Lubricating oil system pollution detection method based on automatic data acquisition technology

Abstract

The invention relates to the technical field of aero-engine state monitoring, in particular to a lubricating oil system pollution detection method based on an automatic data acquisition technology. According to the lubricating oil system pollution detection method based on the automatic data acquisition technology, engine operation parameters and vibration characteristic data in the take-off stage are collected in real time through an airborne data management unit; steady-state operation data are screened, lubricating oil inlet pressure is corrected, and accordingly the pollution accumulation trend slope K changing along with operation time is calculated; and generating a comprehensive pollution risk index value KPI by further combining engine vibration characteristics, and dividing risk grades according to a numerical value interval of the comprehensive pollution risk index value KPI. According to the lubricating oil system pollution detection method based on the automatic data acquisition technology, hardware does not need to be additionally installed or an oil way does not need to be changed, analysis is carried out completely based on existing airborne data, the pollution accumulation process can be quantified, early warning of blockage and other problems can be achieved, the omission ratio is remarkably reduced, preventive maintenance is promoted, and the method is suitable for popularization and application. The operation safety is improved; and the maintenance cost is optimized.

Description

Technical Field

[0001] This invention relates to the field of aircraft engine condition monitoring technology, and in particular to a method for detecting contamination in lubricating oil systems based on automatic data acquisition technology. Background Technology

[0002] The lubricating oil system of an aircraft engine is crucial for ensuring reliable lubrication and cooling of critical components such as bearings and gears. The CFM56-7B, an engine widely used in civil aviation, has long suffered from significant contamination issues in its lubricating oil system, impacting flight safety and operational efficiency. The contamination primarily originates from oil leakage caused by a loose central vent pipe fixing nut. When the oil enters the low-pressure turbine jacket, it remains under high temperatures for an extended period, carbonizing and forming hardened sludge. When this sludge breaks up and flows through the return oil path, it can instantly clog the return oil filter, causing a sudden increase in the pressure difference across the filter. If the pressure difference exceeds a set threshold, it will trigger a bypass warning for the lubricating oil filter, forcing the crew to perform abnormal procedures such as engine shutdown, potentially leading to serious incidents such as in-flight engine failure, return to base, or aborted takeoff. These incidents are characterized by their suddenness and insidious nature, posing a continuous threat to aviation safety.

[0003] Currently, the industry mainly relies on the following traditional methods for monitoring and maintaining lubricating oil system contamination:

[0004] Periodic off-site analysis: Laboratory analysis of lubricating oil samples to detect metal shavings, contaminant content, etc. This method has a time lag, cannot achieve real-time monitoring, and cannot capture state changes within the sampling interval.

[0005] Magnetic plugging inspection: This method assesses wear by examining the metal particles adsorbed within a magnetic plug. However, it is only sensitive to magnetic contaminants and cannot effectively detect non-magnetic contaminants such as carbonized sludge and non-metallic particles, resulting in a significant blind spot.

[0006] Borehole inspection: This method uses borehole equipment to visually inspect the inside of the engine, allowing direct observation of carbon buildup. However, this operation is costly, time-consuming, and intermittent, making it difficult to continuously track the pollution accumulation process.

[0007] Threshold-based real-time alarms rely on hardware sensors such as the oil filter differential pressure switch to trigger a cockpit warning when the differential pressure exceeds the limit. This method is a typical "reactive alarm," only alerting when a fault is about to occur or has already occurred, and cannot provide a predictive maintenance window.

[0008] The above methods have problems such as high maintenance costs, poor real-time performance, insufficient early warning capabilities, or limited detection range. In particular, for the progressive pollution process caused by CVT sludge carbonization in the CFM56-7B engine, existing technologies lack effective means to accurately and in advance quantify risk assessment. Summary of the Invention

[0009] (a) Technical problems to be solved

[0010] This invention provides a method for detecting contamination in lubricating oil systems based on automatic data acquisition technology, which overcomes the problems of existing technologies such as lack of real-time monitoring, high maintenance costs, high false negative rates, and inability to provide early warnings.

[0011] (II) Technical Solution

[0012] To achieve the above objectives, the present invention provides a method for detecting contamination in a lubricating oil system based on automatic data acquisition technology, comprising the following steps:

[0013] Step S1: The engine operating parameters of the target aircraft are collected in real time through the airborne data management unit, and the engine vibration characteristics of the target aircraft during the takeoff phase are extracted from the QAR vibration data of the target aircraft.

[0014] Step S2: Based on the preset engine operating status judgment logic, steady-state operating data is selected from the engine operating parameters, and the steady-state operating data is collected and sent.

[0015] Step S3: Correct the oil inlet pressure OIP to obtain OIP. 修正 Based on OIP 修正 Calculate the slope K of the trend as a function of engine running time, and use it as an indicator of the rate of contamination accumulation in the lubricating oil system.

[0016] Step S4: Combining the trend slope K and engine vibration characteristics, generate the lubricating oil system contamination risk index value KPI;

[0017] Based on the numerical range of the risk indicator value (KPI), the corresponding risk level is output.

[0018] Preferably, the engine operating parameters include: lubricating oil inlet pressure OIP, lubricating oil inlet temperature OIT, high-pressure rotor speed N2, and low-pressure rotor speed N1.

[0019] Preferably, in step S2, the engine operating state judgment logic includes: a first steady-state judgment logic condition and a second steady-state judgment logic condition;

[0020] The first steady-state judgment logic includes:

[0021] Determine whether the target aircraft meets the first steady-state conditions, which include: flight altitude > 17000ft, high-pressure rotor speed N2 > 84%, 0.5 ≤ Mach ≤ 0.9, Mach change rate < 0.015%, total temperature change < 1.0℃, altitude change < 100ft, low-pressure rotor speed N1 speed change rate ≤ 0.8%, and high-pressure rotor speed N2 speed change rate ≤ 0.8% and duration ≥ 60s;

[0022] The second steady-state judgment logic includes:

[0023] After the first steady-state condition is met and the duration is ≥10s, it is determined whether the second steady-state condition is met. The second steady-state condition includes: flight altitude >17000ft, high-pressure rotor speed N2 >84%, 0.6≤Mach≤0.9, Mach change rate <0.006%, total temperature change <1.0℃, altitude change <100ft, low-pressure rotor speed N1 speed change rate ≤0.5%, high-pressure rotor speed N2 speed change rate ≤0.5% and duration ≥60s.

[0024] Preferably, the collection and transmission of steady-state operating data in step S2 includes:

[0025] When the target aircraft enters cruise mode and meets the first steady-state condition, the airborne data management unit performs steady-state data acquisition.

[0026] When the target aircraft meets the second steady-state condition, the airborne data management unit performs steady-state data acquisition again.

[0027] The collected steady-state data is transmitted through the target aircraft's ACARS system when the target aircraft's flight altitude is less than 15,000 ft.

[0028] Preferably, in step S3, the correction of the lubricating oil inlet pressure OIP is a compensation calculation based on the quantitative relationship between the lubricating oil inlet pressure OIP, the high-pressure rotor speed N2, and the lubricating oil inlet temperature OIT.

[0029] The compensation calculation formula is as follows:

[0030]

[0031] Where a is the influence coefficient of N2 rotation speed on OIP, a value is 0.562, b is the reference value of N2, b value is 94, c is the influence coefficient of OIT temperature on OIP, c value is 0.130, and d is the reference value of OIT, d value is 100.

[0032] Preferably, the quantitative relationship is as follows: the lubricating oil inlet pressure OIP is positively correlated with the high-pressure rotor speed N2, and the lubricating oil inlet pressure OIP is negatively correlated with the lubricating oil inlet temperature OIT.

[0033] Preferably, in step S3, the trend slope K is calculated using a univariate linear regression method, with engine running time as the independent variable, and... Using the variable as the dependent variable, a continuous univariate linear regression calculation is performed based on a preset rolling data window to obtain the trend slope K value corresponding to each data window.

[0034] Preferably, in step S4, the formula for generating the lubricating oil system contamination risk index value (KPI) is:

[0035]

[0036] Where J is the mean of K, Let K be the standard deviation of the trend slope. , for The mean, for standard deviation This represents the average vibration of the engine's low-pressure turbine, extracted from QAR data, during the aircraft's runway phase from takeoff to the start of takeoff. This is the reference threshold for VIB_LPT. This represents the standard deviation of the vibration values ​​of the engine low-pressure turbine extracted from QAR data during the aircraft's runway phase, from the start of taxiing to takeoff. for The reference threshold.

[0037] Preferably, the risk levels classified according to risk indicator values ​​(KPIs) include:

[0038] When KPI < 0.6, it is a low-risk level; when 0.6 ≤ KPI < 0.9, it is a medium-risk level; and when KPI ≥ 0.9, it is a high-risk level.

[0039] (III) Beneficial Effects

[0040] This invention provides a method for detecting contamination in a lubricating oil system based on automatic data acquisition technology. By collecting engine operating parameters and QAR vibration data in real time through an airborne data management unit, and combining steady-state data screening and index correction, the method can quantify the contamination accumulation process of the lubricating oil system. This avoids the problem that traditional methods rely on off-site testing or hardware inspection, which often trigger alarms only after a fault occurs. By calculating the trend slope K and risk index KPI, the method can issue early warnings tens of flight hours before serious problems such as return oil filter blockage occur, significantly reducing the missed detection rate.

[0041] This method is entirely based on the calculation of the logic of the airborne data management unit. It does not require the installation of sensors, modification of lubricating oil circuits, or installation of additional hardware, thus reducing the need for manual intervention and lowering the cost of use. By generating comprehensive risk indicators (KPIs) and classifying risk levels, it simplifies complex pollution problems into intuitive numerical outputs, making it easier for maintenance personnel to identify and judge them. This achieves the goals of early warning, preventive maintenance, reducing operational risks, and optimizing maintenance costs. Attached Figure Description

[0042] Figure 1 This diagram illustrates a flow chart of a lubricating oil system contamination detection method based on automatic data acquisition technology according to the present invention.

[0043] Figure 2 This invention presents a flowchart of the first steady-state judgment process for a lubricating oil system contamination detection method based on automatic data acquisition technology.

[0044] Figure 3 This invention presents a flowchart of the second steady-state judgment process for a lubricating oil system contamination detection method based on automatic data acquisition technology.

[0045] Figure 4 The flowchart illustrates the steady-state operation data collection, transmission, and judgment process of a lubricating oil system contamination detection method based on automatic data acquisition technology according to the present invention. Detailed Implementation

[0046] The present invention will now be described in detail with reference to the accompanying drawings and embodiments. The technical solutions in the embodiments of the present invention will be clearly and completely described. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0047] In the description of this invention, it is necessary to understand that the orientations or positional relationships indicated by terms such as "upper," "lower," "left," "right," "inner," "outer," "top," and "bottom" are based on the orientations or positional relationships shown in the accompanying drawings. They are intended only to facilitate the description of this invention and to simplify the description, and are not intended to indicate or imply that the components referred to must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, they should not be construed as limitations on this invention.

[0048] like Figure 1-4 As shown, the present invention provides a method for detecting contamination in a lubricating oil system based on automatic data acquisition technology, comprising the following steps:

[0049] Step S1: The engine operating parameters of the target aircraft are collected in real time through the airborne data management unit, and the engine vibration characteristics of the target aircraft during the takeoff phase are extracted from the QAR vibration data of the target aircraft.

[0050] The engine's operating parameters include: lubricating oil inlet pressure OIP, lubricating oil inlet temperature OIT, and high-pressure rotor speed N2.

[0051] The data collection operation in step S1 is the foundation of the contamination detection method. Its purpose is to solve the hidden risks caused by contamination of the lubricating oil system of CFM56-7B engine. Traditional methods rely on off-site testing or borehole inspection, which are costly and have a high rate of missed detection. In contrast, step S1 transforms post-event alarms into pre-event predictions by collecting onboard data in real time, and realizes quantitative monitoring of processes such as carbon buildup in the cavity and sludge backflow.

[0052] The onboard data management unit (DMU) is an integral part of the aircraft engine electronic control system (EEC). It is responsible for recording and transmitting second-level raw data. The lubricating oil inlet pressure (OIP) is derived from the lubricating oil pressure sensor. This sensor compares the difference between the lubricating oil supply pump pressure and the TGB venting pressure, outputs an electrical signal, and converts it into ARINC429 format by the EEC for DMU recording. The lubricating oil inlet temperature (OIT) is acquired by a temperature sensor to monitor the thermal state of the lubricating oil. The high-pressure rotor speed (N2) is used to reflect the engine power status. Vibration data of the target aircraft during takeoff extracted from the quick access recorder (QAR) is used to identify vibration anomalies caused by lubricating oil contamination.

[0053] Step S2: Based on the preset engine operating status judgment logic, steady-state operating data is selected from the engine operating parameters, and the steady-state operating data is collected and sent.

[0054] Since the operating parameters of aircraft engines are significantly affected by flight conditions, in order to accurately detect small and gradual changes caused by pollution, the analysis benchmark must be unified to a specific and stable operating state. Step S2 captures the high-altitude cruise steady-state segment from the continuous flight data stream through a preset two-level steady-state judgment logic, ensuring that the data used for pollution trend analysis all come from the same physical conditions and excludes parameter fluctuation interference from other transient operating conditions.

[0055] like Figure 2-3 As shown, in step S2, the engine operating state judgment logic includes: a first steady-state judgment logic condition and a second steady-state judgment logic condition;

[0056] This first steady-state judgment logic condition serves as a preliminary filter, designed to identify periods during which the aircraft enters a basically stable cruise state. This first steady-state judgment logic includes:

[0057] Determine whether the target aircraft meets the first steady-state condition, which includes: flight state condition, engine state condition, and duration requirement;

[0058] Flight conditions: flight altitude > 17000ft, Mach 0.5 ≤ Mach ≤ 0.9, Mach change rate < 0.015%, and altitude change < 100ft;

[0059] Engine operating conditions: High-pressure rotor speed N2 > 84%, low-pressure rotor speed N1 speed change rate ≤ 0.8%, high-pressure rotor speed N2 speed change rate ≤ 0.8%, and total temperature change < 1.0℃;

[0060] Duration requirement: All of the above conditions must be met simultaneously and last for at least 60 seconds to ensure that a sufficiently long stable operating range is captured.

[0061] After the first steady-state condition is maintained for at least 10 seconds, a second screening is performed using more stringent criteria to identify the optimal analysis data window and determine whether the second steady-state condition is met.

[0062] The second steady-state condition includes: running condition, engine condition, and duration requirement;

[0063] Flight conditions: altitude > 17000ft, Mach 0.6 ≤ Mach ≤ 0.9, Mach change rate < 0.006%, and altitude change < 100ft;

[0064] Engine operating conditions: High-pressure rotor speed N2 > 84%, low-pressure rotor speed N1 speed change rate ≤ 0.5%, high-pressure rotor speed N2 speed change rate ≤ 0.5%, and total temperature change < 1.0℃;

[0065] Duration requirement: All of the above conditions must be met simultaneously and last for at least 60 seconds. Only data collected within this time period is considered valid steady-state data.

[0066] After filtering out steady-state operating data based on two-level steady-state judgment logic, further judgment is made regarding the acquisition and transmission of engine steady-state data. This judgment is implemented through the airborne data management unit, including:

[0067] When the aircraft enters cruise mode and meets the first steady-state condition for the first time, the engine operating parameters are collected;

[0068] When the aircraft transitions from the first steady state to the second steady state, the engine operating parameters are also collected.

[0069] When the aircraft enters the descent phase and the flight altitude is below 15,000 feet, the collected first and second steady-state engine operating parameter data are transmitted through the onboard ACARS system for subsequent analysis.

[0070] It should be noted that among the collected engine steady-state data, the steady-state data that meets the conditions of the second steady state is the optimal choice, and the data that meets the conditions of the first steady state is the second optimal choice. If steady-state data under the conditions of the second steady state cannot be collected, the data of the first steady state can be used directly for subsequent calculations.

[0071] Step S3: Correct the oil inlet pressure OIP to obtain OIP. 修正 Based on OIP 修正 Calculate the slope K of the trend as a function of engine running time, and use it as an indicator of the rate of contamination accumulation in the lubricating oil system.

[0072] Since even under steady state, the absolute value of the lubricating oil inlet pressure OIP is still directly affected by the high-pressure rotor speed N2 and the lubricating oil inlet temperature OIT, it is necessary to perform compensation calculation and correction on the lubricating oil inlet pressure OIP based on the quantitative relationship between the lubricating oil inlet pressure OIP and the high-pressure rotor speed N2 and the lubricating oil inlet temperature OIT.

[0073] The quantitative relationship is as follows: the lubricating oil inlet pressure OIP is positively correlated with the high-pressure rotor speed N2. The speed of N2 directly drives the lubricating oil pump. The higher the speed, the stronger the pumping capacity and the higher the OIP.

[0074] The lubricating oil inlet pressure (OIP) is negatively correlated with the lubricating oil inlet temperature (OIT). An increase in the lubricating oil temperature (OIT) will lead to a decrease in its viscosity, which will reduce the flow resistance in the system and affect the pump efficiency, thus reducing the lubricating oil inlet pressure (OIP).

[0075] Compensation is performed to correct the actual measured OIP value to a unified reference operating point. The compensation correction calculation formula is as follows:

[0076]

[0077] Where a is the influence coefficient of N2 rotation speed on OIP, a value is 0.562, b is the reference value of N2, b value is 94, c is the influence coefficient of OIT change of 1°C on OIP, c value is 0.130, and d is the reference value of OIT, d value is 100.

[0078] In step S3, the trend slope K is calculated using univariate linear regression, with the target aircraft engine running time as the independent variable. Using the variable as the dependent variable, a continuous univariate linear regression calculation is performed based on a preset rolling data window to obtain the trend slope K value corresponding to each data window.

[0079] Step S4: Combine the trend slope K and engine vibration characteristics to generate the lubricating oil system pollution risk index value KPI, and output the corresponding risk level according to the value range of the risk index value KPI.

[0080] In step S4, the formula for generating the KPI value of the lubricating oil system contamination risk index is:

[0081]

[0082] Where J is the mean of K, Let K be the standard deviation of the trend slope. , for The mean, for standard deviation This represents the average vibration of the engine's low-pressure turbine, extracted from QAR data, during the aircraft's runway phase from takeoff to the start of takeoff. This is the reference threshold for VIB_LPT. This represents the standard deviation of the vibration values ​​of the engine low-pressure turbine extracted from QAR data during the aircraft's runway phase, from the start of taxiing to takeoff. for The reference thresholds are A, B, C, and D, which are the weight coefficients of each item. These weight coefficients need to be adaptively calibrated based on historical data.

[0083] Should For the trend slope term, this The current pressure offset term quantifies the degree to which the current pressure level deviates from the historical normal range; the lower the pressure, the greater the risk contribution. This is a vibration level term used to incorporate the potential mechanical consequences of pollution into the assessment; the higher the vibration level, the greater the risk contribution. This is a vibration stability term. Increased vibration fluctuations may be a sign of intermittent poor lubrication or premature wear of components, and it is used to enhance the sensitivity of the detection.

[0084] Risk levels classified according to risk indicator values ​​(KPIs) include:

[0085] When KPI < 0.6, it is considered a low-risk level. At this time, the system is in a healthy state with a low risk of contamination, and it is recommended to perform routine monitoring.

[0086] When 0.6 ≤ KPI < 0.9, it is considered a medium-risk level. At this point, the system shows signs of early contamination or performance degradation. It is recommended to increase the monitoring frequency and arrange relevant checks during the next scheduled inspection.

[0087] When KPI ≥ 0.9, it is considered a high-risk level. At this point, the system is at high risk of contamination and there may be serious blockages or wear that could lead to failure. It is recommended to immediately arrange borehole inspection, lubricating oil spectral analysis, or more in-depth internal inspection to prevent unplanned shutdowns.

[0088] It is understood that the various embodiments mentioned above in this invention can be combined with each other to form combined embodiments without violating the principle and logic. Due to space limitations, this invention will not elaborate further.

[0089] Those skilled in the art will understand that, in the above-described method of the specific implementation, the order in which each step is written does not imply a strict execution order and does not constitute any limitation on the implementation process. The specific execution order of each step should be determined by its function and possible internal logic.

[0090] This invention provides a method for detecting contamination in a lubricating oil system based on automatic data acquisition technology. By collecting engine operating parameters and QAR vibration data in real time through an airborne data management unit, and combining steady-state data screening and index correction, the method can quantify the contamination accumulation process of the lubricating oil system. This avoids the problem that traditional methods rely on off-site testing or hardware inspection, which often trigger alarms only after a fault occurs. By calculating the trend slope K and risk index KPI, the method can issue early warnings tens of flight hours before serious problems such as return oil filter blockage occur, significantly reducing the missed detection rate.

[0091] This method is entirely based on the calculation of the logic of the airborne data management unit. It does not require the installation of sensors, modification of lubricating oil circuits, or installation of additional hardware, thus reducing the need for manual intervention and lowering the cost of use. By generating comprehensive risk indicators (KPIs) and classifying risk levels, it simplifies complex pollution problems into intuitive numerical outputs, making it easier for maintenance personnel to identify and judge them. This achieves the goals of early warning, preventive maintenance, reducing operational risks, and optimizing maintenance costs.

[0092] The various embodiments of the present invention have been described above. These descriptions are exemplary and not exhaustive, nor are they limited to the disclosed embodiments. Many modifications and variations will be apparent to those skilled in the art without departing from the scope and spirit of the described embodiments. The terminology used herein is chosen to best explain the principles, practical application, or technical improvements to the embodiments in the market, or to enable others skilled in the art to understand the embodiments disclosed herein.

Claims

1. An oil system contamination detection method based on data automatic acquisition technology, characterized in that, The method comprises the following steps: Step S1, collecting engine operation parameters of a target aircraft in real time through an onboard data management unit, and extracting engine vibration characteristics of the target aircraft in a take-off stage from vibration data of a QAR of the target aircraft; Step S2, screening steady-state operation data from the engine operation parameters based on preset engine operation state judgment logic, and collecting and sending the steady-state operation data; Step S3. Correcting the lube oil inlet pressure OIP to obtain OIP 修正 , calculating the trend slope K as a lube oil system pollution accumulation speed index based on OIP 修正 as a function of the engine running time; Step S4, generating an oil system pollution risk index value KPI in combination with the trend slope K and the engine vibration characteristics; According to the numerical interval where the risk index value KPI is located, a corresponding risk level is output.

2. The method of claim 1, wherein the method is characterized by, The engine operation parameters include: oil inlet pressure OIP, oil inlet temperature OIT, high-pressure rotor speed N2, and low-pressure rotor speed N1.

3. The method of claim 2, wherein the method is characterized by, In step S2, the engine operation state judgment logic comprises: a first steady-state judgment logic condition and a second steady-state judgment logic condition; The first steady-state judgment logic comprises: determining whether the target aircraft meets the first steady-state condition, the first steady-state condition comprising: flight altitude > 17000ft, high-pressure rotor speed N2 > 84%, 0.5≤Mach≤0.9, Mach change rate <0.015%, total temperature change <1.0℃, altitude change <100ft, low-pressure rotor speed N1 speed change rate ≤0.8%, high-pressure rotor speed N2 speed change rate ≤0.8% and continuous duration ≥60s; The second steady-state judgment logic comprises: After meeting the first steady-state condition and continuous duration ≥10s, determining whether the second steady-state condition is met, the second steady-state condition comprising: flight altitude > 17000ft, high-pressure rotor speed N2 > 84%, 0.6≤Mach≤0.9, Mach change rate <0.006%, total temperature change <1.0℃, altitude change <100ft, low-pressure rotor speed N1 speed change rate ≤0.5%, high-pressure rotor speed N2 speed change rate ≤0.5% and continuous duration ≥60s.

4. The method of claim 3, wherein the method is characterized by, The collection and sending of the steady-state operation data in step S2 comprises: When the target aircraft enters a cruising state and meets the first steady-state condition, the onboard data management unit collects steady-state data; When the target aircraft meets the second steady-state condition, the onboard data management unit collects steady-state data again; The collected steady-state data is sent through the ACARS system of the target aircraft when the flight altitude of the target aircraft is <15000ft.

5. The method of claim 4, wherein the method is characterized by, In step S3, the oil inlet pressure OIP is corrected based on a quantitative relationship between the oil inlet pressure OIP, the high-pressure rotor speed N2 and the oil inlet temperature OIT; The compensation calculation formula is: wherein a is the influence coefficient of N2 rotating speed on OIP, a is 0.562, b is the reference reference value of N2, b is 94, c is the influence coefficient of OIT temperature on OIP, c is 0.130, d is the reference reference value of OIT, d is 100.

6. The method of claim 5, wherein the method is characterized by, The quantitative relationship is that the oil inlet pressure OIP is positively correlated with the high-pressure rotor speed N2, and the oil inlet pressure OIP is negatively correlated with the oil inlet temperature OIT.

7. The method of claim 6, wherein the method is characterized by, In the step S3, the trend slope K is calculated by a linear regression method, taking the engine running time as the independent variable and taking the engine oil temperature as the dependent variable, and the trend slope K is calculated by continuous linear regression of a preset rolling data window. The trend slope K is calculated by continuous linear regression of a preset rolling data window.

8. The method of claim 7, wherein the method is characterized by, In step S4, the formula for generating the oil system pollution risk index value KPI is: wherein J is the mean of K, is the standard deviation of the trend slope K, , is the mean of , is the standard deviation of , is the average value of the engine low pressure turbine vibration from the start of the runway to the takeoff phase extracted from the QAR data, is the reference threshold of VIB LPT, is the standard deviation of the engine low pressure turbine vibration values from the start of the runway to the takeoff phase extracted from the QAR data, is the reference threshold of .

9. The method of claim 8, wherein the method is characterized by, The risk levels divided according to the risk index value KPI include: When KPI < 0.6, it is a low risk level; when 0.6≤KPI < 0.9, it is a medium risk level; and when KPI≥0.9, it is a high risk level.

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