CN119004861B - Full life cycle management method for electrochemical gas sensors - Google Patents
Full life cycle management method for electrochemical gas sensors Download PDFInfo
- Publication number
- CN119004861B CN119004861B CN202411471829.XA CN202411471829A CN119004861B CN 119004861 B CN119004861 B CN 119004861B CN 202411471829 A CN202411471829 A CN 202411471829A CN 119004861 B CN119004861 B CN 119004861B
- Authority
- CN
- China
- Prior art keywords
- trend
- sensor
- time
- data
- real
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
- 238000007726 management method Methods 0.000 title claims abstract description 10
- 238000000034 method Methods 0.000 claims abstract description 15
- 238000012544 monitoring process Methods 0.000 claims abstract description 9
- 238000012937 correction Methods 0.000 claims description 11
- 230000001174 ascending effect Effects 0.000 claims description 6
- 238000013461 design Methods 0.000 claims description 6
- 230000008569 process Effects 0.000 claims description 3
- 230000032683 aging Effects 0.000 abstract description 12
- 238000005259 measurement Methods 0.000 abstract description 8
- 238000004519 manufacturing process Methods 0.000 abstract description 5
- 238000001514 detection method Methods 0.000 abstract description 3
- 239000003792 electrolyte Substances 0.000 description 7
- 230000035945 sensitivity Effects 0.000 description 7
- 230000008859 change Effects 0.000 description 6
- 230000003139 buffering effect Effects 0.000 description 3
- 230000007423 decrease Effects 0.000 description 3
- 230000007613 environmental effect Effects 0.000 description 3
- 238000005457 optimization Methods 0.000 description 3
- 230000004044 response Effects 0.000 description 3
- 230000000630 rising effect Effects 0.000 description 3
- 238000009825 accumulation Methods 0.000 description 2
- 238000004458 analytical method Methods 0.000 description 2
- 238000004364 calculation method Methods 0.000 description 2
- 238000003487 electrochemical reaction Methods 0.000 description 2
- 238000013178 mathematical model Methods 0.000 description 2
- 230000002411 adverse Effects 0.000 description 1
- 238000013459 approach Methods 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 230000015556 catabolic process Effects 0.000 description 1
- 239000003054 catalyst Substances 0.000 description 1
- 238000010276 construction Methods 0.000 description 1
- 238000005260 corrosion Methods 0.000 description 1
- 230000007797 corrosion Effects 0.000 description 1
- 238000013499 data model Methods 0.000 description 1
- 230000009849 deactivation Effects 0.000 description 1
- 230000007547 defect Effects 0.000 description 1
- 238000006731 degradation reaction Methods 0.000 description 1
- 230000000694 effects Effects 0.000 description 1
- 239000007772 electrode material Substances 0.000 description 1
- 238000012423 maintenance Methods 0.000 description 1
- 239000000463 material Substances 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 238000004806 packaging method and process Methods 0.000 description 1
- 230000000737 periodic effect Effects 0.000 description 1
- 230000002035 prolonged effect Effects 0.000 description 1
- 230000009467 reduction Effects 0.000 description 1
- 238000006467 substitution reaction Methods 0.000 description 1
- 238000012360 testing method Methods 0.000 description 1
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F30/00—Computer-aided design [CAD]
- G06F30/20—Design optimisation, verification or simulation
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N27/00—Investigating or analysing materials by the use of electric, electrochemical, or magnetic means
- G01N27/26—Investigating or analysing materials by the use of electric, electrochemical, or magnetic means by investigating electrochemical variables; by using electrolysis or electrophoresis
- G01N27/416—Systems
- G01N27/4162—Systems investigating the composition of gases, by the influence exerted on ionic conductivity in a liquid
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N27/00—Investigating or analysing materials by the use of electric, electrochemical, or magnetic means
- G01N27/26—Investigating or analysing materials by the use of electric, electrochemical, or magnetic means by investigating electrochemical variables; by using electrolysis or electrophoresis
- G01N27/416—Systems
- G01N27/4163—Systems checking the operation of, or calibrating, the measuring apparatus
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F2119/00—Details relating to the type or aim of the analysis or the optimisation
- G06F2119/04—Ageing analysis or optimisation against ageing
Landscapes
- Physics & Mathematics (AREA)
- Life Sciences & Earth Sciences (AREA)
- Chemical & Material Sciences (AREA)
- Health & Medical Sciences (AREA)
- Engineering & Computer Science (AREA)
- General Physics & Mathematics (AREA)
- Electrochemistry (AREA)
- Molecular Biology (AREA)
- Biochemistry (AREA)
- General Health & Medical Sciences (AREA)
- Chemical Kinetics & Catalysis (AREA)
- Immunology (AREA)
- Pathology (AREA)
- Analytical Chemistry (AREA)
- Theoretical Computer Science (AREA)
- Evolutionary Computation (AREA)
- Geometry (AREA)
- General Engineering & Computer Science (AREA)
- Computer Hardware Design (AREA)
- Investigating Or Analyzing Materials By The Use Of Electric Means (AREA)
Abstract
The invention relates to the technical field of gas detection, in particular to a full life cycle management method of an electrochemical gas sensor, which comprises the following steps of S1, monitoring data noise of the sensor in real time to obtain sensor noise data; S2, counting the accumulated working time of the sensor under different gas concentration, temperature, humidity and atmospheric pressure environment parameters, S3, bringing the production date and data noise of the sensor and the working time under different environments into a life cycle model of the sensor, and calculating to obtain the ageing degree and the residual service life of the sensor. By adopting the method, the sensor can monitor the life cycle of the sensor in real time in practical application without being brought back to a laboratory for measurement.
Description
Technical Field
The invention relates to the technical field of electrochemical gas sensors, belongs to G01N in IPC classification numbers, and particularly relates to a full life cycle management method of an electrochemical gas sensor.
Background
Electrochemical sensors utilize electrochemical reactions to achieve detection of gas concentrations, the basic configuration of which includes a working electrode, a counter electrode, and a reference electrode. When the target gas reacts with the working electrode surface, the resulting current or potential change is proportional to the target gas concentration. By measuring such a current or potential change, the concentration of the target gas can be determined. The lifetime of electrochemical sensors is generally affected by a number of factors, including but not limited to the working environment, the conditions of use, and the quality of the sensor itself, etc., and therefore, there are many challenges in technically measuring the lifetime of electrochemical sensors, currently mainly using the following methods:
1. Laboratory testing
The sensor is placed under control conditions, so that the actual use scene is simulated, and the change condition of the output signal of the sensor is monitored. This approach can provide more accurate predictions of sensor life, but requires a longer time and higher cost to bring the sensor back into the laboratory environment for measurement;
2. analysis and monitoring of sensor data
And (3) utilizing the detection data of the sensor, combining the data history log, and adopting a periodic data reference mode to monitor and evaluate the performance of the sensor in real time. By establishing a proper model and algorithm, the sign of the performance degradation of the sensor can be timely found, and the service life of the sensor can be predicted. However, this method is often found afterwards (the sensor performance is reduced and then found), so that it is difficult to perform early warning in advance (the sensor is found before a problem occurs);
3. model-based life prediction
Based on the principle and performance parameters of the electrochemical sensor, a mathematical model is built, and the service life of the sensor under specific conditions is predicted through the model. The method is mainly used for examining the construction of the mathematical model, can effectively construct the full-parameter data model, and can directly influence the prediction accuracy. The current model-based predictions are based mostly on laboratory data to build models, the accuracy of which gradually becomes worse as the use environment changes and the use time increases, the invention is based on electrochemical sensor principles, the sensor noise, the gas concentration, the ambient temperature and the ambient humidity are used as input parameters to establish a sensor full life cycle model, and the prediction accuracy is higher and higher along with the extension of the application time of the sensor and the change of the use environment.
Disclosure of Invention
Aiming at the defects of the prior art, the invention provides a full life cycle management method of an electrochemical gas sensor, which can be used for real-time monitoring in practical application.
In order to achieve the purpose, the invention is realized by the following technical scheme that the full life cycle management method of the electrochemical gas sensor comprises the following steps:
S1, monitoring data noise of a sensor in real time to obtain sensor noise data;
s2, counting accumulated working time of the sensor under different gas concentration, temperature, humidity and atmospheric pressure environment parameters;
And S3, bringing the production date, data noise and working time under different environments into a life cycle model of the sensor, and calculating the aging degree and the residual service life of the sensor.
Preferably, in S1, a trend prediction algorithm is used to determine the actual value of the current sensor data, and the data noise is calculated in real time through data accumulation statistics.
The method comprises the steps of firstly, reading real-time data of a sensor, calculating a data average value of 90 seconds, secondly, obtaining a current data trend according to a trend algorithm, wherein the data trend is divided into four types of ascending, descending, fluctuation and stability;
VN rtd= |VALrtd- VALavg |formula 1
VN rtd data real-time noise
VAL rtd data real time value
VAL avg data 90 second average.
The trend prediction algorithm comprises four data trends, namely a real-time trend, a current trend, a historical trend and a temporary trend, wherein the four trend states comprise an ascending state, a descending state, a fluctuation state and a stable state, the trend prediction algorithm comprises the steps of firstly obtaining the real-time trend according to real-time data and a data average value, wherein the real-time data is larger than the data average value and is in the ascending state, and is in the descending state, secondly writing the temporary trend into the current data trend when the real-time trend is fluctuation and the same frequency as the temporary trend is larger than 10 times, writing the real-time trend into the temporary trend when the real-time trend is not larger than 10 times, writing the historical trend into the current trend when the real-time data trend is the same as the historical trend, and finally, modifying the current trend into fluctuation when the real-time trend is different, and outputting the current trend.
Preferably, in S2, the gas concentration is classified into 5 categories, namely, 80% of the range, 50% of the range and 80% of the range, 30% of the range and 50% of the range, 15% of the range and 30% of the range and 15% of the range or less, and the working time of the sensor is counted once per minute, then, the temperature is classified into 5 categories, namely, 40 ℃ or more, 30 ℃ to 40 ℃ and 4 ℃ to 30 ℃, 10 ℃ to 4 ℃ and 4 ℃ or less, and the working time is counted respectively, and finally, the humidity is classified into 5 categories, namely, 80% -100%, 50% -80%, 30% -50%, 15% -30% and 15% or less, and the working time is counted respectively, and the total temperature is classified into 125 subdivision categories and the working time of the sensor.
Preferably, the sensor lifecycle model formula is as follows:
Equation 2
T 1 remaining sensor use time
T 2 sensor design time of use
N 1 sensor expected noise
N 2 sensor actual noise
K, working time correction value under different environments
T, working time of sensor under different environments
The method has the beneficial effects that by adopting the method, the sensor can monitor the life cycle of the sensor in real time in practical application without being brought back to a laboratory for measurement. According to the invention, the life cycle curve of the sensor is calculated based on the change trend of sensor data noise and parameter summary statistics of working time, gas concentration, temperature and humidity and the like of the sensor, and is substituted into the life cycle model of the sensor to calculate, so that the residual service time and aging degree of the sensor are obtained, and meanwhile, the measured data is corrected according to the aging degree of the sensor, so that the performance of the sensor is optimized. The life cycle management of electrochemical sensors has important value, not only ensures the performance and reliability of the sensors, but also provides an economical and efficient solution for various applications by reducing maintenance costs and improving system design efficiency.
Drawings
FIG. 1 is a sensor lifecycle management flow chart;
FIG. 2 sensor data noise calculation flow;
FIG. 3 is a flow chart of a sensor trend algorithm;
FIG. 4 is a flow chart of statistical sensor operating environment data.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments.
The full life cycle management method of the electrochemical gas sensor comprises the implementation steps shown in figure 1. Firstly, monitoring data noise of a sensor in real time to obtain sensor noise data, then, counting accumulated working time of the sensor under different environmental parameters such as gas concentration, temperature, humidity, atmospheric pressure and the like, and finally, bringing the production date, noise and working time of the sensor under different environments into a sensor life cycle model to calculate and obtain the ageing degree and the residual service life of the sensor.
The specific implementation mode is as follows:
sensor data noise
There is a certain relation between noise and aging of the electrochemical sensor, and as the service time of the electrochemical sensor increases, the electrode material, electrolyte and other elements of the electrochemical sensor may have problems due to aging, so that the performance of the sensor is reduced, and the noise is increased. Aging can lead to problems with reduced electrode surface area, reduced electrochemical reaction rates, increased electrolyte loss, etc. of electrochemical sensors, which can affect the noise level of the sensor.
Sensor data noise is relatively easy to measure in a laboratory, but there are great challenges to measuring sensor data noise in an application, which is also a way that few people calculate the sensor aging level by monitoring noise in an application. In sensor noise monitoring, noise cannot be measured when data rises or falls, so that the calculated noise deviation is large and is irrelevant to the performance of the sensor, and a trend prediction algorithm is particularly critical in the step.
The invention adopts a trend prediction algorithm to determine the true value of the current sensor data, and calculates the data noise in real time through data accumulation statistics.
The implementation steps of sensor noise monitoring are shown in fig. 2, firstly, the real-time data of the sensor are read, the average value of the data is calculated, the main reason of using the average value of 90 seconds is that the average value needs to exceed a minute value and the response time of the electrochemical sensor is not longer than 90 seconds at the longest, secondly, the current data trend is obtained according to a trend algorithm, the data trend is divided into four types, namely rising, falling, fluctuation and stability, and finally, when the data trend is in fluctuation or stability, the real-time noise of the data is calculated according to a formula 1.
VN rtd= |VALrtd- VALavg |formula 1
VN rtd data real-time noise
VAL rtd data real time value
VAL avg data 90 second average.
The trend algorithm comprises four data trends, namely a real-time trend, a current trend, a historical trend and a temporary trend, and four trend states, namely an ascending state, a descending state, a fluctuation state and a stable state. Meaning analysis is as follows:
Real-time trend, namely calculating trend according to single real-time data and data average value;
the current trend is a current data trend obtained by integrating the historical trend, the temporary trend and the real-time trend;
Historical trend, namely the current trend calculated last time;
Temporary trend, when the real-time trend is different from the historical trend, the last stored real-time trend.
A rising state, VAL rtd＞VALavg and VN rtd > sensor resolution, in a rising state, when no noise is calculated;
a down state, VAL rtd＜VALavg and VN rtd > sensor resolution, in down state, where no noise is calculated;
the fluctuation state is that the sensor data is in the fluctuation state, and the fluctuation of the gas concentration or the aging of the sensor is caused;
steady state VN rtd < sensor resolution, steady state, with small data fluctuations.
The implementation step of the trend algorithm is shown in fig. 3, wherein the real-time trend is obtained according to the real-time data and the data average value, the real-time data is in an ascending state when the real-time data is larger than the data average value, otherwise in a descending state, the temporary trend is written into the current data trend when the real-time trend is in fluctuation and the temporary trend is the same times as the temporary trend for more than 10 times, the real-time trend is written into the temporary trend when the real-time trend is not larger than 10 times, the real-time trend is written into the current trend when the real-time data trend is the same as the historical trend, and the current trend is corrected into fluctuation when the real-time data trend is different from the historical trend. And finally, outputting the current trend. Sensor on-time statistics
Electrochemical sensor aging has close correlation with the length of time the sensor is operated, the environment (temperature, humidity, gas concentration, etc.) in which the sensor is operated. Because the attenuation degree of the sensor in different environments is different, the method for calculating the aging degree by directly counting the working time of the sensor without considering environmental factors can have larger deviation. The invention counts the working time of the sensor by adopting 125 subdivision levels of hierarchical classification, counts the working time of the sensor and achieves comprehensive and comprehensive calculation. As shown in FIG. 4, the gas concentration is firstly classified into 5 categories, namely, 80% of the range, 50% of the range and 80% of the range, 30% of the range and 50% of the range, 15% of the range and 30% of the range and 15% of the range, and the working time of the sensor is counted once per minute, secondly, the temperature is classified into 5 categories, namely, 40 ℃ to 40 ℃ of the range, 4 ℃ to 30 ℃ of the range, 10 ℃ to 4 ℃ of the range and 4 ℃ of the range, and the working time is counted, and finally, the humidity is classified into 5 categories, namely, 80% -100%, 50% -80%, 30% -50%, 15% -30% and 15% of the range, and the working time is counted in the sub-category of the sensor in 125.
Sensor lifecycle model
The life cycle model of an electrochemical sensor is a complex process involving the entire process from the beginning of the use of the sensor to the final failure. The following are some key environmental factors that affect the life of electrochemical sensors:
Temperature is the largest factor affecting the lifetime of the sensor, e.g. at 40 ℃ the lifetime of the sensor may be 6 months, whereas at 5 months the lifetime may be reduced to 3 months, i.e. every 10 ℃ increase in temperature at >40 ℃, the sensor lifetime is almost halved. Extreme temperatures can lead to electrolyte politics or electrode corrosion.
Too high or too low a humidity can adversely affect the sensor. Too high a humidity can cause the sensor electrolyte to overflow, while too low a humidity can cause the electrolyte to dry out, responding to the sensor performance. The ideal working humidity of the electrochemical sensor is 30% -60%.
The gas concentration is that the catalyst deactivation is accelerated and the service life of the sensor is shortened when the gas concentration is excessively frequent or long-time working under high-concentration gas.
Manufacturing quality-manufacturing process, material selection and packaging techniques of the sensor, these factors directly affect the initial performance and durability of the sensor.
The life cycle model of the sensor is designed based on the above factors, and the model formula is as follows:
Equation 2
T 1 remaining sensor use time
T 2 sensor design time of use
N 1 sensor expected noise
N 2 sensor actual noise
K, working time correction value under different environments
And t, working time of the sensor under different environments.
With a brand SO2 gas sensor example, sensor design usage time t 2 = 1051200 minutes (2 years), sensor expected noise from factory measurement is 42.
The sensor measures 60 noise, assuming that the sensor only works at >15% and less than or equal to 30% range, accumulating 13500.
According to equation 1, t1=0.9129× (1051200-16302.5) = 977743.4 minutes is calculated, i.e. the sensor remains in use for 678.99 days.
The gas concentration measured by the sensor is shown in formula 3.
Val= (V cur- Vzero)/Sen formula 3
Val: gas concentration
V cur sensor current value
V zero zero value of sensor (measured in zero-order air)
Sen sensor sensitivity
As the usage time is prolonged, the measurement accuracy of the sensor is gradually reduced, and the main reasons for the reduction of the measurement accuracy are the increase of sensor noise and the change of sensitivity. The correction and optimization of the measurement data of the sensor mainly proceeds from the following two aspects.
Noise optimization is based on the fact that noise increase can lead to fluctuation increase of sensor measured data, the noise optimization method is to increase sensor mean buffering, the buffer size is adjusted by using the default mean buffering quantity of 90× (actual noise/expected noise), the effect of debouncing noise influence is achieved by increasing the buffer size (the increase of the mean buffering is at the cost of sacrificing sensor response time, so that the maximum can not be reached by default, and when the noise is increased, the increase of the buffer size is two ways of taking the weight, and the measurement precision is increased by increasing the response time).
Sensitivity value correction-based on sensor on-time profile, the sensor is counted for on-time greater than 50% humidity and less than 15% humidity, the former is greater than the latter for an increase in sensitivity value (electrolyte moisture increases in the sensor in the high humidity state for a long period of time resulting in an increase in measured current signal), and the former is less than the latter for a decrease in sensitivity value (electrolyte moisture decreases in the sensor resulting in a decrease in measured current signal). The sensor correction formula is calculated according to formula 4.
Equation 4
Sen Correction sensitivity value after correction
Sen Original, original sensitivity value before correction
T High humidity accumulated working time of humidity greater than 50%
T Low humidity accumulated working time of less than 15% humidity
T Total time of total working time of sensor
From the data in table 2 of the above example, t High humidity =6000,t Low humidity =1500,t Total time of =13500, sen Correction =1.33Sen Original, original can be obtained, and the corrected gas concentration value can be obtained by substituting the Sen correction value into formula 2.
The foregoing is only a preferred embodiment of the present invention, but the scope of the present invention is not limited thereto, and any person skilled in the art, who is within the scope of the present invention, should make equivalent substitutions or modifications according to the technical scheme of the present invention and the inventive concept thereof, and should be covered by the scope of the present invention.
Claims (2)
1. The full life cycle management method of the electrochemical gas sensor is characterized by comprising the following steps of:
S1, monitoring data noise of a sensor in real time to obtain sensor noise data;
s2, counting accumulated working time of the sensor under different gas concentration, temperature, humidity and atmospheric pressure environment parameters;
s3, bringing the sensor design service time, sensor noise data and working time under different environments into a sensor life cycle model, and calculating to obtain the residual service life of the sensor;
wherein, the sensor life cycle model formula is as follows:
Equation 2
T 1 remaining sensor use time
T 2 sensor design time of use
N 1 sensor expected noise
N 2 sensor actual noise
K, working time correction value under different environments
T, working time of the sensor under different environments;
The step S1 is as follows:
firstly, reading real-time data of a sensor, and calculating a 90-second data average value;
Secondly, obtaining a current data trend according to a trend prediction algorithm, wherein the trend prediction algorithm comprises four data trends in the implementation process, namely a real-time trend, a current trend, a historical trend and a temporary trend; the method comprises the steps of obtaining a real-time trend according to real-time data and a data average value, wherein the real-time trend is larger than the data average value and is in an ascending state, otherwise is in a descending state, writing the temporary trend into the current trend when the real-time trend is in the fluctuation state and the temporary trend is the same as the temporary trend for 10 times, writing the real-time trend into the temporary trend when the real-time trend is not larger than 10 times, writing the historical trend into the current trend when the real-time trend is the same as the temporary trend, and modifying the current trend into the fluctuation state when the real-time trend is not different from the historical trend and the different times are larger than 10 times, and finally outputting the current trend, wherein the real-time trend is a trend calculated according to the single real-time data and the data average value, the current trend is a current data trend calculated in a comprehensive historical trend and a previous time trend, and the temporary trend is the current trend calculated last time when the real-time trend is different from the previous time trend;
Finally, when the data trend is in a fluctuation state or a stable state, calculating the real-time noise of the data according to a formula 1;
VN rtd = |VALrtd - VALavg |formula 1
VN rtd data real-time noise
VAL rtd data real time value
VAL avg data 90 second average.
2. The method for managing the whole life cycle of the electrochemical gas sensor according to claim 1, wherein in S2, the gas concentration is classified into 5 categories, namely, 80% to 100%, 50% to 80%, 30% to 50%, 15% to 30% and 15% to 15%, the working time of the sensor is counted once per minute, and then the temperature is classified into 5 categories, namely, 40% to 40 ℃ to 30 ℃ and 4 ℃ to 30 ℃ and-10 ℃ to 4 ℃ and the working time is counted, and finally, the humidity is classified into 5 categories, namely, 80% to 100%, 50% to 80%, 30% to 50%, 15% to 30% and 15% and the working time is counted, and the working time of the sensor is counted in 125 sub-categories.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202411471829.XA CN119004861B (en) | 2024-10-22 | 2024-10-22 | Full life cycle management method for electrochemical gas sensors |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202411471829.XA CN119004861B (en) | 2024-10-22 | 2024-10-22 | Full life cycle management method for electrochemical gas sensors |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN119004861A CN119004861A (en) | 2024-11-22 |
| CN119004861B true CN119004861B (en) | 2025-02-18 |
Family
ID=93474651
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202411471829.XA Active CN119004861B (en) | 2024-10-22 | 2024-10-22 | Full life cycle management method for electrochemical gas sensors |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN119004861B (en) |
Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111751508A (en) * | 2020-05-12 | 2020-10-09 | 北京华科仪科技股份有限公司 | Performance evaluation prediction method and system for life cycle of water quality sensor |
| CN117421544A (en) * | 2023-10-19 | 2024-01-19 | 东北大学 | Drift compensation method for periodic time sequence response data of gas sensor |
Family Cites Families (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| EP3462264A1 (en) * | 2017-09-29 | 2019-04-03 | Siemens Aktiengesellschaft | System, method and control unit for diagnosis and life prediction of one or more electro-mechanical systems |
| US20220011282A1 (en) * | 2020-07-08 | 2022-01-13 | Aeroqual Ltd. | Method for Electrochemical Gas Sensor Diagnostics |
| CN117471346A (en) * | 2023-11-06 | 2024-01-30 | 江苏尚鼎新能源科技有限公司 | Method and system for determining remaining life and health status of retired battery module |
-
2024
- 2024-10-22 CN CN202411471829.XA patent/CN119004861B/en active Active
Patent Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111751508A (en) * | 2020-05-12 | 2020-10-09 | 北京华科仪科技股份有限公司 | Performance evaluation prediction method and system for life cycle of water quality sensor |
| CN117421544A (en) * | 2023-10-19 | 2024-01-19 | 东北大学 | Drift compensation method for periodic time sequence response data of gas sensor |
Also Published As
| Publication number | Publication date |
|---|---|
| CN119004861A (en) | 2024-11-22 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN117606578B (en) | An intelligent method for monitoring gas flow | |
| CN109781934A (en) | Processing device, processing method, computer-readable storage medium for environmental sensor detection data, and environmental sensor system | |
| US7957928B2 (en) | Method for function monitoring of a sensor | |
| CN117932501B (en) | Electric energy meter running state management method and system | |
| CN111796064A (en) | TVOC detection equipment data quality control method and system based on remote automatic calibration | |
| CN109668607B (en) | Method for monitoring tiny leakage of gas meter | |
| AU2020100700A4 (en) | A Correction Method for Gas Sensor Based on Machine Learning | |
| CN118917702B (en) | An intelligent assessment management system and method for road construction carbon emission monitoring | |
| CN117909703B (en) | Data quality evaluation method and system based on alarm threshold trigger | |
| CN119132003A (en) | Gas alarm detection method, system, device and gas alarm | |
| CN111814110B (en) | Bridge health monitoring data control chart analysis method | |
| CN119164436A (en) | A self-calibration method and related device for air quality micro-monitoring station | |
| CN119004861B (en) | Full life cycle management method for electrochemical gas sensors | |
| KR20220093597A (en) | Quality inspection system of IoT-based urban meteorological data | |
| CN118936542B (en) | Meter metering detection calibration method and system | |
| CN118965241B (en) | Fault monitoring and early warning method and system for fuel regulator | |
| CN119104016A (en) | A method for measuring building construction | |
| CN117574681B (en) | Sensor service life prediction method based on oil smoke gas interference | |
| CN117740706B (en) | Control method and device of gas monitoring device, electronic equipment and storage medium | |
| CN118310580A (en) | Ocean temperature and salt depth high-precision sensing system based on data analysis model | |
| CN118482753A (en) | An intelligent detection system for shear beam sensors | |
| CN118938110A (en) | A test system for electric power metering equipment | |
| CN116797134A (en) | Dynamic metering management system and method for wine store | |
| CN113836794A (en) | Soft-hard combined fly ash carbon content online monitoring method | |
| CN119715336B (en) | Water meter multiparameter joint detection waterproof early warning method |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |