Reliability optimization evaluation method for temperature sensor

Abstract

The invention relates to a method for optimizing and evaluating the reliability of a temperature sensor. The method comprises the following steps of: counting fault mode data of the temperature sensor under actual working conditions of the temperature sensor; performing simulation analysis on the temperature sensor according to the fault mode data, and determining a vibration weak link and estimating a fatigue life value of the temperature sensor according to a simulation analysis result; carrying out reliability modeling on the temperature sensor and calculating a reliability theoretical value of the temperature sensor; according to the simulation analysis result, reliability optimization is carried out on the weak link of the temperature sensor; and the reliability optimization effect is evaluated by comparing and analyzing the fatigue life value and the reliability theoretical value of the temperature sensor before and after optimization. According to the invention, an accurate reliability optimization and evaluation result can be obtained.

Description

Technical Field

[0001] The invention relates to the technical field of aviation engine temperature sensors, and in particular to a reliability optimization evaluation method for temperature sensors. Background Art

[0002] An aircraft engine is a rotary power machine that uses a continuously flowing gas as a working fluid to convert thermal energy into mechanical energy. To detect changes in the temperature inside an aircraft engine, thermocouple temperature sensors are most widely used to measure airflow temperature. Since aircraft engines are typical high-temperature, high-pressure, and high-flow high-load complex fluid mechanical systems, the temperature sensors used in aircraft engines have a harsh working environment. Under the influence of fluid circulation loads and external vibration excitation, the temperature sensors are easily affected to a certain extent, resulting in poor reliability and short life. In severe cases, the temperature sensor may break, and the fragments will enter the engine, destroying other components inside the engine, directly endangering the safety and reliable operation of the engine.

[0003] Due to the changeable working conditions in modern aircraft engines, the low-cycle fatigue damage of temperature sensors is much greater than the high-cycle fatigue damage. The reliability results obtained through traditional reliability simulation tests often have large deviations. Summary of the invention

[0004] The technical problem to be solved by the present invention is to provide a reliability optimization evaluation method for a sensor, which can obtain accurate reliability optimization and evaluation results.

[0005] The technical solution adopted by the present invention to solve the technical problem is: to provide a reliability optimization evaluation method of a temperature sensor, comprising the following steps:

[0006] Collect statistics on the failure mode data of temperature sensors under their actual working conditions;

[0007] Performing simulation analysis on the temperature sensor according to the failure mode data, and determining its vibration weak link and estimating its fatigue life value according to the simulation analysis results;

[0008] Conduct reliability modeling for temperature sensors and calculate their theoretical reliability values;

[0009] According to the simulation analysis results, reliability optimization is performed on the weak links of the temperature sensor;

[0010] The reliability optimization effect is evaluated by comparing and analyzing the fatigue life value and reliability theoretical value of the temperature sensor before and after optimization.

[0011] Further, the simulation analysis of the temperature sensor according to the failure mode data includes:

[0012] Perform modal analysis on the vibration characteristics of the temperature sensor itself, and apply vibration load based on the modal analysis results to perform power spectrum density analysis;

[0013] The finite element method is used to calculate the thermal stress field and thermal strain field of the temperature sensor under temperature load and pressure load.

[0014] Furthermore, the vibration weak link is determined based on a power spectrum density analysis result.

[0015] Furthermore, the fatigue life value is the fatigue life under the conditions of vibration load, temperature load and pressure load obtained by calculation based on the simulation analysis result.

[0016] Furthermore, the reliability optimization of the weak links of the temperature sensor is achieved by optimizing the installation method, packaging connection, and vibration characteristics of the overall structure.

[0017] Furthermore, the reliability modeling of the temperature sensor is achieved by setting each unit of the temperature sensor to have only two states, normal and faulty, and the life of each unit obeys an exponential distribution, and adopting a series reliability model to model the reliability of the temperature sensor.

[0018] Furthermore, the mathematical model for calculating the theoretical value of the reliability of the temperature sensor is expressed as:

[0019]

[0020] Where n is the number of units contained in the temperature sensor, λ i is the failure probability of the ith unit.

[0021] Furthermore, the reliability optimization effect is evaluated by comparing and analyzing the fatigue life value and the reliability theoretical value of the temperature sensor before and after optimization, including:

[0022] According to the fatigue life value of the temperature sensor, the reliability evaluation index value of the temperature sensor is calculated based on the entropy method; the reliability optimization effect ξ is calculated as

[0023]

[0024] Among them, Ui and U'i are the reliability evaluation index values ​​of the temperature sensor before and after optimization, and M and M' are the reliability theoretical values ​​of the temperature sensor before and after optimization.

[0025] Further, the reliability evaluation index value of the temperature sensor is calculated based on the entropy method according to the fatigue life value of the temperature sensor, including:

[0026] Obtain the simulation analysis results of each unit of the temperature sensor under different loads to obtain an index data matrix;

[0027] Normalizing the indicator data matrix to obtain a first processing matrix;

[0028] Taking the quotient of each element in the first processing matrix and the sum of all elements in the column where it is located, a second processing matrix is ​​obtained;

[0029] Calculate the entropy value of each indicator data based on the second processing matrix, and then calculate the entropy redundancy of each indicator data and the weight of each unit of the temperature sensor;

[0030] The overall reliability level of the temperature sensor is calculated based on the weight of each unit and the indicator data matrix.

[0031] Furthermore, the entropy value of each indicator data is expressed as:

[0032]

[0033] Among them, k is the adjustment coefficient, m=3, respectively vibration load, high temperature load and pressure load, P in is the second processing matrix.

[0034] Beneficial Effects

[0035] Due to the adoption of the above-mentioned technical scheme, the present invention has the following advantages and positive effects compared with the prior art: the present invention performs dynamic simulation analysis and static simulation analysis according to the failure mode data of the temperature sensor under its actual working conditions, and determines the vibration weak links of the sensor and estimates the fatigue life value of the sensor according to the simulation analysis results, and then performs targeted reliability optimization, and quantitatively evaluates the reliability optimization effect of the sensor based on the entropy method, so as to obtain accurate reliability optimization and optimization evaluation results of the thermocouple temperature sensor under the working conditions of the aircraft engine. BRIEF DESCRIPTION OF THE DRAWINGS

[0036] Figure 1 is a schematic diagram of a temperature sensor reliability optimization method according to an embodiment of the present invention;

[0037] Figure 2 is a schematic diagram of the optimized structure of the temperature sensor according to an embodiment of the present invention;

[0038] Figure 3 is a schematic diagram of fatigue life prediction of a temperature sensor according to an embodiment of the present invention;

[0039] Figure 4 is a schematic diagram of a theoretical value calculation model for a temperature sensor reliability according to an embodiment of the present invention;

[0040] Figure 5 It is a principle diagram of evaluating reliability optimization effect using the entropy method according to an embodiment of the present invention. DETAILED DESCRIPTION

[0041] The present invention will be further described below in conjunction with specific embodiments. It should be understood that these embodiments are only used to illustrate the present invention and are not intended to limit the scope of the present invention. In addition, it should be understood that after reading the content taught by the present invention, those skilled in the art can make various changes or modifications to the present invention, and these equivalent forms fall within the scope limited by the appended claims of the application equally.

[0042] The embodiment of the present invention relates to a reliability optimization evaluation method for a thermocouple temperature sensor, such as Figure 1 As shown, the following steps are included:

[0043] Firstly, the failure modes of the temperature sensor in the actual service state of the aircraft engine are collected and counted to obtain the failure modes and causes of the temperature sensor. The failure mode statistics of the temperature sensor mainly collect and count the failure mode categories of the temperature sensor in the service process of the aircraft engine. The failure mode categories mainly include no signal output, unstable output and large output error. According to the actual working condition investigation, the reason is: the sensor is subjected to high pressure, high temperature and vibration working conditions in the aircraft engine, and the failure factors that may be generated include broken wire, loose connection between wire and wire and degradation of insulation material performance. This statistical information provides a data basis for clarifying the weak points of the sensor and analyzing the specific failure mechanism.

[0044] Secondly, the fault mechanism analysis is carried out based on the physical model of the sensor. Through dynamic simulation analysis and static simulation analysis, the weak parts of the sensor are identified, and the fatigue life of the sensor and the theoretical value of the sensor reliability under the comprehensive load are calculated. Among them, the dynamic simulation analysis mainly performs modal analysis on the vibration characteristics of the sensor itself, and applies the vibration load of the aircraft engine based on the modal analysis results of the sensor to perform power spectrum density analysis. The vibration load spectrum refers to the "Strength and Vibration of Aircraft Engines". According to the power spectrum density analysis results, the weak links of the temperature sensor in the vibration environment are identified. The static analysis is mainly based on the actual service status of the temperature sensor, considering the effect of the high temperature and high pressure gas in the engine on the temperature sensor, and using the finite element static simulation analysis method to calculate the thermal stress field and thermal strain field of the temperature sensor, which is used as the basis for fatigue life prediction.

[0045] The fatigue life of the sensor is estimated based on the results of the dynamic simulation analysis and static simulation analysis methods in the sensor failure mechanism. Taking into account the fact that the temperature sensor is serving inside an aircraft engine with high temperature, high pressure and vibration, the dynamic simulation analysis results and static simulation analysis results are used as input loads for fatigue life calculation. The fatigue life of the temperature sensor is calculated under the conditions of vibration load, temperature load and pressure load at the same time.

[0046] The reliability theoretical value calculation refers to the Reliability Prediction Manual. The reliability modeling and theoretical value calculation of the temperature sensor are carried out through the series reliability model. The model assumes that each unit of the temperature sensor has only two states: normal and faulty, and the life of each unit follows an exponential distribution. The obtained reliability theoretical value provides a basis for reliability optimization evaluation and quantitative analysis.

[0047] Then, based on the analysis results of the sensor failure mechanism, the sensor structure, installation method, packaging and connection process are optimized, and the sensor fatigue life and reliability theoretical values ​​are recalculated;

[0048] Finally, the predicted fatigue life values ​​and reliability theoretical calculated values ​​of the temperature sensor after optimization design were compared with those before optimization, and the reliability optimization effect of the sensor was quantitatively evaluated based on the entropy method.

[0049] refer to Figure 2 As shown in the figure, it is a schematic diagram of the temperature sensor structure optimization, (a) is the temperature sensor housing, (b) is the temperature sensor protection tube, (c) is the temperature sensor filler, and (d) is the temperature sensor sensing element. The sensor reliability optimization design is achieved through the step-type reinforcement design of different parts of the sensor housing and the protection tube, the optimization of the shape of the temperature sensing element connection solder joint, the optimization of the wire diameter, and the optimization of the welding material at the wire welding point.

[0050] refer to Figure 3 The figure shows the schematic diagram of the fatigue life prediction of the temperature sensor. Based on the operating parameters of the aircraft engine, the sensor is mainly divided into fatigue damage and mechanical damage. The load of fatigue damage is analyzed by static simulation, and temperature load and pressure load are applied; mechanical damage is caused by sudden load conditions and vibration conditions, and is calculated by applying random vibration load (power spectrum density analysis). Secondly, the stress-life SN curve is determined based on the experimental data of the temperature sensor and the material parameters of each unit of the sensor; the expression is as follows: S a =S ’ f (2N f ) b , where: S a is fatigue stress, N f is fatigue life, S ’ fis the fatigue strength coefficient, b is the fatigue strength index; finally, the fatigue damage and fatigue life results of each unit of the temperature sensor are obtained through fatigue analysis software.

[0051] refer to Figure 4 The figure shows the principle diagram of calculating the theoretical value of temperature sensor reliability. The mathematical model for calculating the theoretical value of reliability is as follows: M = 1 / ∑ n i＝1 λ i , n is the number of units contained in the temperature sensor, λ i is the failure probability of the i-th unit in the sensor.

[0052] refer to Figure 5 The figure shows a schematic diagram of the temperature sensor optimization evaluation method based on the entropy method. The objective weight in the comprehensive evaluation is determined according to the failure rate of each unit of the sensor to evaluate the overall reliability level of the temperature sensor. The specific steps are as follows:

[0053] Step 1: Determine the sensor evaluation index data matrix as follows: X in =[S 11 , S 12 ,S 13 …S 1n ;…;S i1 ,S i2 ,S i3 ,…S in ], where S in It is the stress calculation result of n units of the sensor under type i load in dynamic simulation analysis and static simulation;

[0054] Step 2: Standardization of evaluation index data. Since the standards under different types of loads in dynamic simulation analysis and static simulation analysis are different, the index data matrix is ​​standardized. The formula is: R in =(X in -min(X 1n ,…,X in ) / (max(X 1n ,…,X in )-min(X 1n ,…,X in ))+1; after normalization, the value of the sensor data matrix is ​​between [1,2], and the normalized data matrix is ​​R in ;

[0055] Step 3: Take the quotient of each element in the matrix R and the sum of all elements in its column to get the matrix P in , calculate the entropy value of each evaluation index data: E n =-k∑ m i＝1 P inln(P in ), k is the adjustment coefficient, k = 1 / ln(m), m = 3, which are random vibration load, high temperature load and pressure load respectively;

[0056] Step 4: Calculate the entropy redundancy of each stress data: D n =1-E n , and calculate the weights of the n sensor units: ω n =D n / (n-∑ n i＝1 E n ), where 0<ω n <1,∑ n ω n =1.

[0057] Step 5: Multiply each weight by the index data matrix value to obtain the overall reliability level of the temperature sensor. The evaluation model is: U i =∑ n i＝1 ω n R in。

[0058] Step 6: Calculate the final temperature sensor optimization effect ξ, the formula is ξ=((U ’ i -U i ) / U i +(M ’ -M) / M) / 2, where U ’ i and U i are the evaluation index values ​​of the temperature sensor after optimization and without optimization, M ’ and M are the theoretical calculated values ​​of reliability of the temperature sensor after optimization and without optimization, respectively.

Claims

1. A reliability optimization evaluation method for a temperature sensor, characterized in that: The following steps are involved: Collect statistics on the failure mode data of temperature sensors under their actual working conditions; Performing simulation analysis on the temperature sensor according to the failure mode data, and determining its vibration weak link and estimating its fatigue life value according to the simulation analysis results; Conduct reliability modeling for temperature sensors and calculate their theoretical reliability values; According to the simulation analysis results, reliability optimization is performed on the weak links of the temperature sensor; The reliability optimization effect is evaluated by comparing and analyzing the fatigue life value and reliability theoretical value of the temperature sensor before and after optimization.

2. The method according to claim 1, characterized in that The performing simulation analysis on the temperature sensor according to the failure mode data includes: Perform modal analysis on the vibration characteristics of the temperature sensor itself, and apply vibration load based on the modal analysis results to perform power spectrum density analysis; The finite element method is used to calculate the thermal stress field and thermal strain field of the temperature sensor under temperature load and pressure load.

3. The method according to claim 2, characterized in that The vibration weak link is determined based on the power spectrum density analysis result.

4. The method according to claim 2, characterized in that: The fatigue life value is the fatigue life under the conditions of vibration load, temperature load and pressure load, calculated based on the simulation analysis result.

5. The method according to claim 1, characterized in that The reliability optimization for the weak links of the temperature sensor is achieved by optimizing the installation method, packaging connection, and overall structural vibration characteristics.

6. The method according to claim 1, characterized in that The reliability modeling of the temperature sensor is achieved by setting each unit of the temperature sensor to have only two states, normal and fault, and the life of each unit obeys exponential distribution, and adopting a series reliability model to perform reliability modeling of the temperature sensor.

7. The method according to claim 6, characterized in that The mathematical model for calculating the theoretical value of the temperature sensor's reliability is expressed as: Where n is the number of units contained in the temperature sensor, λ i is the failure probability of the ith unit.

8. The method according to claim 7, characterized in that The reliability optimization effect is evaluated by comparing and analyzing the fatigue life value and the reliability theoretical value of the temperature sensor before and after optimization, including: According to the fatigue life value of the temperature sensor, the reliability evaluation index value of the temperature sensor is calculated based on the entropy method; the reliability optimization effect ξ is calculated as Among them, Ui and U'i are the reliability evaluation index values ​​of the temperature sensor before and after optimization, and M and M' are the reliability theoretical values ​​of the temperature sensor before and after optimization.

9. The method according to claim 8, characterized in that The step of calculating the reliability evaluation index value of the temperature sensor based on the fatigue life value of the temperature sensor and the entropy method includes: Obtain the simulation analysis results of each unit of the temperature sensor under different loads to obtain an index data matrix; Normalizing the indicator data matrix to obtain a first processing matrix; Taking the quotient of each element in the first processing matrix and the sum of all elements in the column where it is located, a second processing matrix is ​​obtained; Calculate the entropy value of each indicator data based on the second processing matrix, and then calculate the entropy redundancy of each indicator data and the weight of each unit of the temperature sensor; The overall reliability level of the temperature sensor is calculated based on the weight of each unit and the indicator data matrix.

10. The method according to claim 9, characterized in that The entropy value of each indicator data is expressed as: Among them, k is the adjustment coefficient, m=3, respectively vibration load, high temperature load and pressure load, P in is the second processing matrix.

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