Aircraft oil system redundant material identification method based on EDS and application

Abstract

The invention discloses an EDS-based aircraft oil system redundant material identification method and application, and relates to the technical field of aircraft oil analysis, and the method comprises the following steps: S1, collecting all material matrix components and surface treatment process components of actual aircraft oil system parts by using an EDS, establishing a part standard component database after the complete technological process; s2, cleaning particle redundant substances obtained by filtering the airplane oil needing to be identified; s3, acquiring component test data of the to-be-identified redundant substances by utilizing EDS, and performing standardized data processing on a test result; and S4, comparing the data in the step S3 with the standard component database in the step S2, and identifying the material of the redundant substances, so that the problems that in the prior art, a method for determining the redundant substances of the oil filter particles is limited by the sizes of the particles, or the determination result is inaccurate, the operation process is complicated, and the detection period is long are solved, and the method is suitable for popularization and application. The method can be applied to evaluation of the internal health state of the aircraft oil system.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of aircraft oil analysis, and particularly relates to a method for identifying the material of excess substances in an aircraft oil system based on EDS and application. BACKGROUND

[0002] Excess substances are one of the main factors affecting the reliability of aircraft oil, hydraulic oil and fuel oil systems. During the production and assembly of pipeline system components, some metal chips, welding slag, sealants and other excess substances may be sealed inside. In addition, during the service use of the system, excess substances such as particles and debris may also be generated due to friction and wear. These particle excess substances may cause the system moving parts (such as hydraulic rudders, electro-hydraulic pressure servo valves, etc.) to be stuck and the surface to be severely worn, resulting in serious failure and even affecting flight safety. Therefore, a multi-stage oil filter device needs to be provided in the aircraft oil system to filter out particle excess substances of different sizes, prevent excess substance particles from entering the aircraft oil system, improve the cleanliness of the oil, and achieve the purpose of minimizing system failure, prolonging service life and ensuring flight safety and reliability. In order to determine the source of the particle excess substances and monitor the health of the system, the material of the excess substances needs to be identified.

[0003] After analysis, the sources of excess substances in the aircraft oil system include the production, manufacturing, assembly and use of parts. In addition to the base material, most parts are protected by surface plating, so the oil system excess substances carry plating layer information in addition to the base material, and the friction and wear between moving parts also cause material transfer. The commonly used analysis methods for the particle excess substances obtained by oil filtration include spectral analysis and ferrography analysis. The spectral analysis method can obtain element characteristic spectrum and thus obtain the composition information of the excess substances, but it has the disadvantage of greater limitation on the size of the excess substances to be detected, such as low efficiency in detecting excess substances larger than 10 μm in the oil. The ferrography analysis technology analyzes the shape, size, material and distribution concentration of particles to obtain rough particle size and information about the type of wear state, but the ferrography analysis operation is complex, time-consuming and highly dependent on manual operation and analysis experience, and the accuracy is difficult to guarantee. Moreover, when the material composition obtained by the above method is compared with the existing material grade, there is always a problem that the excess substances cannot be matched with the existing grade due to the presence of plating layer and friction and wear material transfer components, resulting in doubts about the identification of the material of the excess substances.

[0004] Therefore, in view of the complexity of the excess substances in the aircraft oil system, there is an urgent need for a method that can accurately and effectively identify the material of the excess substances. SUMMARY

[0005] The purpose of the present application is to solve the problems of the prior art, such as being limited by the size of the particles, inaccurate results, complex operation process, and long detection period, and to provide an aircraft oil system excess material identification method based on EDS (energy spectrum analyzer), which can be applied to evaluate the internal health status of the aircraft oil system.

[0006] The present application is realized by the following technical solutions: An aircraft oil system excess material identification method based on EDS, comprising the following steps: S1, using EDS to collect all material matrix components and surface treatment process components of actual aircraft oil system parts, and establishing a standard component database of parts after complete process flow; S2, cleaning the particles excess obtained from the aircraft oil filter to be identified; S3, using EDS to obtain the composition test data of the excess to be identified, and then performing standardized data processing on the test results; S4, comparing the data of step S3 with the standard component database in step S2 to identify the material of the excess.

[0007] Further, in step S1, the establishment of the standard component database comprises the following steps: S1.1, material number and matrix construction: The material number is i , i =1,…, n , and a material standard component matrix is constructed: the matrix is composed of 4 chemical component column vectors arranged according to atomic number, including the upper limit vector A immax of the matrix material component determined based on the material standard, the lower limit vector A immin of the component, and the upper limit vector A icmax of the component of the coated part and the lower limit vector A icmin of the component determined based on the process flow and EDS, and each vector element is expressed in mass percentage, and the value is 0 when there is no corresponding element; the 4 vectors are arranged in the order of A immax , A immin , A icmax , and A icmin to form an m×4 matrix, where m is the number of element types corresponding to the atomic number; S1.2, vector normalization processing: Each chemical component column vector in S1.1 is normalized by minimum-maximum normalization, and the formula is: , wherein: x is the original value, xmin and x max is the minimum and maximum value in the vector.

[0008] Further, in step S1, when collecting information data, at least three different batches of standard samples are extracted, and not less than 3 test areas uniformly distributed on the same sample surface are randomly selected, and the size of the test area is 1 ~10000 μm 2 EDS is used for repeated scanning of each area at least 3 times, and the coefficient of variation of each element composition is calculated CV j σ j μ j ×100%, wherein: σ j is the standard deviation, μ j is the average value, The CV of each element is j ≤10% through data screening or repeated testing, and the data meeting the requirements are used for matrix construction.

[0009] Further, in step S2, the excess of the microparticles is cleaned with anhydrous ethanol or acetone.

[0010] Further, when cleaning, the excess of the microparticles is completely soaked in the cleaning liquid, and the cleaning time is not less than 5 min, and the excess of the microparticles is dried after cleaning.

[0011] Further, when cleaning, the excess of the microparticles is cleaned with an ultrasonic cleaning instrument.

[0012] Further, in step S3, the composition test data of the excess to be identified includes the following steps: S3.1, based on EDS, the composition data matrix of the excess to be identified is obtained, which is composed of 4 chemical composition column vectors arranged according to atomic number: Collect the upper limit vector B mmax and the lower limit vector B mmin of the composition of one side surface of the excess of the microparticles, and the vector elements are expressed by mass percentage, and the value is 0 when there is no corresponding element; Collect the upper limit vector B cmax and the lower limit vector B cmin of the composition of the other side surface, and the vector elements are expressed by mass percentage; S3.2, the matrix is constructed as follows: B1=[B mmax , B mmin , B cmax , B cmin ] and B2=[B​​cmax , B cmin , B mmax , B mmin ], Wherein: the matrix line is the atomic number corresponding to the element mass percentage, and the column is different elements arranged in ascending order of atomic number; S3.3, data processing: calculating the coefficient of variation of each element composition in different regions on the same side CV j = σ j / μ j ×100%, discard CV j >10% of the region data; The column vectors of matrix B1 and B2 are normalized by minimum-maximum, and the formula is: , Wherein: x is the original value, x min and x max is the minimum and maximum value in the vector.

[0013] Further, in step S3.1, when collecting the data information of the excess component to be identified, the one side and the other side surface of the particulate excess are determined according to the shape and surface microstructure of the excess, specifically: a. For sheet-shaped excess, the two sides refer to the upper and lower surfaces, and the sampling direction is perpendicular to the plane; b. For strip-shaped excess, the geometric center axis is taken as the reference, and the symmetrical two side surfaces parallel to the axis are collected c. For spherical excess, the spherical surface is any outer surface area, and the middle cross section is the cross section passing through the center of the sphere. Focused ion beam and other methods are used to obtain the cross section, and the cross section direction is any crystal direction.

[0014] Further, in step S3.1, when collecting the data information of the excess component to be identified, at least 3 test regions with a size of 1-10000 μm 2 are selected on the single side surface, and EDS data is collected in each region.

[0015] Further, in step S4, the deviation between the excess component matrix B1, B2 to be identified and the standard chemical component matrix A i is calculated by the following relationship ε 1 i 、 ε 2 i : ε 1 i= (|A1 i -B1| + |A1 i -B2|) / 2 ε 2 i = |A2 i -B1| + |A2 i -B2|) / 2 In ε 1 i , ε 2 i , if the minimum deviation degree of i=1,…,n is ε 1 min , ε 2 min , then the material grade corresponding to the excess of the to-be-identified excess is i , and no coating layer is included; if the deviation degree ε 1 min > ε 2 min , then the material grade corresponding to the excess of the to-be-identified excess is i , and the corresponding coating layer is included.

[0016] The EDS-based aircraft oil system excess material identification method according to any one of the preceding items is applied to evaluating the internal health state of an aircraft oil system.

[0017] Compared with the prior art, the present application has the following advantages and beneficial effects: 1. The EDS-based aircraft oil system excess material identification method proposed in the present application can be applied to the detection of sub-micron to centimeter wide size range of microparticle excess.

[0018] 2. The identification method proposed in the present application is simple to operate, short in detection period, and low in requirement for analysis experience of the operator. The error caused by subjectivity in the traditional analysis method is avoided.

[0019] 3. The identification method proposed in the present application has wide versatility. By comprehensively analyzing the multi-dimensional information of the coating layer on the surface of the part, combined with the systematic data processing and comparative analysis mechanism, high identification accuracy is achieved, and the identification requirements of various types of excess can be effectively covered, which shows good promotion adaptability in engineering application. BRIEF DESCRIPTION OF DRAWINGS

[0020] Figure 1 is a flowchart for identifying an unknown oil filter excess in the present application. DETAILED DESCRIPTION

[0021] The present application will be further described in detail below in combination with examples, but the implementation manner of the present application is not limited thereto.

[0022] Embodiment 1 In order to facilitate the public to understand the present scheme, the present embodiment takes the identification of the excess material of an unknown oil filter as an example to further illustrate the present scheme.

[0023] An EDS-based method for identifying the material of excess material of an aircraft oil system, the specific steps are as follows: Step S1: Collect all material matrix components and surface treatment process components of actual aircraft oil system components using EDS, and establish a component standard database of components after complete process flow.

[0024] The specific operation is as follows The material number is i ( i =1,…,n), the material standard component matrix is constructed by the following method, which is composed of four chemical component column vectors constructed according to atomic number, including: (1) According to the material standard, the component vector A immax , A immin (mass percentage wt.%) of the matrix material is determined, which respectively represents the upper limit vector of the component and the lower limit vector of the component. If there is no corresponding chemical element in the matrix material, the value of the corresponding element is 0; (2) According to the process flow and EDS spectrum, the component vector A icmax , A icmin (mass percentage wt.%) of the part with plating layer is constructed, which respectively represents the upper limit vector of the component and the lower limit vector of the component. For example, the A immax of 4A13 material is: A i mmax= , The standard chemical component matrix of the material is constructed by the above four vectors: A1 i =[A immax , A immin , A icmax , A icmin ] A2 i =[A immax , A immin , A immax , A immin ] A icmax , A icmin is a standard sample extracted from at least three different batches, and the component information of at least five regions of each standard sample is collected by EDS spectrum to establish the obtained component vector.

[0025] The above vectors need to be normalized, and the above vectors are column vectors.

[0026] In this embodiment, minimum-maximum normalization is applied to the column vector of each of the aforementioned chemical components, and the formula is as follows: , in: x The original value, x min and x max These are the minimum and maximum values ​​in the vector.

[0027] The coefficient of variation (CV) of the chemical composition between different test areas of the same sample must be minimized.

[0028] The specific operating method is as follows: When collecting data, at least three different batches of standard samples should be extracted, and no fewer than three uniformly distributed test areas should be randomly selected on the same sample surface. The size of the test areas should be 1~10000μm. 2 Each region was scanned at least three times using EDS, and the coefficient of variation of each elemental component was calculated. CV j = σ j / μ j ×100%, of which: σ j Standard deviation μ j To obtain the average value, the CV of each element is determined through data filtering or repeated testing. j ≤10%, select data that meets the requirements for matrix construction.

[0029] Step S2: Clean the excess particulate matter obtained from the aircraft oil filter that needs to be identified.

[0030] A foreign metal object was found in the main fuel filter element of an aircraft engine, which required testing and analysis. The object was immersed in anhydrous ethanol and ultrasonically cleaned for five minutes, and then dried.

[0031] Step S3: Use EDS to obtain the compositional test data of the foreign substance to be identified, and then perform standardized data processing on the test results.

[0032] Specifically, the following steps are included: S3.1. Obtain the compositional data matrix of the foreign substance to be identified based on EDS, which consists of four column vectors of chemical composition arranged by atomic number: The upper bound vector B of the composition on one side of the surface of the particulate matter was collected. mmax Lower bound vector B mmin Each vector element is represented as a mass percentage, and the value is 0 when there is no corresponding element. Collect the upper bound vector B of the composition on the other side surface.cmax , lower limit vector B cmin , each vector element is expressed in mass percentage; S3.2, construct the matrix as follows: B1=[B mmax , B mmin , B cmax , B cmin ],B2=[B cmax , B cmin , B mmax , B mmin ], Wherein: the matrix element is the atomic number corresponding to the mass percentage of the element, and the row is arranged in ascending order of atomic number; S3.3, data processing: calculate the coefficient of variation of each element composition in different regions on the same side CV j = σ j / μ j ×100%, discard CV j >10% of the region data; The column vectors of matrix B1 and B2 are normalized by minimum-maximum, and the formula is: , Wherein: x is the original value, x min and x max is the minimum and maximum value in the vector.

[0033] EDS was randomly selected on the surface of the certain unknown oil filter residue to measure the chemical composition, and the results are shown in Table 1. In Table 1, the spectra 1, 2 and 3 are the original collected data, and the fourth row is the range of 4A13 material in the standard, which is used to construct the standard material composition matrix. The data in Table 1 is processed to establish the matrix for comparison.

[0034] Table 1

[0035] The measured data is averaged and normalized to establish the composition characteristic vector of the unknown residue.

[0036] Step S4: residue material identification.

[0037] The deviation between the composition matrix B1, B2 of the residue to be identified and the standard chemical composition matrix A i is calculated by the following relationship ε 1 i ,ε 2 i : ε 1 i =(|A1 i -B1|+|A1 i -B2|) / 2; ε 2 i =|A2 i -B1|+|A2 i |-B2) / 2 In ε 1 i , ε 2 i ,i=1,…,n, if the minimum deviation degree is ε 1 min < ε 2 min , then the material grade corresponding to the excess material to be identified is i , without a coating layer; if the deviation degree ε 1 min > ε 2 min , then the material grade corresponding to the excess material to be identified is i , containing a corresponding coating layer.

[0038] The excess material component information vector to be identified is imported into the established standard database for vector retrieval, and the result shows that the excess material component is most similar to the 4A13 (LT13) alloy component, so it can be judged that the excess material is a 4A13 material.

[0039] In the application, the standard database of matrix components and surface treatment process components of all materials contacted by the oil system is constructed based on EDS, to provide a reference for subsequent information comparison. Specifically, the excess material intercepted by the oil filter is first treated by a standard cleaning step, then the component test data is obtained by EDS, then the test results are standardized data processed, and finally the accurate identification of the excess material quality is realized through the quantitative calculation comparison with the standard database, and the source of the excess material is simultaneously traced back and the internal health status of the system is evaluated.

[0040] The above is only a preferred embodiment of the present application, and does not limit the present application in any form. Any simple modification or equivalent change to the above embodiment according to the technical essence of the present application falls within the scope of protection of the present application.

Claims

1. A method for identifying the material composition of foreign matter in an aircraft oil system based on EDS, characterized in that, It comprises the following steps: S1, using EDS to collect all material matrix components of actual aircraft oil system parts and surface treatment process components, and establishing a standard component database of parts after complete process flow; S2, cleaning the particulate excess of the aircraft oil filter to be identified; S3, using EDS to obtain the composition test data of the excess to be identified, and then performing standardized data processing on the test results; S4, comparing the data of step S3 with the standard component database in step S2 to identify the material quality of the excess.

2. The method of claim 1, wherein the method is based on EDS. 2 In step S1, the establishment of the standard component database comprises the following steps: S1.1, material number and matrix construction: The material number is i , i =1,…, n , the standard component matrix of the construction material is composed of 4 chemical component column vectors arranged according to atomic numbers, including the upper limit vector A immax of the base material component determined based on the material standard, the lower limit vector A immin of the component, the upper limit vector A icmax of the component of the plated part determined based on the process flow and EDS, and the lower limit vector A icmin of the component, the elements of each vector are expressed in mass percentage, and the value is 0 when there is no corresponding element; the 4 vectors are arranged in the order of A immax , A immin , A icmax , and A icmin to form an m×4 matrix, wherein m is the number of element types corresponding to the atomic number; S1.2, vector normalization processing: In S1.1, each chemical component column vector is normalized by minimum-maximum, and the formula is: ， where: x is the original value, x min and x max are the minimum and maximum values in the vector.

3. The method for identifying the material composition of foreign matter in an aircraft oil system based on EDS according to claim 2, characterized in that, In step S1, when collecting information data, at least three different batches of standard samples are extracted, and not less than 3 test regions uniformly distributed on the surface of the same sample are randomly selected, and the size of the test region is 1-10000 μm 2 Each region is scanned at least 3 times by EDS, and the coefficient of variation of each element component is calculated CV j = σ j / μ j ×100%, wherein: σ j is the standard deviation, μ j is the average value, CVs of each element were made by data screening or duplicate testing j ≤10%, and the data meeting the requirements were used for matrix construction.

4. The method of claim 1, wherein the method is based on EDS. In step S2, the particulate excess is cleaned with anhydrous ethanol or acetone. ​ 5. The method for identifying the material composition of foreign matter in an aircraft oil system based on EDS according to claim 4, characterized in that: When cleaning, the particulate excess is completely immersed in the cleaning liquid, and the cleaning time is not less than 5 minutes, and then it is dried.

6. The method of claim 5, wherein the method is based on EDS. When cleaning, the particulate excess is cleaned with an ultrasonic cleaning instrument.

7. The method for identifying the material composition of foreign matter in an aircraft oil system based on EDS according to claim 1, characterized in that, In step S3, obtaining the composition test data of the excess to be identified comprises the following steps: S3.1, obtaining the composition data matrix of the excess to be identified based on EDS, which consists of four chemical component column vectors arranged according to atomic number: B, the upper limit vector of the composition on the surface of the side of the collected microparticle excess mmax B, the lower limit vector mmin , each vector element is expressed in mass percentage, and the value is 0 when there is no corresponding element; Collecting the upper limit vector B of the composition of the other side surface cmax , the lower limit vector B cmin , each vector element is expressed in mass percentage; S3.2, the matrix is constructed as follows: B1=[B mmax , B mmin , B cmax , B cmin ], B2=[B cmax , B cmin , B mmax , B mmin ], Wherein: the matrix column is the mass percentage of the element corresponding to the atomic number, and the row is different elements arranged in ascending order of atomic number; S3.3, data processing: calculate the coefficient of variation of each element composition in different regions of the same side CV j = σ j / μ j ×100%, discard CV j >10% of the region data; The column vectors of matrices B1 and B2 are normalized by minimum-maximum, and the formula is: ， where: x is the original value, x min and x max are the minimum and maximum values in the vector.

8. The method of claim 7, wherein the method is based on EDS.

8. The method of claim 7, wherein the method is based on EDS. In step S3.1, when collecting the composition data information of the excess to be identified, the one side and the other side surface of the particulate excess are determined according to the shape and surface microstructure of the excess, specifically: a. For sheet-shaped excess, the two sides refer to the upper and lower surfaces, and the sampling direction is perpendicular to the plane; b. For strip-shaped excess, the geometric center axis is taken as the reference, and the symmetric two side surfaces parallel to the axis are collected c. For spherical excess, the spherical surface is any outer surface area, the middle cross section is the cross section passing through the center of the sphere, and the cross section direction is any crystal direction.

9. The method for identifying the material composition of foreign matter in an aircraft oil system based on EDS according to claim 8, characterized in that: In step S3.1, when collecting the data information of the excess component to be identified, at least 3 test regions with a size of 1-10000 μm are selected on the single surface in a uniform distribution, and EDS data is collected for each region. 2 of 1-10000 μm are selected on the single surface in a uniform distribution, and EDS data is collected for each region.

10. The method of claim 7, wherein the method is used for identifying the material of the excess substance in the aircraft oil system based on EDS. In step S4, the deviation degree between the excess component matrix B1, B2 to be identified and the standard chemical component matrix A is calculated by the following relational expression i ε 1 i , ε 2 i :​ ε 1 i =(|A1 i -B1|+|A1 i -B2|) / 2; ε 2 i =|A2 i -B1|+|A2 i |-B2) / 2 In ε 1 i , ε 2 i , i = 1, …, n, if the minimum deviation degree of ε 1 min < ε 2 min , the material grade corresponding to the excess to be identified is i , without a coating layer; if the deviation degree ε 1 min > ε 2 min , the material grade corresponding to the excess to be identified is i , containing a corresponding coating layer.

11. The EDS-based aircraft oil system excess material identification method according to any one of claims 1-10 is applied to evaluating the internal health status of the aircraft oil system.

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