A method for identifying influence factors of a working process of a part of an aero-engine power control device

Abstract

The application discloses a kind of identification methods of the influence factor of aero-engine power control device parts working process, it is related to aero-engine maintenance technical field, by the risk level and repair means effectiveness level of data quantization baseline unit are matched diagnosis, to judge whether the current maintenance means can be matched with the risk level of parts, effectively improve the control precision of influence factor in repair process, eliminate internal and external field product quality hidden danger, comprehensively improve the quality prevention ability, process control ability and quality control ability in product repair process, it plays a guiding role to the systematic method of process development and optimization.

Description

Technical Field

[0001] This invention relates to the field of aircraft engine maintenance technology, and more specifically, to a method for identifying factors affecting the working process of components in an aircraft engine power control device. Background Technology

[0002] Aero engines are complex, multi-component coupled systems, and the power control unit is a crucial component of these systems. Due to the unique nature of their functions, the power control unit suffers from a persistently high failure rate both in-flight and field conditions, severely impacting flight safety and combat training support for military units. Currently, repair companies both domestically and internationally only use fault tree analysis to reverse-engineer the faulty component after a failure occurs, failing to move the control checkpoint forward, using the power control unit components as a starting point to identify the influencing factors in their operation and improve the precision of controlling these influencing factors during the repair process.

[0003] When formulating precise control measures for aero-engine power control systems, the first step is to accurately identify the influencing factors of the component's operating process. Different components have different operating processes, resulting in different working effects and deviations. Therefore, it is necessary to identify the influencing factors based on the component's category and operating characteristics. Furthermore, the experience and data accumulated during actual repairs are not well integrated with design data, leading to a weak connection between theory and practice. Without a complete identification of the influencing factors of the operating process of aero-engine power control system components, repair personnel struggle to accurately control the breadth and depth of repairs, resulting in insufficient foresight and potentially leading to the improper handling of repair processes requiring stricter control.

[0004] The performance of components in aero-engine power control systems not only affects the performance of accessories and the engine itself, but also constantly impacts the operational and maintenance efficiency of military aircraft, playing a crucial role in enhancing the combat capabilities of the armed forces. Therefore, while vigorously developing aviation equipment, accurately identifying the influencing factors in the operational process of aero-engine power control system components is a pressing issue that needs to be addressed in the field of aviation equipment maintenance. Summary of the Invention

[0005] The purpose of this invention is to provide a method for identifying factors in the working process of components of an aero-engine power control device. This method can improve the accuracy of controlling influencing factors during the repair process, greatly eliminate potential product quality hazards in both internal and external fields, and comprehensively enhance the quality prevention, process control, and quality assurance capabilities during product repair.

[0006] The embodiments of the present invention are implemented as follows:

[0007] A method for identifying factors influencing the working process of components in an aero-engine power control device, comprising:

[0008] S1. Identify the baseline units of the power control device components and classify the baseline units according to their risk level to obtain the risk level of the baseline units;

[0009] S2. Based on the structural principles of the baseline unit, analyze its operating modes and coupling mechanism;

[0010] S3. Identify the influencing factors of baseline unit failure throughout its entire life cycle by combining operating modes and coupling mechanisms;

[0011] S4. Collect fault data and determine the weight of each influencing factor through deviation analysis;

[0012] S5. Calculate the risk coefficient of the baseline unit based on the weight and risk level of the influencing factors, and perform a matching diagnosis in conjunction with the effectiveness level of repair methods.

[0013] Furthermore, in other preferred embodiments of the present invention, in step S3, the risk factor is calculated by the following formula.

[0014] ,

[0015] Where Z represents the risk coefficient, c j Indicates the risk level, x j ε represents the weight of the influencing factors. j This is the collinearity correction factor.

[0016] Furthermore, in other preferred embodiments of the present invention, collinearity detection is performed before step S4, including:

[0017] Using influencing factors as regression variables, the variance amplification factor method is used to diagnose multicollinearity among the regression variables. If there is no multicollinearity among the regression variables, then ε j = 0; if multicollinearity exists among the regression variables, then ε j ≠ 0. At this point, we can consider setting ε... j The numerical value is used to correct the calculation results. The specific value can be generated based on historical data clustering or set through expert experience.

[0018] Furthermore, in other preferred embodiments of the present invention, if multicollinearity exists among the regression variables, principal component analysis (PCA) is used to analyze and eliminate multicollinearity. PCA eliminates multicollinearity by converting the original correlated variables into mutually independent principal components, while retaining core information.

[0019] Furthermore, in other preferred embodiments of the present invention, in step S2, vector analysis is performed on the operating state of the baseline unit to determine the operating mode and coupling mechanism.

[0020] Furthermore, in other preferred embodiments of the present invention, in step S4, when collecting fault data, a confidence interval is set according to the probability of occurrence of the influencing factors, and the impact of the influencing factors on the performance of accessories and engine is evaluated through deviation analysis, thereby determining the weight of the influencing factors.

[0021] Furthermore, in other preferred embodiments of the present invention, in step S5, the construction of the effectiveness level of the repair means includes:

[0022] Integrate at least two indicators from the tool measurement accuracy, overall equipment efficiency, and defective product return rate; quantify the indicators into graded intervals Q. Typically, they can be divided into Grade I, Grade II, Grade III, and Grade IV, with value ranges of 0~1, 1~2, 2~3, and 3~4, respectively.

[0023] Furthermore, in other preferred embodiments of the present invention, step S5, which involves matching and diagnosing based on the effectiveness level of the repair methods, includes:

[0024] When the Z value is less than the minimum value of the Q interval, it is determined to be oversaturated repair; when the Z value is within the Q interval, it is determined to be saturated repair; when the Z value is greater than the maximum value of the Q interval, it is determined to be undersaturated repair.

[0025] Specifically, it can be determined according to the following formula.

[0026] ,

[0027] Among them, equations (1), (2), and (3) represent oversaturated repair, saturated repair, and undersaturated repair, respectively.

[0028] Furthermore, in other preferred embodiments of the present invention, the division of the hierarchical interval Q is based on generating discrete levels through historical data clustering or setting continuous threshold intervals through expert experience.

[0029] Furthermore, in other preferred embodiments of the present invention, when the determination result is oversaturation, the precision repair measures are reduced, the effectiveness level of the repair means is re-evaluated, and a re-matching diagnosis is performed in combination with the effectiveness level of the repair means.

[0030] Furthermore, in other preferred embodiments of the present invention, when the determination result is undersaturation, more precise repair measures are added, the effectiveness level of the repair measures is re-evaluated, and a second matching diagnosis is performed in combination with the effectiveness level of the repair measures.

[0031] The beneficial effects of the embodiments of the present invention are:

[0032] This invention provides a method for identifying influencing factors in the working process of components in an aero-engine power control device. It quantifies the risk level of a baseline unit and the effectiveness level of repair methods through data analysis and performs matching diagnostics to determine whether the current maintenance methods match the risk level of the component. This effectively improves the control accuracy of influencing factors during the repair process, significantly eliminates potential product quality hazards in both internal and external environments, and comprehensively enhances the quality prevention, process control, and quality assurance capabilities during product repair. It also provides guidance for a systematic approach to process development and optimization. Attached Figure Description

[0033] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings used in the embodiments will be briefly introduced below. It should be understood that the following drawings only show some embodiments of the present invention and should not be regarded as a limitation on the scope. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.

[0034] Figure 1 This is a flowchart illustrating a method for identifying influencing factors in the working process of components of an aero-engine power control device, provided as an embodiment of the present invention. Detailed Implementation

[0035] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention are described clearly and completely. Obviously, the described embodiments are only a part of the embodiments of the present invention, not all of them. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without inventive effort are within the scope of protection of the present invention. Therefore, the following detailed description of the embodiments of the present invention provided in the accompanying drawings is not intended to limit the scope of the claimed invention, but merely to illustrate selected embodiments of the invention. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without inventive effort are within the scope of protection of the present invention.

[0036] The following specific examples will provide further explanation. Example

[0037] This embodiment provides a method for identifying factors influencing the working process of components in an aero-engine power control device, specifically for identifying the n2 command piston rod assembly of the main fuel pump regulator of a certain type of aero-engine. The method includes:

[0038] S1. The n2 command piston rod assembly is disassembled into the n2 command piston rod, the n2 command sliding sleeve, and the bushing, with risk levels of 3, 4, and 2, respectively.

[0039] S2. Based on the structural principle and component characteristics of the main fuel pump regulator and performing vector analysis on the working process of the baseline unit, the n2 command piston rod moves axially under the action of the pressure difference between the left and right chambers of the piston; the n2 command sliding sleeve moves axially on the piston rod under the action of the left transmission rod; the bushing provides the motion space for the axial movement of the n2 command piston rod and remains stationary relative to the housing.

[0040] S3. Based on the working modes and coupling mechanisms of the parts involved in the n2 command piston rod assembly, the influencing factors of the working process of the n2 command piston rod are identified as follows: fit clearance, surface roughness, surface finish of the sharp edge of the oil hole, and residue in the piston rod oil hole; the influencing factors of the working process of the n2 command sliding sleeve include fit clearance, surface finish of the end face, and residue in the inner annular groove; the influencing factors of the working process of the bushing include fit clearance and residue in the oil hole.

[0041] S4. Collect fault data on individual components such as the n2 command piston rod, n2 command sliding sleeve, and bushing. Set the confidence interval to 95% based on the probability of occurrence of fault events in the internal and external fields. Through deviation analysis, evaluate the impact of each baseline unit on the accessories and engine performance. Determine the weight gradient of the influencing factors of the n2 command piston rod, n2 command sliding sleeve, and bushing as shown in Table 1.

[0042] Table 1. Weight gradient of influencing factors on the baseline unit of the n2 command piston rod assembly

[0043]

[0044] S5. Using each influencing factor as a regression variable, the variance inflation factor of the regression variables was calculated using SAS software, i.e., multicollinearity diagnosis was performed on the regression variables. The diagnostic results are shown in Table 2. As can be seen from Table 2, the results show that the variance inflation factor of each regression variable is less than 10, therefore it can be determined that there is no significant multicollinearity relationship among the regression variables.

[0045] Table 2. Influencing Factors and Multicollinearity Diagnosis Results of Baseline Unit of n2 Command Piston Rod Assembly

[0046]

[0047] S6. Substitute the weight gradient of each influencing factor and the risk level of the baseline unit into the calculation formula to calculate the risk coefficient, and let ε j = 0, and the calculation results are shown in Table 3.

[0048] Table 3. Calculation Results of Risk Coefficients for Influencing Factors of Baseline Unit of n2 Command Piston Rod Assembly

[0049]

[0050] S7. Through effectiveness analysis of existing repair methods, the repair effectiveness ratings Q of n2 command piston rod, n2 command sliding sleeve and bushing are obtained as Level II (1~2), Level II (1~2) and Level I (0~1), respectively. The results are shown in Table 4.

[0051] Table 4. Effectiveness Rating Table of Existing Methods

[0052]

[0053] Based on the data in Tables 3 and 4, for the n2 command piston rod, its risk coefficient is 1.188, its effectiveness rating is Level II, its Q range is 1~2, its risk coefficient falls within the Q range, and its matching result is saturation repair.

[0054] For the n2 command sleeve, its risk coefficient is 2.204, its effectiveness rating is Level II, and its Q range is 1~2. The risk coefficient is greater than the maximum value of the Q range, and the matching result is undersaturation repair. Precision repair measures should be added to the n2 command sleeve, and the effectiveness rating should be reassessed to ensure that its effectiveness rating reaches Level III (2~3) to match its risk coefficient.

[0055] For the bushing, the risk coefficient is 0.106, the effectiveness rating is I, the Q range is 0~1, the risk coefficient falls within the Q range, and the matching result is saturation repair.

[0056] In summary, the embodiments of the present invention provide a method for identifying influencing factors in the working process of components of an aero-engine power control device. This method quantifies the risk level of a baseline unit and the effectiveness level of repair methods through data analysis and performs matching diagnostics to determine whether the current maintenance methods match the risk level of the component. This effectively improves the control accuracy of influencing factors during the repair process, significantly eliminates potential product quality hazards in both internal and external fields, and comprehensively enhances the quality prevention, process control, and quality assurance capabilities during product repair. It also provides guidance for a systematic approach to process development and optimization.

[0057] The above description is merely a preferred embodiment of the present invention and is not intended to limit the invention. Various modifications and variations can be made to the present invention by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A method for identifying influencing factors in the working process of components of an aero-engine power control device, characterized in that, It includes: S1. Identify the baseline units of the power control device components and classify the baseline units according to their risk level to obtain the risk level of the baseline units; S2. Based on the structural principle of the baseline unit, analyze its working modes and coupling mechanism; S3. Combining the described operating modes and coupling mechanisms, identify the influencing factors of the baseline unit's full lifecycle failure; S4. Collect fault data and determine the weight gradient of each of the influencing factors through deviation analysis; S5. Calculate the risk coefficient of the baseline unit based on the weight gradient of the influencing factors and the risk level, and perform a matching diagnosis in conjunction with the effectiveness level of the repair methods; In step S5, the construction of the effectiveness level of the repair method includes: Integrate at least two of the following indicators: tool measurement accuracy, overall equipment efficiency, and defective product return rate; quantify the indicators into graded intervals Q; Matching diagnosis based on the effectiveness level of the aforementioned repair methods includes: When the Z value is less than the minimum value of the Q interval, it is determined to be oversaturated repair; when the Z value is within the Q interval, it is determined to be saturated repair; when the Z value is greater than the maximum value of the Q interval, it is determined to be undersaturated repair. When the determination result is oversaturation, the precision repair measures are removed, the effectiveness level of the repair measures is re-evaluated, and a new matching diagnosis is performed based on the effectiveness level of the repair measures. When the determination result is undersaturation, more precise repair measures are added, the effectiveness level of the repair methods is re-evaluated, and a second matching diagnosis is performed based on the effectiveness level of the repair methods.

2. The identification method according to claim 1, characterized in that, In step S3, the risk coefficient is calculated using the following formula. ， Where Z represents the risk coefficient, c j Indicates the risk level, x j ε represents the weight of the influencing factors. j This is the collinearity correction factor.

3. The identification method according to claim 2, characterized in that, Before step S4, collinearity detection is performed, including: Using the aforementioned influencing factors as regression variables, the variance amplification factor method is employed to diagnose multicollinearity among the regression variables. If no multicollinearity exists among the regression variables, then ε... j = 0; if multicollinearity exists among the regression variables, then ε j ≠ 0.

4. The identification method according to claim 3, characterized in that, If multicollinearity exists among the regression variables, principal component analysis should be used to analyze and eliminate multicollinearity.

5. The identification method according to claim 1, characterized in that, In step S2, vector analysis is performed on the operating state of the baseline unit to determine the operating mode and the coupling mechanism.

6. The identification method according to claim 1, characterized in that, In step S4, when collecting fault data, confidence intervals are set according to the occurrence probability of the influencing factors, and the impact of the influencing factors on the performance of accessories and engine is evaluated through deviation analysis, thereby determining the weight of the influencing factors.

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