A method for life extension management of an electrochemical gas detector based on dynamic compensation

Abstract

This invention discloses a dynamic compensation-based method for extending the lifespan of electrochemical gas detectors, belonging to the field of gas detection and industrial safety technology. It includes: adding temperature and humidity sensors to collect environmental parameters; recording the sensor's cumulative operating time, effective working time under load, and idle time; integrating the gas concentration and extracting current noise characteristics to assess the remaining electrolyte activity; constructing an environmental penalty function based on statistical analysis of extreme temperature and humidity and fluctuation characteristics; extracting baseline drift and sensitivity reduction rates and identifying three failure modes—electrolyte depletion, electrode poisoning, and catalyst passivation—based on their ratio; inputting multi-dimensional parameters into a remaining lifespan prediction model, which dynamically adjusts weights based on the failure modes using fuzzy inference and employs a physical-data dual-channel fusion prediction. This invention achieves accurate prediction and proactive early warning of sensor lifespan, eliminates safety blind spots, and reduces operation and maintenance costs.

Description

Technical Field

[0001] This invention belongs to the field of gas detection and industrial safety technology, and in particular relates to a dynamic compensation-based electrochemical gas detector life extension management method for high-risk industries such as petrochemicals, metallurgy, coal mining, gas, pharmaceuticals, and semiconductors. Background Technology

[0002] In high-risk industrial sectors such as petrochemicals, metallurgy, coal mining, gas production, pharmaceuticals, and semiconductors, accurate monitoring of toxic and hazardous gases is a rigid requirement for ensuring personnel safety and stable operation of production facilities. Gas detectors, as a crucial component of safety instrumented systems, directly impact the ability to prevent major accidents such as poisoning, asphyxiation, and explosions. Among existing gas sensing technologies, electrochemical sensors dominate the field of toxic gas and oxygen detection due to their advantages of high selectivity, high sensitivity, wide linear range, and low power consumption. These sensors operate based on the electrochemical reaction of the target gas at the working electrode, quantitatively calculating gas concentration by measuring the diffusion-limiting current generated during the reaction. However, this mechanism also inherently limits the sensor's depletion. The electrolyte, catalytic electrode, and active materials within the sensor are irreversibly consumed during the continuous electrochemical reaction. When the active material is depleted to a critical level, the sensor's output performance deteriorates drastically until it fails completely. Therefore, implementing scientific and effective lifespan management for electrochemical gas sensors is a core element in ensuring measurement reliability throughout the detector's entire lifespan and a critical technical issue that urgently needs to be addressed in the field of industrial safety.

[0003] Currently, most electrochemical gas detectors on the market still employ a rudimentary approach to sensor lifespan management. The common method involves simply timing the sensor at fixed intervals based on the manufacturer's stated lifespan. When the detector's cumulative power-on time reaches this preset value, a replacement prompt is issued. This static management method has serious technical flaws. First, the actual lifespan of a sensor is not a fixed constant but strongly depends on the actual operating conditions: if the target gas concentration in the monitored environment is consistently high, the internal reaction of the sensor is violent, and the active material is consumed more rapidly, potentially resulting in a lifespan far shorter than the stated value; conversely, if the operating environment is relatively clean, the sensor's lifespan may significantly exceed the stated value. Relying solely on working hours cannot reflect these differences in operating conditions. This can lead to premature false alarms and wasted resources, and more dangerously, delayed or missed alarms can create safety blind spots. Secondly, the current industry practice relies on annual inspections to determine sensor failure. This means that during the year-long window between inspections, sensors may have already failed without the user's knowledge. Detectors may then give false low-concentration readings or even fail to respond at critical moments, posing a significant safety hazard. Furthermore, passive annual inspections require substantial manpower and time costs and cannot provide predictive maintenance before sensor failure. This forces users to take emergency measures only after the sensor has completely failed, severely impacting production continuity.

[0004] A search revealed existing technologies closely related to this technical solution, including sensor operating time recording methods mentioned in some patents, and a few attempts at sensor self-diagnosis. For example, existing solutions, such as CN108548855A, disclose a method for extending the overall system lifespan using a dual-sensor redundancy configuration. Its core idea is to achieve seamless switching during failures through hardware backup. However, this solution is essentially a hardware redundancy design and does not involve dynamic quantitative assessment and management of the remaining lifespan of individual sensors. Furthermore, it is costly and complex to implement. In addition, while some high-end gas detectors have internal recording functions for sensor operating time, they are limited to storing duration data and do not perform multi-dimensional fusion analysis of this data with key influencing factors such as gas concentration exposure, ambient temperature and humidity, baseline drift, and sensitivity decay, thus lacking the ability to predict remaining lifespan. In summary, the market currently lacks a lifespan management method that can comprehensively consider multi-dimensional dynamic factors, accurately predict sensor failure time, and proactively provide early warnings before failure. Summary of the Invention

[0005] The technical problem this invention aims to solve is to overcome the shortcomings of existing electrochemical gas detectors that rely solely on fixed-time warnings and cannot accurately predict the actual remaining lifespan of the sensor. This invention provides a dynamic compensation-based method for extending the lifespan of electrochemical gas detectors. By comprehensively monitoring and weighting multiple parameters such as sensor consumption, ambient temperature and humidity, baseline drift, and sensitivity decay, the remaining lifespan of the sensor can be accurately predicted. Furthermore, when the lifespan reaches a set threshold, a replacement warning is proactively issued, ensuring that the detector always operates within an effective and reliable range, thus guaranteeing safe production.

[0006] To achieve the above-mentioned objectives, the present invention adopts the following technical solution: a method for extending the lifespan of an electrochemical gas detector based on dynamic compensation, characterized by comprising the following steps:

[0007] S1: Add a temperature sensor and a humidity sensor to the gas detector to collect real-time temperature and humidity data of the sensor's working environment;

[0008] S2: The timing module records the sensor's cumulative working time, cumulative effective working time under load detection, and cumulative idle time under standby conditions.

[0009] S3: Real-time acquisition of the output current signal of the electrochemical gas sensor and conversion into a gas concentration value; time integration of the gas concentration value; calculation of the cumulative consumption of the sensor.

[0010] S4: Based on the temperature and humidity data collected in step S1, calculate the cumulative time of ultra-high temperature, ultra-low temperature, and ultra-high humidity.

[0011] S5: Periodically extract the baseline drift and sensitivity degradation rate of the sensor;

[0012] S6: Input the multidimensional parameters obtained in steps S2 to S5 into the pre-built remaining lifetime prediction model. The prediction model dynamically adjusts the weight allocation of each influencing factor according to the current failure mode type of the sensor, and calculates the current remaining lifetime percentage of the sensor through weighted comprehensive calculation.

[0013] S7: When the remaining lifespan percentage reaches a preset warning threshold, a sensor replacement warning signal is issued.

[0014] This invention, by adding temperature and humidity sensors, recording load and standby time respectively, calculating gas concentration consumption integrals, extracting baseline drift and sensitivity decline, and dynamically adjusting weights according to failure modes to predict remaining lifespan, and finally proactively reporting when a warning threshold is reached, achieves comprehensive monitoring and dynamic evaluation of all-dimensional degradation factors of electrochemical gas sensors for the first time. Compared with existing technologies that rely solely on fixed-time warnings, this method can accurately reflect the actual consumption progress of the sensor under real operating conditions, eliminate the blind spot of safety monitoring during the annual inspection interval, and realize a leap from passive failure detection to proactive lifespan prediction.

[0015] In the optimized version of step S2, the load detection state is defined as the working state where the amplitude of the sensor output current signal exceeds a preset current threshold, and the standby state is defined as the working state where the amplitude of the sensor output current signal is lower than the preset current threshold and the detector is powered on. The unit-time consumption rate of the sensor's active material under load is determined by the first consumption coefficient k. load Characterized by the second consumption coefficient k in standby mode. idle Characterization, k load >k idle Both are calibrated through accelerated aging experiments; the cumulative effective working time and cumulative idle time are respectively included in the corresponding terms of the comprehensive consumption function. This scheme subdivides the sensor's working state into a load-bearing detection state and a standby state, and assigns different consumption coefficients to each. Existing technologies treat all working times equally, but in reality, the electrochemical reaction of the sensor is intense under load, and the consumption rate of active materials is much higher than in the standby state. This scheme improves the accuracy of lifetime statistics through state subdivision and differentiated measurement, avoids premature false alarms caused by including standby time as full-load consumption, and makes the remaining lifetime prediction more consistent with the actual physical aging process of the sensor.

[0016] Optimized step S3 further includes: real-time extraction of noise features from the output current signal, wherein the noise features include at least one of the root mean square fluctuation amplitude of the current signal, noise power spectral density, and low-frequency drift noise component; and construction of the electrolyte residual activity coefficient η based on the comparison between the measured noise features and the initial noise features. electrolyte As an independent correction factor for predicting the remaining lifespan of the sensor, its calculation formula is as follows:

[0017] η electrolyte =

[0018] Where, N current N represents the current noise characteristic value. initial N represents the initial noise characteristic value of the sensor at the time of manufacture or during the initial period of use. failure η is the sensor failure threshold noise characteristic value pre-calibrated through accelerated aging tests;electrolyte The value ranges between 0 and 1, with smaller values ​​indicating lower residual electrolyte activity. This scheme utilizes noise as valuable diagnostic information. As the electrolyte ages, the noise characteristics exhibit regular changes. Incorporating this into the prediction model adds an independent physical dimension to the lifespan assessment, improving the robustness and reliability of the prediction.

[0019] In an optimized version, step S4 further includes: constructing the temperature fluctuation amplitude characteristic parameter ΔT. amp and humidity fluctuation frequency characteristic parameter F RH The temperature fluctuation amplitude characteristic parameter is the difference between the maximum and minimum temperature values ​​within a preset time window, and the humidity fluctuation frequency characteristic parameter is the number of times the humidity crosses a set humidity fluctuation threshold within a preset time window; based on the temperature fluctuation amplitude characteristic parameter and the humidity fluctuation frequency characteristic parameter, an environmental stress comprehensive penalty function g( ), used to characterize the accelerated aging effect of environmental stress on sensor lifespan.

[0020] Optimized, in step S5, based on the ratio ρ of baseline drift direction to sensitivity decrease rate, Construct a sensor failure mode classification criterion, where ΔI baseline δ represents the baseline drift. sensitivity The sensitivity degradation rate (ρ) is defined as follows: when ρ is below a first preset threshold, it is determined to be an electrolyte depletion failure; when ρ is between the first and second preset thresholds, it is determined to be an electrode poisoning failure; when ρ is above the second preset threshold, it is determined to be a catalyst passivation failure. The first and second preset thresholds are determined by cluster analysis of historical aging data of sensors under multiple failure modes. Specifically, multiple sets of sensor lifecycle aging data with known failure modes are collected, the ρ value sequence of each set of data is calculated, and the K-means clustering algorithm is used to cluster the ρ value samples into three categories. The median of the first and second cluster centers is taken as the first preset threshold, and the median of the second and third cluster centers is taken as the second preset threshold. This invention establishes the correspondence between the ratio of baseline drift to sensitivity degradation rate and the three failure modes, and provides an operable threshold determination method through clustering algorithms. This scheme provides a key basis for subsequent differentiated early warning and handling through failure mode classification, realizing information upgrade.

[0021] In the optimized step S6, the basic structure of the comprehensive consumption function is as follows:

[0022] f=α(m) +β(m) )+γ(m) g( )+δ(m) h(ΔIbaseline ,δ sensitivity ,η electrolyte )

[0023] Where m∈{electrolyte depletion type, electrode poisoning type, catalyst passivation type} is the failure mode type determined in step S5; α(m), β(m), γ(m), and δ(m) are the dynamic weight coefficients corresponding to different failure modes m, satisfying α(m)+β(m)+γ(m)+δ(m)=1, and the values ​​of each weight coefficient are different under different failure modes; Q exp Q represents the cumulative consumption. max This represents the sensor's nominal maximum power consumption; T load To accumulate effective working hours, T idle To accumulate idle time, k load k is the consumption coefficient under load. idle T is the standby power consumption coefficient. rated The nominal rated service life is represented by g(·), which is the comprehensive environmental stress penalty function; h(·) is the comprehensive performance degradation function; η is the nominal rated service life. electrolyte This represents the residual activity coefficient of the electrolyte.

[0024] The innovation of the above scheme lies in the fact that the weight coefficients α(m), β(m), γ(m), and δ(m) are not fixed values, but dynamically change with the failure mode type m. Through adaptive weight allocation of failure modes, the prediction model can capture the most important life consumption factors under different aging mechanisms, thereby improving the predictive model's adaptability and generalization accuracy to various working conditions and failure scenarios.

[0025] The optimized dynamic weight coefficients are adaptively adjusted through a fuzzy inference system: the failure mode type m and the current percentage of the sensor's service life are used as input variables for the fuzzy inference system. After fuzzification, fuzzy rule inference, and defuzzification, the adjustment amount of each weight coefficient is output. The fuzzy rules are jointly formulated based on prior knowledge of the sensor aging mechanism and accelerated aging experimental data, enabling the prediction model to assign higher weights to the dominant lifespan factors under different failure modes. This invention uses a fuzzy inference system to achieve adaptive adjustment of the weight coefficients. Fuzzy inference systems are good at handling classification problems with unclear boundaries. However, there are transitional intervals between sensor failure modes, which are not completely separate. Compared with simple table lookup switching or hard threshold judgment, fuzzy inference can perform smooth and continuous weight adjustment based on the current service life of the sensor and the membership degree of the failure mode, avoiding abrupt weight changes caused by mode misjudgment, making the prediction model output more stable, smooth, and reliable.

[0026] The optimization also includes step S8: after issuing a replacement warning signal but before the sensor is actually replaced, automatically switch to the enhanced dynamic compensation mode, wherein the compensation coefficient for the sensitivity reduction rate in the enhanced dynamic compensation mode is changed from (1-δ) in the conventional mode. sensitivity ) -1 Adjusted to (1-δ) sensitivity ) -p Where p is the enhancement compensation factor, 1 < p ≤ 1.5, and the value of p is related to the failure mode type determined in step S5. Different failure modes correspond to different p values, which are pre-calibrated through accelerated aging experiments. This solution effectively maintains the accuracy of concentration readings without increasing hardware costs by automatically enhancing the compensation after an early warning, providing users with a time window for timely handling and achieving seamless integration of early warning and measurement protection functions.

[0027] In the optimized version, the remaining lifetime prediction model in step S6 adopts a dual-channel parallel prediction architecture: the first channel is a physical-driven model based on the Arrhenius aging kinetic equation, used to calculate the intrinsic aging lifetime baseline of the sensor; the second channel is a data-driven model based on a long short-term memory network or a gated recurrent unit, used to learn the lifetime deviation caused by individual sensor differences and random fluctuations in operating conditions; the outputs of the physical-driven model and the data-driven model are fused by Bayesian methods to obtain the remaining lifetime prediction value and the confidence level of the prediction interval. This scheme adopts a dual-channel parallel prediction architecture of physical-driven and data-driven methods and outputs the results through Bayesian fusion. A single physical model is difficult to cover individual differences and random fluctuations in operating conditions, while a single data-driven model is prone to failure when there are small samples or sudden changes in operating conditions. This scheme complements the advantages of the two models: the physical model provides the intrinsic aging baseline, the data model captures individual deviations, and then Bayesian fusion provides a prediction interval rather than a single-point estimate.

[0028] This invention also introduces a dynamic compensation-based electrochemical gas detector lifetime extension management system, employing the aforementioned electrochemical gas detector lifetime extension management method. The system includes:

[0029] An electrochemical gas sensor is used to detect the concentration of a target gas and output a corresponding current signal.

[0030] Temperature and humidity sensors are used to collect temperature and humidity data of the sensor's operating environment, respectively.

[0031] The signal acquisition and conditioning circuit is used to convert, amplify, filter, and convert the current signal from the electrochemical gas sensor to IV and then send it to the microprocessor.

[0032] The timing module is used to record the sensor's cumulative working time, cumulative effective working time under load detection state, and cumulative idle time under standby state, respectively.

[0033] The noise feature extraction module is used to extract the root mean square fluctuation amplitude, noise power spectral density and low-frequency drift noise component in the output current signal of the electrochemical gas sensor in real time.

[0034] The microprocessor has a built-in failure mode classifier, fuzzy inference weight adjuster and remaining lifetime prediction model. It is used to receive data from various sensors and perform failure mode discrimination, dynamic weight allocation and weighted comprehensive calculation, and output the remaining lifetime percentage, failure mode type and early warning control signal.

[0035] The storage module is used to store the weight coefficient library, threshold parameters, accelerated aging test calibration data and historical monitoring data corresponding to different failure modes;

[0036] The display module is used to display real-time gas concentration, remaining lifetime percentage, and failure mode type information.

[0037] The alarm module is used to issue audible and visual alarm signals when the warning conditions are triggered, and to output differentiated prompt information according to the failure mode type.

[0038] The communication interface is used to upload early warning information, failure mode types, and sensor status data to the equipment management system or user terminal.

[0039] This solution is formalized into a complete hardware-algorithm integrated system architecture, clearly defining the physical implementation and connections of each functional module, including core components such as a noise feature extraction module, a failure mode classifier, and a fuzzy inference weight adjuster. The integrated design of this system allows for direct deployment in existing industrial gas detector products without significant modifications to the gas path and sensor installation structure. It boasts low implementation costs, low barriers to entry, and plug-and-play industrialization capabilities, providing a complete technical solution for the intelligent upgrading of existing product lines.

[0040] Compared with the prior art, the beneficial effects of the present invention are:

[0041] 1. The most fundamental advancement of this invention compared to existing technologies lies in its transformation of the traditional reactive safety management model for gas detectors, establishing a proactive early warning and protection mechanism. In existing technologies, sensor failure can only be detected during annual inspections, leaving a safety monitoring vacuum of several weeks between inspections. In the event of a hazardous gas leak, a failed detector will not be able to issue an alarm in time. This invention, through real-time tracking and dynamic weighted calculation of multi-dimensional parameters such as sensor consumption integration, accumulated extreme environmental stress, baseline drift, and sensitivity decay, can proactively trigger an early warning when the sensor's lifespan reaches a preset threshold. This provides users with ample time to address the issue before the sensor completely fails, allowing them to calmly complete replacement or maintenance, eliminating safety blind spots, and possessing irreplaceable practical value in protecting the lives of personnel in high-risk industries.

[0042] 2. This invention reduces the overall lifecycle maintenance cost. On the one hand, it reduces reliance on manual annual inspections and periodic calibration with standard gases. For widely distributed detector networks, the labor, time, and standard gas consumption costs are considerable. This invention, through its built-in multi-parameter self-diagnosis and remaining lifespan prediction functions, allows users to take targeted action only after the system issues a warning, thus reducing maintenance costs. On the other hand, through dynamic compensation for sensitivity decay, it fully utilizes the effective lifespan of the sensor without sacrificing measurement accuracy. This avoids resource waste caused by premature replacement due to simple timing, and also avoids safety hazards caused by blindly extending use. Furthermore, it replaces emergency repairs after sudden failures with planned, early warning-based replacement, avoiding downtime losses and the premium associated with emergency procurement.

[0043] 3. This invention fills a gap in the field of electrochemical gas detectors regarding multi-parameter fusion and dynamic lifetime management. Existing solutions either record only a single operating duration parameter or employ hardware redundancy to circumvent the challenge of single-sensor lifetime prediction. This invention, for the first time, introduces large-model-assisted prediction into sensor lifetime management, achieving continuous and accurate prediction and self-optimization of remaining lifetime. This upgrades gas detectors from simple concentration measurement tools into intelligent sensing terminals with self-health management capabilities, providing key technical support for the construction of predictive maintenance systems in the industrial safety field. Attached Figure Description

[0044] Figure 1 This is a flowchart illustrating the overall method of the electrochemical gas detector lifetime extension management method based on dynamic compensation of the present invention.

[0045] Figure 2 This is a block diagram of the hardware structure in this invention;

[0046] Figure 3 This is a logic diagram of the weighted calculation model in this invention;

[0047] Figure 4 This is a diagram illustrating the failure mode classification criteria in this invention.

[0048] Figure 5 This is a diagram of the dual-channel fusion prediction architecture in this invention;

[0049] Figure 6 This is a flowchart of the early warning output process in this invention;

[0050] Figure 7 This is a schematic diagram of the reinforcement compensation principle in this invention;

[0051] Figure 8 This is a schematic diagram of the hardware structure circuit used in this invention;

[0052] Figure 9 This is a circuit schematic diagram of the MCU unit in this invention. Detailed Implementation

[0053] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0054] like Figures 1-9 As shown, the present invention provides a method for extending the lifespan of an electrochemical gas detector based on dynamic compensation, comprising the following steps S1 to S8:

[0055] Step S1: Real-time acquisition of ambient temperature and humidity: A temperature sensor and a humidity sensor are added inside the gas detector or near the electrochemical gas sensor. Both are electrically connected to the ADC sampling port or I²C / SPI communication interface of the detector's microprocessor unit, respectively. The temperature sensor and humidity sensor continuously acquire the temperature data T(t) and relative humidity data RH(t) of the microenvironment in which the sensor is located at a fixed sampling period and store them in the detector's storage module as the raw data source for subsequent environmental stress statistics.

[0056] Step S2: Working time statistics by state: The three working time parameters are recorded separately by the timing module inside the detector:

[0057] (1) Cumulative working time T total The total time the detector has been powered on since the sensor was put into use;

[0058] (2) Cumulative effective working hours T load The cumulative duration of the sensor in a load detection state;

[0059] (3) Cumulative idle time T idle : The cumulative duration of the sensor in standby mode.

[0060] The load detection state is defined as the amplitude of the sensor output current signal exceeding a preset current threshold I. threshold The sensor is in its operating state, during which significant electrochemical reactions occur internally, and the active material is consumed at a high rate. The standby state is defined as the sensor's output current signal amplitude being lower than the preset current threshold while the detector remains powered on. In this state, the internal electrochemical reactions are extremely weak or nonexistent, and the active material consumption rate is much lower than in the loaded state. Duration statistics are not included during power outages; they resume after power is restored.

[0061] The rate of consumption of the sensor's active material per unit time under load is based on the first consumption coefficient k. load Characterized by the second consumption coefficient k in standby mode. idle Representation, satisfying k load >k idle The two consumption coefficients were independently calibrated through accelerated aging experiments. Specifically, multiple sensors of the same model were operated under constant standard gas concentration and without target gas conditions, and the amount of remaining active material was periodically measured. The consumption rate per unit time, T, was then fitted. load and T idle These are respectively included in the corresponding items of the subsequent comprehensive consumption function.

[0062] Step S3: Gas Concentration Consumption Integration and Noise Feature Extraction: This step includes two parallel sub-functions:

[0063] (a) Statistics on gas concentration consumption integral:

[0064] The output current signal of the electrochemical gas sensor is acquired in real time through a signal acquisition and conditioning circuit. After IV conversion, amplification, filtering, and analog-to-digital conversion, the microprocessor converts it into the corresponding gas concentration value C(t). The gas concentration value is then integrated over time to calculate the cumulative consumption Q of the sensor since it was put into use. exp Q exp =

[0065] Where, k gas The consumption coefficient for the target gas represents the amount of sensor active material consumed per unit concentration × time, obtained through experimental calibration. This integral value directly reflects the actual chemical consumption of the sensor due to detecting the target gas and is the most critical consumption item in lifetime assessment.

[0066] (II) Current noise feature extraction and electrolyte activity assessment:

[0067] During the acquisition of the sensor's output current signal, noise features are extracted simultaneously. Specifically, within a time window when the detector is in a stable operating state and there are no sudden changes in concentration, the continuously acquired current signal sequence undergoes digital signal processing to extract at least one of the following noise feature parameters:

[0068] Root mean square fluctuation amplitude σ current The root mean square deviation of the current signal within a preset time window, such as 10 minutes;

[0069] Noise Power Spectral Density (PSD): The power spectral density obtained by performing a Fourier transform on a current signal sequence, with a focus on the low-frequency band, such as the energy distribution from 0.01 Hz to 1 Hz.

[0070] Low-frequency drift noise component N drift The drift power of current signals in the low-frequency range, such as 0.01Hz to 0.1Hz.

[0071] As the electrolyte inside the electrochemical sensor ages and is consumed, the electrode interface state changes, ion mobility decreases, and the aforementioned noise characteristics show a trend change. Based on this principle, the measured noise characteristic value N... current With the initial noise characteristic value N initial and the critical noise characteristic value N failure By comparison, the electrolyte residual activity coefficient η was constructed. electrolyte η electrolyte =

[0072] Where, N failure η represents the critical noise characteristic value corresponding to sensor failure, obtained through accelerated aging experiments. electrolyte The value ranges between 0 and 1. A value closer to 1 indicates that the electrolyte activity is close to its initial state, while a value closer to 0 indicates that the electrolyte has severely aged. This coefficient serves as an independent correction factor in the subsequent remaining lifetime prediction model, complementing traditional indicators such as sensor sensitivity decline. When the sensitivity has not yet declined significantly but the electrolyte has already begun to age, the noise characteristics can reflect this trend in advance.

[0073] Step S4: Multidimensional Statistics of Environmental Stress and Construction of Comprehensive Penalty Function: Based on the temperature data T(t) and humidity data RH(t) collected in Step S1, Step S4 performs the following statistics and calculations:

[0074] (a) Cumulative time statistics under extreme conditions:

[0075] Accumulated time of extreme high temperature T HT Temperature T(t) exceeds the set upper limit threshold T high For example, the cumulative duration of 50℃;

[0076] Ultra-low temperature cumulative time T LT Temperature T(t) is lower than the set lower threshold T low For example, the cumulative duration of -20℃;

[0077] Ultra-high humidity cumulative time T HH The relative humidity RH(t) exceeds the set upper limit threshold RH. high For example, the cumulative duration of 95%RH.

[0078] The above thresholds are preset according to the sensor's specifications and factory parameters.

[0079] (II) Extraction of Temperature and Humidity Fluctuation Features: In addition to the cumulative time under extreme conditions, frequent fluctuations in ambient temperature and drastic changes in humidity also have a cumulative damaging effect on sensor lifespan. Therefore, this step further constructs:

[0080] Temperature fluctuation amplitude characteristic parameter ΔT amp The difference between the maximum and minimum temperature measurements within a preset time window, such as 24 hours.

[0081] Humidity fluctuation frequency characteristic parameter F RH Within a preset time window, such as 24 hours, the cumulative number of times the relative humidity rises or falls across the set humidity fluctuation threshold.

[0082] (III) Environmental Stress Comprehensive Penalty Function: Based on the above statistical parameters, an environmental stress comprehensive penalty function g(·) is constructed to quantify the accelerated aging effect of thermal and wet stresses on the sensor's lifespan in actual working environments. The basic form of this function is:

[0083] g( )=λ HT +λ LT +λ HH +λ ΔT +λ RH

[0084] Where: λ HT , λ LT , λ HH The penalty coefficients for ultra-high temperature, ultra-low temperature, and ultra-high humidity are respectively calibrated through accelerated aging tests; λ ΔT ΔT is the penalty coefficient for temperature fluctuations. ref λ is the reference temperature fluctuation amplitude. RH F is the penalty coefficient for the frequency of humidity fluctuations. refThe reference humidity fluctuation frequency is used. All penalty coefficients are positive numbers; the larger the value, the more significant the accelerating effect of that factor on the sensor's lifespan. The larger the value of g(·), the more severe the environmental stress, and the greater the deduction in sensor lifespan.

[0085] Step S5: Performance Degradation Feature Extraction and Failure Mode Classification: Step S5 is executed periodically and includes the following sub-functions:

[0086] (a) Baseline drift and sensitivity decrease rate extraction:

[0087] Baseline drift ΔI baseline The deviation of the sensor's current output current value from the factory initial baseline value under clean air or zero air conditions, expressed as an absolute value or percentage;

[0088] Sensitivity decrease rate δ sensitivity The sensitivity decay rate is calculated by comparing the current response value of the sensor with the initial response value. This can be achieved in two ways: (a) by automatically measuring using the detector's built-in self-test program; or (b) by periodically introducing a standard gas of known concentration into the detector and calculating the sensitivity decay rate based on the deviation between the output reading and the standard concentration.

[0089] (II) Failure Mode Classification Criteria:

[0090] Studies have shown that the failure of electrochemical sensors is not a single mode, and there are at least three main failure mechanisms: (1) Electrolyte depletion - the liquid or solid electrolyte inside the sensor is reduced due to evaporation, leakage or electrochemical consumption, resulting in a decrease in ionic conductivity; (2) Electrode poisoning - impurities in the target gas or interfering substances in the environment are adsorbed on the electrode surface, occupying active sites and hindering the electrochemical reaction; (3) Catalyst passivation - the catalyst on the electrode surface has reduced activity due to long-term use, resulting in a decrease in reaction rate.

[0091] The degradation trajectories of the three failure modes are different: electrolyte depletion is mainly manifested as baseline drift; electrode poisoning usually leads to a rapid decrease in sensitivity while baseline drift is relatively small; catalyst passivation is manifested as a slow change in sensitivity and baseline in sync.

[0092] Based on this mechanism, this step constructs a failure mode classification criterion—the ratio ρ of baseline drift to sensitivity degradation rate: ρ =

[0093] The above ratios reflect the degree to which baseline drift dominates relative to sensitivity reduction. When ρ is below the first preset threshold ρ1, it is determined to be an electrolyte depletion type failure; when ρ is between ρ1 and the second preset threshold ρ2, it is determined to be an electrode poisoning type failure; when ρ is above ρ2, it is determined to be a catalyst passivation type failure.

[0094] The method for determining the first preset threshold ρ1 and the second preset threshold ρ2 is as follows: Collect multiple sets of sensor lifecycle aging data with known failure modes, and calculate the ρ value sequence for each set of data. Use the K-means clustering algorithm to cluster all ρ value samples into three classes, corresponding to the three failure modes mentioned above. Take the median of the first and second cluster centers as ρ1, and the median of the second and third cluster centers as ρ2. This clustering process is completed during the system development phase, and the obtained ρ1 and ρ2 are preset as fixed thresholds in the detector's storage module.

[0095] Step S6: Multi-parameter weighted fusion and remaining lifetime prediction: Input the multi-dimensional parameters obtained in steps S2 to S5 into the pre-built remaining lifetime prediction model. The core of the remaining lifetime prediction model is a weighted comprehensive consumption function based on failure mode adaptation.

[0096] (I) Basic Structure of the Comprehensive Consumption Function: The mathematical expression of the comprehensive consumption function f is:

[0097] f=α(m) +β(m) )+γ(m) g( )+δ(m) h(ΔI baseline ,δ sensitivity ,η electrolyte )

[0098] The meanings of each symbol are as follows:

[0099] symbol meaning source m Failure mode type, ∈ {electrolyte depletion type, electrode poisoning type, catalyst passivation type} Step S5 determination α(m), β(m), γ(m), δ(m) The dynamic weighting coefficient satisfies α+β+γ+δ=1, and its value varies under different failure modes. Fuzzy inference system regulation Q exp ]]> Cumulative consumption of gas concentration Step S3 (I) Q max ]] The sensor's nominal maximum consumption (i.e., the cumulative exposure corresponding to the total amount of active material in the sensor). Manufacturer parameters or experimental calibration <![CDATA[T load 、T idle ]]> Cumulative effective working hours, cumulative idle time Step S2 <![CDATA[k load 、k idle ]]> Load state consumption coefficient, standby state consumption coefficient Step S2 Accelerated Aging Calibration <![CDATA[T rated ]]> Nominal rated service life Manufacturer parameters g(·) Environmental stress comprehensive penalty function Step S4 h(·) <![CDATA[comprehensive performance degradation function, which is a weighted combination of ΔI baseline , δ sensitivity , η electrolyte > Experimental calibration <![CDATA[η electrolyte ]]> Electrolyte residual activity coefficient Step S3 (II)

[0100] The physical meaning of the above function is as follows: the cumulative lifespan of the sensor is contributed by the following factors: gas concentration consumption, pure operating time consumption, additional environmental stress consumption, and performance degradation consumption. The weights of each factor are not fixed, but are dynamically adjusted according to the current failure mode of the sensor.

[0101] (II) Adaptive Adjustment of Dynamic Weighting Coefficients: The aforementioned weighting coefficients α(m), β(m), γ(m), and δ(m) are adaptively adjusted through a fuzzy inference system. The specific process is as follows:

[0102] The failure mode type *m* determined in step S5 and the current percentage of the sensor's service life are used as the two input variables of the fuzzy inference system. The failure mode type is encoded into three fuzzy subsets: {electrolyte depletion, electrode poisoning, catalyst passivation}, and the percentage of service life is fuzzified into three membership functions: {early, middle, late}. A fuzzy rule base is established based on prior knowledge of the sensor's aging mechanism and accelerated aging experimental data. For example:

[0103] Rule 1: If the failure mode is "electrolyte depletion" and the service life is "mid-term", then α should be the larger value;

[0104] Rule 2: If the failure mode is "electrode poisoning" and the service life is "late", then δ should be the larger value;

[0105] Rule 3: If the failure mode is "catalyst passivation", then β should be a larger value.

[0106] After fuzzification, fuzzy inference, and defuzzification, the adjustment amount of each weight coefficient is output, so that the prediction model assigns higher weights to the dominant lifetime factors under different failure modes.

[0107] (III) Calculation of Remaining Lifetime Percentage: The current cumulative lifetime consumption rate is calculated from the comprehensive consumption function f, then the current remaining lifetime percentage R is calculated. remaining for:

[0108] R remaining =(1−f)×100%

[0109] When f=0, it means the sensor is not consumed at all; when f=1, it means the lifespan has been exhausted.

[0110] (iv) Dual-channel parallel prediction architecture (preferred option): To further improve prediction accuracy and reliability, in some preferred embodiments, the remaining lifetime prediction model in step S6 adopts a dual-channel parallel architecture:

[0111] The first channel—the physics-driven model—is based on the Arrhenius aging kinetic equation, using temperature as the primary accelerating stress to calculate the intrinsic aging lifetime baseline of the sensor. This physics-driven model starts from the aging mechanism of sensor materials, reflects the physical essence of lifetime, and has good extrapolation and physical interpretability.

[0112] The second channel—the data-driven model—is based on a long short-term memory network or a gated recurrent unit. It uses the multi-dimensional time-series parameters collected in steps S2 to S5 as input to learn the lifespan deviation caused by individual sensor differences and random fluctuations in operating conditions. This data-driven model excels at capturing complex nonlinear relationships within the data.

[0113] The outputs of the physical-driven model and the data-driven model are fused using a Bayesian fusion method. The predictions from the two models are treated as two independent information sources. The fused posterior distribution is calculated using a Bayesian update formula, ultimately outputting the remaining lifetime prediction and its prediction interval confidence level. The fused prediction retains the mechanistic constraints of the physical model while incorporating the adaptive ability of the data model to individual differences, resulting in better prediction robustness than a single model.

[0114] Step S7: Early Warning Judgment and Output: Calculate the remaining lifespan percentage R obtained in step S6. remainingThe result is compared with a preset warning threshold. The warning threshold can be set to any value between 80% and 95% of the product's lifespan has been consumed, preferably 90%, depending on the industry's safety level requirements.

[0115] When the warning conditions are met, the following warning output operation will be performed:

[0116] (1) The detector’s local alarm module emits an audible and visual alarm signal;

[0117] (2) The local display module of the detector displays the text prompt "Sensor remaining lifespan: X%, please replace in time" and displays the failure mode type determined in step S5, providing users with differentiated handling reference information; (3) The warning information, remaining lifespan percentage, failure mode type and current status parameters of the sensor are uploaded to the equipment management system or user terminal through the communication interface.

[0118] Step S8: Enhanced Dynamic Compensation After Warning: During the transition period after the replacement warning signal is issued in step S7 and before the sensor is actually replaced, in order to ensure that the detector can still provide a concentration reading with a certain degree of reliability, the system automatically switches from the conventional measurement mode to the enhanced dynamic compensation mode.

[0119] In normal mode, the decrease in sensor sensitivity due to aging has been largely compensated for, and the correction formula for the concentration measurement value is: =

[0120] That is, the conventional compensation coefficient is (1-δ) sensitivity ) -1 After entering the enhanced dynamic compensation mode, the compensation coefficient is adjusted as follows: =

[0121] Where p is the enhancement compensation factor, and its value ranges from 1 < p ≤ 1.5. The value of p is related to the failure mode type determined in step S5. Different failure modes correspond to different p values, which are pre-calibrated through accelerated aging experiments: for example, when electrode poisoning failure occurs, the sensitivity decreases the fastest, so a higher p value is taken (e.g., 1.4 to 1.5); when catalyst passivation failure occurs, the degradation is relatively slow, so a lower p value is taken (e.g., 1.1 to 1.2).

[0122] The physical meaning of the aforementioned enhanced compensation mode is as follows: at the end of the sensor's lifespan, at the cost of sacrificing some measurement resolution, the overall accuracy of the concentration readings is maintained within an acceptable range. This provides users with still-reliable monitoring data during the transition time window required to obtain a replacement sensor and complete the replacement, ensuring that there is no completely unmonitored vacuum period from the triggering of the warning to the completion of the replacement. Once the sensor has been replaced, the system exits the enhanced dynamic compensation mode, resets all lifespan statistical parameters, and begins a new lifespan management cycle.

[0123] This invention also introduces a dynamic compensation-based electrochemical gas detector lifetime extension management system, employing the aforementioned dynamic compensation-based electrochemical gas detector lifetime extension management method. The system includes:

[0124] An electrochemical gas sensor is used to detect the concentration of a target gas and output a corresponding current signal.

[0125] Temperature and humidity sensors are used to collect temperature and humidity data of the sensor's operating environment, respectively.

[0126] The signal acquisition and conditioning circuit is used to convert, amplify, filter, and convert the current signal from the electrochemical gas sensor to IV and then send it to the microprocessor.

[0127] The timing module is used to record the sensor's cumulative working time, cumulative effective working time under load detection state, and cumulative idle time under standby state, respectively.

[0128] The noise feature extraction module is used to extract the root mean square fluctuation amplitude, noise power spectral density and low-frequency drift noise component in the output current signal of the electrochemical gas sensor in real time.

[0129] The microprocessor has a built-in failure mode classifier, fuzzy inference weight adjuster and remaining lifetime prediction model. It is used to receive data from various sensors and perform failure mode discrimination, dynamic weight allocation and weighted comprehensive calculation, and output the remaining lifetime percentage, failure mode type and early warning control signal.

[0130] The storage module is used to store the weight coefficient library, threshold parameters, accelerated aging test calibration data and historical monitoring data corresponding to different failure modes;

[0131] The display module is used to display real-time gas concentration, remaining lifetime percentage, and failure mode type information.

[0132] The alarm module is used to issue audible and visual alarm signals when the warning conditions are triggered, and to output differentiated prompt information according to the failure mode type.

[0133] The communication interface is used to upload early warning information, failure mode types, and sensor status data to the equipment management system or user terminal.

[0134] The implementation process of the technical solution of this invention will be described in detail below, taking into account specific sensor types and application scenarios.

[0135] Scenario Setting: An electrochemical carbon monoxide gas detector is installed in a petrochemical plant area. The sensor model is City Technology 3CO-F, with a nominal rated service life of T. rated =730 days, nominal maximum consumption Q max =20000ppm·h, target gas consumption coefficient k gas =1.0. The detector's MCU is an STM32F407, equipped with a real-time clock, ADC sampling, I²C bus, and RS485 communication interface. The user upgraded their existing detector according to the solution of this invention.

[0136] I. Hardware Modification (corresponding to step S1)

[0137] A digital temperature sensor chip DS18B20 and a humidity sensor chip SHT30 are installed inside the detector housing near the CO sensor. Both are connected to the STM32F407 via the I²C bus. The MCU reads the temperature and humidity data at a sampling rate of once every 30 seconds and stores it in an external EEPROM for subsequent statistics. The original CO sensor signal is sent to the MCU's ADC after I-V conversion and second-order low-pass filtering, with the sampling rate set to 1Hz.

[0138] II. Work Hour Statistics (corresponding to step S2)

[0139] Accelerated aging tests were conducted to determine the load consumption coefficient k of this CO sensor model. load =1.0, standby power consumption coefficient k idle =0.05, meaning that the consumption rate of active materials under load is 20 times that under standby conditions.

[0140] Preset current threshold I threshold The corresponding output current value for a CO concentration of 0.5 ppm. When the sensor output signal amplitude is ≥0.15 μA, the MCU determines that the current state is under load detection. load Accumulation; when the signal amplitude is <0.15μA and the detector is powered on, it is determined to be in standby mode, T idle Accumulation. The RTC does not accumulate any duration during the detector power-off period.

[0141] To date, the aforementioned detectors have been operating for 150 days, of which T load A total of 450 hours, T idleA total of 3150 hours.

[0142] III. Consumption Integration and Noise Extraction (corresponding to step S3)

[0143] Consumption Integration: The MCU digitally filters the ADC sampled values ​​every second, converts them into concentration values ​​C(t), and accumulates Q in a discrete manner. exp = Σ(C(ti)·Δt·k gas ). 150-day cumulative Q exp = 4800 ppm·h.

[0144] Noise Feature Extraction: A noise measurement program is automatically executed between 2:00 AM and 4:00 AM daily, continuously collecting 3000 current data points and calculating the root mean square fluctuation amplitude σ. current And the noise power spectral density in the frequency band below 1 Hz. Initial value N initial =0.12μA, current measured value N current =0.18μA. Accelerated aging tests determined the critical noise characteristic value N for this sensor model to be 0.18μA. failure =0.45μA. Therefore:

[0145] η electrolyte = ≈ 0.818

[0146] That is, the remaining electrolyte activity is about 81.8%, which is basically consistent with the actual consumption integral result.

[0147] IV. Environmental stress statistics (corresponding to step S4)

[0148] The aforementioned detectors are installed in an outdoor cabinet and have been operating for 150 days.

[0149] Cumulative T of ultra-high temperature (>50℃) HT =120 hours;

[0150] Cumulative T at ultra-low temperatures (< -20℃) LT =0 hours;

[0151] Ultra-high humidity (>95%RH) cumulative T HH =45 hours;

[0152] Temperature fluctuation amplitude ΔT amp (24-hour window) Daily average temperature is about 8℃, with a maximum daily fluctuation of 15℃;

[0153] Humidity fluctuation frequency F RH (24-hour window) Approximately 3 times per day on average.

[0154] Each penalty coefficient was calibrated through accelerated aging tests: λ HT =0.30、λ LT=0.20、λ HH =0.15、λ ΔT =0.10、λ RH =0.05, reference value ΔT ref =10℃, F ref =5 times / 24h. Calculate according to formula in step S4:

[0155] The sum of the components of the environmental stress term = 0.30×(120 / 3600) + 0.20×(0 / 3600) + 0.15×(45 / 3600) + 0.10×(8 / 10) + 0.05×(3 / 5) ≈ 0.010 + 0 + 0.0019 + 0.08 + 0.03 = 0.1219

[0156] This figure indicates that environmental stress accounts for approximately 12.19% of the total lifespan.

[0157] V. Performance Degradation and Failure Mode Classification (corresponding to step S5)

[0158] Perform a self-test procedure after 150 days of operation:

[0159] Baseline drift ΔI baseline = +0.04μA, normalized to approximately +33%;

[0160] Sensitivity decrease rate δ sensitivity = 8%.

[0161] ρ = = 0.5

[0162] K-means clustering was performed on accelerated aging test data of multiple groups of sensors of the same model during the R&D phase to calibrate the sensor model to ρ1=0.3 and ρ2=0.9. The current ρ=0.5 is between the two, which is judged as electrode poisoning failure—there may be trace amounts of interfering substances such as H2S in the air of the factory area, which cause the active sites of the electrode to be gradually occupied.

[0163] VI. Weighted Fusion and Lifetime Prediction (corresponding to step S6)

[0164] Based on the failure mode m = electrode poisoning, the output weight coefficients of the fuzzy inference system are: α = 0.25, β = 0.15, γ = 0.20, δ = 0.40.

[0165] The calibrated performance degradation synthesis function h(·) currently has a value of 0.15, based on ΔI. baseline =33%, δ sensitivity =8%, η electrolyte =0.818 Comprehensive calculation.

[0166] Substitute the above parameters into the overall consumption function:

[0167] f=0.25×(4800 / 20000) + 0.15×[(450×1.0 + 3150×0.05) / (730×24)] +0.20×0.1219 + 0.40×0.15

[0168] = 0.25×0.24 + 0.15×[(450+157.5) / 17520] + 0.02438 + 0.06

[0169] = 0.06 + 0.15 × 0.0347 + 0.02438 + 0.06

[0170] = 0.06 + 0.0052 + 0.02438 + 0.06 = 0.14958

[0171] R remaining = (1 - 0.14958) × 100% ≈ 85.0%

[0172] This means the sensor has consumed about 15% of its lifespan, with about 85% remaining. Based on the current rate of consumption, it is expected to reach the end of its lifespan in about 850 days.

[0173] Meanwhile, the dual-channel prediction architecture outputs a remaining lifetime prediction of approximately 820 days, with a 95% confidence interval of [750 days, 900 days].

[0174] VII. Early Warning Output and Enhanced Compensation (corresponding steps S7 and S8)

[0175] Current R remaining =85% > 10%, no warning triggered. The system continues to monitor.

[0176] When it runs for about 660 days, R remaining When the lifespan drops to 10%, an early warning is triggered. The local LCD displays: "Remaining lifespan: 10% | Failure type: Electrode poisoning | Recommendation: Replace the sensor as soon as possible," and simultaneously sends an early warning message to the DCS system via RS485, activating the on-site audible and visual alarm.

[0177] Upon receiving the warning, operators planned to arrange a replacement the following week. During this period, the system automatically switched to enhanced dynamic compensation mode. Current δ sensitivity The value has reached 25%, and the standard compensation coefficient is (1-0.25). -1 =1.333. The p-value corresponding to the electrode poisoning type is calibrated as 1.4, and the enhancement compensation coefficient is (1-0.25). -1.4=1.506, the compensation strength is increased by about 13%, ensuring that the concentration readings are still of reference value during the transition period.

[0178] 8. Sensor Replacement and System Reset

[0179] After the operator completes the installation of the new sensor, they execute the "Sensor Replacement Confirmation" command via the detector's button. The system then performs the following reset action: Zero T. load T idle Q exp T HT T LT T HH After accumulating all parameters, recalibrate the initial baseline and initial noise characteristic values, and reset R. remaining Once the value reaches 100%, exit the enhanced compensation mode, resume regular measurements, and begin the next life management cycle.

[0180] The above implementation details the complete process of the method of the present invention, from hardware modification, parameter acquisition, multi-dimensional statistics, weighted fusion prediction to early warning output and replacement / reset. The source of parameters, calculation formulas and physical meanings of each step are clearly traceable, and it has clear operability and reproducibility.

[0181] Finally, it should be noted that the above 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 preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.

Claims

1. A method for extending the lifespan of an electrochemical gas detector based on dynamic compensation, characterized in that, Includes the following steps: S1: Add a temperature sensor and a humidity sensor to the gas detector to collect real-time temperature and humidity data of the sensor's working environment; S2: The timing module records the sensor's cumulative working time, cumulative effective working time under load detection, and cumulative idle time under standby conditions. S3: Real-time acquisition of the output current signal of the electrochemical gas sensor and conversion into a gas concentration value; time integration of the gas concentration value; calculation of the cumulative consumption of the sensor. S4: Based on the temperature and humidity data collected in step S1, calculate the cumulative time of ultra-high temperature, ultra-low temperature, and ultra-high humidity. S5: Periodically extract the baseline drift and sensitivity degradation rate of the sensor; S6: Input the multidimensional parameters obtained in steps S2 to S5 into the pre-built remaining lifetime prediction model. The prediction model dynamically adjusts the weight allocation of each influencing factor according to the current failure mode type of the sensor, and calculates the current remaining lifetime percentage of the sensor through weighted comprehensive calculation. S7: When the remaining lifespan percentage reaches a preset warning threshold, a sensor replacement warning signal is issued.

2. The method for extending the lifespan of an electrochemical gas detector based on dynamic compensation according to claim 1, characterized in that, In step S2, the loaded detection state is defined as the working state where the amplitude of the sensor output current signal exceeds a preset current threshold, and the standby state is defined as the working state where the amplitude of the sensor output current signal is lower than the preset current threshold and the detector is powered on; the consumption rate of the sensor's active material per unit time under the loaded state is measured by the first consumption coefficient k. load Characterized by the second consumption coefficient k in standby mode. idle Characterization, k load >k idle Both were calibrated through accelerated aging experiments; the cumulative effective working time and cumulative idle time were respectively included in the corresponding terms of the comprehensive consumption function.

3. The method for extending the lifespan of an electrochemical gas detector based on dynamic compensation according to claim 1, characterized in that, Step S3 further includes: extracting noise features from the output current signal in real time, wherein the noise features include at least one of the root mean square fluctuation amplitude of the current signal, noise power spectral density, and low-frequency drift noise component; and constructing the electrolyte residual activity coefficient η based on the comparison between the measured noise features and the initial noise features. electrolyte As an independent correction factor for predicting the remaining lifespan of the sensor, its calculation formula is: or electrolyte = Where, N current N represents the current noise characteristic value. initial N represents the initial noise characteristic value of the sensor at the time of manufacture or during the initial period of use. failure η is the sensor failure threshold noise characteristic value pre-calibrated through accelerated aging tests; electrolyte The value ranges between 0 and 1, with smaller values ​​indicating lower residual electrolyte activity.

4. The method for extending the lifespan of an electrochemical gas detector based on dynamic compensation according to claim 1, characterized in that, Step S4 further includes: constructing the temperature fluctuation amplitude characteristic parameter ΔT amp and humidity fluctuation frequency characteristic parameter F RH The temperature fluctuation amplitude characteristic parameter is the difference between the maximum and minimum temperature values ​​within a preset time window, and the humidity fluctuation frequency characteristic parameter is the number of times the humidity crosses a set humidity fluctuation threshold within a preset time window; based on the temperature fluctuation amplitude characteristic parameter and the humidity fluctuation frequency characteristic parameter, an environmental stress comprehensive penalty function g( ), used to characterize the accelerated aging effect of environmental stress on sensor lifespan.

5. The method for extending the lifespan of an electrochemical gas detector based on dynamic compensation according to claim 1, characterized in that, In step S5, the ratio ρ of the baseline drift direction to the sensitivity decrease rate is used. Construct a sensor failure mode classification criterion, where ΔI baseline δ represents the baseline drift. sensitivity The sensitivity decrease rate is defined as follows: when ρ is lower than the first preset threshold, it is determined to be an electrolyte depletion failure; when ρ is between the first preset threshold and the second preset threshold, it is determined to be an electrode poisoning failure; when ρ is higher than the second preset threshold, it is determined to be a catalyst passivation failure. The first and second preset thresholds are determined by cluster analysis of the sensor's historical aging data under multiple failure modes. Specifically, multiple sets of sensor lifecycle aging data with known failure modes are collected, the ρ value sequence of each set of data is calculated, and the K-means clustering algorithm is used to cluster the ρ value samples into three categories. The median of the first and second cluster centers is taken as the first preset threshold, and the median of the second and third cluster centers is taken as the second preset threshold.

6. The method for extending the lifespan of an electrochemical gas detector based on dynamic compensation according to claim 1, characterized in that, In step S6, the basic structure of the comprehensive consumption function is as follows: f=α(m) +β(m) )+γ(m) g( )+δ(m) h(ΔI baseline ,d sensitivity ,or electrolyte ) Where m∈{electrolyte depletion type, electrode poisoning type, catalyst passivation type} is the failure mode type determined in step S5; α(m), β(m), γ(m), and δ(m) are the dynamic weight coefficients corresponding to different failure modes m, satisfying α(m)+β(m)+γ(m)+δ(m)=1, and the values ​​of each weight coefficient are different under different failure modes; Q exp Q represents the cumulative consumption. max This represents the sensor's nominal maximum power consumption; T load To accumulate effective working hours, T idle To accumulate idle time, k load k is the consumption coefficient under load. idle T is the standby power consumption coefficient. rated The nominal rated service life; g(·) is the environmental stress comprehensive penalty function; h(·) is the performance degradation comprehensive function; η electrolyte This represents the residual activity coefficient of the electrolyte.

7. The method for extending the lifespan of an electrochemical gas detector based on dynamic compensation according to claim 6, characterized in that, The dynamic weight coefficients are adaptively adjusted through a fuzzy inference system: the failure mode type m and the current percentage of the sensor's service life are used as input variables of the fuzzy inference system, and the adjustment amount of each weight coefficient is output after fuzzification, fuzzy rule inference and defuzzification. The fuzzy rules are jointly formulated based on prior knowledge of sensor aging mechanisms and accelerated aging experimental data, enabling the prediction model to assign higher weights to dominant lifespan factors under different failure modes.

8. The method for extending the lifespan of an electrochemical gas detector based on dynamic compensation according to claim 1, characterized in that, The process also includes step S8: After issuing a replacement warning signal but before the sensor is actually replaced, automatically switch to enhanced dynamic compensation mode, wherein the compensation coefficient for the sensitivity reduction rate in enhanced dynamic compensation mode is changed from (1-δ) in the conventional mode. sensitivity ) -1 Adjusted to (1-δ) sensitivity ) -p , where p is the reinforcement compensation factor, 1<p≤1.5, the value of p is related to the failure mode type determined in step S5, different failure modes correspond to different p values, and are pre-calibrated through accelerated aging experiments.

9. The method for extending the lifespan of an electrochemical gas detector based on dynamic compensation according to claim 1, characterized in that, The remaining lifetime prediction model in step S6 adopts a dual-channel parallel prediction architecture: the first channel is a physical driving model based on the Arrhenius aging dynamics equation, used to calculate the intrinsic aging lifetime baseline of the sensor; the second channel is a data driving model based on a long short-term memory network or a gated recurrent unit, used to learn the lifetime deviation caused by individual sensor differences and random fluctuations in operating conditions; the outputs of the physical driving model and the data driving model are fused by Bayes to obtain the remaining lifetime prediction value and the prediction interval confidence.

10. A dynamic compensation-based electrochemical gas detector lifetime extension management system, characterized in that, The system comprising the method according to any one of claims 1 to 9, wherein the system includes: An electrochemical gas sensor is used to detect the concentration of a target gas and output a corresponding current signal. Temperature and humidity sensors are used to collect temperature and humidity data of the sensor's operating environment, respectively. The signal acquisition and conditioning circuit is used to convert, amplify, filter, and convert the current signal from the electrochemical gas sensor to IV and then send it to the microprocessor. The timing module is used to record the sensor's cumulative working time, cumulative effective working time under load detection state, and cumulative idle time under standby state, respectively. The noise feature extraction module is used to extract the root mean square fluctuation amplitude, noise power spectral density and low-frequency drift noise component in the output current signal of the electrochemical gas sensor in real time. The microprocessor has a built-in failure mode classifier, fuzzy inference weight adjuster and remaining lifetime prediction model. It is used to receive data from various sensors and perform failure mode discrimination, dynamic weight allocation and weighted comprehensive calculation, and output the remaining lifetime percentage, failure mode type and early warning control signal. The storage module is used to store the weight coefficient library, threshold parameters, accelerated aging test calibration data and historical monitoring data corresponding to different failure modes; The display module is used to display real-time gas concentration, remaining lifetime percentage, and failure mode type information. The alarm module is used to issue audible and visual alarm signals when the warning conditions are triggered, and to output differentiated prompt information according to the failure mode type. The communication interface is used to upload early warning information, failure mode types, and sensor status data to the equipment management system or user terminal.

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