Method and system for evaluating application reliability of electric power microsensor

Abstract

The invention provides an application reliability evaluation method and system for an electric power micro sensor, and the method comprises the steps: applying a target combination environment factor to a sensor based on the environment data of a target application scene, and carrying out the dynamic testing of the sensor under the target combination environment factor, collecting preset performance index data of the sensor under the dynamic test based on a preset performance index, and generating performance degradation information of the sensor based on the preset performance index data by using a pre-constructed multi-scale performance degradation model, quantifying the contribution degree of each environmental factor in the target combination environmental factors to sensor degradation based on the performance degradation information, and generating a reliability evaluation result of the sensor based on the contribution degree and a multi-scale performance degradation model. Through a systematized evaluation method and experimental verification, the application reliability evaluation of the electric power micro sensor is realized, and the requirements of the traditional accelerated life test can also be met.

Description

Technical Field

[0001] The present invention relates to the field of power micro sensors, and in particular to an application reliability evaluation method and system for power micro sensors. Background Art

[0002] As power systems accelerate their transformation toward digitalization and intelligentization, high-precision, highly reliable real-time monitoring methods are urgently needed to ensure safe and stable operation of the power system and the efficient integration of new energy. Especially in the context of renewable energy grid integration, dynamic power load sensing, and high renewable energy penetration, real-time, accurate monitoring and analysis capabilities have become key to optimizing power system management and ensuring efficient operation.

[0003] Advanced sensing technology is a crucial foundation for the development of new power systems. Microsensors, based on MEMS (micro-electromechanical systems) technology, boast high sensitivity, miniaturization, low power consumption, and multifunctional integration, and are becoming a crucial component of power system sensing and monitoring. These sensors can accurately measure a variety of physical quantities, including current, voltage, temperature, air pressure, and infrared, meeting the sensing and measurement needs of diverse power scenarios.

[0004] However, in practical applications, power microsensors face harsh and complex environmental challenges. Extreme temperatures, high humidity, strong electromagnetic interference, and vibration and shock pose severe challenges to sensor reliability. Furthermore, sensors must remain stable over long periods of operation and provide consistent measurements under a wide variety of operating conditions and application scenarios. Due to their integrated micromechanical electronic structures, power microsensors exhibit complex inherent failure mechanisms and coupling relationships. Traditional sensor fault diagnosis methods based on output value fluctuations struggle to uncover the underlying issues. The unique operating mechanisms of power microsensors present numerous challenges to their reliable operation.

[0005] However, the existing sensor reliability evaluation methods still have at least the following deficiencies:

[0006] 1) Limitations of the evaluation system. Existing evaluation methods mostly focus on laboratory environments or single-dimensional performance testing, failing to form a comprehensive evaluation system covering multiple environments, functions, and operating conditions. They also lack a comprehensive assessment of the comprehensive performance of sensors in actual operating environments. These shortcomings can lead to performance degradation or even failure of microsensors in complex application environments, posing a potential threat to the safe operation of power systems.

[0007] 2) Evaluation requirements for multi-environment coupling and structural complexity. Existing evaluation methods often ignore the coupling effects and influencing mechanisms of sensors under various environmental factors. Power microsensors usually have complex micromechanical electronic structures, which are very different from traditional sensing and measurement devices, and the integrated microsystem issues are complex. Power microsensors cover the combination of micromechanical structures and electronic circuits, and the coordinated operation of multifunctional modules. The influence and coupling mechanisms of various factors such as temperature, humidity, electromagnetic interference and mechanical vibration are relatively complex, which puts higher requirements on the stability and reliability of sensor performance.

[0008] 3) Insufficient compatibility with actual applications. Existing evaluation methods mostly focus on reliability verification during the sensor manufacturing process, but lack reliability assessment of sensors under actual operating conditions. This laboratory-focused evaluation method cannot deeply analyze the various operating conditions that sensors may face in actual applications. In particular, under the combined influence of multiple environmental factors, the performance evolution and degradation mechanisms of sensors are difficult to predict. This limitation makes it impossible to effectively evaluate their stability and lifespan over long-term use, which may lead to unexpected performance degradation or failure.

[0009] Therefore, how to scientifically and comprehensively evaluate the reliability of microsensors in complex application environments has become an important issue in the development of power sensing. Summary of the Invention

[0010] In order to solve the problem that the existing technology cannot scientifically and comprehensively evaluate the reliability of micro sensors in complex application environments, the present invention proposes an application reliability evaluation method and system for power micro sensors.

[0011] In a first aspect, a method for evaluating the application reliability of a power micro sensor is provided, comprising:

[0012] Applying target combined environmental factors to the sensor based on environmental data of the target application scenario, and performing dynamic testing on the sensor under the target combined environmental factors;

[0013] Collecting preset performance indicator data of the sensor under dynamic testing based on preset performance indicators;

[0014] Generating performance degradation information of the sensor using a pre-built multi-scale performance degradation model based on the preset performance indicator data, and quantifying the contribution of each environmental factor in the target combined environmental factor to the degradation of the sensor based on the performance degradation information;

[0015] A reliability evaluation result of the sensor is generated based on the contribution and the multi-scale performance degradation model.

[0016] Preferably, generating the performance degradation information of the sensor based on the preset performance indicator data using a pre-built multi-scale performance degradation model includes:

[0017] Analyzing the failure mode of the sensor from a macroscopic scale, a mesoscopic scale, and a microscopic scale based on the preset performance indicator data;

[0018] Determining a target failure mode corresponding to the sensor based on the analysis result, and determining a corresponding target multi-scale performance degradation model from a plurality of pre-built multi-scale performance degradation models based on the target failure mode;

[0019] The preset performance indicator data is input into the multi-scale performance degradation model to generate performance degradation information of the sensor that changes over time.

[0020] Preferably, quantifying the contribution of each environmental factor in the target combined environmental factor to the sensor degradation based on the performance degradation information includes:

[0021] Calculating the partial derivative of the key performance degradation indicator of the sensor with respect to the quantized value of each environmental factor corresponding to the target combined environmental factor in the performance degradation information to obtain a sensitivity coefficient of each environmental factor in the target combined environmental factor;

[0022] The contribution of each environmental factor to the sensor degradation is calculated based on the quantified value of each environmental factor and the sensitivity coefficient.

[0023] Preferably, the calculation formula of the contribution is as follows:

[0024]

[0025] Among them, S i is the sensitivity coefficient of the i-th environmental factor, X i is the quantitative value of the i-th environmental factor, CR i is the contribution rate of the i-th environmental factor, Represents the weighted sum of j environmental factors.

[0026] Preferably, generating the reliability evaluation result of the sensor based on the contribution and the multi-scale performance degradation model includes:

[0027] Predicting the service life of the sensor under the target combination of factors based on the multi-scale performance degradation model;

[0028] Calculating the failure rate of the sensor at a target time point based on the predicted service life;

[0029] A reliability evaluation result of the sensor is generated based on the predicted service life, the failure rate, the multi-scale degradation model, the target combined environmental factors, and the preset performance indicator data.

[0030] Preferably, the dynamic testing of the sensor under the target combined environmental factors includes:

[0031] Adjusting the initial environmental factor value of each environmental factor in the target combined environmental factor to the environmental factor limit value corresponding to the sensor;

[0032] Based on the environmental factor limit value and the environmental factor initial value of each environmental factor, the sensor is dynamically tested according to the preset limit operation time.

[0033] Preferably, the target combination of environmental factors includes at least two of the following: temperature, humidity, mechanical vibration and shock, electromagnetic interference, chemical environment, light or radiation.

[0034] Preferably, the preset performance indicators include but are not limited to: measurement accuracy, response time, repeatability, linearity, sensitivity, noise, power consumption, availability or anti-electromagnetic interference.

[0035] Preferably, after generating the reliability evaluation result of the sensor based on the contribution and the multi-scale performance degradation model, the method further includes:

[0036] Optimizing the sensor based on the reliability evaluation result of the sensor, and continuing to perform the application reliability evaluation method of the power micro sensor on the optimized sensor to generate a reliability optimization evaluation result;

[0037] Analyze the difference between the reliability optimization evaluation result and the reliability evaluation result, and determine whether to optimize the application reliability evaluation method of the power microsensor based on the analysis result.

[0038] In a second aspect, the present invention provides an application reliability evaluation system for a power micro sensor, comprising:

[0039] A testing module, configured to apply target combined environmental factors to the sensor based on environmental data of a target application scenario, and to perform a dynamic test on the sensor under the target combined environmental factors;

[0040] An acquisition module, configured to acquire preset performance index data of the sensor under dynamic testing based on preset performance indexes;

[0041] a quantification module, configured to generate performance degradation information of the sensor using a pre-built multi-scale performance degradation model based on the preset performance indicator data, and quantify the contribution of each environmental factor in the target combination of environmental factors to the degradation of the sensor based on the performance degradation information;

[0042] A generating module is used to generate a reliability evaluation result of the sensor based on the contribution and the multi-scale performance degradation model.

[0043] Preferably, the quantification module generates the performance degradation information of the sensor based on the preset performance indicator data using a pre-built multi-scale performance degradation model, including:

[0044] Analyzing the failure mode of the sensor from a macroscopic scale, a mesoscopic scale, and a microscopic scale based on the preset performance indicator data;

[0045] Determining a target failure mode corresponding to the sensor based on the analysis result, and determining a corresponding target multi-scale performance degradation model from a plurality of pre-built multi-scale performance degradation models based on the target failure mode;

[0046] The preset performance indicator data is input into the multi-scale performance degradation model to generate performance degradation information of the sensor that changes over time.

[0047] Preferably, the quantification module quantifies the contribution of each environmental factor in the target combined environmental factor to the sensor degradation based on the performance degradation information, including:

[0048] Calculating the partial derivative of the key performance degradation indicator of the sensor with respect to the quantized value of each environmental factor corresponding to the target combined environmental factor in the performance degradation information to obtain a sensitivity coefficient of each environmental factor in the target combined environmental factor;

[0049] The contribution of each environmental factor to the sensor degradation is calculated based on the quantified value of each environmental factor and the sensitivity coefficient.

[0050] Preferably, the calculation formula of the contribution in the quantification module is as follows:

[0051]

[0052] Among them, S i is the sensitivity coefficient of the i-th environmental factor, X i is the quantitative value of the i-th environmental factor, CR i is the contribution rate of the i-th environmental factor, Represents the weighted sum of j environmental factors.

[0053] Preferably, the generating module is specifically used for:

[0054] Predicting the service life of the sensor under the target combination of factors based on the multi-scale performance degradation model;

[0055] Calculating the failure rate of the sensor at a target time point based on the predicted service life;

[0056] A reliability evaluation result of the sensor is generated based on the predicted service life, the failure rate, the multi-scale degradation model, the target combined environmental factors, and the preset performance indicator data.

[0057] Preferably, the testing module performs a dynamic test on the sensor under the target combined environmental factors, including:

[0058] Adjusting the initial environmental factor value of each environmental factor in the target combined environmental factor to the environmental factor limit value corresponding to the sensor;

[0059] Based on the environmental factor limit value and the environmental factor initial value of each environmental factor, the sensor is dynamically tested according to the preset limit operation time.

[0060] Preferably, the target combination environmental factors in the test module include at least two of the following: temperature, humidity, mechanical vibration and shock, electromagnetic interference, chemical environment, light or radiation.

[0061] Preferably, the preset performance indicators in the test module include but are not limited to: measurement accuracy, response time, repeatability, linearity, sensitivity, noise, power consumption, availability or anti-electromagnetic interference.

[0062] Preferably, the system further comprises:

[0063] an optimization module, configured to optimize the sensor based on the reliability evaluation result of the sensor, and continue to execute the application reliability evaluation method of the power micro sensor on the optimized sensor to generate a reliability optimization evaluation result;

[0064] Analyze the difference between the reliability optimization evaluation result and the reliability evaluation result, and determine whether to optimize the application reliability evaluation method of the power microsensor based on the analysis result.

[0065] In another aspect, the present application further provides an electronic device, comprising: at least one processor and a memory; the memory and the processor are connected via a bus;

[0066] The memory is used to store one or more programs;

[0067] When the one or more programs are executed by the at least one processor, the above-mentioned method for evaluating the application reliability of a power micro sensor is implemented.

[0068] On the other hand, the present application also provides a computer-readable storage medium having a computer program stored thereon. When the computer program is executed, the above-mentioned method for evaluating the application reliability of a power microsensor is implemented.

[0069] Compared with the prior art, the present invention has the following beneficial effects:

[0070] The present invention provides a method and system for evaluating the application reliability of power microsensors. This method applies a target combination of environmental factors to the sensor based on environmental data from a target application scenario, performs dynamic testing on the sensor under the target combination of environmental factors, collects preset performance indicator data of the sensor under dynamic testing based on preset performance indicators, generates performance degradation information of the sensor based on the preset performance indicator data using a pre-built multi-scale performance degradation model, quantifies the contribution of each environmental factor in the target combination of environmental factors to sensor degradation based on the performance degradation information, and then generates sensor reliability evaluation results based on the contribution and the multi-scale performance degradation model. Through a systematic evaluation method and experimental verification, the application reliability evaluation of power microsensors is achieved. This solution not only meets the requirements of traditional accelerated life testing, but also makes significant expansions in degradation models, coupling effect research, and multi-scale penetration. The quantitative analysis of the coupling effects of multiple scenarios enables this solution to more accurately characterize the damage path of real complex environments to sensors. BRIEF DESCRIPTION OF THE DRAWINGS

[0071] Figure 1 This is a flow chart of the application reliability evaluation method of the power microsensor of the present invention;

[0072] Figure 2 Schematic diagram of the overall evaluation framework of the application reliability evaluation method of the power microsensor of the present invention;

[0073] Figure 3 Schematic diagram of the overall evaluation framework of the application reliability evaluation method of the power microsensor of the present invention;

[0074] Figure 4 A flow chart of the reliability evaluation steps of the application reliability evaluation method of the power microsensor of the present invention;

[0075] Figure 5 A schematic diagram of a multi-scale through-analysis of an application reliability evaluation method for a power microsensor according to the present invention;

[0076] Figure 6A schematic diagram of a degradation model and data analysis of an application reliability evaluation method for a power microsensor according to the present invention;

[0077] Figure 7 Schematic diagram of reliability evaluation and optimization feedback of the application reliability evaluation method of the power micro sensor of the present invention;

[0078] Figure 8 This is a schematic diagram of the structure of the application reliability evaluation system of the power micro sensor of the present invention;

[0079] Figure 9 The figure is a schematic structural diagram of an electronic device of the present invention. DETAILED DESCRIPTION

[0080] The present invention proposes a method and system for evaluating the application reliability of power microsensors. By constructing a comprehensive evaluation system covering multiple environments, multiple dimensions, and multiple working conditions, the method comprehensively evaluates the performance stability and reliability of microsensors in complex application environments, reveals the performance evolution laws and degradation mechanisms faced by sensors in long-term operation, and provides a scientific basis for sensor design optimization, engineering application, and reliability improvement.

[0081] In order to better understand the present invention, the present invention will be further described below with reference to the accompanying drawings and embodiments.

[0082] Example 1:

[0083] A method for evaluating the reliability of power micro sensors Figure 1 Shown, including:

[0084] Step 1: applying target combined environmental factors to the sensor based on environmental data of the target application scenario, and performing dynamic testing on the sensor under the target combined environmental factors;

[0085] Step 2: collecting preset performance index data of the sensor under dynamic testing based on preset performance indexes;

[0086] Step 3: Generating performance degradation information of the sensor using a pre-built multi-scale performance degradation model based on the preset performance indicator data, and quantifying the contribution of each environmental factor in the target combined environmental factor to the degradation of the sensor based on the performance degradation information;

[0087] Step 4: Generate a reliability evaluation result of the sensor based on the contribution and the multi-scale performance degradation model.

[0088] Specifically, the present invention establishes a comprehensive evaluation system with "multiple environmental factors, multiple performance indicators, and multi-scale integration" as the core, ensuring coverage of key influencing factors of sensors in complex practical applications, analyzing the sensor performance degradation mechanism and long-term reliability, revealing the coupling effect of the environment and working conditions on sensor performance, and clarifying the performance limits and adaptability of power micro sensors in complex power system scenarios. The overall framework is mainly divided into three main parts: reliability evaluation preparation, evaluation steps, and evaluation results. Figure 2 shown.

[0089] Reliability evaluation preparation: This includes three components: a multi-environmental factor module, a multi-performance indicator module, and a multi-scale penetration module. This module defines the external environmental parameters that affect the performance and lifespan of power microsensors. A quantifiable indicator system is constructed based on measurement accuracy, power consumption, noise, linearity, sensitivity, and electromagnetic interference resistance. Finally, a multi-scale analysis of microsensor failure mechanisms and performance changes is conducted from the microstructure, meso-module, and macro-system levels.

[0090] Reliability evaluation steps include five steps: evaluation requirements analysis, environmental condition simulation and control, multi-dimensional performance testing, data collection and analysis, and reliability assessment and reporting. Application scenarios and specific performance requirements are clarified, and environmental factor data is collected. Combined environmental stress loading is achieved by combining experimental devices for temperature and humidity, EMI, vibration, shock, and chemical corrosion. Changes in various sensor performance indicators are measured and recorded in single and combined environments for subsequent degradation and failure analysis. Data cleaning, statistical analysis, and model building are used to identify and quantify the contribution of environmental factors to sensor degradation. A systematic reliability evaluation report is generated for the sensor using methods such as degradation models, life prediction, and FMEA (Failure Mode and Effects Analysis).

[0091] Reliability evaluation results include performance degradation mechanism analysis, coupling effect analysis, performance limit and adaptability determination, and optimization and iteration. Performance degradation mechanism analysis includes failure mode identification, degradation process modeling, influencing factor quantification, and reliability prediction. Coupling effect analysis includes multi-factor interaction modeling, coupling effect quantification, and comprehensive performance evaluation. Performance limit and adaptability determination includes performance limit testing, adaptability assessment, and safety margin analysis. Optimization and iteration includes feedback mechanism establishment, database construction, model optimization, and standardized processes.

[0092] In one embodiment, this evaluation method divides currently common environmental factors into the following five categories and further quantifies them: temperature, humidity, mechanical vibration and shock, electromagnetic interference, and special environment (including chemical environment, light, radiation, etc.).

[0093] (1) Temperature (T)

[0094] In high-temperature environments, micromechanical materials are prone to oxidation or stress relaxation, causing electronic components to drift faster or age faster; in low-temperature environments, mechanical structures may fail due to thermal stress or low-temperature brittleness.

[0095] (2) Humidity (RH)

[0096] A humid environment can easily lead to problems such as material corrosion, circuit short circuits, or failure of packaging and sealing materials. Especially at the microscopic scale, the impact of water vapor penetration on sensitive components is more significant.

[0097] (3) Mechanical vibration and shock (M)

[0098] Vibration can cause fatigue or microcrack propagation in micromechanical structures, while acceleration shock may cause damage to diaphragms, cantilever beams or sensitive components within a transient state.

[0099] (4) Electromagnetic interference (EMI)

[0100] For microsensors, electromagnetic interference may cause increased circuit noise, distorted measurement values, or local failure of some sensitive components, or even trigger parasitic effects.

[0101] (5) Special Environment (SE)

[0102] In addition to the above four categories, environmental factors that need to be considered and affect the operation of power microsensors can be classified as special environments, symbolized as SE. Special environments are generally only analyzed as environmental factors in special application scenarios.

[0103] Common special environments include: chemical environment, light, and radiation.

[0104] In highly corrosive or radiation-radiating environments, the degradation of the material surface and interior is accelerated, significantly affecting the reliability of microscopic electrodes, packaging materials, and solder joints.

[0105] In one embodiment, this evaluation method sets multiple performance indicators for the sensor's performance: measurement accuracy, response time, repeatability, linearity, sensitivity, noise, power consumption, availability, anti-electromagnetic interference capability, etc., and provides corresponding calculation formulas for quantitative evaluation.

[0106] (1) Measurement accuracy (A)

[0107]

[0108] Among them, V measured is the sensor measurement value, V ture is the true value.

[0109] (2) Response time (τ)

[0110] τ=t 90％ -t 10％ (2)

[0111] That is, the time difference required for the sensor to stabilize its output from 10% to 90%.

[0112] (3) Repeatability (R)

[0113]

[0114] Among them, V i is the i-th measurement value, V avg is the average value of N measurements.

[0115] (4) Linearity (L)

[0116]

[0117] Among them, V linear is the ideal linear response value.

[0118] (5) Sensitivity (S)

[0119]

[0120] Among them, IN max 、IN min are the maximum and minimum input values, V measured,max 、V measured,min The maximum and minimum sensor measurements, respectively.

[0121] (6) Noise level (N)

[0122]

[0123] Among them, σ noise is the standard deviation of the noise signal, V signal is the effective signal amplitude.

[0124] (7) Power consumption (P)

[0125] P=V×I (7)

[0126] Among them, V is the power supply voltage and I is the operating current.

[0127] (8) Availability (HA)

[0128]

[0129] Among them, MTBF is the mean time between failures and MTTR is the mean time to repair.

[0130] (9) Anti-electromagnetic interference capability (I EMI )

[0131]

[0132] Where ΔA is the change in measurement accuracy due to electromagnetic interference, and EMI is the electromagnetic interference intensity.

[0133] In this embodiment, the process of dynamically testing the sensor under the target combination of environmental factors in step 1 includes:

[0134] Adjusting the initial environmental factor value of each environmental factor in the target combined environmental factor to the environmental factor limit value corresponding to the sensor;

[0135] Based on the environmental factor limit value and the environmental factor initial value of each environmental factor, the sensor is dynamically tested according to the preset limit operation time.

[0136] In one embodiment, before conducting a reliability evaluation, it is necessary to analyze the evaluation requirements to clarify the sensor's application scenarios, performance requirements, and environmental factor collection requirements, including:

[0137] (1) Determine the application scenario

[0138] Clarify the sensor's performance requirements and environmental conditions for specific power application scenarios. For example, renewable energy grid-connected scenarios require high-precision current and voltage measurements, with fast response times to accommodate the volatility of renewable energy generation. Dynamic load sensing scenarios require sensors with high repeatability and stability to ensure real-time monitoring of load changes.

[0139] (2) Definition of performance requirements

[0140] Specific requirements are given for measurement accuracy, response time, repeatability, linearity, sensitivity, noise, power consumption, availability, and anti-electromagnetic interference capability.

[0141] (3) Environmental factor data collection

[0142] Collect data distribution of key environmental factors such as temperature, humidity, electromagnetic interference, mechanical vibration, chemical environment, light and radiation in the target application scenario, and collect the changing patterns of environmental factors on time scales such as days, months and years.

[0143] In a specific embodiment, after the evaluation requirements of the sensor are clarified, the environmental conditions can be simulated based on the evaluation requirements of the sensor, such as Figure 3 As shown, before simulating environmental conditions, it is also necessary to determine how to accurately control various environmental factors, including:

[0144] (1) Temperature and humidity control

[0145] Use a temperature and humidity test chamber equipped with a PID controller to achieve precise regulation of temperature and humidity:

[0146]

[0147] Among them, T set is the set temperature, T measeured is the set temperature, K p , K i , K d is the PID parameter, and T(t) represents the temperature at time t.

[0148] The humidity control principles are basically the same.

[0149] (2) Electromagnetic interference simulation

[0150] Electromagnetic compatibility (EMC) test equipment is used to generate electromagnetic interference signals of different frequencies and intensities to simulate electric field interference (E) and magnetic field interference (H).

[0151] The maximum and minimum intensities of the electric field interference are E max 、E min .

[0152] The maximum and minimum intensities of the magnetic field interference are H max 、H min .

[0153] And consider more EMI forms such as pulse interference, harmonic interference and random modulation interference.

[0154] (3) Vibration and shock test

[0155] Use a six-degree-of-freedom vibration table to simulate the mechanical vibration and impact in actual work.

[0156] Vibration acceleration:

[0157] a(t)=Asin(2πft+φ) (11)

[0158] Where A is the vibration acceleration amplitude, f is the vibration frequency, φ is the phase angle, and a(t) represents the vibration acceleration at time t.

[0159] Impact strength:

[0160]

[0161] Among them, I0 is the vibration acceleration, γ I is the attenuation coefficient, and I(t) represents the impact intensity at time t.

[0162] (4) Chemical environment test

[0163] The concentration of corrosive gas and dust is controlled by the gas generator.

[0164] Corrosive gases:

[0165]

[0166] Where C0 is the initial concentration, γ C is the attenuation coefficient, and C(t) represents the gas concentration at time t.

[0167] Dust particles:

[0168]

[0169] Where D0 is the vibration acceleration, μ D is the growth rate, and D(t) represents the dust particles at time t.

[0170] (5) Lighting and radiation simulation

[0171] Use xenon lamps and radiation sources to simulate different light intensities and radiation levels.

[0172] In a specific embodiment, after the control method of each environmental factor is clarified, multi-dimensional performance testing can be performed, such as Figure 4 As shown, it specifically includes:

[0173] (1) Benchmark

[0174] Standard environmental conditions: 25°C, 50% RH, no electromagnetic interference, no vibration.

[0175] Under standard environmental conditions or specified conditions, test the basic performance indicators of the sensor, such as measurement accuracy, response speed, stability, repeatability, linearity, sensitivity, noise level, power consumption, etc., as a benchmark for subsequent comparison.

[0176] (2) Environmental adaptability test

[0177] 1) Temperature test

[0178] Increase the ambient temperature gradually to 150°C or the specified temperature, and record the changes in the sensor's performance indicators. Use the temperature sensitivity model to calculate the temperature sensitivity coefficient of the measurement accuracy:

[0179] A(T)=A0+k T T (15)

[0180] For example, k TIf the temperature is 0.05% / ℃, the measurement accuracy changes by 5% at 100℃. A(T) represents the temperature sensitivity coefficient at time T, and A0 represents the reference accuracy value.

[0181] 2) Humidity test

[0182] Similarly, gradually increase the ambient humidity to 95% RH or the specified humidity and record the performance changes of the sensor.

[0183] Evaluate the impact of high humidity on sensor measurement accuracy and signal repeatability, and establish a humidity sensitivity model:

[0184] A(RH)=A0+k RH RH (16)

[0185] For example, k RH If the humidity is 0.02% / %RH, then at 95%RH, the measurement accuracy changes by 1.9%. A(RH) represents the temperature sensitivity coefficient under RH humidity, and A0 represents the reference accuracy value.

[0186] 3) Electromagnetic interference test

[0187] ① Electric field interference

[0188] Test the sensor's performance at different frequencies and intensities, such as 50Hz, 1kHz, 10kHz, 100kHz, and 1MHz, and at different intensities (10V / m, 20V / m, 30V / m, 40V / m, and 50V / m). Record the sensor's measurement error and signal repeatability under electric field interference.

[0189] Establish a model for the impact of electric field interference on measurement accuracy:

[0190]

[0191] like, If it is 0.03% / V / m, then at 30V / m, the measurement accuracy changes by 0.9%, and A0 represents the reference accuracy value.

[0192] ②Magnetic field interference

[0193] Similarly, the sensor's performance was tested under different magnetic field strengths (10A / m, 20A / m, 30A / m, 40A / m, and 50A / m). The sensor's measurement error and signal repeatability under electric field interference were recorded.

[0194]

[0195] like, If it is 0.02% / A / m, then at 50A / m, the measurement accuracy changes by 1%, and A0 represents the reference accuracy value.

[0196] 4) Mechanical vibration and shock test

[0197] Test the performance changes of the sensor under different vibration frequencies and accelerations, such as different vibration frequencies (10Hz, 50Hz, 100Hz) and different accelerations (0.5g, 1g, 2g, 5g, 10g).

[0198] Establish vibration tolerance model A(M):

[0199] A(M)=A0+k M a (19)

[0200] Among them, k M is the vibration sensitivity coefficient, a is the acceleration, and A0 represents the benchmark accuracy value.

[0201] 5) Chemical environment testing

[0202] The performance changes of the sensor were tested under different corrosive gas concentrations, such as different corrosive gas concentrations (100ppm, 500ppm, 1000ppm, 5000ppm).

[0203] Establishing the corrosive gas tolerance model A(C):

[0204] A(C)=A0+k C C (20)

[0205] Among them, k C is the chemical environment sensitivity coefficient, C is the gas concentration, and A0 represents the benchmark accuracy value.

[0206] 6) Light and radiation test

[0207] The performance of the sensor was tested under different light intensities and radiation doses, such as different light intensities (100lx, 500lx, 1000lx, 5000lx) and radiation doses (0.1mSv, 0.5mSv, 1mSv, 5mSv).

[0208] Referring to the above content, the light sensitivity model A(L) is established:

[0209] A(L)=A0+k L I (21)

[0210] Among them, k L is the light sensitivity coefficient, L is the light intensity, and A0 represents the benchmark accuracy value.

[0211] (3) Combined environmental testing

[0212] The comprehensive performance of the sensor is tested by simultaneously applying various environmental factors, such as high temperature and humidity (150°C, 95% RH), high electromagnetic interference (50V / m, 50A / m), and high vibration (10Hz, 10g).

[0213] Use a multi-factor model to evaluate the combined environmental impact on sensor performance:

[0214] A composite =A0+∑k i A i +∑k i,j A i,j +… (22)

[0215] Among them, A0 represents the benchmark accuracy value, A composite represents the performance index of the sensor in the combined environment, k i Represents the influence coefficient of a single environmental factor, A i represents the intensity or change of the i-th environmental factor, k i,j Indicates the coupling influence coefficient between environmental factors, A i,j represents the interaction between the i-th and j-th environmental factors.

[0216] (4) Long-term operation test

[0217] Test the sensor for a long time and observe its performance degradation, such as for a long time (≥1000 hours).

[0218] The service life prediction model predicts the reliability of the sensor:

[0219]

[0220] Among them, λ is the failure rate (the probability of failure per unit time), P is the reliability level (the probability of still being able to work normally at a certain point in time), and R represents the predicted life.

[0221] In one embodiment, under the aforementioned multi-dimensional performance test, performance test data corresponding to the sensor is collected and analyzed, which specifically includes:

[0222] (1) Data preprocessing

[0223] Use outlier detection, such as the 3σ principle, to remove noise and detect and eliminate outliers to ensure data accuracy and reliability. Based on benchmark test results, perform error correction to eliminate the impact of systematic and environmental errors.

[0224] (2) Performance index calculation

[0225] Calculate various performance indicators based on the defined formulas. Use statistical analysis methods to evaluate the impact of environmental factors on performance indicators.

[0226] (3) Degradation model establishment

[0227] Based on long-term test data, a mathematical model of sensor performance degradation is established using time series analysis and regression models, such as the exponential decay degradation model A(t):

[0228] A(t)=A0e -βt +∈(t) (24)

[0229] Where β is the degradation rate and ∈(t) is the random error term.

[0230] 2.3.5 Reliability Assessment and Reporting

[0231] (1) Reliability index calculation

[0232] Failure rate:

[0233]

[0234] Mean time between failures:

[0235]

[0236] (2) Determination of performance limits

[0237] Use the limit analysis method to determine the limit points of each performance indicator, such as:

[0238] A(T limit )=A threshold (27)

[0239] Among them, A(T limit ) is the performance index under the limit, A threshold is the performance threshold.

[0240] (3) Risk Assessment

[0241] Identify the risk points that sensors may face in actual applications, use the failure mode and effects analysis (FMEA) method to identify the critical paths and probabilities of failure of micromechanical structures and circuit components, and assess the potential impact, probability of occurrence, and difficulty of detection after failure occurs:

[0242] RPN=S×O×D (28)

[0243] Among them, S is severity, O is probability of occurrence, and D is difficulty of detection.

[0244] The severity (S) rating scale assesses the potential impact of each failure mode on system functionality or safety, using a rating scale of 1 to 10. 1-3: Minor impact, no impact on overall system functionality; 4-6: Moderate impact, may require maintenance or repair, but does not compromise safety; 7-10: Severe impact, may result in system downtime, major safety incidents, or high repair costs.

[0245] The probability of occurrence (O) rating scale assesses the likelihood of each failure mode occurring within a specified timeframe, using a 1 to 10 rating scale. 1-3: Very low probability, based on historical data or experience, the failure mode rarely occurs; 4-6: Medium probability, the failure mode occurs occasionally and requires regular monitoring; 7-10: High probability, the failure mode occurs frequently and requires immediate action.

[0246] The Detection Difficulty (D) rating scale assesses the difficulty of detecting the failure mode after it occurs, using a rating scale of 1 to 10. 1-3: Easy to detect, existing monitoring systems or testing methods can quickly detect it; 4-6: Moderate difficulty, may require specialized detection equipment or methods; 7-10: High difficulty, existing means are difficult to effectively detect.

[0247] (4) Evaluation report generation

[0248] The evaluation report should include: test methods, test data, data analysis results, reliability evaluation conclusions and improvement suggestions. For example:

[0249] Introduction: Purpose and scope of the evaluation.

[0250] Test Method: A detailed description of the test procedures and equipment.

[0251] Test results: Displays the test data and statistical analysis results of various performance indicators.

[0252] Data analysis: including degradation model, coupling effect analysis, etc.

[0253] Reliability assessment: Summarize reliability indicators and risk assessment results.

[0254] Conclusion and suggestions: Suggestions for sensor design optimization and application improvement are put forward.

[0255] In this embodiment, the process of generating the performance degradation information of the sensor based on the preset performance indicator data using the pre-built multi-scale performance degradation model in step 3 includes:

[0256] Analyzing the failure mode of the sensor from a macroscopic scale, a mesoscopic scale, and a microscopic scale based on the preset performance indicator data;

[0257] Determining a target failure mode corresponding to the sensor based on the analysis result, and determining a corresponding target multi-scale performance degradation model from a plurality of pre-built multi-scale performance degradation models based on the target failure mode;

[0258] The preset performance indicator data is input into the multi-scale performance degradation model to generate performance degradation information of the sensor that changes over time.

[0259] Specifically, this evaluation method introduces a multi-scale analysis method, including macro, meso and micro levels, such as Figure 5 As shown in the figure, the performance changes of sensors at different time and space scales are deeply analyzed. Multi-scale through-analysis integrates data at different scales to build a comprehensive performance model, which deeply reveals the performance changes and failure mechanisms of sensors at various levels.

[0260] The macroscopic scale can be understood as the performance evaluation at the overall system level, considering the integration performance of the sensor in the entire power system and the reliability at the system level. For example, the data transmission stability and real-time performance of the sensor in actual applications; the mesoscopic scale can be understood as the performance analysis at the module level, focusing on the mutual influence and collaborative work between the sub-modules within the sensor, such as the coupling relationship between the sensor's micromechanical structure and electronic circuit, and evaluating the independent and collaborative performance of each sub-module under different environmental conditions; the microscopic scale can be understood as observing the micromorphological changes of the micromechanical structure and electronic circuit under stress such as high temperature and high humidity through scanning electron microscopy (SEM), transmission electron microscopy (TEM), X-ray tomography and other technologies, such as crack propagation, material phase change, and solder joint degradation.

[0261] In a specific embodiment, after obtaining the preset performance indicator data, it is necessary to analyze the preset performance indicator data to determine the degradation mechanism corresponding to the sensor, such as Figure 6 As shown, it specifically includes:

[0262] (1) Failure mode identification

[0263] By analyzing long-term test data, the main failure modes of sensors are identified. The following are several typical failure modes:

[0264] 1) Material aging

[0265] Materials in micromechanical structures oxidize, corrode, or fatigue under high temperature and high humidity environments, resulting in a decrease in structural strength, such as spring breakage and cantilever beam deformation.

[0266] 2) Circuit loss

[0267] Components in electronic circuits age under high voltage, current, or electromagnetic interference, resulting in degradation of circuit performance, such as resistance drift and capacitor failure.

[0268] 3) Mechanical structure deformation

[0269] Micromechanical structures undergo permanent deformation under long-term vibration and impact, affecting the sensitivity and response speed of the sensor, such as permanent bending of the sensor diaphragm.

[0270] 4) Failure of sensitive components

[0271] Failure in high-intensity electromagnetic environments results in a decrease in the overall anti-interference capability of the sensor.

[0272] (2) Degradation process modeling

[0273] Based on the identified failure modes, a process model of sensor performance degradation is established to describe its changes with time and environmental factors.

[0274] 1) Low cycle fatigue life model

[0275] Describes the fatigue life of a material at low cycle counts:

[0276]

[0277] Where Δε is the strain range; ε′ f is the fatigue ductility coefficient; N f is the fatigue life; c is the fatigue extension index.

[0278] Applicable to materials with lower cycle times (usually less than 10 4 Fatigue analysis of a large strain range can describe the fatigue life under large strain amplitude and is suitable for the prediction of early fatigue failure of structural parts.

[0279] 2) High cycle fatigue life model

[0280] Describes the fatigue life of a material at high cycle counts:

[0281] α a =α′ f (2N f ) b (30)

[0282] Among them, α a is the stress amplitude; α′ f is the fatigue strength coefficient; N f is the fatigue life; b is the Basquin index.

[0283] Suitable for materials with high cycle times (usually more than 10 4 Fatigue analysis of a smaller strain range is suitable for life prediction of components subjected to small vibrations or cyclic stresses during long-term operation.

[0284] 3) Crack propagation model

[0285] Describe the crack growth rate in each cycle:

[0286]

[0287] in, is the crack length expansion per cycle; C and m are material constants; ΔK is the stress intensity factor range.

[0288] It is applicable to structural parts with existing cracks, describes the crack expansion rate in each cycle, can predict the crack growth trend, and is suitable for monitoring and evaluating the impact of crack expansion on structural integrity.

[0289] 4) Arrhenius model

[0290] Describe the effect of temperature on the rate of a chemical reaction or diffusion process:

[0291]

[0292] Where k is the reaction rate constant, A is the pre-exponential factor, Q is the activation energy, R is the gas constant, and T is the absolute temperature.

[0293] It is suitable for situations where temperature has a significant impact on chemical reaction rates or diffusion processes. It can quantify the impact of temperature changes on failure processes and is suitable for material aging analysis in high-temperature environments.

[0294] 5) Irene Model

[0295] Describe the relationship between strain rate and the combined effects of stress and temperature:

[0296]

[0297] in, is the strain rate, ΔV is the activation volume, σ is the stress, k is the Boltzmann constant, and T is the absolute temperature.

[0298] It can describe the nonlinear effect of strain rate on material properties, is suitable for analyzing material behavior under dynamic loads, and is suitable for complex failure processes where strain rate, stress, and temperature have a common influence.

[0299] 6) Inverse power law model

[0300] Describe the relationship between fatigue life and stress amplitude:

[0301] N f =C(Δσ) -m (34)

[0302] Among them, N fis the fatigue life, C and m are material constants; Δσ is the stress amplitude.

[0303] It is applicable to materials whose fatigue life is inversely proportional to stress amplitude, can describe the significant influence of stress amplitude on fatigue life, and is suitable for quantitative analysis of material fatigue strength.

[0304] 7) Ohmic contact degradation Kirkendall void model

[0305] Describe the degradation and void formation processes of materials under electrical contact, combining Ohm's law with the dynamics of void propagation.

[0306]

[0307] Where A is the cavity area, I is the current, α and β are material constants, and n is the power law exponent.

[0308] It is applicable to the process of material degradation and void formation under electrical contact, can describe the influence of current on the change of material microstructure, and is suitable for reliability analysis of contact points of electronic components.

[0309] 8) Linear additive model

[0310] Describe the cumulative damage assessment:

[0311]

[0312] Among them, n i is the actual number of cycles at the i-th stress level, N i is the fatigue life at the i-th stress level; k is the number of different stress levels, and D represents the cumulative damage factor.

[0313] It can quantify the cumulative contribution of different stress levels to overall fatigue damage, and is suitable for life prediction under various load conditions and for evaluating cumulative damage under various stress levels.

[0314] In this embodiment, in step 3, the process of quantifying the contribution of each environmental factor in the target combination of environmental factors to sensor degradation based on the performance degradation information includes:

[0315] Calculating the partial derivative of the key performance degradation indicator of the sensor with respect to the quantized value of each environmental factor corresponding to the target combined environmental factor in the performance degradation information to obtain a sensitivity coefficient of each environmental factor in the target combined environmental factor;

[0316] The contribution of each environmental factor to the sensor degradation is calculated based on the quantified value of each environmental factor and the sensitivity coefficient.

[0317] In a specific embodiment, the influence of various environmental factors on the sensor degradation process is quantitatively analyzed to identify the main influencing factors and their mechanisms of action, which specifically includes:

[0318] 1) Sensitivity analysis

[0319] Principal component analysis (PCA) and partial least squares regression (PLS) methods were used to evaluate the contribution of each environmental factor to performance degradation:

[0320]

[0321] Among them, S i is the sensitivity coefficient of the i-th environmental factor, X i is the quantitative value of the i-th environmental factor, and A represents the performance index.

[0322] 2) Quantification of coupling effects

[0323] Through multivariate regression models, the impact of the interaction of different environmental factors on the degradation process is quantified, such as:

[0324]

[0325] Among them, β T,RH It represents the coupling effect coefficient between temperature (T) and humidity (RH), and A represents the performance index.

[0326] 3) Contribution rate analysis

[0327] Calculate the contribution of each environmental factor and its interaction to the overall performance degradation and identify the key influencing factors:

[0328]

[0329] Among them, CR i is the contribution rate of the i-th environmental factor.

[0330] In this embodiment, the process of generating the reliability evaluation result of the sensor based on the contribution and the multi-scale performance degradation model in step 4 includes:

[0331] Predicting the service life of the sensor under the target combination of factors based on the multi-scale performance degradation model;

[0332] Calculating the failure rate of the sensor at a target time point based on the predicted service life;

[0333] A reliability evaluation result of the sensor is generated based on the predicted service life, the failure rate, the multi-scale degradation model, the target combined environmental factors, and the preset performance indicator data.

[0334] In a specific embodiment, the reliability index of the sensor in different application scenarios is predicted by using a degradation model and influencing factor analysis, which specifically includes:

[0335] 1) Lifespan prediction

[0336] Predict the service life of the sensor under specific environmental conditions based on the degradation model:

[0337]

[0338] Where Λ is the life prediction value, η is the model coefficient, A0 is the initial performance index, A(t) is the performance index at time t, and β is the degradation rate.

[0339] 2) Failure rate assessment

[0340] Based on the lifetime prediction, calculate the failure rate at a specific point in time:

[0341]

[0342] Among them, f(t) is the failure rate function and R(t) is the reliability function.

[0343] In another specific embodiment, the coupled effects of environmental factors and environmental conditions may be analyzed to accurately describe the combined effects of different environmental factors on the sensor, specifically including:

[0344] (1) Multi-factor interaction modeling

[0345] 1) Multiple linear regression model

[0346] Establish an interaction model between multiple factors to describe the comprehensive impact of different environmental factors on sensor performance under the joint action of A:

[0347] A=β0+β1T+β2RH+β3EMI E +β4EMI H +β5(T×RH)+β6(T×EMI E )+β7(T×EMI H )+β8(RH×EMI E )+β9(RH×EMI H )+∈ (42)

[0348] Among them, β i is the regression coefficient, and ∈ is the error term.

[0349] This model is suitable for situations where there is a linear relationship between environmental factors and performance indicators. The model structure is simple and easy to interpret. The normality, independence, and homoscedasticity of the residuals are checked to ensure that the model assumptions are met.

[0350] 2) Nonlinear interaction model

[0351] Add quadratic terms and more interaction terms for each environmental factor to more accurately describe the complex nonlinear coupling relationship:

[0352]

[0353] It is applicable to situations where there are nonlinear relationships or complex interactions between environmental factors and performance indicators, and can more accurately capture system behavior. 2 ), adjusted R 2 Indicators such as , are used to evaluate how well the model fits the data.

[0354] (2) Quantification of coupling effect

[0355] 1) Calculation of coupling effect coefficient

[0356] Use multiple regression analysis to calculate the regression coefficient of each interaction term and quantify the strength of the coupling effect, such as the interaction effect coefficient of temperature and electric field interference. And the interaction effect coefficient of temperature and magnetic field interference

[0357] 2) Sensitivity ranking

[0358] Rank the coupling effects based on their coefficients to identify the interactions that most significantly impact sensor performance. Based on the ranking results, identify the coupling effects that have the greatest impact on sensor performance as key coupling effects. For example, "high temperature + high humidity" > "high temperature + EMI."

[0359] (3) Comprehensive performance evaluation

[0360] 1) Comprehensive performance score

[0361] According to the weight and evaluation results of each performance indicator, the comprehensive performance score S of the sensor is calculated. total :

[0362]

[0363] Among them, ω i is the weight of the i-th performance indicator, S i is the score of the i-th performance indicator.

[0364] Performance indicator weights are determined using expert evaluation, the Analytic Hierarchy Process, or a data-driven approach. Based on actual application feedback and new data, performance indicator weights are regularly adjusted and optimized to maintain the adaptability and accuracy of the scoring system.

[0365] Expert evaluation method is a commonly used qualitative method in which experts in multiple fields evaluate various performance indicators and use their professional knowledge and experience to make weight judgments.

[0366] The analytic hierarchy process (AHP) is suitable for situations where the impact of multiple performance indicators on the overall system evaluation needs to be considered. It constructs a hierarchical structure to conduct multi-indicator decision analysis, then analyzes the eigenvalues ​​of the pairwise comparison matrix to calculate the weight of each performance indicator.

[0367] The data-driven approach relies on actual test data and historical performance data, using statistical analysis or machine learning models to determine the weights of various performance indicators. By analyzing the impact of different indicators on sensor performance degradation, the weight of each indicator can be automatically calculated.

[0368] 2) Performance level classification

[0369] Based on the comprehensive performance score, the sensors are divided into different performance levels. total ≥90: Excellent; 80≤S total <90: Good; 70≤S total <80: Medium; S total <70: Poor. To guide selection and layout in different scenarios.

[0370] In another embodiment, a long-term performance limit test may be performed on the sensor to intuitively demonstrate the performance limit of the sensor, which specifically includes:

[0371] (1) Performance limit test

[0372] At each incremental step, a complete performance test is conducted, recording the changes in each performance indicator until sensor performance significantly degrades or fails. This determines the sensor's limits under various environmental factors. A curve is then drawn showing the relationship between each environmental factor and the performance indicator, visually demonstrating the sensor's performance limits.

[0373] (2) Adaptability assessment

[0374] Evaluate the adaptability of sensors in different application scenarios, including response speed to environmental changes, recovery ability, and stability under extreme conditions.

[0375] 1) Resilience Assessment

[0376] Evaluate the sensor's ability to recover after environmental changes to ensure it can return to baseline performance or close to it:

[0377]

[0378] Among them, A recoveris the performance index after recovery, A baseline is the baseline performance indicator, and R represents the recovery capability of the sensor.

[0379] 2) Stability evaluation under extreme conditions

[0380] Evaluate the stability of the sensor after extreme conditions and characterize the ability of the sensor to not undergo drastic changes in performance under extreme conditions:

[0381]

[0382] Among them, σ extreme is the standard deviation of the output signal under extreme conditions, μ extreme is the mean value of the output signal under extreme conditions, S extreme Indicates the coefficient of variation of the signal under extreme conditions.

[0383] (3) Safety margin analysis

[0384] Based on the performance limit and adaptability evaluation results, the sensor's safety margin is analyzed to ensure that it has sufficient reliability and stability in actual applications. The safety margin is defined as the gap between the sensor's performance under extreme environmental conditions and the actual application requirements:

[0385]

[0386] Among them, M is the safety margin, A limit is the performance limit, A threshold is the application requirement threshold.

[0387] In this embodiment, after generating the reliability evaluation result of the sensor based on the contribution and the multi-scale performance degradation model in step 4, the method further includes:

[0388] Optimizing the sensor based on the reliability evaluation result of the sensor, and continuing to perform the application reliability evaluation method of the power micro sensor on the optimized sensor to generate a reliability optimization evaluation result;

[0389] Analyze the difference between the reliability optimization evaluation result and the reliability evaluation result, and determine whether to optimize the application reliability evaluation method of the power microsensor based on the analysis result.

[0390] like Figure 7 As shown, Figure 7 The reliability evaluation and optimization feedback diagram of the reliability evaluation method of the power micro sensor of the present invention is shown below. Figure 7 The reliability evaluation of the present invention is described in detail.

[0391] (1) Feedback mechanism

[0392] Establish a feedback mechanism during the evaluation process, and adjust the evaluation plan and methods in a timely manner based on the problems and deficiencies found in the actual test: collect problems encountered during the evaluation process, analyze the causes of the problems, and develop solutions; adjust the test steps and methods based on the results of the problem analysis to ensure that the adjusted plan can solve the existing problems and improve the accuracy of the evaluation.

[0393] (2) Database

[0394] Establish a sensor performance test database to accumulate test data of different sensors under various environmental conditions to support subsequent model optimization and reliability prediction: design the database table structure to cover sensor model, test environment parameters, performance index data, test time and other information.

[0395] (3) Model optimization

[0396] Based on the rich data in the database, we continuously optimize the degradation model and coupling effect model to improve the accuracy and predictive ability of the evaluation method: use the new test data in the database to verify the existing degradation model and coupling effect model. According to the verification results, adjust the model parameters to improve the model's fit and prediction accuracy; expand the scope of application of the model according to actual application needs; apply the optimized model to new evaluation projects and collect application feedback.

[0397] (4) Standardized processes

[0398] Develop standardized processes for evaluation methods to ensure consistency and repeatability across different testers and laboratories, and improve the reliability of evaluation results: Prepare detailed evaluation process documentation covering various test steps, operating specifications, data processing methods, etc.; regularly review the evaluation process to identify deficiencies and areas for improvement.

[0399] In a specific embodiment, this embodiment provides a reliability evaluation of a current sensor in a high temperature and high humidity environment, the details of which include:

[0400] (1) Evaluation preparation

[0401] 1) Performance requirements

[0402] The sensor needs to maintain a measurement accuracy of ±1% under high temperature and high humidity conditions

[0403] Response time ≤ 100ms

[0404] Anti-electromagnetic interference capability ≥1% / V / m.

[0405] 2) Environmental factors

[0406] Temperature: -40°C to +150°C

[0407] Humidity: 10% RH to 95% RH

[0408] Electromagnetic interference (EMI): Electric field interference 10V / m to 100V / m, magnetic field interference 10A / m to 100A / m Mechanical vibration and shock: Vibration frequency 10Hz to 100Hz, acceleration 0.1g to 2g

[0409] Special environment: no significant chemical environment, little impact from light and radiation

[0410] 3) Evaluation scale

[0411] The stability and real-time performance of sensor data transmission;

[0412] The coupling relationship between the sensor's internal circuit and micromechanical structure;

[0413] The oxidation state of the micromechanical structure was observed by scanning electron microscopy (SEM).

[0414] (2) Evaluation steps

[0415] 1) Environmental condition simulation and control

[0416] A temperature and humidity chamber was used to maintain the temperature at 150°C and the humidity at 95% RH. EMC testing equipment was used to generate a 50V / m electric field and a 50A / m magnetic field. A vibration table was used to apply 10Hz, 2g vibration.

[0417] 2) Multi-dimensional performance testing

[0418] Basic performance indicators are tested under conditions of 25°C, 50% RH, no EMI, and no vibration.

[0419] Gradually increase the temperature to 150°C and record changes in performance. Gradually increase the humidity to 95% RH and record changes in performance. Apply a 50V / m electric field and a 50A / m magnetic field and record changes in performance. Apply 10Hz, 2g vibration and record changes in performance.

[0420] At the same time, 150°C, 95% RH, 50V / m EMI and 10Hz, 10g vibration were applied to conduct comprehensive performance tests.

[0421] Operate under the combined environment for 1000 hours, recording performance degradation regularly.

[0422] 3) Data collection and analysis

[0423] The 3σ principle was used to remove outliers and perform error correction.

[0424] Fit the exponential decay model to analyze degradation trends and predict long-term performance changes.

[0425] (3) Reliability assessment and reporting

[0426] 1) Reliability index calculation

[0427] Based on the test data, calculate the failure rate and MTBF of the sensor in the combined environment.

[0428] The multivariate linear regression model showed that the interaction between high temperature and high humidity significantly affected the measurement accuracy (β T，RH =0.04% / ℃·%RH).

[0429] Contribution rate analysis: temperature contribution rate is 40%, humidity contribution rate is 35%, EMI contribution rate is 15%, and vibration contribution rate is 10%.

[0430] According to the expert evaluation method, the comprehensive performance score S total =85, rated as “good”.

[0431] 2) Performance limit determination

[0432] At 150°C and 95% RH, the sensor measurement accuracy drops to 1%, which meets the performance threshold, and the safety margin M = 0%.

[0433] 3) Risk Assessment

[0434] The main failure modes were identified as oxidation of micromechanical structures and drift of circuit components caused by high temperature. Failure mode and effects analysis was applied with RPN=280, where severity was 8, probability of occurrence was 7, and difficulty of detection was 5.

[0435] 4) Optimization and iteration

[0436] Select materials that are more resistant to high temperatures to reduce oxidation of the micromechanical structure. Optimize the design of the micromechanical structure to improve its stability under high temperature and high humidity conditions, thereby increasing the safety margin.

[0437] In a specific embodiment, this embodiment provides a reliability evaluation of a voltage sensor in a high vibration environment, the details of which include:

[0438] (1) Evaluation preparation

[0439] 1) Performance requirements

[0440] The sensor must maintain a voltage measurement accuracy of ±0.5% under 15g vibration acceleration

[0441] Response time ≤50ms

[0442] Anti-electromagnetic interference capability ≥1% / V / m

[0443] 2) Environmental factors

[0444] Temperature: -20℃ to +70℃

[0445] Humidity: 20% RH to 90% RH

[0446] Electromagnetic interference: Electric field interference 5V / m to 80V / m, magnetic field interference 5A / m to 80A / m

[0447] Mechanical vibration and shock: vibration frequency 5Hz to 200Hz, acceleration 0.2g to 15g

[0448] Special environment: exposed to sand and dust environment, no significant chemical corrosion

[0449] 3) Evaluation scale

[0450] The stability and real-time performance of sensor data transmission;

[0451] The coupling relationship between the sensor's internal circuit and micromechanical structure;

[0452] Transmission electron microscopy (TEM) was used to observe the microcracks in the circuit caused by vibration.

[0453] (2) Evaluation steps

[0454] 1) Environmental condition simulation and control

[0455] A six-degree-of-freedom vibration table was used to apply 200Hz, 15g vibration and shock. A gas generator was used to simulate a dusty environment with a dust concentration of D = 50mg / m 3 Use EMC test equipment to apply 40V / m electric field interference and 40A / m magnetic field interference.

[0456] 2) Multi-dimensional performance testing

[0457] Basic performance indicators are tested under conditions of 25°C, 50% RH, no EMI, and no vibration.

[0458] Apply 200Hz, 15g vibration and record the change in measurement accuracy. Apply 40V / m electric field interference and 40A / m magnetic field interference and record the change in measurement accuracy.

[0459] Simultaneously apply 15g vibration, 40V / m EMI and 50mg / m 3 Dust, conduct comprehensive performance test.

[0460] Operate under the combined environment for 1500 hours, recording performance degradation regularly.

[0461] 3) Data collection and analysis

[0462] The boxplot method was used to remove outliers and perform error correction.

[0463] The inverse power law model is applied to analyze degradation trends and predict long-term performance changes.

[0464] (3) Reliability assessment and reporting

[0465] 1) Reliability index calculation

[0466] Based on the test data, calculate the failure rate and MTBF of the sensor in the combined environment.

[0467] The multivariate linear regression model shows that the interaction between high vibration and high EMI significantly affects the measurement accuracy (β V，EMI =0.05% / g·V / m).

[0468] Contribution rate analysis: vibration contribution rate 50%, EMI contribution rate 30%, and dust contribution rate 20%.

[0469] According to the expert evaluation method, the comprehensive performance score S total =78, rated as “medium”.

[0470] 2) Performance limit determination

[0471] Under 15g vibration acceleration, the sensor measurement accuracy drops to 0.5%, which meets the performance threshold, and the safety margin M = 0%.

[0472] 3) Risk Assessment

[0473] The main failure modes were identified as micro cracks in the circuit and dust particle intrusion caused by high vibration. The Failure Mode and Effects Analysis (RMEA) RPN was 252, with a severity of 7, a probability of occurrence of 6, and a detection difficulty of 6.

[0474] 4) Optimization and iteration

[0475] The anti-vibration design and optimized circuit layout reduce the occurrence of micro-cracks in the circuit. The enhanced dust-proof packaging design prevents dust particles from invading the circuit, thereby improving the safety margin.

[0476] In a specific embodiment, this embodiment provides a reliability evaluation of a pressure sensor in a high humidity environment, the details of which include:

[0477] (1) Evaluation preparation

[0478] 1) Performance requirements

[0479] The sensor needs to maintain a pressure measurement accuracy of ±0.3% in a high humidity environment of 98% RH

[0480] Anti-EMI capability ≥1% / V / m

[0481] 2) Environmental factors

[0482] Temperature: -10℃ to +70℃

[0483] Humidity: 40% RH to 98% RH

[0484] Electromagnetic interference (EMI): Electric field interference 15V / m to 90V / m, magnetic field interference 15A / m to 90A / m Mechanical vibration and shock: Vibration frequency 15Hz to 120Hz, acceleration 0.3g to 1g

[0485] Special environment: no significant chemical corrosion, appropriate dust particles

[0486] 3) Evaluation scale

[0487] The stability and real-time performance of sensor data transmission;

[0488] Humidity coupling analysis between the sensor's internal circuit and micromechanical structure;

[0489] The corrosion of the micromechanical structure under high humidity was observed by SEM.

[0490] (2) Evaluation steps

[0491] 1) Environmental condition simulation and control

[0492] A high-humidity test chamber was used to maintain humidity at 98% RH and temperature at 70°C. EMC testing equipment was used to apply an electric field of 75V / m and a magnetic field of 75A / m. A vibration table was used to apply vibration at 120Hz and 1g.

[0493] 2) Multi-dimensional performance testing

[0494] Basic performance indicators are tested under conditions of 25°C, 50% RH, no EMI, and no vibration.

[0495] Adjust to 98% RH and record changes in measurement performance. Apply 75V / m electric field interference and 75A / m magnetic field interference and record changes in measurement performance. Apply 120Hz, 1g vibration and record changes in measurement performance.

[0496] At the same time, 70°C, 98% RH, 75V / m EMI, and 1g vibration were applied to conduct comprehensive performance tests.

[0497] Operate under the combined environment for 1200 hours, recording performance degradation regularly.

[0498] 3) Data collection and analysis

[0499] The 3σ principle was used to remove outliers and perform error correction.

[0500] Apply linear cumulative model to analyze degradation trends and predict long-term performance changes.

[0501] (3) Reliability assessment and reporting

[0502] 1) Reliability index calculation

[0503] Based on the test data, calculate the failure rate and MTBF of the sensor in the combined environment.

[0504] The multivariate linear regression model showed that the interaction between high humidity and high EMI significantly affected the measurement accuracy (β V，EMI =0.04% / RH·V / m).

[0505] Contribution rate analysis: humidity contribution rate 50%, EMI contribution rate 30%, vibration contribution rate 20%.

[0506] According to the expert evaluation method, the comprehensive performance score S total =82, rated as “good”.

[0507] 2) Performance limit determination

[0508] At 70°C, 98% RH, 75V / m EMI, and 1g vibration, the sensor measurement accuracy drops to 0.3%, meeting the performance threshold, with a safety margin of M = 0%.

[0509] 3) Risk Assessment

[0510] The main failure modes were identified as microcracks in the circuit and dust particle intrusion caused by high vibration. The Failure Mode and Effects Analysis (RMEA) RPN was 192, with a severity of 6, a probability of occurrence of 8, and a detection difficulty of 4.

[0511] 4) Optimization and iteration

[0512] Anti-corrosion coatings are used to prevent corrosion of micromechanical structures in high-humidity environments. Optimized circuit design enhances EMI resistance, reduces the risk of short circuits, and improves safety margins.

[0513] In one possible embodiment, this embodiment provides a reliability evaluation of a temperature sensor in a coastal high salt fog environment, the details of which include:

[0514] (1) Evaluation preparation

[0515] 1) Performance requirements

[0516] The sensor needs to be at 50mg / m 3 Maintains ±0.25% temperature measurement accuracy under salt spray concentration

[0517] Strong resistance to salt spray corrosion, ensuring long-term stability under extreme conditions.

[0518] 2) Environmental factors

[0519] Temperature: 0℃ to +80℃

[0520] Humidity: 50% RH to 98% RH

[0521] Electromagnetic interference (EMI): Electric field interference 10V / m to 100V / m, magnetic field interference 10A / m to 100A / m Mechanical vibration and shock: Vibration frequency 10Hz to 150Hz, acceleration 0.2g to 1g

[0522] Special environment: high salt fog environment, salt fog concentration S = 5mg / m 3 Up to 50 mg / m 3

[0523] 3) Evaluation scale

[0524] The stability and real-time performance of sensor data transmission;

[0525] Coupling analysis of the sensor's internal anti-corrosion coating and micromechanical structure;

[0526] The corrosion of the micromechanical structure in salt spray environment was observed by SEM.

[0527] (2) Evaluation steps

[0528] 1) Environmental condition simulation and control

[0529] A salt spray test chamber was used to simulate different salt spray concentrations. A high-temperature, high-humidity test chamber was used to maintain the temperature at 80°C and the humidity at 98% RH. An EMC test device was used to apply an 80V / m electric field and an 80A / m magnetic field. A vibration table was used to apply 150Hz, 1g vibration.

[0530] 2) Multi-dimensional performance testing

[0531] Basic performance indicators are tested under 25°C, 60% RH, no EMI, and no vibration conditions.

[0532] Adjust to 50 mg / m 3 Apply salt spray and record changes in measurement performance. Adjust to 80°C, 98% RH and record changes in measurement performance. Apply 80V / m electric field interference and 80A / m magnetic field interference and record changes in measurement performance. Apply 150Hz, 1g vibration and record changes in measurement performance.

[0533] Simultaneously apply 80°C, 98% RH, 80V / m, 1g vibration and 50mg / m 3 Salt spray, conduct comprehensive performance test.

[0534] Run under the combined environment for 1800 hours and record performance degradation.

[0535] 3) Data collection and analysis

[0536] The 3σ principle was used to remove outliers and perform error correction.

[0537] Apply the Arrhenius model to analyze degradation trends and predict long-term performance changes.

[0538] (3) Reliability assessment and reporting

[0539] 1) Reliability index calculation

[0540] Calculate the failure rate and MTBF of the sensor under the combined environment based on the test data

[0541] The multivariate linear regression model shows that the interaction between high salt spray and high EMI significantly affects the measurement accuracy (β T，EMI =0.05% / mg / m 3 ·V / m).

[0542] Contribution rate analysis: salt spray contribution rate 50%, temperature contribution rate 30%, EMI contribution rate 15%, and vibration contribution rate 5%.

[0543] According to the expert evaluation method, the comprehensive performance score S total =88, rated as “good”.

[0544] 2) Performance limit determination

[0545] At 80°C, 98% RH, 80V / m EMI, 10g vibration and 50mg / m 3 The sensor's measurement accuracy under salt fog drops to 0.25%, which meets the performance threshold, and the safety margin M = 0%.

[0546] 3) Risk Assessment

[0547] The main failure modes are corrosion of micromechanical structure materials and circuit short circuit caused by high salt spray. The Failure Mode and Effects Analysis RPN=252, with severity 7, probability of occurrence 6, and detection difficulty 6.

[0548] 4) Optimization and iteration

[0549] A highly salt-spray-resistant coating is used to prevent corrosion of micromechanical structures. Optimized circuit design enhances EMI resistance and reduces the risk of short circuits, thereby increasing safety margins.

[0550] The reliability evaluation method proposed in this paper is based on laboratory equipment and an engineering test environment, combined with advanced data analysis and modeling techniques. It has high feasibility and promotional value. Through systematic reliability evaluation, it can effectively improve the engineering application level of power microsensors, ensure their stable operation in complex environments, and provide strong support for the intelligent development of power systems.

[0551] 1. This invention fully considers the impact of various environmental stresses on the performance of power microsensors in power system sites. In actual power system operation and maintenance and device selection, sensors often face the coupled effects of multiple stresses, such as high and low temperatures, high and low humidity, strong electromagnetic interference, mechanical vibration, chemical corrosion, high-pressure gas shock, and dust pollution. Traditional methods often only conduct accelerated life or reliability assessments based on a single environmental parameter, ignoring the complexity of multiple factors in real-world operating conditions. This invention, based on fundamental principles, incorporates factors such as temperature, humidity, electromagnetic interference, vibration and shock, and the chemical environment into the evaluation system. Particularly under extreme conditions such as high temperature and humidity, high electromagnetic interference, and high corrosion, this invention, by capturing and analyzing long-term sensor operating data, can effectively reveal the degradation patterns of sensor performance under the influence of multiple fields. For example, in an environment coupled with high temperature and chemical corrosion, packaging material aging, circuit component oxidation, and microcrack propagation accelerate over time. Traditional single-factor testing cannot fully simulate this complex failure path. However, this invention captures these degradation characteristics through multi-dimensional environmental loading and multi-stage testing. Therefore, at the application level, the comprehensive coverage of the present invention ensures that practical reliability test results can be obtained in various complex scenarios of the power system (such as substations, distribution rooms, high-altitude cold areas, seaside salt spray areas, chemical plant areas, etc.), fundamentally improving the applicability and safety margin of sensors in real working conditions.

[0552] 2. Building on traditional accuracy and stability testing, this invention further incorporates multiple indicators, including response time, repeatability, linearity, sensitivity, noise level, power consumption, availability, and electromagnetic interference resistance, forming a multi-dimensional performance evaluation matrix. Through quantitative formulas (such as relative error calculation for measurement accuracy, standard deviation assessment for noise level, and MTBF / MTTR metric for availability), evaluation results can be quantified and accurately compared. More importantly, under long-term operation and multi-field coupling, sensors may experience more than just simple drift in measurement accuracy. They may also exhibit a variety of phenomena, including response hysteresis (longer response time), decreased repeatability (increased output fluctuations), degraded linearity, increased noise floor, and reduced overall availability. Measuring sensor reliability solely based on measurement accuracy or linearity is no longer sufficient to meet the needs of high-level control and fault prediction in new power systems. This invention incorporates these key indicators into a unified evaluation and analysis framework, enabling a multi-dimensional overview of sensor operating conditions within a single testing process. This allows for precise identification of weak indicators that impact measurement quality and lifespan, providing more targeted data support for design optimization and engineering selection.

[0553] 3. The present invention is not limited to viewing the sensor as a "black box" to observe the simple correspondence between input and output, but analyzes it from three scales: macro (system-level integration and operation), meso (module-level coordination), and micro (material level). The macro level focuses on the operating performance of the sensor in the complex network of the power system, such as the coordination with the communication network or the upper-level dispatching system and the quality of information transmission; the meso level focuses on the coupling between the core modules inside the sensor (such as the mutual influence between the micromechanical structure and the signal conditioning circuit), including the temperature drift of key components, vibration fatigue, signal conditioning amplification drift, digital quantization error, etc.; the micro level goes deeper into materials and processes, such as the corrosion of the packaged metal, cracks in the micromechanical structure under high stress, delamination or void formation on the surface of the sensitive element, and other physical mechanisms. It is through the data penetration of these three scales that the present invention analyzes the failure mode from the outside to the inside and obtains a complete link explanation of the sensor performance degradation. In applications, this cross-scale analysis can, on the one hand, help designers find weak links where sensors are prone to failure, and on the other hand, can be used to guide on-site operation and maintenance and fault diagnosis, fundamentally enhancing the scientific content and guiding value of the evaluation method.

[0554] 4. In the data acquisition and modeling link, the present invention not only includes the time series of the sensor output itself, but also includes real-time records of environmental stress parameters (temperature, humidity, electromagnetic interference intensity, gas concentration, etc.), as well as necessary internal monitoring data of components. The quantitative correspondence between environmental stress and sensor performance degradation can be effectively analyzed. In the selection of degradation models, the present invention integrates a variety of curve fitting methods such as the inverse power law model, the Arrhenius model, exponential decay, and logarithmic linear. Traditional evaluations usually only use a certain accelerated life model, which not only lacks flexibility for multiple failure modes, but also has difficulty in taking into account different failure mechanisms. The multivariate degradation model framework of the present invention can select the most appropriate formula for fitting and prediction for different failure modes (such as accelerated aging caused by high-temperature oxidation, strain aggregation caused by vibration fatigue, material erosion caused by chemical corrosion, etc.), and finally obtain a comprehensive reliability index through regression analysis and partial least squares (PLS). Not only does it ensure the fitting degree of the model, but it also ensures the adaptability of the prediction results to the actual working conditions, providing a basis for subsequent engineering decisions and sensor evolution design.

[0555] 5. The present invention highlights the concept of giving equal importance to "combined working condition testing" and "long-term operation observation": applying multiple environmental stresses (such as high temperature + high humidity + high EMI + vibration) simultaneously in the laboratory to simulate the most stringent or most representative actual scenarios, and observing the performance decay rate and failure characteristics of the sensor under these coupling conditions; during a long period of continuous operation (such as hundreds of hours or even thousands of hours), regularly record the evolution trajectory of various performance indicators of the sensor to check for potential chronic degradation or sudden failure. Based on this dual testing method, the true service life and risk points of the sensor in a complex power environment can be restored as much as possible. For example, some packaging defects will only appear after long-term immersion in high temperature and high humidity, while the oxidation and micro-arc discharge phenomena of some connectors will not appear until hundreds of hours after the superposition of EMI. The multi-stage, multi-angle observation mode proposed by the present invention is precisely to solve the problem of identifying such "slow-heating" or "combination effect" failures, and truly ensure the accuracy and comprehensiveness of the evaluation results.

[0556] 6. The present invention is not only for the one-time measurement and registration of the reliability index of the sensor, but also emphasizes the dynamic feedback mechanism of "evaluation-design-re-evaluation". Specifically, after the evaluation is completed, based on the deep understanding of failure modes, degradation mechanisms and environmental coupling, the R&D and design work can determine which links are most prone to problems. The optimized new version of the sensor is then put back into the evaluation system of the present invention for repeated testing in multiple environments and multiple working conditions. By comparing and observing the changes in the reliability index before and after the improvement, the effectiveness of the optimization measures can be verified. This cyclical process enables the present invention to not only meet the detection needs of existing sensors, but also become an important tool and technical platform for the design department to continuously iterate and improve product reliability, effectively promoting the technological upgrading and industrial progress of power micro sensors.

[0557] In summary, the present invention combines multiple disciplines such as reliability engineering theory, micromechanical failure theory, material mechanics, and electromagnetic compatibility analysis, and realizes the application reliability evaluation of power microsensors through systematic model construction and experimental verification. The present invention not only meets the requirements of traditional accelerated life testing, but also makes a lot of expansion in degradation models, coupling effect research, and multi-scale penetration. The quantitative analysis of multi-scenario coupling effects (such as temperature × electromagnetic interference, humidity × corrosion, etc.) enables the present invention to more accurately characterize the damage path of real complex environments to sensors, and realize early prediction of fault risks in long-term monitoring and online diagnosis.

[0558] Example 2:

[0559] The present invention based on the same inventive concept also provides an application reliability evaluation system for power micro sensors, such as Figure 8 Shown, including:

[0560] A testing module, configured to apply target combined environmental factors to the sensor based on environmental data of a target application scenario, and to perform a dynamic test on the sensor under the target combined environmental factors;

[0561] An acquisition module, configured to acquire preset performance index data of the sensor under dynamic testing based on preset performance indexes;

[0562] a quantification module, configured to generate performance degradation information of the sensor using a pre-built multi-scale performance degradation model based on the preset performance indicator data, and quantify the contribution of each environmental factor in the target combination of environmental factors to the degradation of the sensor based on the performance degradation information;

[0563] A generating module is used to generate a reliability evaluation result of the sensor based on the contribution and the multi-scale performance degradation model.

[0564] Preferably, the quantification module generates the performance degradation information of the sensor based on the preset performance indicator data using a pre-built multi-scale performance degradation model, including:

[0565] Analyzing the failure mode of the sensor from a macroscopic scale, a mesoscopic scale, and a microscopic scale based on the preset performance indicator data;

[0566] Determining a target failure mode corresponding to the sensor based on the analysis result, and determining a corresponding target multi-scale performance degradation model from a plurality of pre-built multi-scale performance degradation models based on the target failure mode;

[0567] The preset performance indicator data is input into the multi-scale performance degradation model to generate performance degradation information of the sensor that changes over time.

[0568] Preferably, the quantification module quantifies the contribution of each environmental factor in the target combined environmental factor to the sensor degradation based on the performance degradation information, including:

[0569] Calculating the partial derivative of the key performance degradation indicator of the sensor with respect to the quantized value of each environmental factor corresponding to the target combined environmental factor in the performance degradation information to obtain a sensitivity coefficient of each environmental factor in the target combined environmental factor;

[0570] The contribution of each environmental factor to the sensor degradation is calculated based on the quantified value of each environmental factor and the sensitivity coefficient.

[0571] Preferably, the calculation formula of the contribution in the quantification module is as follows:

[0572]

[0573] Among them, Si is the sensitivity coefficient of the i-th environmental factor, X i is the quantitative value of the i-th environmental factor, CR i is the contribution rate of the i-th environmental factor, Represents the weighted sum of j environmental factors.

[0574] Preferably, the generating module is specifically used for:

[0575] Predicting the service life of the sensor under the target combination of factors based on the multi-scale performance degradation model;

[0576] Calculating the failure rate of the sensor at a target time point based on the predicted service life;

[0577] A reliability evaluation result of the sensor is generated based on the predicted service life, the failure rate, the multi-scale degradation model, the target combined environmental factors, and the preset performance indicator data.

[0578] Preferably, the testing module performs a dynamic test on the sensor under the target combined environmental factors, including:

[0579] Adjusting the initial environmental factor value of each environmental factor in the target combined environmental factor to the environmental factor limit value corresponding to the sensor;

[0580] Based on the environmental factor limit value and the environmental factor initial value of each environmental factor, the sensor is dynamically tested according to the preset limit operation time.

[0581] Preferably, the target combination environmental factors in the test module include at least two of the following: temperature, humidity, mechanical vibration and shock, electromagnetic interference, chemical environment, light or radiation.

[0582] Preferably, the preset performance indicators in the test module include but are not limited to: measurement accuracy, response time, repeatability, linearity, sensitivity, noise, power consumption, availability or anti-electromagnetic interference.

[0583] Preferably, the system further comprises:

[0584] an optimization module, configured to optimize the sensor based on the reliability evaluation result of the sensor, and continue to execute the application reliability evaluation method of the power micro sensor on the optimized sensor to generate a reliability optimization evaluation result;

[0585] Analyze the difference between the reliability optimization evaluation result and the reliability evaluation result, and determine whether to optimize the application reliability evaluation method of the power microsensor based on the analysis result.

[0586] Example 3

[0587] like Figure 9 As shown, the present invention also provides an electronic device, which may be a computer, a single-chip microcomputer, a smart mobile device, or the like. The electronic device in this embodiment may include a processor, a memory, a transceiver component, and the like. The memory, processor, and transceiver component are connected via a bus; the memory may be used to store an execution program, which may include instructions; and the processor may be used to execute the instructions stored in the memory. The memory may also be used to store data, which may be accessed and / or modified during the execution of the instructions.

[0588] The processor may be a central processing unit (CPU), or it may be other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. It is the computing core and control core of the terminal, which is suitable for implementing one or more instructions, specifically suitable for loading and executing one or more instructions in the storage medium to implement the corresponding method flow or corresponding function, so as to realize the steps of the application reliability evaluation method of an electric micro sensor in the above embodiment.

[0589] Example 4

[0590] Based on the same inventive concept, the present invention also provides a readable storage medium, specifically an electronic device readable storage medium (Memory), which is a memory device in the electronic device for storing programs and data. It can be understood that the storage medium here can include both the built-in storage medium in the electronic device and the extended storage medium supported by the electronic device. The storage medium provides a storage space, which stores the operating system of the terminal. In addition, one or more instructions suitable for being loaded and executed by the processor are also stored in the storage space. These instructions can be one or more execution programs (including program codes). It should be noted that the storage medium here can be a high-speed RAM memory or a non-volatile memory, such as at least one disk memory. The processor loads and executes one or more instructions stored in the storage medium, which can implement the steps of the application reliability evaluation method of an electric micro sensor in the above embodiment.

[0591] It will be understood by those skilled in the art that embodiments of the present invention may be provided as methods, systems, or computer program products. Thus, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware. Furthermore, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to magnetic disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0592] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowcharts and / or block diagrams, as well as combinations of processes and / or blocks in the flowcharts and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowcharts and / or block diagrams. Figure 1 a process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0593] These computer program instructions may also be stored in a computer readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 a process or multiple processes and / or boxes Figure 1 The function specified in one or more boxes.

[0594] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operational steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing the instructions executed on the computer or other programmable device for implementing the process. Figure 1 a process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0595] The above are merely embodiments of the present invention and are not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention are included in the scope of the claims of the present invention to be approved.

Claims

1. A method for evaluating the application reliability of a power microsensor, characterized in that: include: Applying target combined environmental factors to the sensor based on environmental data of the target application scenario, and performing dynamic testing on the sensor under the target combined environmental factors; Collecting preset performance indicator data of the sensor under dynamic testing based on preset performance indicators; Generating performance degradation information of the sensor using a pre-built multi-scale performance degradation model based on the preset performance indicator data, and quantifying the contribution of each environmental factor in the target combined environmental factor to the degradation of the sensor based on the performance degradation information; A reliability evaluation result of the sensor is generated based on the contribution and the multi-scale performance degradation model.

2. The method according to claim 1, characterized in that Generating the performance degradation information of the sensor based on the preset performance indicator data using a pre-built multi-scale performance degradation model includes: Analyzing the failure mode of the sensor from a macroscopic scale, a mesoscopic scale, and a microscopic scale based on the preset performance indicator data; Determining a target failure mode corresponding to the sensor based on the analysis result, and determining a corresponding target multi-scale performance degradation model from a plurality of pre-built multi-scale performance degradation models based on the target failure mode; The preset performance indicator data is input into the multi-scale performance degradation model to generate performance degradation information of the sensor that changes over time.

3. The method according to claim 1, characterized in that The quantifying, based on the performance degradation information, the contribution of each environmental factor in the target combined environmental factor to the sensor degradation includes: Calculating the partial derivative of the key performance degradation indicator of the sensor with respect to the quantized value of each environmental factor corresponding to the target combined environmental factor in the performance degradation information to obtain a sensitivity coefficient of each environmental factor in the target combined environmental factor; The contribution of each environmental factor to the sensor degradation is calculated based on the quantified value of each environmental factor and the sensitivity coefficient.

4. The method according to claim 3, characterized in that The calculation formula of the contribution is as follows: Among them, S i is the sensitivity coefficient of the i-th environmental factor, X i is the quantitative value of the i-th environmental factor, CR i is the contribution rate of the i-th environmental factor, Represents the weighted sum of j environmental factors.

5. The method according to claim 1, wherein Generating a reliability evaluation result of the sensor based on the contribution and the multi-scale performance degradation model includes: Predicting the service life of the sensor under the target combination of factors based on the multi-scale performance degradation model; Calculating the failure rate of the sensor at a target time point based on the predicted service life; A reliability evaluation result of the sensor is generated based on the predicted service life, the failure rate, the multi-scale degradation model, the target combined environmental factors, and the preset performance indicator data.

6. The method according to claim 1, characterized in that The dynamically testing the sensor under the target combined environmental factors includes: Adjusting the initial environmental factor value of each environmental factor in the target combined environmental factor to the environmental factor limit value corresponding to the sensor; Based on the environmental factor limit value and the environmental factor initial value of each environmental factor, the sensor is dynamically tested according to the preset limit operation time.

7. The method according to claim 1, characterized in that The target combination of environmental factors includes at least two of the following: temperature, humidity, mechanical vibration and shock, electromagnetic interference, chemical environment, light or radiation.

8. The method according to claim 1, characterized in that The preset performance indicators include but are not limited to: measurement accuracy, response time, repeatability, linearity, sensitivity, noise, power consumption, availability or anti-electromagnetic interference.

9. The method according to claim 1, characterized in that After generating the reliability evaluation result of the sensor based on the contribution and the multi-scale performance degradation model, the method further includes: Optimizing the sensor based on the reliability evaluation result of the sensor, and continuing to perform the application reliability evaluation method of the power micro sensor on the optimized sensor to generate a reliability optimization evaluation result; Analyze the difference between the reliability optimization evaluation result and the reliability evaluation result, and determine whether to optimize the application reliability evaluation method of the power microsensor based on the analysis result.

10. An application reliability evaluation system for power microsensors, characterized in that: include: A testing module, configured to apply target combined environmental factors to the sensor based on environmental data of a target application scenario, and to perform a dynamic test on the sensor under the target combined environmental factors; An acquisition module, configured to acquire preset performance index data of the sensor under dynamic testing based on preset performance indexes; a quantification module, configured to generate performance degradation information of the sensor using a pre-built multi-scale performance degradation model based on the preset performance indicator data, and quantify the contribution of each environmental factor in the target combination of environmental factors to the degradation of the sensor based on the performance degradation information; A generating module is used to generate a reliability evaluation result of the sensor based on the contribution and the multi-scale performance degradation model.

11. An electronic device, characterized in that: include: at least one processor and memory; The memory and the processor are connected via a bus; The memory is used to store one or more programs; When the one or more programs are executed by the at least one processor, the application reliability evaluation method of the power microsensor according to any one of claims 1 to 9 is implemented.

12. A readable storage medium, characterized in that: An execution program is stored thereon, and when the execution program is executed, the application reliability evaluation method of the power microsensor according to any one of claims 1 to 9 is implemented.

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