

[0103] Please see Figure 3 In one embodiment, embodiments of this application also provide a performance determination module for an intelligent detection system for hydrogen compressor performance, comprising:

[0104] Error acquisition module: used to acquire the allowable error data of the reference data;

[0105] Range determination module: used to determine the benchmark judgment range based on the benchmark data and the error allowable data;

[0106] Data judgment module: used to determine whether the real-time performance data is within the benchmark judgment range;

[0107] Performance determination module: If the real-time performance data is within the benchmark judgment range, then the performance of the hydrogen compressor under test is determined to be normal; otherwise, the performance of the hydrogen compressor under test is abnormal.

[0108] Based on the same idea as the method in the above embodiments, the intelligent detection system for hydrogen compressor performance provided in this application can implement the method in the above embodiments. For ease of explanation, the structural schematic diagram of the system embodiment only shows the parts related to the embodiments of this application. Those skilled in the art can understand that the illustrated structure does not constitute a limitation on the system, and may include more or fewer components than illustrated, or combine certain components, or have different component arrangements.

[0109] The above-described embodiments are only used to illustrate the technical solutions of the present invention, and are not intended to limit it. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A smart detection method for the performance of a hydrogen compressor, characterized in that, include: Using hydrogen compressor sensors, real-time performance data of multiple operating performance parameters of the hydrogen compressor under test are obtained; Obtain historical data of the hydrogen compressor to be tested; Based on the historical data, baseline data for each operating performance level are determined; Based on the real-time performance data and the baseline data, determine whether the performance of the hydrogen compressor under test is normal; The step of determining the baseline data for each operational performance based on the historical data includes: Based on the detection data and the preset detection state, the first state data of the hydrogen compressor to be tested is determined, wherein the first state data is the state data of the historical data starting point; Based on the historical data and the preset historical state, the second state data of the hydrogen compressor to be tested is determined, wherein the second state data is the state data at the end point of the historical data. Based on the first state data and the second state data, the reference data of the hydrogen compressor to be tested is determined.

2. The method according to claim 1, characterized in that, The real-time performance data for the various operating parameters are voltage data, current data, temperature data, pressure data, and vibration data.

3. The method according to claim 2, characterized in that, The method of using a hydrogen compressor sensor to acquire real-time performance data of multiple operating performance parameters of the hydrogen compressor under test includes: Using a hydrogen compressor sensor, the voltage amplitude data, voltage amplitude change curve data, current amplitude data, current amplitude change curve data, first temperature data, and second temperature data of the hydrogen compressor under test are acquired. The voltage data and current data are determined based on the voltage amplitude data, the voltage amplitude change curve data, the current amplitude data, and the current amplitude change curve data. Generate thermoelectric potential difference data based on the first temperature data and the second temperature data; The temperature data is determined based on the thermoelectric potential difference data; Acquire the first pressure data, the second pressure data, and the pressure change curve data collected by the hydrogen compressor sensor; The pressure data is generated based on the first pressure data and the second pressure data; The vibration data is obtained based on the pressure change curve data; Based on the voltage data, current data, temperature data, pressure data, and vibration data, the performance data of the multiple operating performance parameters are determined.

4. The method according to claim 1, characterized in that, After acquiring the historical data of the hydrogen compressor to be tested, the following steps are included: Acquire the analog data collected by the sensor of the hydrogen compressor to be tested; Acquire digital data from the simulation data, classify the digital data, and calculate the actual data of the hydrogen compressor to be tested; Based on the actual data, obtain the fluctuation curve within a preset time range.

5. The method according to claim 1, characterized in that, The step of determining whether the performance of the hydrogen compressor under test is normal based on the real-time performance data and the benchmark data includes: Obtain the allowable error data of the reference data; The benchmark judgment range is determined based on the benchmark data and the error allowable data; Determine whether the real-time performance data is within the benchmark judgment range; If the real-time performance data is within the benchmark judgment range, the performance of the hydrogen compressor under test is determined to be normal; otherwise, the performance of the hydrogen compressor under test is abnormal.

6. The method according to claim 5, characterized in that, After determining that the performance of the hydrogen compressor under test is normal if the real-time performance data is within the benchmark judgment range, and otherwise that the performance of the hydrogen compressor under test is abnormal, the process includes: If the performance of the hydrogen compressor under test is normal, then the predicted lifespan data of the hydrogen compressor under test is obtained. If the performance of the hydrogen compressor under test is abnormal, abnormal data is sent to the maintenance terminal, and maintenance personnel information is determined.

7. An intelligent detection system for the performance of a hydrogen compressor, characterized in that, A smart detection system for hydrogen compressor performance is used to execute the smart detection method for hydrogen compressor performance according to any one of claims 1 to 6, wherein the smart detection system for hydrogen compressor performance includes: Performance acquisition module: Used to acquire real-time performance data of multiple operating performance characteristics of the hydrogen compressor under test using hydrogen compressor sensors; Historical data acquisition module: used to acquire historical data of the hydrogen compressor under test; Benchmark determination module: used to determine the benchmark data for each operating performance based on the historical data; Performance judgment module: used to determine whether the performance of the hydrogen compressor under test is normal based on the real-time performance data and the benchmark data.

8. The system according to claim 7, characterized in that, The performance judgment module includes: Error acquisition module: used to acquire the allowable error data of the reference data; Range determination module: used to determine the benchmark judgment range based on the benchmark data and the error allowable data; Data judgment module: used to determine whether the real-time performance data is within the benchmark judgment range; Performance determination module: If the real-time performance data is within the benchmark judgment range, then the performance of the hydrogen compressor under test is determined to be normal; otherwise, the performance of the hydrogen compressor under test is abnormal.

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