CN117571200A - Pressure sensor water pressure fatigue test system based on artificial intelligence - Google Patents
Pressure sensor water pressure fatigue test system based on artificial intelligence Download PDFInfo
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- 238000009661 fatigue test Methods 0.000 title claims abstract description 17
- 238000013473 artificial intelligence Methods 0.000 title claims abstract description 14
- XLYOFNOQVPJJNP-UHFFFAOYSA-N water Substances O XLYOFNOQVPJJNP-UHFFFAOYSA-N 0.000 title abstract description 7
- 238000012360 testing method Methods 0.000 claims abstract description 292
- 238000004458 analytical method Methods 0.000 claims abstract description 91
- 238000001514 detection method Methods 0.000 claims abstract description 47
- 230000003068 static effect Effects 0.000 claims abstract description 47
- 238000004891 communication Methods 0.000 claims abstract description 4
- 238000000034 method Methods 0.000 claims description 65
- 238000005457 optimization Methods 0.000 claims description 24
- 230000002159 abnormal effect Effects 0.000 claims description 16
- 238000011056 performance test Methods 0.000 claims description 14
- 238000013433 optimization analysis Methods 0.000 claims description 12
- 238000004364 calculation method Methods 0.000 claims description 7
- 238000012935 Averaging Methods 0.000 claims description 6
- 230000005856 abnormality Effects 0.000 claims description 6
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01L—MEASURING FORCE, STRESS, TORQUE, WORK, MECHANICAL POWER, MECHANICAL EFFICIENCY, OR FLUID PRESSURE
- G01L27/00—Testing or calibrating of apparatus for measuring fluid pressure
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F18/00—Pattern recognition
- G06F18/20—Analysing
- G06F18/21—Design or setup of recognition systems or techniques; Extraction of features in feature space; Blind source separation
- G06F18/217—Validation; Performance evaluation; Active pattern learning techniques
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F18/00—Pattern recognition
- G06F18/20—Analysing
- G06F18/24—Classification techniques
- G06F18/241—Classification techniques relating to the classification model, e.g. parametric or non-parametric approaches
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F18/00—Pattern recognition
- G06F18/20—Analysing
- G06F18/24—Classification techniques
- G06F18/243—Classification techniques relating to the number of classes
- G06F18/2433—Single-class perspective, e.g. one-against-all classification; Novelty detection; Outlier detection
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- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02E—REDUCTION OF GREENHOUSE GAS [GHG] EMISSIONS, RELATED TO ENERGY GENERATION, TRANSMISSION OR DISTRIBUTION
- Y02E30/00—Energy generation of nuclear origin
- Y02E30/30—Nuclear fission reactors
Abstract
The invention belongs to the field of pressure testing, relates to a data analysis technology, and is used for solving the problem that a pressure sensor hydraulic fatigue testing system in the prior art cannot simulate various application environments of a pressure sensor, in particular to a pressure sensor hydraulic fatigue testing system based on artificial intelligence, which comprises a dynamic testing module, a static testing module, a testing analysis module and an optimizing analysis module, wherein the dynamic testing module, the static testing module, the testing analysis module and the optimizing analysis module are sequentially in communication connection; the dynamic test module is used for carrying out dynamic test analysis on the pressure sensor; the invention can dynamically test and analyze the pressure sensor, acquire and analyze data at a test time point set in a test period to obtain a normal coefficient, and judge the accuracy of the detection data of the pressure sensor when the water pressure slowly and uniformly increases by the normal coefficient, thereby screening the pressure sensor with unqualified accuracy.
Description
Technical Field
The invention belongs to the field of pressure testing, relates to a data analysis technology, and particularly relates to a pressure sensor water pressure fatigue testing system based on artificial intelligence.
Background
Pressure sensor hydraulic fatigue test system is mainly used for testing the durability and reliability of a pressure sensor, and the system simulates the long-term pressure change which the sensor can experience in actual use by continuously applying hydraulic pressure and repeatedly performing pressure circulation so as to detect the performance and stability of the sensor.
In the prior art, the hydraulic fatigue test system of the pressure sensor generally adopts a single pressure circulation mode to carry out hydraulic fatigue test, but can not simulate various application environments of the pressure sensor, so that the accuracy of a test result is low, and meanwhile, the process optimization direction can not be given when the test performance of the pressure sensor is unqualified.
Aiming at the technical problems, the application provides a solution.
Disclosure of Invention
The invention aims to provide a pressure sensor hydraulic fatigue test system based on artificial intelligence, which is used for solving the problem that the pressure sensor hydraulic fatigue test system in the prior art cannot simulate various application environments of a pressure sensor;
the technical problems to be solved by the invention are as follows: how to provide a pressure sensor water pressure fatigue test system based on artificial intelligence which can simulate various application environments of the pressure sensor.
The aim of the invention can be achieved by the following technical scheme:
the system comprises a dynamic test module, a static test module, a test analysis module and an optimization analysis module, wherein the dynamic test module, the static test module, the test analysis module and the optimization analysis module are sequentially in communication connection;
the dynamic test module is used for carrying out dynamic test analysis on the pressure sensor: marking a pressure sensor to be tested as a test object, acquiring the pressure test range of the test object, setting a test period, and dynamically testing in the test period to obtain the normal coefficient of the test object; marking the test object as a normal object or an abnormal object through a normal coefficient;
the static test module is used for carrying out static test analysis on a normal object: randomly selecting a plurality of pressure values as test values within a pressure test range, randomly scrambling and reorganizing the test values to obtain a test sequence, and performing performance test on a normal object according to the test sequence to obtain a plurality of delay values; all the test values and the delay values are sent to a test analysis module;
the test analysis module is used for evaluating and analyzing the test performance of the normal object;
the optimization analysis module is used for analyzing the process optimization direction of the normal object;
the specific process of dynamic test comprises: setting the minimum value and the maximum value of the pressure test range as a pressure small value and a pressure large value respectively, marking the initial value of the output pressure value of the pressure pump as the pressure small value, controlling the output pressure value of the pressure pump to increase at a constant speed in a test period, and enabling the output pressure value of the pressure pump to reach the pressure large value at the ending time of the test period; setting a plurality of test time points in a test period, acquiring an output pressure value of a pressure pump at the test time point and a pressure detection value of a test object, marking an absolute value of a difference value between the output pressure value and the pressure detection value as a test deviation value at the test time point, and comparing the test deviation value with a preset test deviation threshold value: if the test deviation value is smaller than the test deviation threshold value, judging that the detection data of the pressure sensor at the test time point meets the requirement, and marking the corresponding test time point as a normal time point; if the test deviation value is greater than or equal to the test deviation threshold value, judging that the detection data of the pressure sensor at the test time point does not meet the requirement, and marking the corresponding test time point as an abnormal time point; marking the number ratio of the normal time point to the test time point in the test period as a normal coefficient;
the specific process of marking the test object as a normal object or an abnormal object comprises the following steps: comparing the normal coefficient with a preset normal threshold value: if the normal coefficient is smaller than the normal threshold, judging that the accuracy of the detection data of the corresponding test object does not meet the requirement, and marking the corresponding test object as an abnormal object; if the normal coefficient is greater than or equal to the normal threshold, judging that the accuracy of the detection data of the corresponding test object meets the requirement, and marking the corresponding test object as a normal object;
the specific process for performing performance test on the normal object according to the test sequence comprises the following steps: selecting a first test value in a test sequence, marking the first test value as a static value, instantaneously adjusting the output pressure value of the pressure pump to the static value, marking the numerical value of the static value as M1, forming a static range by M1-t and M1+t, and marking the difference between the moment when the detection data of a normal object reaches the static range and the output pressure adjusting moment of the pressure pump as a delay value; then selecting a second test value in the test sequence to replace the value of the static value, and after L1 seconds, regulating the output pressure value of the pressure pump to the static value again, and re-acquiring the static range and the delay value; and the like, until all test values in all test sequences complete numerical replacement of the static value and obtain corresponding delay values.
As a preferred embodiment of the invention, the specific process of evaluating and analyzing the test performance of the normal object by the test analysis module comprises the following steps: summing all delay values, taking an average value to obtain a delay coefficient, carrying out variance calculation on all delay values to obtain a uniform coefficient, and comparing the delay coefficient and the uniform coefficient with a preset delay threshold and a preset uniform threshold respectively: if the delay coefficient is greater than or equal to the delay threshold, judging that the detection performance of the normal object does not meet the requirement, generating a performance abnormality signal and sending the performance abnormality signal to a mobile phone terminal of a manager; if the delay coefficient is smaller than the delay threshold value and the uniformity coefficient is smaller than the uniformity threshold value, judging that the detection performance of the normal object meets the requirement, generating a performance qualified signal and sending the performance qualified signal to a mobile phone terminal of a manager; if the delay coefficient is smaller than the delay threshold and the uniformity coefficient is larger than or equal to the uniformity threshold, judging that the detection performance of the normal object does not meet the requirement, generating an optimized analysis signal and sending the optimized analysis signal to an optimized analysis module.
As a preferred embodiment of the invention, the specific process of analyzing the process optimization direction of the normal object by the optimization analysis module comprises the following steps: the method comprises the steps of marking an output pressure value instantaneous adjustment process of a pressure pump in a performance test process as an analysis process, arranging the analysis process according to a sequence of small to large delay value values to obtain a delay sequence, arranging the analysis process according to a sequence of front to back execution time of the analysis process to obtain a time sequence, arranging test value values of the analysis process according to a sequence of small to large analysis process execution time to obtain a pressure sequence, marking an absolute value of a difference value between a serial number of the analysis process in the delay sequence and a serial number of the time sequence as a time influence value of the analysis process, marking an absolute value of a difference value between a serial number of the analysis process in the delay sequence and a serial number of the pressure sequence as a pressure influence value of the analysis process, summing and averaging the time influence values of all the analysis processes to obtain a time influence coefficient, summing and averaging the pressure influence values of all the analysis processes to obtain a pressure influence coefficient, and comparing the time influence coefficient with the pressure influence coefficient: if the time influence coefficient is greater than or equal to the pressure influence coefficient, generating a pressure-resistant optimization signal and sending the pressure-resistant optimization signal to a mobile phone terminal of a manager; and if the time influence coefficient is smaller than the pressure influence coefficient, generating a durable optimized signal and sending the durable optimized signal to a mobile phone terminal of the manager.
As a preferred embodiment of the invention, the working method of the artificial intelligence-based pressure sensor hydraulic fatigue testing system comprises the following steps:
step one: dynamic test analysis is carried out on the pressure sensor: marking a pressure sensor to be tested as a test object, acquiring the pressure test range of the test object, setting a test period, and controlling the output pressure value of the pressure pump to increase at a constant speed in the test period; setting a plurality of test time points in a test period, acquiring an output pressure value of a pressure pump at the test time points and a pressure detection value of a test object, obtaining a normal coefficient, and marking the test object as a normal object or an abnormal object through the normal coefficient;
step two: static test analysis was performed on normal subjects: randomly selecting a plurality of pressure values as test values within a pressure test range, randomly scrambling and reorganizing the test values to obtain a test sequence, and performing performance test on a normal object according to the test sequence to obtain a plurality of delay values;
step three: evaluation analysis is carried out on the test performance of the normal object: summing all the delay values, taking an average value to obtain a delay coefficient, carrying out variance calculation on all the delay values to obtain a uniform coefficient, and judging whether the detection performance of a normal object meets the requirement or not through the delay coefficient and the uniform coefficient;
step four: analyzing the process optimization direction of the normal object, obtaining a time influence coefficient and a pressure influence coefficient, comparing the time influence coefficient and the pressure influence coefficient, and generating a corresponding process optimization signal through a comparison result.
The invention has the following beneficial effects:
1. the dynamic test module can be used for carrying out dynamic test analysis on the pressure sensor, data acquisition and analysis are carried out at a test time point set in a test period to obtain a normal coefficient, and the accuracy of the detection data of the pressure sensor when the water pressure slowly and uniformly increases is judged through the normal coefficient, so that the pressure sensor with unqualified accuracy is screened;
2. the static test module can perform static test analysis on the normal object, analyze the detection timeliness of the normal object under the condition of instantaneous pressurization or depressurization to obtain a delay value, feed back the instantaneous detection performance of the normal object through the delay value, and provide data support for the test analysis process;
3. the test analysis module can evaluate and analyze the test performance of the normal object to obtain a delay coefficient and a uniformity coefficient, and evaluate whether the detection performance of the normal object is qualified or not according to the delay coefficient and the uniformity coefficient, and meanwhile, judge the necessity of the optimization analysis of the normal object;
4. the process optimization direction of the normal object can be analyzed through the optimization analysis module, the time influence coefficient and the pressure influence coefficient are obtained by comparing the delay value of the performance test result with the time characteristic and the pressure characteristic in the test process, so that the process influence factors influencing the abnormal test performance of the normal object are marked through the time influence coefficient and the pressure influence coefficient, and decision analysis is provided for the process optimization direction of the pressure sensor.
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In order to more clearly illustrate the embodiments of the invention or the technical solutions in the prior art, the drawings that are required in the embodiments or the description of the prior art will be briefly described, it being obvious that the drawings in the following description are only some embodiments of the invention, and that other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 is a system block diagram of a first embodiment of the present invention;
fig. 2 is a flowchart of a method according to a second embodiment of the invention.
Detailed Description
The technical solutions of the present invention will be clearly and completely described in connection with the embodiments, and it is obvious that the described embodiments are only some embodiments of the present invention, not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
Example 1
As shown in FIG. 1, the system for testing the hydraulic fatigue of the pressure sensor based on the artificial intelligence comprises a dynamic testing module, a static testing module, a testing analysis module and an optimizing analysis module, wherein the dynamic testing module, the static testing module, the testing analysis module and the optimizing analysis module are sequentially in communication connection.
The dynamic test module is used for carrying out dynamic test analysis on the pressure sensor: marking a pressure sensor to be tested as a test object, acquiring a pressure test range of the test object, setting a test period, respectively setting a minimum value and a maximum value of the pressure test range as a pressure small value and a pressure large value, marking an initial value of an output pressure value of a pressure pump as the pressure small value, controlling the output pressure value of the pressure pump to increase at a uniform speed in the test period, and enabling the output pressure value of the pressure pump to reach the pressure large value at the end time of the test period; setting a plurality of test time points in a test period, acquiring an output pressure value of a pressure pump at the test time point and a pressure detection value of a test object, marking an absolute value of a difference value between the output pressure value and the pressure detection value as a test deviation value at the test time point, and comparing the test deviation value with a preset test deviation threshold value: if the test deviation value is smaller than the test deviation threshold value, judging that the detection data of the pressure sensor at the test time point meets the requirement, and marking the corresponding test time point as a normal time point; if the test deviation value is greater than or equal to the test deviation threshold value, judging that the detection data of the pressure sensor at the test time point does not meet the requirement, and marking the corresponding test time point as an abnormal time point; marking the number ratio of the normal time points to the test time points in the test period as a normal coefficient, and comparing the normal coefficient with a preset normal threshold value: if the normal coefficient is smaller than the normal threshold, judging that the accuracy of the detection data of the corresponding test object does not meet the requirement, and marking the corresponding test object as an abnormal object; if the normal coefficient is greater than or equal to the normal threshold, judging that the accuracy of the detection data of the corresponding test object meets the requirement, and marking the corresponding test object as a normal object; and carrying out dynamic test analysis on the pressure sensor, carrying out data acquisition and analysis at a test time point set in a test period to obtain a normal coefficient, and judging the accuracy of detection data of the pressure sensor when the water pressure slowly and uniformly increases through the normal coefficient, so as to screen the pressure sensor with unqualified accuracy.
The static test module is used for carrying out static test analysis on the normal object: randomly selecting a plurality of pressure values as test values within a pressure test range, randomly disturbing and reorganizing the test values to obtain a test sequence, and performing performance test on a normal object according to the test sequence: selecting a first test value in a test sequence, marking the first test value as a static value, instantaneously adjusting the output pressure value of the pressure pump to the static value, marking the numerical value of the static value as M1, forming a static range by M1-t and M1+t, and marking the difference between the moment when the detection data of a normal object reaches the static range and the output pressure adjusting moment of the pressure pump as a delay value; then selecting a second test value in the test sequence to replace the value of the static value, and adjusting the output pressure value of the pressure pump to the static value again after L1 seconds, wherein L1 is a constant value, and the specific value of L1 is set by a manager; and re-acquiring a static range and a delay value; and the like, until all test values in all test sequences complete numerical replacement of the static value and obtain corresponding delay values; all the test values and the delay values are sent to a test analysis module; and carrying out static test analysis on the normal object, analyzing the detection timeliness of the normal object under the condition of instantaneous pressurization or depressurization to obtain a delay value, feeding back the instantaneous detection performance of the normal object through the delay value, and providing data support for the test analysis process.
The test analysis module is used for evaluating and analyzing the test performance of the normal object: summing all delay values, taking an average value to obtain a delay coefficient, carrying out variance calculation on all delay values to obtain a uniform coefficient, and comparing the delay coefficient and the uniform coefficient with a preset delay threshold and a preset uniform threshold respectively: if the delay coefficient is greater than or equal to the delay threshold, judging that the detection performance of the normal object does not meet the requirement, generating a performance abnormality signal and sending the performance abnormality signal to a mobile phone terminal of a manager; if the delay coefficient is smaller than the delay threshold value and the uniformity coefficient is smaller than the uniformity threshold value, judging that the detection performance of the normal object meets the requirement, generating a performance qualified signal and sending the performance qualified signal to a mobile phone terminal of a manager; if the delay coefficient is smaller than the delay threshold and the uniformity coefficient is larger than or equal to the uniformity threshold, judging that the detection performance of the normal object does not meet the requirement, generating an optimized analysis signal and sending the optimized analysis signal to an optimized analysis module; and evaluating and analyzing the test performance of the normal object to obtain a delay coefficient and a uniformity coefficient, evaluating whether the detection performance of the normal object is qualified or not according to the delay coefficient and the uniformity coefficient, and judging the necessity of the optimization analysis of the normal object.
The optimization analysis module is used for analyzing the process optimization direction of the normal object: the method comprises the steps of marking an output pressure value instantaneous adjustment process of a pressure pump in a performance test process as an analysis process, arranging the analysis process according to a sequence of small to large delay value values to obtain a delay sequence, arranging the analysis process according to a sequence of front to back execution time of the analysis process to obtain a time sequence, arranging test value values of the analysis process according to a sequence of small to large analysis process execution time to obtain a pressure sequence, marking an absolute value of a difference value between a serial number of the analysis process in the delay sequence and a serial number of the time sequence as a time influence value of the analysis process, marking an absolute value of a difference value between a serial number of the analysis process in the delay sequence and a serial number of the pressure sequence as a pressure influence value of the analysis process, summing and averaging the time influence values of all the analysis processes to obtain a time influence coefficient, summing and averaging the pressure influence values of all the analysis processes to obtain a pressure influence coefficient, and comparing the time influence coefficient with the pressure influence coefficient: if the time influence coefficient is greater than or equal to the pressure influence coefficient, generating a pressure-resistant optimization signal and sending the pressure-resistant optimization signal to a mobile phone terminal of a manager; if the time influence coefficient is smaller than the pressure influence coefficient, generating a durable optimized signal and sending the durable optimized signal to a mobile phone terminal of a manager; and analyzing the process optimization direction of the normal object, and comparing the delay value of the performance test result with the time characteristic and the pressure characteristic in the test process to obtain a time influence coefficient and a pressure influence coefficient, so that the process influence factors influencing the abnormal test performance of the normal object are marked through the time influence coefficient and the pressure influence coefficient, and decision analysis is provided for the process optimization direction of the pressure sensor.
Example two
As shown in fig. 2, a method for testing hydraulic fatigue of a pressure sensor based on artificial intelligence comprises the following steps:
step one: dynamic test analysis is carried out on the pressure sensor: marking a pressure sensor to be tested as a test object, acquiring the pressure test range of the test object, setting a test period, and controlling the output pressure value of the pressure pump to increase at a constant speed in the test period; setting a plurality of test time points in a test period, acquiring an output pressure value of a pressure pump at the test time points and a pressure detection value of a test object, obtaining a normal coefficient, and marking the test object as a normal object or an abnormal object through the normal coefficient;
step two: static test analysis was performed on normal subjects: randomly selecting a plurality of pressure values as test values within a pressure test range, randomly scrambling and reorganizing the test values to obtain a test sequence, and performing performance test on a normal object according to the test sequence to obtain a plurality of delay values;
step three: evaluation analysis is carried out on the test performance of the normal object: summing all the delay values, taking an average value to obtain a delay coefficient, carrying out variance calculation on all the delay values to obtain a uniform coefficient, and judging whether the detection performance of a normal object meets the requirement or not through the delay coefficient and the uniform coefficient;
step four: analyzing the process optimization direction of the normal object, obtaining a time influence coefficient and a pressure influence coefficient, comparing the time influence coefficient and the pressure influence coefficient, and generating a corresponding process optimization signal through a comparison result.
When the pressure sensor hydraulic fatigue testing system based on artificial intelligence works, the pressure sensor to be tested is marked as a test object, the pressure testing range of the test object is obtained, the testing period is set, and the output pressure value of the pressure pump is controlled to increase at a constant speed in the testing period; setting a plurality of test time points in a test period, acquiring an output pressure value of a pressure pump at the test time points and a pressure detection value of a test object, obtaining a normal coefficient, and marking the test object as a normal object or an abnormal object through the normal coefficient; randomly selecting a plurality of pressure values as test values within a pressure test range, randomly scrambling and reorganizing the test values to obtain a test sequence, and performing performance test on a normal object according to the test sequence to obtain a plurality of delay values; summing all the delay values, taking an average value to obtain a delay coefficient, carrying out variance calculation on all the delay values to obtain a uniform coefficient, and judging whether the detection performance of a normal object meets the requirement or not through the delay coefficient and the uniform coefficient; analyzing the process optimization direction of the normal object, obtaining a time influence coefficient and a pressure influence coefficient, comparing the time influence coefficient and the pressure influence coefficient, and generating a corresponding process optimization signal through a comparison result.
The foregoing is merely illustrative of the structures of this invention and various modifications, additions and substitutions for those skilled in the art can be made to the described embodiments without departing from the scope of the invention or from the scope of the invention as defined in the accompanying claims.
In the description of the present specification, the descriptions of the terms "one embodiment," "example," "specific example," and the like, mean that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the present invention. In this specification, schematic representations of the above terms do not necessarily refer to the same embodiments or examples. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples.
The preferred embodiments of the invention disclosed above are intended only to assist in the explanation of the invention. The preferred embodiments are not intended to be exhaustive or to limit the invention to the precise form disclosed. Obviously, many modifications and variations are possible in light of the above teaching. The embodiments were chosen and described in order to best explain the principles of the invention and the practical application, to thereby enable others skilled in the art to best understand and utilize the invention. The invention is limited only by the claims and the full scope and equivalents thereof.
Claims (4)
1. The system is characterized by comprising a dynamic test module, a static test module, a test analysis module and an optimization analysis module, wherein the dynamic test module, the static test module, the test analysis module and the optimization analysis module are sequentially in communication connection;
the dynamic test module is used for carrying out dynamic test analysis on the pressure sensor: marking a pressure sensor to be tested as a test object, acquiring the pressure test range of the test object, setting a test period, and dynamically testing in the test period to obtain the normal coefficient of the test object; marking the test object as a normal object or an abnormal object through a normal coefficient;
the static test module is used for carrying out static test analysis on a normal object: randomly selecting a plurality of pressure values as test values within a pressure test range, randomly scrambling and reorganizing the test values to obtain a test sequence, and performing performance test on a normal object according to the test sequence to obtain a plurality of delay values; all the test values and the delay values are sent to a test analysis module;
the test analysis module is used for evaluating and analyzing the test performance of the normal object;
the optimization analysis module is used for analyzing the process optimization direction of the normal object;
the specific process of dynamic test comprises: setting the minimum value and the maximum value of the pressure test range as a pressure small value and a pressure large value respectively, marking the initial value of the output pressure value of the pressure pump as the pressure small value, controlling the output pressure value of the pressure pump to increase at a constant speed in a test period, and enabling the output pressure value of the pressure pump to reach the pressure large value at the ending time of the test period; setting a plurality of test time points in a test period, acquiring an output pressure value of a pressure pump at the test time point and a pressure detection value of a test object, marking an absolute value of a difference value between the output pressure value and the pressure detection value as a test deviation value at the test time point, and comparing the test deviation value with a preset test deviation threshold value: if the test deviation value is smaller than the test deviation threshold value, judging that the detection data of the pressure sensor at the test time point meets the requirement, and marking the corresponding test time point as a normal time point; if the test deviation value is greater than or equal to the test deviation threshold value, judging that the detection data of the pressure sensor at the test time point does not meet the requirement, and marking the corresponding test time point as an abnormal time point; marking the number ratio of the normal time point to the test time point in the test period as a normal coefficient;
the specific process of marking the test object as a normal object or an abnormal object comprises the following steps: comparing the normal coefficient with a preset normal threshold value: if the normal coefficient is smaller than the normal threshold, judging that the accuracy of the detection data of the corresponding test object does not meet the requirement, and marking the corresponding test object as an abnormal object; if the normal coefficient is greater than or equal to the normal threshold, judging that the accuracy of the detection data of the corresponding test object meets the requirement, and marking the corresponding test object as a normal object;
the specific process for performing performance test on the normal object according to the test sequence comprises the following steps: selecting a first test value in a test sequence, marking the first test value as a static value, instantaneously adjusting the output pressure value of the pressure pump to the static value, marking the numerical value of the static value as M1, forming a static range by M1-t and M1+t, and marking the difference between the moment when the detection data of a normal object reaches the static range and the output pressure adjusting moment of the pressure pump as a delay value; then selecting a second test value in the test sequence to replace the value of the static value, and after L1 seconds, regulating the output pressure value of the pressure pump to the static value again, and re-acquiring the static range and the delay value; and the like, until all test values in all test sequences complete numerical replacement of the static value and obtain corresponding delay values.
2. The artificial intelligence based pressure sensor hydraulic fatigue testing system according to claim 1, wherein the specific process of evaluating and analyzing the testing performance of the normal subject by the testing and analyzing module comprises: summing all delay values, taking an average value to obtain a delay coefficient, carrying out variance calculation on all delay values to obtain a uniform coefficient, and comparing the delay coefficient and the uniform coefficient with a preset delay threshold and a preset uniform threshold respectively: if the delay coefficient is greater than or equal to the delay threshold, judging that the detection performance of the normal object does not meet the requirement, generating a performance abnormality signal and sending the performance abnormality signal to a mobile phone terminal of a manager; if the delay coefficient is smaller than the delay threshold value and the uniformity coefficient is smaller than the uniformity threshold value, judging that the detection performance of the normal object meets the requirement, generating a performance qualified signal and sending the performance qualified signal to a mobile phone terminal of a manager; if the delay coefficient is smaller than the delay threshold and the uniformity coefficient is larger than or equal to the uniformity threshold, judging that the detection performance of the normal object does not meet the requirement, generating an optimized analysis signal and sending the optimized analysis signal to an optimized analysis module.
3. The artificial intelligence based pressure sensor hydraulic fatigue testing system according to claim 2, wherein the specific process of analyzing the process optimization direction of the normal object by the optimization analysis module comprises: the method comprises the steps of marking an output pressure value instantaneous adjustment process of a pressure pump in a performance test process as an analysis process, arranging the analysis process according to a sequence of small to large delay value values to obtain a delay sequence, arranging the analysis process according to a sequence of front to back execution time of the analysis process to obtain a time sequence, arranging test value values of the analysis process according to a sequence of small to large analysis process execution time to obtain a pressure sequence, marking an absolute value of a difference value between a serial number of the analysis process in the delay sequence and a serial number of the time sequence as a time influence value of the analysis process, marking an absolute value of a difference value between a serial number of the analysis process in the delay sequence and a serial number of the pressure sequence as a pressure influence value of the analysis process, summing and averaging the time influence values of all the analysis processes to obtain a time influence coefficient, summing and averaging the pressure influence values of all the analysis processes to obtain a pressure influence coefficient, and comparing the time influence coefficient with the pressure influence coefficient: if the time influence coefficient is greater than or equal to the pressure influence coefficient, generating a pressure-resistant optimization signal and sending the pressure-resistant optimization signal to a mobile phone terminal of a manager; and if the time influence coefficient is smaller than the pressure influence coefficient, generating a durable optimized signal and sending the durable optimized signal to a mobile phone terminal of the manager.
4. A pressure sensor hydraulic fatigue testing system based on artificial intelligence according to any of claims 1-3, wherein the working method of the pressure sensor hydraulic fatigue testing system based on artificial intelligence comprises the following steps:
step one: dynamic test analysis is carried out on the pressure sensor: marking a pressure sensor to be tested as a test object, acquiring the pressure test range of the test object, setting a test period, and controlling the output pressure value of the pressure pump to increase at a constant speed in the test period; setting a plurality of test time points in a test period, acquiring an output pressure value of a pressure pump at the test time points and a pressure detection value of a test object, obtaining a normal coefficient, and marking the test object as a normal object or an abnormal object through the normal coefficient;
step two: static test analysis was performed on normal subjects: randomly selecting a plurality of pressure values as test values within a pressure test range, randomly scrambling and reorganizing the test values to obtain a test sequence, and performing performance test on a normal object according to the test sequence to obtain a plurality of delay values;
step three: evaluation analysis is carried out on the test performance of the normal object: summing all the delay values, taking an average value to obtain a delay coefficient, carrying out variance calculation on all the delay values to obtain a uniform coefficient, and judging whether the detection performance of a normal object meets the requirement or not through the delay coefficient and the uniform coefficient;
step four: analyzing the process optimization direction of the normal object, obtaining a time influence coefficient and a pressure influence coefficient, comparing the time influence coefficient and the pressure influence coefficient, and generating a corresponding process optimization signal through a comparison result.
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Citations (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN108318357A (en) * | 2018-01-11 | 2018-07-24 | 浙江大学 | Damage monitoring device and method for composites gas cylinder fatigue test |
| CN108345976A (en) * | 2017-01-24 | 2018-07-31 | 中国石油化工股份有限公司 | A kind of long-distance oil ＆ gas pipeline dynamic risk appraisal procedure and device |
| CN108496206A (en) * | 2015-12-21 | 2018-09-04 | 赛峰电子与防务公司 | The method of the aging of undercarriage for monitoring aircraft |
| CN109030252A (en) * | 2018-07-25 | 2018-12-18 | 浙江大学 | Monitoring device and method for composites gas cylinder fatigue damage under hot environment |
| CN116915824A (en) * | 2023-09-13 | 2023-10-20 | 长沙弘汇电子科技有限公司 | Hydraulic engineering brake pump remote monitoring system based on Internet of things |
| WO2023213041A1 (en) * | 2022-05-05 | 2023-11-09 | 江苏科技大学 | Seawater fatigue cycle simulation experimental device and experimental method |
| CN117058826A (en) * | 2023-08-29 | 2023-11-14 | 北京怀芯物联技术有限公司 | Acoustic security sensor operation detecting system based on artificial intelligence |
| CN117408641A (en) * | 2023-12-11 | 2024-01-16 | 无锡芯感智半导体有限公司 | Pressure sensor production line processing operation supervision system based on data analysis |
-
2024
- 2024-01-16 CN CN202410059395.6A patent/CN117571200B/en active Active
Patent Citations (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN108496206A (en) * | 2015-12-21 | 2018-09-04 | 赛峰电子与防务公司 | The method of the aging of undercarriage for monitoring aircraft |
| CN108345976A (en) * | 2017-01-24 | 2018-07-31 | 中国石油化工股份有限公司 | A kind of long-distance oil ＆ gas pipeline dynamic risk appraisal procedure and device |
| CN108318357A (en) * | 2018-01-11 | 2018-07-24 | 浙江大学 | Damage monitoring device and method for composites gas cylinder fatigue test |
| CN109030252A (en) * | 2018-07-25 | 2018-12-18 | 浙江大学 | Monitoring device and method for composites gas cylinder fatigue damage under hot environment |
| WO2023213041A1 (en) * | 2022-05-05 | 2023-11-09 | 江苏科技大学 | Seawater fatigue cycle simulation experimental device and experimental method |
| CN117058826A (en) * | 2023-08-29 | 2023-11-14 | 北京怀芯物联技术有限公司 | Acoustic security sensor operation detecting system based on artificial intelligence |
| CN116915824A (en) * | 2023-09-13 | 2023-10-20 | 长沙弘汇电子科技有限公司 | Hydraulic engineering brake pump remote monitoring system based on Internet of things |
| CN117408641A (en) * | 2023-12-11 | 2024-01-16 | 无锡芯感智半导体有限公司 | Pressure sensor production line processing operation supervision system based on data analysis |
Non-Patent Citations (1)
| Title |
|---|
| 王蕊;李东风;杨鹏;王亚龙;韩军;: "水压疲劳试验应变采集系统的不确定度分析", 工业计量, no. 05, 26 September 2017 (2017-09-26) * |
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