Method and device for evaluating washing effect of aero-engine and predicting recession of whole machine

Abstract

The invention provides an aero-engine washing effect evaluation and complete machine decline prediction method and device, and the method comprises the steps: selecting an EGTM of an aero-engine as a washing effect evaluation and engine state monitoring indication quantity, and carrying out the preprocessing of the EGTM data; based on the preprocessed EGTM data, analyzing an EGTM recession rule by using an index model, and establishing a non-intervened EGTM operation recession model, a recoverable recession model and an unrecoverable recession model; according to the recoverable decline model and the washing performance recovery reliability model, a prediction method under the EGTM flight cycle number is established, and evaluation of the washing effect of the aero-engine and prediction of complete machine decline are achieved. According to the method, the washing effect of the aero-engine after washing can be evaluated, the change trend of the EGTM of the aero-engine can be predicted, and help is provided for reasonably arranging the washing nodes of the aero-engine.

Description

Technical Field

[0001] The present invention relates to the technical field of aero-engines, and in particular to a method and a device for evaluating the water washing effect of an aero-engine and predicting the decline of the entire engine. Background Art

[0002] Aircraft engines will degrade over time, resulting in reduced performance such as thrust and efficiency. Aircraft engine performance is directly related to flight safety and operating costs. Aircraft engine degradation refers to a decrease in performance, manifested as thrust loss and reduced thermal efficiency. The reduction in thrust and efficiency can be caused by erosion and fouling caused by impurities in the air inhaled by the engine. Although each component of the engine degrades at a different rate, the degradation of the entire engine is essentially the combined effect of the degradation contributions of each of its components. Dirt, corrosion and foreign matter impacting the compressor and turbine blades are the main factors that cause engine degradation. In order to offset these performance reductions, it is a standard procedure to "clean" the engine on schedule. Water washing can remove dirt from the inside of the engine, restore the engine's performance to a certain extent, and extend the engine's on-wing time. From a maintenance and operation perspective, engine cleaning is an effective measure to prevent permanent engine damage and help restore performance. Properly scheduling the engine cleaning schedule is very important to ensure the best operation and maintenance of the engine. Only by accurately tracking the engine's decline process can this condition-based water washing maintenance be achieved, providing maintenance time points, and then formulating an optimized maintenance schedule, reducing unplanned aircraft downtime, and significantly improving the fleet's operating performance. At the same time, during operation, it is also crucial to determine the effectiveness of each cleaning and its corresponding performance recovery.

[0003] Aircraft engines will degrade as the number of flight cycles accumulates. The performance degradation of aircraft engines mainly includes: 1. Recoverable degradation: can be eliminated by actions during the operation of the aircraft engine; 2. Irrecoverable degradation: can be recovered through overhaul, but cannot be recovered during operation; 3. Permanent degradation: residual degradation still exists even after overhaul. Specifically, recoverable degradation refers to the partial performance that can be restored by water washing during the use of the engine, and recoverable degradation is the degradation that can be mitigated by online and offline water washing components. Irrecoverable degradation cannot be mitigated by washing or cleaning alone, but requires an engine overhaul, that is, repairing or replacing parts to restore the degradation of performance loss. Irrecoverable degradation also includes permanent degradation, in which full performance cannot be restored even with an overhaul.

[0004] However, the existing water washing method for aircraft engines adopts a fixed time arrangement, which leads to low water washing efficiency and unreasonable engine working arrangement. In addition, the existing water washing method cannot evaluate the water washing effect of the engine performance, nor can it predict the performance of the water washing node. Therefore, it is necessary to provide an aircraft engine water washing effect evaluation and whole machine degradation prediction method to reasonably arrange the aircraft engine water washing. Summary of the invention

[0005] In view of the defects in the prior art, the purpose of the present invention is to provide a method and device for evaluating the water washing effect of an aircraft engine and predicting the decline of the whole machine. The present invention can evaluate the water washing effect of an aircraft engine after water washing, predict the changing trend of the aircraft engine EGTM, and provide assistance for the reasonable arrangement of aircraft engine water washing nodes.

[0006] To solve the above problems, the technical solution of the present invention is:

[0007] A method for evaluating the water washing effect of an aircraft engine and predicting the decline of the entire engine comprises the following steps:

[0008] The EGTM of aircraft engines is selected as the indicator for water washing effect evaluation and engine status monitoring, and the EGTM data is preprocessed;

[0009] Based on the pre-processed EGTM data, the exponential model was used to analyze the EGTM decline law, and the non-intervention EGTM operation decline model, recoverable decline model and irrecoverable decline model were established.

[0010] According to the recoverable degradation model and the water wash performance recovery reliability model, a prediction method under the EGTM flight cycle number is established to evaluate the water wash effect of aircraft engines and predict the degradation of the whole machine.

[0011] Preferably, the step of selecting the EGTM of the aircraft engine as an indicator for water washing effect evaluation and engine status monitoring, and preprocessing the EGTM data specifically includes: EGTM is an important parameter for monitoring engine performance, and is used to monitor the engine water washing effect. In order to accurately describe the true EGTM performance status of the engine, gross errors are removed and smoothed by hampel filtering, and the fluctuation interference in the data is separated by singular value decomposition to eliminate factors that are unfavorable to trend analysis. After the singular value decomposition processing, the upper and lower envelopes of the EGTM sequence are extracted, and the average value of the upper and lower envelopes is obtained to eliminate local interference in the EGTM sequence.

[0012] Preferably, the non-intervention EGTM operation decline model, the recoverable decline model and the irrecoverable decline model are respectively:

[0013]

[0014] y3=y2-y1

[0015] Wherein, y1 represents the unintervention EGTM operating recession model, a1 represents the starting coefficient of the unintervention EGTM operating recession model, and b1 represents the recession rate coefficient of the unintervention EGTM operating recession model; y2 represents the irreversible recession model, a2 represents the starting coefficient of the irreversible recession model, b2 represents the recession rate coefficient of the irreversible recession model, and y3 represents the recoverable recession model.

[0016] Preferably, the coefficients a1 and a2 are mainly related to the initial EGTM state of the engine and are determined by adaptively determining the initial value of EGTM; the coefficients b1 and b2 are mainly related to the EGTM decay rate, and the coefficients b1 and b2 are statistically analyzed using lognormal distribution, and the lognormal distribution probability distribution function is:

[0017]

[0018] Where X is the mean and σ is the standard deviation.

[0019] Preferably, in the step of establishing a prediction method under the EGTM flight cycle number based on the recoverable decay model and the water washing performance recovery reliability model to realize the evaluation of the water washing effect of the aircraft engine and the prediction of the whole machine decay, the recovery data after EGTM water washing is extracted, and the engine water washing EGTM recovery data satisfies the Weibull distribution, and a water washing performance recovery reliability model based on the Weibull distribution is established.

[0020] Preferably, in the step of establishing a prediction method under the EGTM flight cycle number based on the recoverable decay model and the water wash performance recovery reliability model to achieve the evaluation of the aircraft engine water wash effect and the prediction of the whole machine decay, the prediction method is defined as follows:

[0021] y(t)=y u (t)-y c (t),0<t<t1

[0022] y(t)=y(t i -1)+c i ,t=t i

[0023] y(t)=y u (t)-y c (tt i )+(y(t i )-y u (t i )),t i <t<t i+1 or t>t end

[0024] Where: y is the predicted EGTM; y u is an irreversible decline, y c is a recoverable recession; c i is the amount of EGTM recovered after the i-th washing, which is a Weibull distribution; t i is the i-th washing time point.

[0025] Furthermore, the present invention also provides an aircraft engine water washing effect evaluation and whole machine degradation prediction device, comprising a processor and a memory for storing executable instructions of the processor, wherein the processor is configured to execute the above-mentioned aircraft engine water washing effect evaluation and whole machine degradation prediction method by executing the executable instructions.

[0026] Compared with the prior art, the present invention provides an evaluation method for aircraft engine water washing and establishes an EGTM index prediction method for engine degradation trend, which can better predict the degradation trend of the entire engine and evaluate the water washing effect of the aircraft engine after water washing, providing assistance for the reasonable arrangement of water washing nodes. BRIEF DESCRIPTION OF THE DRAWINGS

[0027] Other features, objects and advantages of the present invention will become more apparent from the detailed description of non-limiting embodiments made with reference to the following drawings:

[0028] Figure 1 This is a flowchart of the method for evaluating the water washing effect of an aircraft engine and predicting the decline of the entire engine according to the present invention;

[0029] Figure 2 This is a detailed flow chart of the method for evaluating the water washing effect of an aircraft engine and predicting the decline of the entire engine according to the present invention;

[0030] Figure 3 This is the exponential recession model diagram;

[0031] Figure 4 Diagrams for single engine EGTM, uninterrupted EGTM operation, recoverable and non-recoverable models;

[0032] Figure 5 It is a diagram of the prediction results of the prediction method of the present invention. DETAILED DESCRIPTION

[0033] The present invention is described in detail below in conjunction with specific embodiments. The following embodiments will help those skilled in the art to further understand the present invention, but are not intended to limit the present invention in any form. It should be noted that, for those of ordinary skill in the art, several changes and improvements can also be made without departing from the concept of the present invention. These all belong to the protection scope of the present invention.

[0034] Specifically, the present invention provides a method for evaluating the water washing effect of an aircraft engine and predicting the decline of the entire aircraft, such as Figure 1 and Figure 2 As shown, the method comprises the following steps:

[0035] S1: Select the EGTM of the aircraft engine as the indicator for water washing effect evaluation and engine status monitoring, and pre-process the EGTM data;

[0036] Specifically, the larger the engine turbine outlet exhaust gas temperature margin (Exhaust Gas Temperature Margin, EGTM), the lower the engine's EGT and fuel consumption will be when the same thrust is obtained under the same external environment. EGTM is an important parameter for monitoring engine performance, and is also often used to monitor the engine water washing effect. EGTM is selected as the engine status monitoring indicator. Engine water washing improves compressor efficiency by cleaning the compressor blades and increasing EGTM, thereby achieving the purpose of saving fuel and extending the life of the engine's hot parts. Different operating environments lead to different EGTM decay rates, so the recovery effect and fuel saving benefits of each engine water washing will be different. Only on the basis of a full understanding of the EGTM decay law can a better water washing maintenance plan for the fleet be formulated.

[0037] The larger the engine turbine outlet exhaust temperature margin (EGTM), the lower the engine's EGT and fuel consumption will be when the same thrust is achieved under the same external environment. EGTM is an important parameter for monitoring engine performance and is also often used to monitor the engine water washing effect.

[0038] Due to the harsh working environment of the engine, the acquired performance data is often mixed with strong random noise, and there are a few data mutations or development trend mutations. There are some obvious outliers in the original EGTM, and the data fluctuates greatly. In order to accurately describe the real EGTM performance state of the engine, the gross error removal and smoothing processing are carried out through hampel filtering. With the increase of flight cycles, EGTM first decreases; after water washing, EGTM has a certain recovery and increase. Since the smoothed data can only show the general law of the engine performance state, and EGTM is a dynamic nonlinear time series data, it is stable as a whole, but there are fluctuations. Singular value decomposition SVD (Singular Value Decomposition) will lag slightly for instantaneous mutation data, and can be used for EGTM trend analysis between each water washing point. In order to better analyze the changing trend of EGTM with flight cycles, the present invention uses SVD to separate the fluctuation interference in the data and eliminate factors that are unfavorable to trend analysis.

[0039] After SVD processing, a large amount of local interference still remains in the EGTM sequence. In order to remove the smaller local interference, the upper and lower envelopes of the EGTM sequence are extracted, and the average value of the upper and lower envelopes is calculated, which effectively eliminates the local interference of the EGTM sequence. By preprocessing the EGTM flight cycle sequence, the decay trend is effectively extracted.

[0040] S2: Based on the pre-processed EGTM data, the exponential model is used to analyze the EGTM decline law, and the non-intervention EGTM operation decline model, the recoverable decline model and the irrecoverable decline model are established;

[0041] Specifically, after the engine is put into use, the EGTM decline trend is roughly fast at first and then slow, so it can be assumed that both recoverable and irrecoverable decline follow an exponential pattern. Therefore, the exponential model is used to analyze the EGTM decline law, and the following non-intervention EGTM operation decline model, recoverable decline model, and irrecoverable decline model are established:

[0042]

[0043] y3=y2-y1

[0044] In the formula, y1 represents the unintervention EGTM operating recession model, a1 represents the starting coefficient of the unintervention EGTM operating recession model, and b1 represents the recession rate coefficient of the unintervention EGTM operating recession model; y2 represents the irreversible recession model, a2 represents the starting coefficient of the irreversible recession model, b2 represents the recession rate coefficient of the irreversible recession model, and y3 represents the recoverable recession model. The specific exponential recession model is as follows Figure 3 shown.

[0045] The EGTM sequence from the first installation of the engine to the first water washing is extracted and fitted through an exponential degradation model to obtain the uninterventional EGTM operation decay model of the engine. The recoverable decay model estimates the entire decay process by the functional relationship between known data; the peak value of the EGTM sequence after water washing is extracted and fitted through an exponential degradation model to obtain the irrecoverable decay model. The single engine EGTM, uninterventional EGTM operation, recoverable and irrecoverable models are as follows: Figure 4 shown.

[0046] The EGTM of the engine shows a continuous downward trend when it is not washed; the irrecoverable model fits the upper limit of the EGTM sequence in different cycles, which is a description of the upper limit of EGTM recovery after washing, showing the irrecoverable decline of EGTM after the engine is washed on time; with the increase of the operating cycle, the recoverable decline also increases. By batch processing the EGTM data of multiple engines, the decline model coefficient of the unit can be obtained.

[0047] The coefficients a1 and a2 are mainly related to the initial EGTM state of the engine, so they are determined by adaptive initial EGTM values; the coefficients b1 and b2 are mainly related to the EGTM decay rate, and the log-normal distribution is used to perform statistical analysis on the coefficients b1 and b2. The log-normal distribution probability distribution function is shown below:

[0048]

[0049] Where X is the mean and σ is the standard deviation.

[0050] S3: Based on the recoverable degradation model and the water wash performance recovery reliability model, a prediction method under the EGTM flight cycle number is established to evaluate the water wash effect of aircraft engines and predict the degradation of the entire machine.

[0051] Specifically, the recovery data of EGTM after water washing is extracted. The water washing EGTM recovery data of this type of engine satisfies the Weibull distribution, and a water washing performance recovery reliability model based on the Weibull distribution is established.

[0052] A prediction method under the EGTM flight cycle number is established. Since the recoverable decay is mainly caused by dust and scaling, it is assumed that after one water washing, the recoverable decay rate returns to a clean state, and the recovery amount of each water washing is generated by the Weibull distribution of the water washing recovery model.

[0053] The prediction method is defined as follows:

[0054] y(t)=y u (t)-y c (t),0<t<t1

[0055] y(t)=y(t i -1)+c i ,t=t i

[0056] y(t)=y u (t)-y c (tt i )+(y(t i )-y u (t i )),t i <t<t i+1 or t>t end

[0057] Where: y is the predicted EGTM; y u is an irreversible decline, y c is a recoverable recession; c i is the amount of EGTM recovered after the i-th washing, which is a Weibull distribution; t i is the i-th washing time point.

[0058] The prediction results are as follows Figure 5 As shown, the prediction method of the present invention can better simulate the decreasing trend of EGTM of aircraft engines.

[0059] Furthermore, the present invention also provides an aircraft engine water washing effect evaluation and whole machine degradation prediction device, comprising a processor and a memory for storing executable instructions of the processor, wherein the processor is configured to execute the above-mentioned aircraft engine water washing effect evaluation and whole machine degradation prediction method by executing the executable instructions.

[0060] The above describes the specific embodiments of the present invention. It should be understood that the present invention is not limited to the above specific embodiments, and those skilled in the art can make various changes or modifications within the scope of the claims, which does not affect the essence of the present invention. In the absence of conflict, the embodiments of the present application and the features in the embodiments can be combined with each other arbitrarily.

Claims

1. A method for evaluating the water washing effect of an aircraft engine and predicting the decline of the entire aircraft, characterized in that: The method comprises the following steps: The EGTM of aircraft engines is selected as the indicator for water washing effect evaluation and engine status monitoring, and the EGTM data is preprocessed; Based on the pre-processed EGTM data, the exponential model was used to analyze the EGTM decline law, and the non-intervention EGTM operation decline model, recoverable decline model and irrecoverable decline model were established. According to the recoverable degradation model and the water wash performance recovery reliability model, a prediction method under the EGTM flight cycle number is established to evaluate the water wash effect of aircraft engines and predict the degradation of the whole machine.

2. The method for evaluating the water washing effect of an aircraft engine and predicting the decline of the entire aircraft engine according to claim 1, characterized in that: The steps of selecting the EGTM of the aircraft engine as an indicator for water washing effect evaluation and engine status monitoring, and preprocessing the EGTM data specifically include: EGTM is an important parameter for monitoring engine performance and is used to monitor the engine water washing effect. In order to accurately describe the actual EGTM performance status of the engine, gross errors are removed and smoothed by using hampel filtering, and fluctuation interference in the data is separated by singular value decomposition to eliminate factors that are unfavorable to trend analysis. After the singular value decomposition processing, the upper and lower envelopes of the EGTM sequence are extracted, the average value of the upper and lower envelopes is obtained, and the local interference of the EGTM sequence is eliminated.

3. The method for evaluating the water washing effect of an aircraft engine and predicting the decline of the entire aircraft engine according to claim 1, characterized in that: The non-intervention EGTM operation decline model, the recoverable decline model and the irrecoverable decline model are respectively: y3=y2-y1 Wherein, y1 represents the unintervention EGTM operating recession model, a1 represents the starting coefficient of the unintervention EGTM operating recession model, and b1 represents the recession rate coefficient of the unintervention EGTM operating recession model; y2 represents the irreversible recession model, a2 represents the starting coefficient of the irreversible recession model, b2 represents the recession rate coefficient of the irreversible recession model, and y3 represents the recoverable recession model.

4. The method for evaluating the water washing effect of an aircraft engine and predicting the decline of the entire aircraft engine according to claim 3, characterized in that: The coefficients a1 and a2 are mainly related to the initial EGTM state of the engine and are determined by adaptively determining the initial EGTM value; the coefficients b1 and b2 are mainly related to the EGTM decay rate. The coefficients b1 and b2 are statistically analyzed using log-normal distribution. The log-normal distribution probability distribution function is: Where X is the mean and σ is the standard deviation.

5. The method for evaluating the water washing effect of an aircraft engine and predicting the decline of the entire aircraft engine according to claim 1, characterized in that: According to the recoverable decay model and the water washing performance recovery reliability model, a prediction method under the EGTM flight cycle number is established to realize the step of evaluating the water washing effect of the aircraft engine and predicting the decay of the whole machine. The recovery data after EGTM water washing is extracted. The water washing EGTM recovery data of the engine satisfies the Weibull distribution, and a water washing performance recovery reliability model based on the Weibull distribution is established.

6. The method for evaluating the water washing effect of an aircraft engine and predicting the decline of the entire aircraft engine according to claim 5, characterized in that: In the step of establishing a prediction method under the EGTM flight cycle number based on the recoverable decay model and the water wash performance recovery reliability model to achieve the evaluation of the aircraft engine water wash effect and the prediction of the whole machine decay, the prediction method is defined as follows: y(t)=y u (t)-y c (t),0<t<t1 y(t)=y(t i -1)+c i ,t=t i y(t) = y u (t) - y c (t - t i )) + (y(t i )) - y u (t i ))), t i <t<t i+1 or t > t end Where: y is the predicted EGTM; y u is an irreversible decline, y c is a recoverable recession; c i is the amount of EGTM recovered after the i-th washing, which is a Weibull distribution; t i is the i-th washing time point.

7. An aircraft engine water washing effect evaluation and whole engine decline prediction device, characterized in that: The device includes a processor and a memory for storing executable instructions of the processor, and the processor is configured to execute the aircraft engine water washing effect evaluation and whole machine degradation prediction method as described in any one of claims 1 to 6 by executing the executable instructions.

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