Sensor multi-scale through reliability evaluation method and system

Abstract

The invention discloses a sensor multi-scale through reliability evaluation method and system, and the method comprises the steps: building a material-level model based on the key parameters, failure modes and failure criteria of a sensor material, and calibrating the parameters of the material-level model through an acceleration test or a database; external environment stress is mapped to key parts of the sensor through a finite element or analysis method, and a device-level model is established based on structural features of a material-level model; establishing a system-level model based on the multi-stress coupling influence, the actual use scene of the sensor and the device-level model; obtaining failure data through a multi-stress coupling accelerated life test, and calibrating key parameters of the system-level model based on the failure data; on-line monitoring data of the sensor in operation are collected, and parameters of the system-level model are dynamically corrected based on the on-line monitoring data; and based on the corrected system-level model, evaluating the residual life or failure risk of the in-service sensor, and outputting a reliability evaluation result.

Description

Technical Field

[0001] This invention relates to the field of power sensing technology, and more specifically, to a method and system for multi-scale interconnected reliability assessment of sensors. Background Technology

[0002] With the development of smart grids, miniature sensors are widely deployed in various power equipment for real-time monitoring of parameters such as temperature, pressure, current, and electromagnetic fields. However, these sensors often operate in harsh environments with high voltage, electromagnetic radiation, and frequent temperature changes, directly impacting the safety and stability of the power system. Traditional methods for predicting the reliability of electronic products mainly fall into the following categories: one is based on empirical statistical models of failure data, calculating indicators such as the overall MTBF (Mean Time Between Failures) using component failure rate benchmarks and stress factors (π factors). For example, MIL-HDBK-217F provides environmental factors πE and quality factors πQ under different environments to adjust the benchmark failure rate. The IEC 61709 international standard provides stress models that convert the benchmark failure rate to actual operating conditions. However, these manual methods have significant shortcomings: their data largely comes from past device statistics, making it difficult to reflect the actual failure mechanisms of new sensors; and the calculations assume average environmental and component characteristics, lacking models for specific manufacturers and structures. They typically consider each stress factor individually, without deeply revealing the physical root causes of failure. Component failures are often caused by specific physical processes such as material aging, solder joint fatigue, and package cracking, but manual methods cannot distinguish these mechanisms or handle situations involving multiple stress couplings.

[0003] Another type is physical failure analysis methods, which emphasize building models based on failure mechanisms. Typical examples include thermal failure of semiconductor devices, mechanical fatigue failure using the Coffin-Manson relationship to describe the power-law relationship between strain and life, and the Peck model used in humid and hot environments. These models extract material parameters (such as activation energy Ea) through accelerated testing and then extrapolate them to predict life under actual conditions. However, single-mechanism models often assume that a single stress source dominates the failure, making it difficult to cover situations where multiple mechanisms act together under complex environments. For example, for packaged solder joints, load cycling may simultaneously accelerate their aging. In addition, different failure mechanisms may compete or be correlated (e.g., vibration exacerbates crack propagation, while temperature increases reduce material strength), making life prediction under multiple stresses even more complex.

[0004] In recent years, with the widespread application of MEMS sensors and fiber optic sensors in industry, researchers have begun to focus on reliability issues under multi-physics coupling. Literature indicates that in the past five or six years, many studies have only explored the failure of MEMS devices under single stress conditions, while systematic research on composite stresses remains lacking, and effective models are insufficient to evaluate device reliability under multiple harsh environments. A review by Peng et al. points out the urgent need to develop research on multi-field coupling failure modes and mechanisms, as well as corresponding reliability assessment models. For example, in the field of avionics, devices often experience vibration, shock, and high temperatures simultaneously, and the failure modes observed in high-accelerated life testing (HALT) differ significantly from those in single-stress testing. If models that allow each stress to operate independently are still used, it will be impossible to accurately predict the actual lifespan.

[0005] Furthermore, the impact of system-level factors on the reliability of micro-sensors is receiving increasing attention. While individual sensor tests in the laboratory may yield certain lifespan indicators, integration into power equipment systems introduces new failure factors such as installation stress, heat dissipation, and electromagnetic interference. For example, some on-board packaged MEMS accelerometers can withstand 20,000g acceleration in device-level impact tests, but when soldered to a PCB and fixed to a transformer casing for drop tests, the actual acceleration waveform transmitted to the sensor is amplified due to structural resonance, leading to premature device failure. Studies testing MEMS gyroscopes under on-board impact showed that approximately 79% of failures occurred in the package structure, with only 21% occurring in the silicon chip microstructure. This indicates that packaging and system integration are crucial to sensor reliability. Focusing solely on the sensor chip itself while ignoring the packaging reveals buffering or amplification effects during stress transmission, leading to overly optimistic or conservative assessments. Preliminary surveys indicate that the vast majority of MEMS sensor failures originate from the packaging (79%) rather than the sensor chip itself (21%). This indicates that when designing and evaluating reliability, sensor packaging and its interface with the system must be taken into account to achieve multi-scale integrated analysis; otherwise, the failure risk caused by packaging factors may be underestimated.

[0006] There are differences between field operating conditions and accelerated testing. Laboratory accelerated life tests can induce failure in a short time, but the stress levels often far exceed normal operating conditions. Accurately converting life data measured under accelerated stress to actual service conditions has always been a challenge in reliability engineering. The traditional approach is to use models for extrapolation, such as the Arrhenius formula to extrapolate high-temperature life to room temperature. However, when multiple stresses exist simultaneously, simple extrapolation may be inaccurate. Furthermore, in actual field conditions, sensors may experience random stress spectra rather than constant stress, making accelerated models potentially unsuitable. Therefore, in recent years, methods such as Bayesian updates have emerged to fuse field operating data with accelerated test data to improve prediction accuracy. For example, combining failure data from real-world scenarios with accelerated life test results and correcting the model through calibration factors can more accurately predict the product's field life. Bayesian-based methods fuse degradation test and field data, using the Wiener process and calibration factors to unify laboratory and field conditions, thereby improving the accuracy of reliability assessments.

[0007] In summary, current reliability assessment methods for power micro-sensors suffer from the following shortcomings: 1) A lack of unified cross-scale models; how material-level defects affect device failure and how device failure manifests at the system level lacks comprehensive research; 2) A lack of lifetime prediction methods under multi-stress coupling; the acceleration mechanism of multiple physical fields is unclear, and existing methods are often limited to single stresses; 3) Existing assessments are mostly open-loop offline analyses, lacking dynamic feedback from field data, making it difficult for prediction models to be corrected in a timely manner to adapt to actual conditions; 4) A lack of specialized reliability assessment frameworks for the harsh environments of the power industry; existing technologies or patents mostly focus on applications in general environments or other fields, which are inconsistent with the needs of power micro-sensors. For example, existing technology (CN104915558B) proposes a MEMS reliability assessment method that estimates MEMS reliability by calculating the failure rate of cantilever beams, chips, and packages and multiplying it by coefficients such as vibration and temperature, thereby reducing the need for extensive testing. However, this method still simply multiplies the factors without considering the interaction between stresses and the evolution of failure modes.

[0008] Therefore, there is a need to propose a new reliability evaluation method that can integrate multi-scale physical models and multi-source data to achieve accurate prediction and continuous improvement of the reliability of power micro-sensors under real operating conditions. Summary of the Invention

[0009] The present invention provides a method and system for multi-scale interconnect reliability assessment of sensors, in order to solve the problem of how to conduct multi-scale interconnect reliability assessment of sensors for power system applications.

[0010] To address the above problems, this invention provides a multi-scale interconnectivity reliability assessment method for sensors, characterized in that the method includes:

[0011] Based on the key parameters, failure modes, and failure criteria of sensor materials, a material-level model is established, and the parameters of the material-level model are calibrated through accelerated testing or database.

[0012] By using finite element or analytical methods, external environmental stress is mapped to the key components of the sensor, and a device-level model is established based on the structural features of the material-level model.

[0013] Based on the effects of multi-stress coupling, the actual application scenarios of sensors, and the device-level model, a system-level model is established.

[0014] Failure data is obtained through multi-stress coupling accelerated life testing, and key parameters of the system-level model are calibrated based on the failure data.

[0015] Collect online monitoring data from sensors during operation, and dynamically correct the parameters of the system-level model based on the online monitoring data;

[0016] Based on the corrected system-level model, the remaining lifespan or failure risk of in-service sensors is assessed, and reliability assessment results are output.

[0017] Preferably, the method further includes correcting the parameters of the device-level model and the system-level model:

[0018] Finite element or multiphysics simulation methods are used to analyze the coupling stress of the sensor under temperature, vibration, electromagnetic and humidity environments, obtain the stress amplification factor or attenuation factor of the key components of the sensor, and correct the parameters of the device-level model and system-level model by the amplification factor or the attenuation factor.

[0019] Preferably, the method further includes establishing a multi-stress coupling model; the multi-stress coupling model adopts the generalized Eyring equation or the competing failure model, and considers the interactive effects of temperature T, mechanical stress S1, electromagnetic stress S2, and humidity; wherein the γ·S1·S2 term is used to represent the coupling coefficient between stresses, and the coupling coefficient is obtained through multi-stress coupling accelerated test;

[0020] It also includes testing the system-level model using a multi-stress coupling model based on key stresses, wherein the multi-stress coupling model is:

[0021]

[0022] Where T is temperature, S1 and S2 are stresses, the γS1S2 term can represent the interaction term between stresses S1 and S2, L is life, C is a constant, B is a constant, α is the stress coefficient, and β is the stress coefficient.

[0023] Preferably, the system-level model includes a lifetime distribution model, in which lifetime distribution models are established for each major failure mechanism, and the mechanisms are combined using a method of parallel or competitive failure to obtain the overall lifetime distribution of the sensor; when there are multiple stress interactions, by correcting the lifetime parameters of each mechanism or introducing a synergistic factor, a lifetime prediction result that is closer to the actual working condition can be obtained.

[0024] Preferably, the multi-stress coupling accelerated life test includes:

[0025] Single stress constant test: Life test is conducted under single-factor conditions such as different temperatures or different vibration amplitudes to extract the parameters of the Arrhenius and Coffin-Manson basic models;

[0026] Multi-stress coupling accelerated test: The test life of the sensor is observed under the combined stress conditions of high temperature + vibration + humidity. If the test life is significantly lower than the single-factor predicted life, it indicates that there is a negative synergistic effect under the combined stress conditions. The coupling term coefficients of the system-level model are corrected based on the negative synergistic effect.

[0027] The stress level, sample size, and test cycle of multi-stress coupled accelerated life tests are designed to identify the main failure modes and more closely approximate reality.

[0028] Preferably, the test lifetime obtained from the multi-stress coupled accelerated life test is fitted with a Weibull distribution to extract characteristic lifetime and shape factor parameters; and the failure mechanism parameters or stress coefficients of the system-level model are corrected based on the deviation between the test results of the multi-stress coupled accelerated life test and the evaluation results of the system-level model.

[0029] Preferably, the online monitoring data includes sensor performance degradation curves, on-site environmental stress spectrum, and actual failure time; the online monitoring data is combined with the prior model in the system-level model to construct a likelihood function and use Bayes' theorem to update the parameters of the system-level model, thereby obtaining the posterior distribution of the system-level model; when the cumulative amount of the online monitoring data reaches a threshold, the parameter update process of the system-level model is repeated to achieve dynamic adaptive correction of the system-level model.

[0030] Preferably, when the predicted remaining lifespan of the in-service sensor is lower than a preset lifespan threshold or the performance degradation rate exceeds a preset rate threshold, an early warning signal is triggered, and maintenance or replacement recommendations are generated; at the same time, the failure symptom information is recorded and fed back to the system-level model.

[0031] According to another aspect of the present invention, the present invention provides a sensor multi-scale interconnect reliability assessment system, characterized in that the system comprises:

[0032] The first establishment unit is used to establish a material-level model based on the key parameters, failure modes and failure criteria of the sensor material, and to calibrate the parameters of the material-level model through accelerated testing or database.

[0033] The second building unit is used to map external environmental stress to key components of the sensor using finite element or analytical methods, and to build a device-level model based on the structural features of the material-level model.

[0034] The third establishment unit is used to establish a system-level model based on the effects of multi-stress coupling, the actual use scenarios of the sensor, and the device-level model.

[0035] The calibration unit is used to obtain failure data through multi-stress coupling accelerated life tests and to calibrate the key parameters of the system-level model based on the failure data;

[0036] The calibration unit is used to collect online monitoring data from the sensors in operation and to dynamically calibrate the parameters of the system-level model based on the online monitoring data.

[0037] The results unit is used to assess the remaining lifespan or failure risk of in-service sensors based on the corrected system-level model and output reliability assessment results.

[0038] Preferably, the correction unit is further configured to correct the parameters of the device-level model and the system-level model:

[0039] Finite element or multiphysics simulation methods are used to analyze the coupling stress of the sensor under temperature, vibration, electromagnetic and humidity environments, obtain the stress amplification factor or attenuation factor of the key components of the sensor, and correct the parameters of the device-level model and system-level model by the amplification factor or the attenuation factor.

[0040] Preferably, the third establishment unit is also used to establish a multi-stress coupling model; the multi-stress coupling model adopts the generalized Eyring equation or the competing failure model, and considers the interactive effects of temperature T, mechanical stress S1, electromagnetic stress S2, and humidity; wherein the γ·S1·S2 term is used to represent the coupling coefficient between stresses, and the coupling coefficient is obtained through multi-stress coupling accelerated test;

[0041] It also includes testing the system-level model using a multi-stress coupling model based on key stresses, wherein the multi-stress coupling model is:

[0042]

[0043] Where T is temperature, S1 and S2 are stresses, the γS1S2 term can represent the interaction term between stresses S1 and S2, L is life, C is a constant, B is a constant, α is the stress coefficient, and β is the stress coefficient.

[0044] Preferably, the system-level model includes a lifetime distribution model, in which lifetime distribution models are established for each major failure mechanism, and the mechanisms are combined using a method of parallel or competitive failure to obtain the overall lifetime distribution of the sensor; when there are multiple stress interactions, by correcting the lifetime parameters of each mechanism or introducing a synergistic factor, a lifetime prediction result that is closer to the actual working condition can be obtained.

[0045] Preferably, the multi-stress coupling accelerated life test includes:

[0046] Single stress constant test: Life test is conducted under single-factor conditions such as different temperatures or different vibration amplitudes to extract the parameters of the Arrhenius and Coffin-Manson basic models;

[0047] Multi-stress coupling accelerated test: The test life of the sensor is observed under the combined stress conditions of high temperature + vibration + humidity. If the test life is significantly lower than the single-factor predicted life, it indicates that there is a negative synergistic effect under the combined stress conditions. The coupling term coefficients of the system-level model are corrected based on the negative synergistic effect.

[0048] The stress level, sample size, and test cycle of multi-stress coupled accelerated life tests are designed to identify the main failure modes and more closely approximate reality.

[0049] Preferably, the correction unit is further configured to perform Weibull distribution fitting on the test lifetime obtained from the multi-stress coupling accelerated life test, extract characteristic lifetime and shape factor parameters; and correct the failure mechanism parameters or stress coefficients of the system-level model based on the deviation between the test results of the multi-stress coupling accelerated life test and the evaluation results of the system-level model.

[0050] Preferably, the online monitoring data includes sensor performance degradation curves, on-site environmental stress spectra, and actual failure times;

[0051] The correction unit is further configured to combine the online monitoring data with the prior model in the system-level model, construct a likelihood function and use Bayes' theorem to update the parameters of the system-level model to obtain the posterior distribution of the system-level model; when the cumulative amount of the online monitoring data reaches a threshold, the parameter update process of the system-level model is repeated to achieve dynamic adaptive correction of the system-level model.

[0052] Preferably, the result unit is further configured to trigger an early warning signal and generate maintenance or replacement recommendations when the predicted remaining lifespan of the in-service sensor is lower than a preset lifespan threshold or the performance degradation rate exceeds a preset rate threshold; at the same time, it records the failure symptom information and feeds it back to the system-level model.

[0053] According to another aspect of the present invention, the present invention provides a computer-readable storage medium, characterized in that the computer-readable storage medium stores a computer program for executing a sensor multi-scale interconnect reliability assessment method.

[0054] According to another aspect of the present invention, the present invention provides an electronic device, characterized in that the electronic device comprises: a processor and a memory; wherein,

[0055] The memory is used to store the processor-executable instructions;

[0056] The processor is configured to read the executable instructions from the memory and execute the instructions to implement a sensor multi-scale interconnect reliability assessment method.

[0057] This invention provides a multi-scale integrated reliability assessment method and system for sensors. The method includes: establishing a material-level model based on key parameters, failure modes, and failure criteria of the sensor material, and calibrating the parameters of the material-level model through accelerated testing or database analysis; mapping external environmental stress to key components of the sensor using finite element analysis or analytical methods, and establishing a device-level model based on the structural features of the material-level model; establishing a system-level model based on the effects of multi-stress coupling, the actual usage scenarios of the sensor, and the device-level model; obtaining failure data through accelerated life testing with multi-stress coupling, and calibrating the key parameters of the system-level model based on the failure data; collecting online monitoring data from the operating sensor, and dynamically correcting the parameters of the system-level model based on the online monitoring data; and assessing the remaining lifespan or failure risk of the in-service sensor based on the corrected system-level model, and outputting the reliability assessment results. This invention proposes a multi-scale modeling process combining top-down and bottom-up approaches: first, establishing a material-to-device failure model from bottom to top, and then refining and mapping the system environmental load to the device / material level from top to bottom, thereby connecting all scales. The technical solution of this invention, by integrating multiple scales, makes the reliability evaluation results more comprehensive and accurate, enabling early prediction of potential problems and improving the foresight of the assessment. Attached Figure Description

[0058] Exemplary embodiments of the present invention can be more fully understood by referring to the following figures:

[0059] Figure 1This is a flowchart of a sensor multi-scale through-connection reliability assessment method according to a preferred embodiment of the present invention;

[0060] Figure 2 A flowchart of a multi-scale through-connection reliability evaluation method according to a preferred embodiment of the present invention;

[0061] Figure 3 This is a tree diagram illustrating the failure mechanism of a typical power micro-sensor according to a preferred embodiment of the present invention.

[0062] Figure 4 This is a schematic diagram of a sensor lifetime prediction curve according to a preferred embodiment of the present invention;

[0063] Figure 5 This is a stress distribution cloud map of the sensor packaging structure according to a preferred embodiment of the present invention under thermal shock stress.

[0064] Figure 6 A schematic flowchart illustrating the data fusion and Bayes update process according to a preferred embodiment of the present invention; and

[0065] Figure 7 This is a structural diagram of a sensor multi-scale through-reliability evaluation system according to a preferred embodiment of the present invention. Detailed Implementation

[0066] Exemplary embodiments of the invention will now be described with reference to the accompanying drawings. However, the invention may be embodied in many different forms and is not limited to the embodiments described herein. These embodiments are provided to fully and completely disclose the invention and to fully convey its scope to those skilled in the art. The terminology used in the exemplary embodiments illustrated in the drawings is not intended to limit the invention. In the drawings, the same units / elements are referred to by the same reference numerals.

[0067] Unless otherwise stated, the terms used herein (including technical terms) have their common meaning as understood by one of ordinary skill in the art. Furthermore, it is understood that terms defined in commonly used dictionaries should be understood to have a meaning consistent with the context of their relevant field, and not to be interpreted as having an idealized or overly formal meaning.

[0068] Figure 1 This is a flowchart of a sensor multi-scale through-reliability assessment method according to a preferred embodiment of the present invention.

[0069] This invention provides a multi-scale, integrated reliability assessment method for sensors used in power systems. This invention combines material-level, device-level, and system-level reliability assessments of microelectronic devices to perform comprehensive reliability evaluation of power micro-sensors. It is also applicable to various sensors operating in harsh environments and falls within the technical scope of electronic equipment reliability prediction and assessment.

[0070] This invention establishes a cross-scale reliability assessment framework. It connects sensor material-level defects, device-level structural characteristics, and system-level application conditions to form a hierarchical model system, solving the prediction inaccuracies caused by the fragmentation of traditional methods across different levels. This invention aims to establish a clear failure propagation path from materials to devices to the system through multi-scale modeling, enabling the tracing of sensor failure causes and the comprehensive quantification of factors affecting sensor lifespan.

[0071] This invention introduces a multi-physics coupling mechanism. Addressing the common multi-field coupled stresses in power field applications, including temperature, mechanical, electrical, and chemical stresses, this invention constructs a comprehensive reliability analysis method to reveal failure modes under the interaction of different stress factors. The goal of this invention is to simultaneously consider factors such as thermal stress, vibration and shock, electromagnetic pulse, and humidity corrosion in the model, providing a more realistic lifespan prediction than a single stress model, thereby avoiding underestimating or overestimating sensor failure rates.

[0072] This invention achieves dynamic evaluation with a closed-loop data system. It aims to establish a reliability assessment mechanism that integrates laboratory accelerated testing data with on-site online monitoring data. By continuously collecting and analyzing the status data of operating sensors and comparing and correcting it with initial model predictions, a closed-loop feedback for reliability assessment is formed, improving the real-time performance and accuracy of the assessment. The goal is to enable the lifetime prediction model to be updated over time, promptly reflecting the actual degradation state of the sensors.

[0073] This invention provides a basis for guiding design improvements and operational decisions. Through the aforementioned models and data analysis, this invention aims to obtain specific reliability indicators and information on weak points to guide the improved design of sensor materials, structures, and packaging, and optimize manufacturing processes. Furthermore, it provides decision-making support for operations and maintenance departments, such as determining maintenance and replacement cycles and developing condition monitoring strategies, thereby reducing the risk of mass failures and operational costs.

[0074] like Figure 1 As shown, the present invention provides a method for multi-scale interconnect reliability assessment of sensors, characterized in that the method includes:

[0075] Step 101: Based on the key parameters, failure modes, and failure criteria of the sensor material, establish a material-level model and calibrate the parameters of the material-level model through accelerated testing or database.

[0076] This invention performs multi-scale reliability modeling, including establishing a material-level model:

[0077] Material-level models primarily focus on the microscopic failure mechanisms of core sensor components and encapsulation materials. For materials commonly used in power micro-sensors (such as silicon, metal solders, and polymer encapsulation materials), this invention first identifies their main failure modes and establishes corresponding physical models. For example, fatigue fracture of silicon MEMS structures can be described using the Coffin-Manson low-cycle fatigue model, which describes the relationship between strain amplitude and lifetime; electromigration failure of aluminum electrodes follows Black's formula, with lifetime dependent on current density and temperature; solder joint creep fatigue can be predicted using a modified Norris-Landzberg model, combining parameters such as temperature cycle amplitude, average temperature, and frequency. For various materials, this invention lists key parameters (such as activation energy Ea, material constants, etc.) and their failure criteria. For example, for a certain silver paste interconnect material, where temperature is the main stress factor, its lifetime model can be derived using the Arrhenius formula. For humidity-sensitive materials such as conductive epoxy, a humidity factor is introduced, and the Peck model is used. Where H represents relative humidity. This invention is not limited to a specific formula, but provides a framework that uses the lifetime model parameters of relevant materials, calibrated through accelerated testing, as input to a lower-level model.

[0078] The material-level model of this invention also considers potential defects, such as microcracks on the silicon surface and the porosity of the coating. These microscopic defects can reduce material strength or accelerate aging. This invention obtains initial defect distribution data of the material experimentally (e.g., by observing crack density through SEM) and sets corresponding reduction coefficients or reliability distributions in the model. For example, the critical size of microcracks in silicon can be estimated using the energy release rate criterion of fracture mechanics; defects exceeding this size will lead to premature failure. The material-level model outputs the probability distribution or lifetime distribution of material failure under a certain stress.

[0079] Step 102: Using finite element or analytical methods, map the external environmental stress to the key components of the sensor, and establish a device-level model based on the structural features of the material-level model;

[0080] This invention includes establishing a device-level model:

[0081] The device-level focus is on the reliability of the sensor as a whole and its internal substructures (such as the sensing chip, substrate, package, leads, etc.). Based on the material-level model output, this invention further constructs an equivalent failure model for the sensor device. Specific steps include: dividing the device into key components, listing the failure modes of each component and their correlation with the material-level model. For example, a typical MEMS temperature sensor includes: a silicon MEMS chip, metal leads, a ceramic substrate, and epoxy encapsulation. Possible failure modes include: silicon chip breakage, metal lead fatigue breakage, solder joint fatigue, package cracking, and moisture intrusion causing sensitive element drift. This invention classifies these failure modes into two categories: hard failure (such as physical fracture causing the sensor to stop working) and soft failure (such as sensitivity drift exceeding the limit but not complete failure). For each mode, a failure criterion and lifetime model are established at the device level. For example, taking the solder joint fatigue mode as an example, the material-level creep fatigue model is used to predict the probability of solder joint failure after N cycles of cyclic stress at a given temperature; taking the sensitive film drift mode as an example, the material-level chemical aging model is used to predict the time required for the sensor output drift to reach the 5% threshold under humid and hot conditions.

[0082] Based on this, this invention equates device-level reliability to a system composed of multiple failure modes. Commonly used methods include series-parallel models or competing failure models. For hard failure modes, they can be considered as a series relationship (the occurrence of any hard failure results in device failure); for soft failure modes, if one of them reaches its limit, the device is considered to have reached its end of life, which is also similar to a series relationship. Therefore, the overall system can be approximated as a series system for treating the major failure mechanisms, and the system failure rate λ(t) can be expressed as the sum of the failure rates of each mechanism (assuming that the failure processes are independent of each other). Of course, the actual situation may be more complex, such as different mechanisms developing simultaneously. This invention also supports the use of a competing risk model, obtaining the distribution of the first failure time through statistical analysis. In addition, stress distribution transmission coefficients are introduced into the device-level model: since material-level models are usually based on uniform samples, while the stress on each component may be different from the external stress during actual device operation. For example, the chip temperature inside the package may be higher than the external ambient temperature, and vibration stress may be attenuated or amplified by the supporting structure. This invention obtains these stress transmission coefficients through finite element simulation or analytical calculation. If simulations show that the chip experiences an equivalent acceleration of 1.5 times the external acceleration under a certain impact, then the stress input for the chip's failure mechanism in the lifetime calculation will be multiplied by 1.5. This step essentially establishes multi-scale coupling: mapping system-level / device-level stress conditions to a material-level model, thereby embedding the microscopic model into the actual operating conditions.

[0083] Step 103: Based on the effects of multi-stress coupling, the actual application scenarios of the sensor, and the device-level model, establish a system-level model;

[0084] This invention establishes a system-level model:

[0085] The system-level design primarily considers the sensor's operating environment within the power equipment and its interaction with the surrounding environment. This invention views system-level reliability as the performance of the sensor as a component of the system. On one hand, the system-level model defines the time series (load spectrum) of the stress environment in which the sensor operates. For example, for a temperature microsensor on a transformer top cover, its operating temperature cycle (day-night temperature difference cycle, temperature rise cycle caused by load, etc.), mechanical stress (wind vibration, equipment start-up and shutdown impact), electromagnetic environment (surrounding electric and magnetic field strength), and chemical environment (dissolved gases in oil, humidity) can be defined over time. On the other hand, the system-level design also involves sensor redundancy configuration and failure criteria. If multiple sensors are redundantly installed at a monitoring point, system-level failure needs to be defined as the failure of all sensors for the monitoring function to fail (parallel connection). However, for a single sensor, this invention primarily considers its own reliability; therefore, the system-level design mainly provides environmental input.

[0086] This invention proposes a multi-scale modeling process combining top-down and bottom-up approaches: first, a failure model from material to device is established from bottom to top; then, the system environmental load is refined and mapped to the device / material level from top to bottom, thus connecting all scales. In this way, the reliability index of a sensor under a specific application scenario can be predicted. For example, this invention can output the "lifetime distribution function of a sensor in an outdoor environment of a substation," which already includes factors such as material degradation, structural stress amplification, and environmental spectral impact.

[0087] Step 104: Obtain failure data through multi-stress coupling accelerated life testing, and calibrate the key parameters of the system-level model based on the failure data;

[0088] Preferably, the method further includes establishing a multi-stress coupling model; the multi-stress coupling model adopts the generalized Eyring equation or the competing failure model, and considers the interactive effects of temperature T, mechanical stress S1, electromagnetic stress S2, and humidity; wherein the γ·S1·S2 term is used to represent the coupling coefficient between stresses, and the coupling coefficient is obtained through multi-stress coupling accelerated test;

[0089] It also includes testing the system-level model using key stresses based on a multi-stress coupling model. The multi-stress coupling model is as follows:

[0090]

[0091] Where T is temperature, S1 and S2 are stresses, the γS1S2 term can represent the interaction term between stresses S1 and S2, L is life, C is a constant, B is a constant, α is the stress coefficient, and β is the stress coefficient.

[0092] Preferably, the multi-stress coupled accelerated life test includes:

[0093] Single stress constant test: Life test is conducted under single-factor conditions such as different temperatures or different vibration amplitudes to extract the parameters of the Arrhenius and Coffin-Manson basic models;

[0094] Multi-stress coupling accelerated test: The test life of the sensor is observed under the combined stress conditions of high temperature + vibration + humidity. If the test life is significantly lower than the single-factor predicted life, it indicates that there is a negative synergistic effect under the combined stress conditions. The coupling term coefficients of the system-level model are corrected based on the negative synergistic effect.

[0095] The stress level, sample size, and test cycle of multi-stress coupled accelerated life tests are designed to identify the main failure modes and more closely approximate reality.

[0096] Preferably, the test lifetime obtained from the multi-stress coupling accelerated life test is fitted with a Weibull distribution to extract characteristic lifetime and shape factor parameters; and the failure mechanism parameters or stress coefficients of the system-level model are corrected based on the deviation between the test results of the multi-stress coupling accelerated life test and the evaluation results of the system-level model.

[0097] This invention designs multiphysics coupling and acceleration experiments, including:

[0098] (1) Multi-stress coupling modeling

[0099] Within the aforementioned model framework, this invention focuses on introducing multi-physics coupling factors. Traditional models often assume that stresses act independently, but in reality, different stresses may act simultaneously or sequentially, producing complex effects. This invention addresses this through two approaches: one is a unified model method, which directly establishes a lifetime model incorporating multiple stress factors. For example, the generalized Eyring model can be used.

[0100]

[0101] Where T represents temperature, S1 and S2 represent other stresses (such as stress amplitude and humidity), and the γS1S2 term can represent the interaction term between stresses. An empirical formula can be fitted through multiple regression to capture the main coupling effects. For example, for temperature-humidity coupling, a product term of humidity and temperature can be added to represent the accelerating effect of humidity on corrosion at high temperatures.

[0102] If experimental data is insufficient to fit complex models, a second approach is adopted: the parallel mechanism-based method. This involves treating failure mechanisms dominated by different stresses as competing failure processes in parallel, each described by a corresponding model, and then using these competing models to derive the composite lifetime distribution. For example, a sensor may simultaneously face aging of the sensitive element caused by high temperature (mechanism A) and solder joint fatigue caused by vibration (mechanism B). This invention simulates the two mechanisms separately to obtain the lifetime distribution F. A(t),F B (t), then the total failure distribution F(t) = 1 - [1 - F A (t)][1-F B (t) (assuming failure events are independent). This avoids the difficulty of directly constructing a high-dimensional model. If there are mutual influences between mechanisms (e.g., increased temperature reduces solder joint fatigue life), this invention introduces a coupling coefficient into the model, such as making the life parameter of the solder joint fatigue model dependent on temperature, thereby achieving coupling. In summary, through model modification, this invention can reflect the combined effect of multiple stresses acting simultaneously to a considerable extent.

[0103] It is important to emphasize that for coupling effects that are difficult to characterize through analytical models, this invention employs finite element / multiphysics simulation methods for guidance. For example, for thermo-mechanical coupling, this invention uses software such as ANSYS to establish a sensor structural model, applies mechanical loads at different temperatures, and obtains the stress-strain response curves of the components. Simulations may reveal that the material enters the plastic stage earlier at a certain temperature, thus inferring that high temperatures reduce mechanical strength, and adding a correction factor for "high temperature shortens fatigue life" to the life model. Similarly, for electro-thermal-mechanical coupling, the positive feedback effect of increased resistance due to temperature rise, resulting in more heat generation, is obtained through coupled field simulation, and this is incorporated as a special failure mode (thermal runaway) into the model criterion. Literature indicates that for small devices such as MEMS, multiphysics coupling complicates reliability assessment, and currently there is no universal formula. This invention, through the above simulation and decomposition methods, provides a practical approach for reliability evaluation under the combined effects of multiple fields.

[0104] This invention designs an accelerated testing scheme, including:

[0105] To obtain the parameters required for the model and verify its accuracy, this invention plans an accelerated life testing scheme for the system. The accelerated test is conducted using a multi-stress combination approach to simulate the degradation process under real service conditions as closely as possible. The specific design is as follows:

[0106] 1) Constant Stress Test: Traditional constant stress life tests are conducted for the main single stress factor. Examples include high-temperature aging tests (aging the sensor for several hours at 125℃, 150℃, etc., recording performance drift or failure time) and vibration durability tests (continuous vibration at different acceleration amplitudes until failure). These tests obtain stress-life relationship curves for each single factor, such as the slope of the Arrhenius line between temperature and life, to determine model parameters (e.g., activation energy Ea). Referring to the general methods of MIL-HDBK-217 and IEC 61709, the stress level should be selected to cover 1.5 to 2 times the upper limit of actual conditions to accelerate failure. At the same time, the principles in GJB 299C are followed to ensure that accelerated stress does not introduce entirely new failure modes that are inconsistent with reality.

[0107] 2) Accelerated Combined Stress Testing: To capture the stress coupling effect, this invention specifically incorporates tests that apply multiple factors simultaneously. For example, a "high temperature + vibration" combined acceleration test is designed: the sample is placed in an 80℃ environment and subjected to random vibration to accelerate the fatigue of the solder joints and package. A "temperature cycling + humidity" test is designed: the relative humidity is controlled above 85% in a temperature cycling chamber, subjecting the sensor to condensation stress while undergoing periodic temperature changes, thus accelerating corrosion and seal deterioration. Through these tests, it is possible to observe whether the failure mode differs from that of a single factor, and to record lifetime data to verify the accuracy of the coupling terms in the model. If the lifetime under a certain combined stress is found to be significantly lower than the result estimated according to the single factor, it indicates the existence of a negative synergistic effect. Based on this, this invention adjusts the stress interaction term parameters in the model to fit the result. If the lifetime is observed to be 20% lower than the product of temperature and humidity alone in the "temperature + humidity" test, a negative interaction term is added to the Eyring model to increase the influence of humidity on the lifetime.

[0108] 3) Statistical Experimental Design: This invention employs the statistical design of experiments (DOE) to optimize the experimental matrix, acquiring as much information as possible with a smaller number of experiments. For example, two-level orthogonal experiments are used to distinguish the main effects and interaction effects of each stress, or partial factorial experiments are used to focus on lifetime changes in high-stress areas. Each experiment ensures a certain sample size (e.g., no less than 10 sensors under each condition) for reliability statistical analysis. The obtained lifetime data is fitted using a Weibull distribution to extract characteristic lifetimes and shape factors, which are then used to inversely calculate model parameters.

[0109] Through the aforementioned multi-stress accelerated testing, this invention establishes a reliability model database covering major failure mechanisms. This experimental data is used, on the one hand, to calibrate model parameters (such as Ea in the Arrhenius formula and the fatigue index in the Coffin-Manson formula), and on the other hand, to verify the model's predictive ability. In model validation, the predicted lifetime distribution is compared with the actual experimental results, and the error is calculated. If the prediction deviation is large under certain stress conditions, this invention further analyzes the reasons and improves the model. If the model fails to consider a certain second-order effect leading to an overestimation of lifetime, the corresponding mechanism is supplemented or the model's conservatism coefficient is corrected.

[0110] Step 105: Collect online monitoring data from the sensors during operation, and dynamically correct the parameters of the system-level model based on the online monitoring data;

[0111] Preferably, the method further includes correcting the parameters of the device-level model and the system-level model:

[0112] Finite element or multiphysics simulation methods are used to analyze the coupling stress of sensors under temperature, vibration, electromagnetic and humidity environments, obtain the stress amplification factor or attenuation factor of key components of the sensor, and correct the parameters of the device-level model and system-level model by the amplification factor or attenuation factor.

[0113] This invention performs online monitoring and data fusion:

[0114] (1) Deployment of online monitoring system

[0115] This invention deploys an online monitoring device at the actual operating site of sensors to obtain data on sensor performance degradation and environmental stress. Specifically, a certain number of sensor samples are selected from typical equipment (e.g., 100 temperature sensors of the same model installed in a substation), and the key parameters of each sensor are recorded over time using a data acquisition system. These parameters include zero-point drift, noise level, and sensitivity coefficient changes as Health Indicators (HIs), along with corresponding environmental conditions (temperature, voltage, vibration acceleration, etc.). Simultaneously, a sensor failure judgment standard is established; if a sensor reading becomes abnormal (e.g., exceeds the calibration deviation range) or communication is interrupted, it is judged as a failure event. During the monitoring process, the collected data includes: the survival time (successful operating time) or failure time of several sensors, and the environmental stress history during operation. This field data directly reflects the reliability under real operating conditions and is of significant value for improving the model.

[0116] (2) Bayes integration and model update

[0117] After acquiring field data, this invention employs a Bayesian update method to fuse field information with a prior accelerated testing model, achieving dynamic correction of model parameters. The specific steps are as follows: using the initial model parameters as the prior distribution, a likelihood function is constructed based on the failure time data observed in the field (e.g., assuming the model's predicted lifetime follows a Weibull distribution, the likelihood is calculated based on the actual failure / non-failure data in the field); the posterior distribution is calculated using the Bayesian formula, from which the updated parameter estimates are extracted. If the amount of field data is large, frequency correction can also be performed directly on the model, such as calculating the ratio of the actual MTBF in the field to the model's predicted MTBF as an empirical calibration factor. The research of Pan et al. provides an example of this fusion: they combined field failure data with laboratory accelerated testing results, introduced a correction factor to correct the model, and considered the uncertainty of field stress. In this invention, a similar environmental calibration factor θ is defined; if the actual environment is more severe than the laboratory standard environment, it indicates a shorter accelerated lifetime. The most probable values ​​of the posterior parameters can be solved through simulation or numerical optimization methods. For degradation-related data (gradual changes in sensor performance), this invention employs the Wiener process model, integrating the monitored degradation trajectory into the Bayes update to improve the accuracy of remaining lifetime prediction. The fused model better reflects actual operating conditions: if a stress source in the field is lower than the laboratory assumption, the model will adjust accordingly to extend the lifetime prediction; conversely, if it is higher, the model will adjust accordingly.

[0118] (3) Dynamic reliability assessment and early warning

[0119] After the model parameters are updated, this invention can perform dynamic reliability assessments on a group of sensors. Specifically, the lifetime distribution and reliability function R(t) of each sensor are recalculated using a posterior model. For in-service sensors that have not yet failed, their remaining lifetime (RUL) can be predicted based on their current operating time and environmental conditions. If the RUL of some sensors is found to be lower than a preset threshold, a preventative maintenance warning is issued, prompting maintenance personnel to replace or repair them in advance. This closed-loop mechanism ensures that reliability assessment is no longer a one-time result at the factory, but a real-time indicator that is continuously corrected during operation. For example, after 6 months of field data feedback, if the model adjusts the expected lifetime of sensor A from 2 years to 1.8 years (because of the higher temperature at its location), the system will incorporate this information into the maintenance plan. Compared to static assessments without feedback, the dynamic closed-loop scheme of this invention significantly improves prediction accuracy and the effectiveness of maintenance decisions.

[0120] Step 106: Based on the corrected system-level model, assess the remaining lifespan or failure risk of in-service sensors and output the reliability assessment results.

[0121] Preferably, the system-level model includes a lifetime distribution model, in which lifetime distribution models are established for each major failure mechanism, and the mechanisms are combined using a method of parallel or competitive failure to obtain the overall lifetime distribution of the sensor; when there are multiple stress interactions, the lifetime prediction results that are closer to the actual working conditions are obtained by correcting the lifetime parameters of each mechanism or introducing a synergistic factor.

[0122] Preferably, the online monitoring data includes sensor performance degradation curves, on-site environmental stress spectrum, and actual failure time; the online monitoring data is combined with the prior model in the system-level model to construct a likelihood function and use Bayes' theorem to update the parameters of the system-level model, thereby obtaining the posterior distribution of the system-level model; when the cumulative amount of online monitoring data reaches a threshold, the parameter update process of the system-level model is repeated to achieve dynamic adaptive correction of the system-level model.

[0123] Preferably, when the predicted remaining lifespan of the in-service sensor is lower than a preset lifespan threshold or the performance degradation rate exceeds a preset rate threshold, an early warning signal is triggered, and maintenance or replacement recommendations are generated; at the same time, the failure symptom information is recorded and fed back to the system-level model.

[0124] This invention establishes an optimization design and feedback mechanism:

[0125] (1) Failure Mechanism Location

[0126] Multi-scale models can identify the key mechanisms driving sensor failure. For example, comparing the failure probabilities of various mechanisms revealed that the primary failure mode of a certain sensor model under the target environment was "encapsulation and sealing failure leading to moisture intrusion." This provides direction for improvement. Furthermore, stress simulations showed severe stress concentration at the sensor's mounting points, a major cause of fracture. With this information output by the model, researchers can make targeted design improvements.

[0127] (2) Structural and process optimization

[0128] Based on the model analysis results, design improvement suggestions are proposed. For example, regarding the packaging failure in the example above, it can be suggested to adopt a higher level of sealing process or use moisture-resistant materials to improve reliability at the material level. To address the stress concentration problem, the sensor shape can be modified or buffer pads can be added to reduce shock transmission at the system level. Furthermore, if the model predicts insufficient solder joint lifespan under high-frequency vibration, the soldering process can be improved at the device level or supports can be added to reduce vibration. All these improvements can be verified again using model simulation before implementation, forming a closed loop of design-evaluation-redesign.

[0129] (3) Reliability Redundancy and Maintenance Strategy

[0130] For applications with extremely high reliability requirements, the evaluation results of this invention can also be used to develop redundancy schemes and maintenance strategies. For example, if the model estimates that a batch of sensors may have a 5% failure rate within 5 years, it can be recommended to deploy redundant sensors to replace potentially failed nodes, or to proactively replace them every 4 years, in order to control the failure risk to below 1%. These strategies are all based on the quantitative results of the model and are more scientific than those formulated based on experience.

[0131] Through the above mechanisms, the method of this invention is not merely a passive evaluation tool, but rather a proactive optimization auxiliary system. It can feed back the knowledge gained from reliability assessments into product development and operation and maintenance management, thereby continuously improving the reliability level of micro-sensors. For example, after improving the packaging process guided by the model, the lifespan of a new batch of sensors is expected to be extended by 20%, and the failure rate in actual operation is reduced by more than 30%. This positive feedback makes reliability assessment a true part of the product's entire lifecycle management.

[0132] 1) Multi-scale integrated reliability modeling method: This invention innovatively integrates material-level, device-level, and system-level reliability models into a single framework, enabling the seamless transfer of failure mechanisms across different scales. This hierarchical model can quantitatively describe how microscopic defects lead to macroscopic failures, filling the gap in existing technologies that rely on isolated analysis at each level.

[0133] 2) Multiphysics Coupling Reliability Verification Method: This invention proposes a reliability assessment mechanism that considers the simultaneous effects of multiple stresses, including temperature, mechanical stress, humidity, and electrical stresses. This includes multi-stress accelerated test design and multi-field coupling model calibration. In particular, the impact of stress coupling on lifetime is quantified through a method combining finite element simulation and experimental data.

[0134] 3) Life Prediction Framework Based on Accelerated Testing and Field Data Fusion: This invention develops a Bayesian data fusion algorithm that combines high-stress accelerated testing results with actual field operation data to dynamically update the life model and achieve closed-loop feedback for reliability assessment. This framework overcomes the limitations of traditional static reliability prediction, enabling the model to adaptively adjust and improving prediction accuracy and real-time performance.

[0135] 4) Optimization feedback mechanism for reliability assessment results: Unlike existing technologies that only provide assessments, this invention further includes a feedback loop that uses the assessment results to improve design and operation and maintenance decisions. For example, by using models to identify and optimize weak points in sensors, product reliability is improved; and by using real-time assessment results to provide early warnings and guide maintenance and replacement.

[0136] The beneficial effects of this invention are reflected in the following aspects:

[0137] 1) Multi-scale integration improves evaluation accuracy.

[0138] Traditional methods are often limited to testing or statistics at a single scale (material or device level), making it difficult to promptly identify cross-level failure causes. This invention, through multi-scale coupled analysis at the material, device, and system levels, can predict potential device-level failure paths early in the appearance of microscopic defects in materials and assess their impact on system operation. For example, in one instance, this method uses a microcrack growth model to predict the development trend of encapsulation resin cracks under repeated temperature shocks and warns of the potential for sensor seal failure six months later. In contrast, relying solely on device-level lifetime testing only reveals the cause after failure occurs, exhibiting a time lag. This invention, spanning multiple scales, makes reliability assessment results more comprehensive and accurate, enabling early detection of potential problems and improving the foresight of the assessment.

[0139] 2) Multiphysics coupling makes the evaluation results closer to reality.

[0140] Sensors in power environments are often subjected to multiple stresses simultaneously, including temperature, mechanical stress, electromagnetic radiation, and chemical corrosion. Estimating lifespan by considering only a single stress source can easily underestimate or overestimate the sensor's failure rate. This invention incorporates multi-field coupling (thermal, mechanical, electrical, and humidity) into a unified simulation and experimental framework. Through finite element analysis and combined stress-accelerated testing, the reliability prediction is significantly improved.

[0141] 3) Data closed-loop dynamic updates enhance reliability management.

[0142] This invention establishes a closed-loop feedback mechanism for reliability assessment by introducing on-site online monitoring data, enabling the life prediction model to be continuously corrected based on actual conditions. This brings two direct benefits: First, life prediction is more accurate and dynamic. For example, for a batch of sensors, the traditional static model estimates their lifespan as 5±1 years, while this method adjusts the prediction to 4.8±0.5 years after incorporating on-site data from the previous year, making it more accurate. Second, it enables early warning and proactive maintenance. Because the model assesses the health status of each sensor in real time, this invention issues timely warning signals when abnormal performance indicators of certain sensors are detected. In a real-world case, this method detected a sudden drop in the RUL (Remaining Lifespan) of a sensor on a circuit breaker tank (predicted remaining lifespan less than 10%), recommending that maintenance personnel check it. It was found that the sensor had become loose due to an accidental mechanical impact, and timely replacement prevented a monitoring failure. Therefore, this invention extends reliability assessment from the laboratory to the operational field, realizing online reliability management and significantly improving operational efficiency and system reliability.

[0143] 4) Extend sensor lifespan and reduce maintenance costs

[0144] By accurately locating the failure mechanism and feeding it back into design improvements, the method of this invention can substantially improve the lifespan and reliability of sensor products. Statistics show that in power system applications with large-scale sensor deployment, any improvement in reliability significantly reduces the risk of mass failures and emergency repairs, creating substantial economic and social benefits for power companies.

[0145] 5) Universality and scalability

[0146] The method of this invention is not only applicable to the evaluation of micro-sensors in the power industry, but also has good potential for widespread application. Its multi-scale, multi-physics modeling approach can be transferred to fields with stringent reliability requirements, such as petrochemicals, aerospace, and automotive electronics. For example, it can be used for reliability analysis of pressure sensors in aero-engines, or for lifetime prediction of automotive MEMS gyroscopes under vibration and temperature environments. By adjusting the specific failure mechanism model and stress spectrum input, the framework of this method can evaluate sensor reliability in different application scenarios. Furthermore, this method can also be used to evaluate higher-level systems (such as sensor networks or modules), providing a basis for system-level reliability design. In summary, this invention has strong versatility and potential application value in multiple fields.

[0147] Figure 2 This is a flowchart illustrating the multi-scale reliability evaluation method of this invention. The diagram shows the main steps of the method from left to right, including multi-scale modeling (material level, device level, system level), multiphysics simulation and accelerated testing, data fusion calibration, and result feedback. Figure 2 The diagram uses different modules to represent "model building" and "data flow": the material-level model output is passed to the device-level model via arrows, and the device-level model is then combined with the system-level environmental input before entering the reliability prediction module. Online monitoring data is returned via the feedback arrows below, compared with the prediction results, and then enters the Bayes update module. The updated model is then used again for lifetime prediction. The method of this invention forms a closed loop: the prior model combines with experimental data to generate initial predictions, field data continuously corrects the model, and finally outputs a reliability assessment report and improvement suggestions. This diagram highlights the characteristics of multi-scale, multi-source data fusion.

[0148] Figure 3 This is a failure mechanism tree diagram for a typical power micro-sensor. The diagram lists potential failure modes at each level, from the material level to the device level, in a tree structure: the root node represents overall sensor failure; the second layer includes chip failure, packaging failure, and solder interconnect failure; the third layer further subdivides the failure mechanism, such as cantilever beam fracture and diffusion layer degradation under chip failure, and cracks and poor sealing under packaging failure. Each terminal leaf node indicates the main driving stress (e.g., thermal cycling, humidity, mechanical stress) and the name of the reference physical model. Figure 3As can be seen, this invention systematically decomposes complex failure possibilities and establishes a clear hierarchical causal relationship. This helps to identify the main failure paths through layer-by-layer analysis during the assessment and to quantitatively calculate the lifetime accordingly.

[0149] Figure 4 This is a graph showing the sensor lifetime prediction curves based on multi-stress accelerated testing, as presented in this invention. The solid line represents the predicted lifetime curve considering the coupling effect of temperature and vibration, while the dashed line represents the traditional lifetime curve considering only temperature and single stress. The vertical axis represents the sensor reliability R(t), and the horizontal axis represents the operating time (logarithmic scale). As can be seen from the graph, the dashed curve decreases relatively slowly, indicating that the traditional model predicts the sensor's reliability will remain high over a longer period. However, the solid curve decreases more rapidly, showing a significant drop in reliability within a shorter time, which is closer to the failure observed in actual field conditions. The difference in lifetime Δt between the two at R = 0.5 (i.e., 50% failure probability) indicates that the traditional model overestimates the lifetime. This graph visually demonstrates the importance of multi-physics models: when vibration acceleration mechanisms are considered simultaneously, lifetime assessments become more rigorous and reliable. Figure 4 The data points in the figure are the results of the accelerated experiment, while the curves are the results of the model fitting. The two match well, which verifies the accuracy of the model.

[0150] Figure 5 This is a stress distribution cloud map of the sensor packaging structure under thermal shock stress. Different colors in the graph represent the equivalent stress magnitude of different parts of the packaging, with blue to red indicating increasing stress. Under sudden temperature changes, a high-stress red area appears at the junction of the packaging material and the substrate, while the sensor chip area shows lower stress, appearing as blue-green. This indicates that the packaging interface is a stress concentration point and may become the location of preferential failure. Figure 5 It demonstrates how stress is transferred and amplified locally within the device, with peak stress reaching 50 MPa, exceeding the fatigue strength of the packaging material, and packaging cracking becoming the primary failure mode.

[0151] Figure 6 This is a schematic flowchart illustrating the data fusion and Bayes update process of this invention. The diagram shows how the model parameters are progressively updated based on field data: the initial parameter distribution (prior) is obtained by acquiring new failure data and applying Bayes' theorem to obtain the posterior distribution. The curve in the diagram vividly represents the convergence process of parameter uncertainty—as the amount of data increases, the posterior distribution gradually narrows, and the parameter estimation becomes more accurate. Finally, the updated model is used for the next round of reliability prediction, achieving adaptive improvement in the assessment. This diagram demonstrates that this invention does not provide an assessment conclusion all at once, but rather continuously learns from field information, allowing the assessment results to evolve synchronously with the actual state of the sensors.

[0152] This embodiment relates to a wireless temperature microsensor installed at a hot spot in a transformer winding. Its structure includes a MEMS temperature-sensitive chip, a flexible printed antenna, a lithium-ion button battery, and an epoxy encapsulation shell. The operating environment is in transformer oil, with a temperature range of -20 to 120°C, and is subject to vibration, shock, and strong electric field interference. For this scenario, we conducted a reliability assessment step-by-step according to the method of this invention.

[0153] (1) Determine the evaluation indicators and failure criteria

[0154] This embodiment focuses on the sensor's ability to retain functionality over its 10-year lifespan. Sensor failure is determined by: temperature measurement error exceeding ±2°C or loss of wireless signal, rendering communication impossible. Evaluation metrics include: B10 lifespan (time to 10% failure rate), Mean Time To Failure (MTTF), and the proportion of major failure modes.

[0155] (2) Establish a multi-scale model

[0156] Materials-level analysis: The sensor's sensing chip is a silicon-based MEMS thermistor, the encapsulating material is epoxy resin, the antenna is copper foil, and the solder joints are Sn-Ag-Cu lead-free solder. Potential failure mechanisms are listed: aging drift of the silicon thermistor, fatigue fracture of the copper antenna, creep cracking of the solder joints, high-temperature aging and cracking of the epoxy resin, and battery capacity decay. For each mechanism, the following models are selected: silicon thermistor drift is modeled using the Arrhenius model (given that similar materials have an Ea≈0.9eV); copper fatigue is modeled using the Coffin-Manson model (exponent approximately 5.5, obtained from literature); solder joint creep fatigue is modeled using the Norris-Landzberg model (considering temperature cycling); and epoxy aging is modeled using the Eyring model, considering the combined effects of temperature and electric field.

[0157] At the device level, soft failure modes such as sensitive chip drift and battery failure are included in the reliability block diagram along with hard failure modes such as antenna breakage and open solder joints. Since any hard failure will lead to communication interruption, hard failures affect overall reliability in a cascading manner; soft failures such as temperature drift are equivalent to failure when they reach a 2°C threshold. The device-level model uses competitive failure simulation and combines the lifetime distributions of each mode to generate the overall system lifetime distribution.

[0158] System level. Considering the sensor is embedded in the transformer windings, with external insulating oil for heat transfer and temperature fluctuations due to transformer load cycles. Thermal network analysis shows the chip temperature is approximately 5°C higher than the oil temperature (due to self-heating and ambient temperature difference), which is incorporated into the model. Vibration and shock are transmitted through the mounting structure, experiencing some attenuation; the actual vibration stress of the sensor is measured to be approximately 50% of the casing's. The electric field accelerates the aging of the sensor's electronic components; based on experimental experience, the model shows that for every 10kV / m increase in electric field strength, the silicon chip drift rate increases by 5%. These system influence coefficients are applied to the device-level model.

[0159] (3) Conduct accelerated testing

[0160] Fifty sensor samples were fabricated for accelerated life testing. Three sets of experiments were designed:

[0161] High-temperature aging: 20 samples were continuously run in a 150℃ constant-temperature oil bath, and their error drift was measured every 100 hours. The results showed that after about 800 hours, the average drift reached 2℃, indicating failure. The acceleration factor of the fitted Arrhenius model was approximately 50 times that of the actual 120℃ condition.

[0162] Temperature cycling + electric field combination: 15 samples were cycled in oil at 80℃~+130℃ for 24 hours, while a 20kV / m electric field was applied simultaneously. After 300 cycles (approximately 300 days), 4 samples exhibited drift failure, mainly due to cracking of the encapsulating resin leading to oil ingress and affecting temperature measurement. Compared with the group without an electric field, the presence of an electric field accelerated drift failure, shortening the lifespan by approximately 30%.

[0163] Vibration and shock test: 15 samples were subjected to a 10g half-sine shock every 10 seconds at room temperature for a total of 100,000 shocks (equivalent to an accelerated number of shocks accumulated over 10 years). Dissection after the test revealed that the copper foil solder joints of two antennas had cracked and failed; the rest functioned normally. This indicates that mechanical shock has a relatively small but not negligible impact on the reliability of this sensor.

[0164] Based on these experimental data, the model parameters were calibrated. For example, the parameters of the epoxy encapsulation failure model were adjusted to reduce its lifetime under electric field conditions by 30%, consistent with the experiments; the impact acceleration factor of the solder joint model was also updated.

[0165] (4) Online monitoring data fusion

[0166] Twenty sensors of this type were installed on an actual operating transformer for field testing and continuous monitoring for two years. During this period, two sensor failures were recorded: one experienced communication interruption after 18 months (determined to be premature failure due to battery depletion, related to the model's failure to consider battery life; the battery life factor needs to be added to the model); the other exhibited temperature drift exceeding 2°C after 22 months. Comparing this data with model predictions revealed that the model predicted a 90% reliability over the two years, and the actual observed reliability was 90% (18 / 20), a good match. However, the model did not cover battery issues and could not predict this failure. Therefore, the method of this invention incorporates a battery capacity decay model at the device level. For drift failure data, a Bayesian update was used to slightly correct the activation energy estimate of silicon drift.

[0167] (5) Prediction and Verification

[0168] After updating the model by integrating field data, the predicted B10 lifespan of the sensor under its operating environment is approximately 5.5 years, meaning there is a 10% probability of failure within 5.5 years. The predicted percentages of major failure modes are: encapsulation resin failure (40%), battery failure (25%), solder joint cracking (15%), chip drift (15%), and others (5%). To address the encapsulation issue, improvements such as replacing the resin with a high-temperature resistant one are proposed. Subsequently, an improved version of the sensor was produced and subjected to another high-temperature lifespan test, resulting in a lifespan improvement of approximately 50%, validating the effectiveness of the improvements.

[0169] This invention's method integrates information from testing, simulation, and field observations to reliably assess the lifespan of power sensors and pinpoint key weaknesses for targeted improvements. Sensors optimized using this method achieve their expected lifespan (>5 years of trouble-free operation) in actual operation, reducing maintenance costs.

[0170] Figure 7 This is a structural diagram of a sensor multi-scale through-reliability evaluation system according to a preferred embodiment of the present invention.

[0171] like Figure 7 As shown, the present invention provides a sensor multi-scale through-reliability evaluation system, characterized in that the system includes:

[0172] The first establishment unit 701 is used to establish a material-level model based on the key parameters, failure modes and failure criteria of the sensor material, and to calibrate the parameters of the material-level model through accelerated testing or database.

[0173] The second establishment unit 702 is used to map external environmental stress to key components of the sensor through finite element or analytical methods, and establish a device-level model based on the structural features of the material-level model;

[0174] The third unit 703 is used to establish a system-level model based on the effects of multi-stress coupling, the actual application scenarios of sensors, and device-level models.

[0175] The calibration unit 704 is used to obtain failure data through multi-stress coupling accelerated life tests and to calibrate the key parameters of the system-level model based on the failure data.

[0176] The calibration unit 705 is used to collect online monitoring data from sensors in operation and to dynamically calibrate the parameters of the system-level model based on the online monitoring data.

[0177] Result unit 706 is used to assess the remaining life or failure risk of in-service sensors based on the corrected system-level model and output reliability assessment results.

[0178] Preferably, the correction unit 705 is also used to correct the parameters of the device-level model and the system-level model:

[0179] Finite element or multiphysics simulation methods are used to analyze the coupling stress of sensors under temperature, vibration, electromagnetic and humidity environments, obtain the stress amplification factor or attenuation factor of key components of the sensor, and correct the parameters of the device-level model and system-level model by the amplification factor or attenuation factor.

[0180] Preferably, the third establishing unit 703 is also used to establish a multi-stress coupling model; the multi-stress coupling model adopts the generalized Eyring equation or the competing failure model, and considers the interactive effects of temperature T, mechanical stress S1, electromagnetic stress S2, and humidity; wherein the γ·S1·S2 term is used to represent the coupling coefficient between stresses, and the coupling coefficient is obtained through multi-stress coupling accelerated test;

[0181] It also includes testing the system-level model using key stresses based on a multi-stress coupling model. The multi-stress coupling model is as follows:

[0182]

[0183] Where T is temperature, S1 and S2 are stresses, the γS1S2 term can represent the interaction term between stresses S1 and S2, L is life, C is a constant, B is a constant, α is the stress coefficient, and β is the stress coefficient.

[0184] Preferably, the system-level model includes a lifetime distribution model, in which lifetime distribution models are established for each major failure mechanism, and the mechanisms are combined using a method of parallel or competitive failure to obtain the overall lifetime distribution of the sensor. When there are multiple stress interactions, the lifetime prediction results that are closer to the actual working conditions are obtained by correcting the lifetime parameters of each mechanism or introducing a synergistic factor.

[0185] Preferably, the multi-stress coupled accelerated life test includes:

[0186] Single stress constant test: Life test is conducted under single-factor conditions such as different temperatures or different vibration amplitudes to extract the parameters of the Arrhenius and Coffin-Manson basic models;

[0187] Multi-stress coupling accelerated test: The test life of the sensor is observed under the combined stress conditions of high temperature + vibration + humidity. If the test life is significantly lower than the single-factor predicted life, it indicates that there is a negative synergistic effect under the combined stress conditions. The coupling term coefficients of the system-level model are corrected based on the negative synergistic effect.

[0188] The stress level, sample size, and test cycle of multi-stress coupled accelerated life tests are designed to identify the main failure modes and more closely approximate reality.

[0189] Preferably, the correction unit 705 is further configured to perform Weibull distribution fitting on the test lifetime obtained from the multi-stress coupling accelerated life test, extract characteristic lifetime and shape factor parameters; and correct the failure mechanism parameters or stress coefficients of the system-level model based on the deviation between the test results of the multi-stress coupling accelerated life test and the evaluation results of the system-level model.

[0190] Preferably, the online monitoring data includes sensor performance degradation curves, on-site environmental stress spectra, and actual failure times;

[0191] The correction unit 705 is also used to combine the online monitoring data with the prior model in the system-level model, construct the likelihood function and use Bayes' formula to update the parameters of the system-level model to obtain the posterior distribution of the system-level model; when the cumulative amount of online monitoring data reaches the threshold, the parameter update process of the system-level model is repeated to realize the dynamic adaptive correction of the system-level model.

[0192] Preferably, the result unit 706 is further configured to trigger an early warning signal and generate maintenance or replacement recommendations when the predicted remaining lifespan of the in-service sensor is lower than a preset lifespan threshold or the performance degradation rate exceeds a preset rate threshold; at the same time, it records the failure symptom information and feeds it back to the system-level model.

[0193] The present invention provides a computer-readable storage medium, characterized in that the computer-readable storage medium stores a computer program, the computer program being used to execute a sensor multi-scale interconnect reliability assessment method.

[0194] This invention provides an electronic device, which includes: a processor and a memory; wherein,

[0195] Memory, used to store processor-executable instructions;

[0196] A processor is used to read executable instructions from memory and execute the instructions to implement a multi-scale through reliability assessment method for sensors.

[0197] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code. The solutions in the embodiments of the present invention can be implemented using various computer languages, such as the object-oriented programming language Java and the interpreted scripting language JavaScript.

[0198] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations 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, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0199] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0200] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0201] Although preferred embodiments of the invention have been described, those skilled in the art, upon learning the basic inventive concept, can make other changes and modifications to these embodiments. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments as well as all changes and modifications falling within the scope of the invention.

[0202] Obviously, those skilled in the art can make various modifications and variations to this invention without departing from its spirit and scope. Therefore, if these modifications and variations fall within the scope of the claims of this invention and their equivalents, this invention also intends to include these modifications and variations.

[0203] The invention has been described with reference to a few embodiments. However, as will be known to those skilled in the art, and as defined in the appended claims, other embodiments besides those disclosed above fall equivalently within the scope of the invention.

[0204] Generally, all terms used in the claims are to be interpreted according to their ordinary meaning in the art, unless otherwise expressly defined herein. All references to “a / the / the [device, component, etc.]” ​​are openly interpreted as at least one instance of said device, component, etc., unless otherwise expressly stated. The steps of any method disclosed herein need not be performed in the exact order disclosed unless explicitly stated otherwise.

Claims

1. A method for evaluating the multi-scale continuity reliability of sensors, characterized in that, The method includes: Based on the parameters, failure modes, and failure criteria of sensor materials, a material-level model is established, and the parameters of the material-level model are calibrated through accelerated testing or database. Using finite element or analytical methods, external environmental stress is mapped to the components of the sensor, and a device-level model is established based on the structural features of the material-level model. Based on the effects of multi-stress coupling, the actual application scenarios of sensors, and the device-level model, a system-level model is established. Failure data is obtained through multi-stress coupling accelerated life testing, and the parameters of the system-level model are calibrated based on the failure data. Collect online monitoring data from sensors during operation, and dynamically correct the parameters of the system-level model based on the online monitoring data; Based on the corrected system-level model, the remaining lifespan or failure risk of in-service sensors is assessed, and reliability assessment results are output.

2. The method according to claim 1, characterized in that, It also includes correcting the parameters of the device-level model and the system-level model: Finite element or multiphysics simulation methods are used to analyze the coupling stress of the sensor under temperature, vibration, electromagnetic and humidity environments, obtain the stress amplification factor or attenuation factor of the key components of the sensor, and correct the parameters of the device-level model and system-level model by the amplification factor or the attenuation factor.

3. The method according to claim 1 further includes establishing a multi-stress coupling model; the multi-stress coupling model adopts the generalized Eyring equation or the competing failure model, considering the interactive effects of temperature T, mechanical stress S1, electromagnetic stress S2, and humidity; wherein the γ·S1·S2 term is used to represent the coupling coefficient between stresses, and the coupling coefficient is obtained through multi-stress coupling accelerated test; It also includes testing the system-level model using a multi-stress coupling model based on key stresses, wherein the multi-stress coupling model is: in, T represents temperature, S1 and S2 represent stress, the γS1S2 term can represent the interaction term between stresses S1 and S2, L represents life, C is a constant, B is a constant, α is the stress coefficient, and β is the stress coefficient.

4. The method according to claim 3, characterized in that, The system-level model includes a lifetime distribution model, which establishes lifetime distribution models for each major failure mechanism and combines them using a method of parallel or competitive failure of the mechanisms to obtain the overall lifetime distribution of the sensor. When there are multiple stress interactions, the lifetime prediction results that are closer to the actual working conditions are obtained by correcting the lifetime parameters of each mechanism or introducing a synergistic factor.

5. The method according to claim 3, characterized in that, The multi-stress coupling accelerated life test includes: Single stress constant test: Life test is conducted under single-factor conditions such as different temperatures or different vibration amplitudes to extract the parameters of the Arrhenius and Coffin-Manson basic models; Multi-stress coupling accelerated test: The test life of the sensor is observed under the combined stress conditions of high temperature + vibration + humidity. If the test life is significantly lower than the single-factor predicted life, it indicates that there is a negative synergistic effect under the combined stress conditions. The coupling term coefficients of the system-level model are corrected based on the negative synergistic effect. The stress level, sample size, and test cycle of multi-stress coupled accelerated life tests are designed to determine the main failure modes and make the test design more consistent with actual working conditions.

6. The method according to claim 5, characterized in that, The test lifetimes obtained from the multi-stress coupled accelerated life test are fitted with a Weibull distribution to extract characteristic lifetimes and shape factor parameters; and the failure mechanism parameters or stress coefficients of the system-level model are corrected based on the deviation between the test results of the multi-stress coupled accelerated life test and the evaluation results of the system-level model.

7. The method according to claim 1, characterized in that, The online monitoring data includes sensor performance degradation curves, on-site environmental stress spectra, and actual failure times. The online monitoring data is combined with the prior model in the system-level model to construct a likelihood function and use Bayes' theorem to update the parameters of the system-level model, thereby obtaining the posterior distribution of the system-level model. When the cumulative amount of the online monitoring data reaches a threshold, the parameter update process of the system-level model is repeated to achieve dynamic adaptive correction of the system-level model.

8. The method according to claim 1, characterized in that, When the predicted remaining lifespan of an in-service sensor is lower than a preset lifespan threshold or the performance degradation rate exceeds a preset rate threshold, an early warning signal is triggered, and maintenance or replacement recommendations are generated; at the same time, the failure symptom information is recorded and fed back to the system-level model.

9. A multi-scale through-system reliability assessment system for sensors, characterized in that, The system includes: The first establishment unit is used to establish a material-level model based on the key parameters, failure modes and failure criteria of the sensor material, and to calibrate the parameters of the material-level model through accelerated testing or database. The second building unit is used to map external environmental stress to key components of the sensor using finite element or analytical methods, and to build a device-level model based on the structural features of the material-level model. The third establishment unit is used to establish a system-level model based on the effects of multi-stress coupling, the actual use scenarios of the sensor, and the device-level model. The calibration unit is used to obtain failure data through multi-stress coupling accelerated life tests and to calibrate the key parameters of the system-level model based on the failure data; The calibration unit is used to collect online monitoring data from the sensors in operation and to dynamically calibrate the parameters of the system-level model based on the online monitoring data. The results unit is used to assess the remaining lifespan or failure risk of in-service sensors based on the corrected system-level model and output reliability assessment results.

10. The system according to claim 9, characterized in that, The correction unit is also used to correct the parameters of the device-level model and the system-level model: Finite element or multiphysics simulation methods are used to analyze the coupling stress of the sensor under temperature, vibration, electromagnetic and humidity environments, obtain the stress amplification factor or attenuation factor of the key components of the sensor, and correct the parameters of the device-level model and system-level model by the amplification factor or the attenuation factor.

11. The system according to claim 9, wherein the third establishing unit is further configured to establish a multi-stress coupling model; the multi-stress coupling model adopts the generalized Eyring equation or a competing failure model, considering the interactive effects of temperature T, mechanical stress S1, electromagnetic stress S2, and humidity; wherein the γ·S1·S2 term is used to represent the coupling coefficient between stresses, and the coupling coefficient is obtained through multi-stress coupling accelerated test; It also includes testing the system-level model using a multi-stress coupling model based on key stresses, wherein the multi-stress coupling model is: in, T represents temperature, S1 and S2 represent stress, the γS1S2 term can represent the interaction term between stresses S1 and S2, L represents life, C is a constant, B is a constant, α is the stress coefficient, and β is the stress coefficient.

12. The method according to claim 11, characterized in that, The system-level model includes a lifetime distribution model, which establishes lifetime distribution models for each major failure mechanism and combines them using a method of parallel or competitive failure of the mechanisms to obtain the overall lifetime distribution of the sensor. When there are multiple stress interactions, the lifetime prediction results that are closer to the actual working conditions are obtained by correcting the lifetime parameters of each mechanism or introducing a synergistic factor.

13. The system according to claim 9, characterized in that, The multi-stress coupling accelerated life test includes: Single stress constant test: Life test is conducted under single-factor conditions such as different temperatures or different vibration amplitudes to extract the parameters of the Arrhenius and Coffin-Manson basic models; Multi-stress coupling accelerated test: The test life of the sensor is observed under the combined stress conditions of high temperature + vibration + humidity. If the test life is significantly lower than the single-factor predicted life, it indicates that there is a negative synergistic effect under the combined stress conditions. The coupling term coefficients of the system-level model are corrected based on the negative synergistic effect. The stress level, sample size, and test cycle of multi-stress coupled accelerated life tests are designed to determine the main failure modes and make the test design more consistent with actual working conditions.

14. The system according to claim 13, characterized in that, The correction unit is also used to perform Weibull distribution fitting on the test lifetime obtained from the multi-stress coupling accelerated life test, and extract characteristic lifetime and shape factor parameters. Based on the deviation between the test results of the multi-stress coupling accelerated life test and the evaluation results of the system-level model, the failure mechanism parameters or stress coefficients of the system-level model are corrected.

15. The system according to claim 9, characterized in that, The online monitoring data includes sensor performance degradation curves, on-site environmental stress spectra, and actual failure times. The correction unit is further configured to combine the online monitoring data with the prior model in the system-level model, construct a likelihood function and use Bayes' theorem to update the parameters of the system-level model to obtain the posterior distribution of the system-level model; when the cumulative amount of the online monitoring data reaches a threshold, the parameter update process of the system-level model is repeated to achieve dynamic adaptive correction of the system-level model.

16. The system according to claim 9, characterized in that, The result unit is also used to trigger an early warning signal and generate maintenance or replacement suggestions when the predicted remaining lifespan of the in-service sensor is lower than a preset lifespan threshold or the performance degradation rate exceeds a preset rate threshold; at the same time, it records the failure symptom information and feeds it back to the system-level model.

17. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program for performing the method of any one of claims 1-8.

18. An electronic device, characterized in that, The electronic device includes: a processor and a memory; wherein, The memory is used to store the processor-executable instructions; The processor is configured to read the executable instructions from the memory and execute the instructions to implement the method of any one of claims 1-8.

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