A method for intelligently locating a faulty component of an aeroengine

Abstract

The application particularly relates to a method for intelligently positioning a fault component of an aero-engine, which comprises the following steps: constructing a performance evaluation digital engineering model of each component in a factory test and a performance evaluation digital model of each component of an aero-engine in a field, comparing a performance degradation amount of each component of the aero-engine after a flight cycle with a performance degradation curve of each component in a factory simulation field test, and determining whether the monitored component is in a normal degradation state or a fault state according to whether the difference is greater than a set threshold, so as to determine which component or components have the most serious performance degradation and realize rapid and accurate positioning of the fault component.

Description

Technical Field

[0001] This invention belongs to the field of aero-engine fault diagnosis technology, specifically relating to a method for intelligently locating faulty components in an aero-engine. Background Technology

[0002] For aero-engines operating in field flights, real-time assessment of their overall performance and monitoring of the performance degradation of individual components are crucial for ensuring flight safety and diagnosing engine faults. As flight time increases, the performance degradation of aero-engine components due to mechanical wear gradually increases. When severe mechanical wear occurs, such as blade breakage, it can lead to a significant decline in overall engine performance, even resulting in aircraft destruction and loss of life. When a field engine exhibits severe performance degradation, fault diagnosis is necessary. The first step is to pinpoint which component(s) are malfunctioning, causing the severe performance degradation. Traditional methods for locating faulty components require disassembling the entire engine and following a specific procedure to troubleshoot each component, which is extremely resource-intensive. During the factory testing and field flight phases, engines accumulate a wealth of monitoring data. Building intelligent analysis models based on this data can significantly reduce costs.

[0003] For aero-engines operating in the field, there are very few onboard real-time monitoring parameters. For each component, its corresponding performance parameters, such as pressure ratio, fuel-air ratio, and pressure drop ratio, are generally not monitored. Therefore, to monitor the real-time performance status of each engine component, it is necessary to be able to predict the performance parameters of each component in real time during flight. Currently, some relatively mature real-time prediction models for aero-engine performance parameters exist, and this technology also includes a digital engineering model capable of accurately predicting the performance parameters of each aero-engine component in real time.

[0004] While achieving real-time prediction of the performance parameters of each component has been achieved, the problem of real-time assessment of the performance degradation of each component has not been completely solved. This is because the factors affecting the performance status of engine components include three main parts: environmental conditions, flight conditions, and mechanical wear. Direct comparison of performance parameters cannot distinguish whether the performance parameter degradation is caused by mechanical wear. Therefore, it is necessary to establish a method to assess the performance degradation of each engine component due to mechanical wear, ensuring that the performance degradation comparison is conducted under the same environmental and flight conditions. Summary of the Invention

[0005] To address the problem of accurately locating faulty components, this invention provides an intelligent method for locating faulty components in aero-engines. This method can quickly and accurately locate faulty components when aero-engines experience severe performance degradation, avoiding the problem of traditional methods that require disassembling the engine and consuming a large amount of manpower, material resources, and financial resources.

[0006] To achieve the above-mentioned objectives, the technical solution adopted by this invention is: a method for intelligently locating faulty components in an aero-engine, comprising the following steps:

[0007] S1: Establish a digital engineering model for performance evaluation of each component during factory testing: Based on the data of the aero-engine undergoing factory testing, select environmental parameters and state parameters as model inputs, and the performance parameters of each component as model outputs, and train the digital engineering model for performance evaluation of each component during factory testing.

[0008] S2: Construct the performance degradation curves of each component during factory testing: Simulate the field mission and input the monitored test data into the digital engineering model for performance evaluation of each component trained in step S1, evaluate the performance degradation of each component in real time, and fit the performance degradation curve of the aero-engine over time.

[0009] S3: Establish a digital engineering model for field engine performance evaluation: Use environmental parameters and state parameters from the flight monitoring data of field engine test flights as input to the model, and real-time performance parameters of each engine component as output to train the digital engineering model for field engine performance evaluation.

[0010] S4: Calculate the performance degradation of each component in the field: Input the environmental parameters and state parameters in the monitoring data of the field engine evaluation flights into the field engine performance evaluation digital engineering model in step S3 to obtain the performance degradation of each component at each time in subsequent flights.

[0011] S5: Compare the performance degradation of each component: Compare the actual performance degradation of each component of the field engine at each time point in step S4 with the performance degradation curve in step S2, and determine the degradation status of each component based on the difference and the set threshold.

[0012] Preferably, in step S1, the selected environmental and state parameters are data that can be monitored on both the engine in the factory and the engine during field flight; the performance parameters of each component are parameters that can reflect the performance status of each component, and the performance parameters of each component can be directly measured or calculated during the factory test phase.

[0013] Preferably, in step S3, the real-time performance parameters of each engine component are obtained by establishing a real-time prediction digital engineering model for engine performance parameters. Specifically, the method is as follows: based on the data from the test run of the aero-engine in the factory, environmental parameters, state parameters, and overall engine performance parameters are selected as model inputs, and the performance parameters of each component are used as model outputs to train the real-time prediction digital engineering model for engine performance parameters; based on the real-time prediction digital engineering model for engine performance parameters, the real-time performance parameters of each engine component are obtained by combining the field monitoring data.

[0014] The selected environmental parameters, state parameters, and overall performance parameters are data that will be monitored on both the engine in the factory and the engine during field flight; the performance parameters of each component are parameters that can reflect the performance status of each component, and the performance parameters of each component can be directly measured or obtained by conversion during the factory test phase.

[0015] Preferably, the real-time performance parameters of each engine component are obtained by selecting the corresponding characteristic parameters from the data monitored in real time by the engine sensors in the field and inputting them into the digital engineering model for real-time prediction of engine performance parameters.

[0016] Preferably, the overall performance parameter mentioned in step S3 is the low-pressure turbine afterburner gas temperature, and the performance parameters of each component include the intake duct total pressure recovery coefficient, fan boost ratio, high-pressure compressor boost ratio, combustion chamber oil-gas ratio or combustion efficiency, high-pressure turbine pressure drop ratio, low-pressure turbine pressure drop ratio, and tail nozzle flow coefficient or thrust coefficient.

[0017] Preferably, the component performance degradation is calculated using the following formula:

[0018]

[0019] Among them, D i p represents the percentage degradation of component performance parameters at time i. i p0 represents the component performance parameter value at time i, and p0 represents the initial state engine, that is, the component performance parameter value exhibited by the engine under the same operating conditions at time i when its performance has not degraded.

[0020] Preferably, in step S3, the field engine test flights are the initial few flight flights of the field engine; in step S4, the field engine evaluation flights are the subsequent flight flights of the field engine excluding the initial few flight flights.

[0021] Preferably, the data from the factory test run mentioned in step S1 includes: intake manifold inlet temperature, compressor boost ratio, fuel flow rate, turbine pressure ratio, exhaust nozzle throat diameter, engine speed, ambient temperature, ambient pressure, and temperature and pressure of each section of the engine's internal flow passage.

[0022] An electronic device, comprising:

[0023] One or more processors;

[0024] Storage device for storing one or more programs;

[0025] When the one or more programs are executed by the one or more processors, the one or more processors implement the method for intelligently locating faulty components of an aero-engine as described above.

[0026] A computer-readable medium storing a computer program that, when executed by a processor, implements the method for intelligently locating faulty components in an aero-engine as described above.

[0027] The present invention has the following beneficial effects:

[0028] 1. The method of this invention can be used to assess the performance degradation of each component of the engine in real time. Then, by comparing it with the performance degradation curve established in the factory using the same method, it can be determined which component or components have excessive performance degradation at each time, thus enabling rapid and accurate location of the faulty component.

[0029] 2. The digital engineering model for evaluating the performance of each component during factory testing established in this invention is consistent with the environmental and state parameters selected in the digital engineering model for evaluating engine performance in the field. This ensures that the model reflects the physical matching law of each component during engine operation, so that the performance degradation directly reflects the performance loss caused by mechanical wear compared to the initial flight stage, thereby avoiding the influence of environmental and state parameters. Attached Figure Description

[0030] Figure 1 This is a flowchart of the method for locating faulty components according to the present invention;

[0031] Figure 2 A schematic diagram for locating the performance degradation of components. Detailed Implementation

[0032] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Unless otherwise specified, the technical means used in the embodiments are conventional means well known to those skilled in the art.

[0033] In the description of this invention, it should be understood that the terms "longitudinal", "lateral", "up", "down", "front", "rear", "left", "right", "vertical", "horizontal", "top", "bottom", "inner", "outer", etc., indicate the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings, and are only for the convenience of describing this invention, and are not intended to indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation, and therefore should not be construed as a limitation of this invention.

[0034] like Figure 1-2 As shown, firstly, engine monitoring data from various stages of testing within the factory are acquired. Environmental parameters, state parameters, and some overall engine performance parameters monitored both within the factory and in the field are selected, such as the low-pressure turbine afterburner temperature T6, to establish a digital engineering model for predicting the performance parameters of each engine component. Simultaneously, environmental and state parameters are selected to establish a digital engineering model for evaluating the performance of each engine component. The prediction models are then used to predict the performance parameters of each engine component in the field in real time. Together with the engine field monitoring parameters, a digital engineering model for evaluating the engine performance in the field is constructed.

[0035] After establishing the above-mentioned digital engineering models for in-plant performance evaluation and field performance evaluation, the performance degradation of components at each moment during the simulated field test phase in the plant can be calculated. By fitting, the performance degradation curve of each component can be obtained. Using the field performance evaluation model, the performance degradation of each component at each moment in the field can be calculated. By comparing the degradation amount with the degradation amount at the corresponding moment of the degradation curve, it can be determined which component or components have the most severe performance degradation, thus achieving rapid and accurate location of the faulty component.

[0036] Specifically, it includes the following steps:

[0037] S1: Establish a digital engineering model for evaluating the performance of various components during factory testing: Based on data from factory testing of the aero-engine, mainly including: inlet temperature, compressor pressure ratio, fuel flow rate, turbine pressure ratio, nozzle throat diameter, speed, ambient temperature, ambient pressure, and temperature and pressure of various cross-sections of the engine's internal flow channels; select environmental parameters and state parameters as model inputs, and component performance parameters as model outputs, to train the digital engineering model for evaluating the performance of various components during factory testing; the selected environmental and state parameters are data that will be monitored on the engine both in the factory and during field flight, such as atmospheric temperature, atmospheric pressure, atmospheric humidity, and altitude, and state parameters including high-pressure rotor speed, low-pressure rotor speed, low-pressure inlet guide angle, high-pressure inlet guide angle, throttle position, nozzle critical cross-section diameter, and lubricating oil parameters; the component performance parameters are parameters that can reflect the performance status of each component, and these component performance parameters can be directly measured or calculated during the factory testing phase.

[0038] Based on real-time monitoring data from the testing and commissioning phase, environmental and state parameters are selected as model inputs, and component performance parameters are outputs. This allows for the training of digital engineering models for evaluating the performance of each component during in-plant testing. When selecting environmental and state parameters, it is necessary to ensure that these parameters are also monitored on the engine during field flight, guaranteeing consistency in input parameter characteristics between the in-plant and field models. The component performance parameters reflect the performance state of each component, such as the pressure ratio of the compressor component and the pressure drop ratio of the turbine component. These performance parameters can be directly measured or calculated during in-plant testing.

[0039] S2: Construct the performance degradation curves of each component during factory testing: Simulate the field mission and input the monitored test data into the digital engineering model for performance evaluation of each component trained in step S1, evaluate the performance degradation of each component in real time, and fit the performance degradation curve of the aero-engine over time.

[0040] Simulated field tests are conducted, and the monitoring data obtained from these tests is input into the digital engineering model for performance evaluation of each component, trained in step S1, according to the corresponding input feature parameters. This allows for real-time evaluation of the performance degradation of each component and the fitting of curves showing the change in performance degradation over engine operating time. Since the parameters input into the performance evaluation model are environmental and state parameters at each moment, ensuring consistency in environmental and state factors during performance evaluation, the obtained performance evaluation results only reflect the impact of mechanical wear on the performance of each component. The resulting performance degradation curves for each component reflect the relationship between the amount of performance degradation caused by mechanical wear and engine operating time.

[0041] S3: Establish a digital engineering model for field engine performance evaluation: Use environmental parameters and state parameters from the flight monitoring data of field engine test flights as input to the model, and real-time performance parameters of each engine component as output to train the digital engineering model for field engine performance evaluation.

[0042] The real-time performance parameters of each engine component are obtained by establishing a digital engineering model for real-time prediction of engine performance parameters. The specific method for constructing the digital engineering model for real-time prediction of engine performance parameters is as follows: based on the data of the aero-engine test in the factory, environmental parameters, state parameters and overall engine performance parameters are selected as model inputs, and the performance parameters of each component are used as model outputs to train the digital engineering model for real-time prediction of engine performance parameters.

[0043] Based on test and monitoring data of the engine at various stages and tasks within the factory, environmental parameters, state parameters, and a portion of overall engine performance parameters measured both in the field and at the factory are selected as inputs to the digital engineering model. The outputs are the performance parameters of each component, and the model is trained to predict the performance parameters of each engine component in real time. The parameter selection requirements in this step are basically the same as those in step S1, except that when selecting input features, a portion of performance parameters monitored both in the field and at the factory need to be added as input features. Adding this feature as input ensures that the model reflects the physical matching laws of each component during engine operation, thus guaranteeing accurate prediction of the performance parameters of each component.

[0044] By using a real-time predictive digital engineering model for engine performance parameters, the performance of various engine components can be predicted. This involves selecting relevant characteristic parameters from the real-time data monitored by engine sensors in the field. For example: intake manifold characteristic parameters include intake inlet temperature and pressure; fan characteristic parameters include low-pressure inlet guide angle, low-pressure rotor speed, fan outlet temperature, and outlet pressure; high-pressure compressor characteristic parameters include high-pressure inlet guide angle, high-pressure rotor speed, and high-pressure compressor outlet total pressure; combustion chamber characteristic parameters include throttle position and fuel flow rate; high-pressure turbine characteristic parameters include high-pressure rotor speed and turbine inlet gas temperature; low-pressure turbine characteristic parameters include low-pressure rotor speed and turbine outlet gas temperature; and exhaust nozzle characteristic parameters include critical cross-sectional area. These parameters are then input into the real-time predictive digital engineering model for engine performance parameters to obtain the real-time performance parameters of each engine component. Then, based on the environmental and state parameters in the flight monitoring data of the initial engine test flights (the first few flights), the real-time performance parameters obtained by the engine performance parameter real-time prediction digital engineering model are used as the output to train the field engine performance evaluation digital engineering model. The field engine performance evaluation digital engineering model and the in-plant test component performance evaluation digital engineering model in step S1 are consistent in architecture and training to ensure comparability in subsequent comparisons.

[0045] The overall engine performance parameters include the low-pressure turbine afterburner gas temperature. The performance parameters of individual components vary depending on the engine's structure: the intake manifold, fan, high-pressure compressor, combustion chamber, high-pressure turbine, low-pressure turbine, and exhaust nozzle. For example, the intake manifold's performance parameters include the total pressure recovery coefficient; the fan's performance parameters include the fan boost ratio; the high-pressure compressor's performance parameters include the high-pressure compressor boost ratio; the combustion chamber's performance parameters include the air-fuel ratio and combustion efficiency; the high-pressure turbine's performance parameters include the high-pressure turbine pressure drop ratio; the low-pressure turbine's performance parameters include the low-pressure turbine pressure drop ratio; and the exhaust nozzle's performance parameters include the thrust coefficient and flow coefficient.

[0046] S4: Calculate the performance degradation of each component in the field: Input the environmental parameters and state parameters in the field engine evaluation flight monitoring data into the field engine performance evaluation digital engineering model in step S3 to obtain the performance degradation of each component at each time point in subsequent flights.

[0047] The field engine evaluation flights are the subsequent flights that are different from the initial flights mentioned above. The monitoring data of the subsequent flights are input into the field engine performance evaluation digital engineering model to obtain the component performance degradation of the engine at each time point. Similarly, since the input features only include the environmental and state parameters at the corresponding time point, the performance degradation reflects the performance loss caused by mechanical wear compared to the initial flight phase.

[0048] S5: Compare the performance degradation of each component: Compare the actual performance degradation of each component of the field engine at each time point in step S4 with the performance degradation curve in step S2, and determine the degradation status of each component based on the difference and the set threshold.

[0049] The degradation amount of components at various times in the field is compared with the performance degradation curves of each engine component established in step S2. The difference between the actual degradation amount and the theoretical degradation amount obtained from the degradation curve is calculated and compared with a set threshold to determine the degradation status of each component and to pinpoint which component's performance degradation is mainly causing the severe degradation of the overall engine performance. That is, if the difference between the actual degradation amount and the theoretical degradation amount obtained from the degradation curve is less than the threshold, it is judged as normal degradation; if the difference between the actual degradation amount and the theoretical degradation amount obtained from the degradation curve is greater than the threshold, it is judged as a fault.

[0050] Example

[0051] S1: Establish a digital engineering model for evaluating the performance of various components during factory testing: Real-time monitoring of test data such as intake manifold temperature, compressor boost ratio, fuel flow rate, turbine pressure ratio, nozzle throat diameter, engine speed, ambient temperature, ambient pressure, and temperature and pressure of various cross-sections of the engine's internal flow channels; Select environmental parameters, such as atmospheric temperature, atmospheric pressure, atmospheric humidity, and altitude; and state parameters, such as high-pressure rotor speed, low-pressure rotor speed, low-pressure inlet guide angle, high-pressure inlet guide angle, throttle position, nozzle critical cross-section diameter, and lubricating oil parameters, as inputs to train the model. Component performance parameters from the factory test monitoring data (such as the compressor boost ratio mentioned above) are used as outputs to train the digital engineering model for evaluating the performance of various test components.

[0052] S2: Constructing performance degradation curves for each component during in-plant testing: Simulate field testing and acquire testing monitoring data. Input the data into the established digital engineering model for evaluating the performance of each component during in-plant testing to evaluate the performance degradation of each component in real time. The performance degradation is calculated using the following formula:

[0053]

[0054] Among them, Di p represents the percentage degradation of component performance parameters at time i. i p0 represents the component performance parameter value at time i, and p0 represents the initial state engine, that is, the component performance parameter value exhibited by the engine under the same operating conditions at time i when its performance has not degraded.

[0055] By fitting the performance degradation values ​​obtained at each time point, a performance degradation curve as a function of engine operating time is obtained, as shown in the attached figure. Figure 2 The black curve in the image.

[0056] S3: Establish a digital engineering model for field engine performance evaluation: Based on the initial flight monitoring data of the field engine, select the same environmental parameters and state parameters as those in step S1 (same category) as the model input, and use the real-time predicted performance parameters of each component of the field engine as the output to train the digital engineering model for field engine performance evaluation.

[0057] The real-time performance parameters of each component are obtained through the digital engineering model for real-time prediction of engine performance parameters. This model is constructed based on environmental parameters, state parameters, and some overall engine performance parameters measured in the field and in the factory, as well as the performance parameters of each component. By using the data monitored in real time by the sensors on the engine in the field as input data, the digital engineering model for real-time prediction of engine performance parameters can predict the performance parameters of each engine component in real time.

[0058] S4: Calculate the performance degradation of each component in the field: This involves calculating the performance degradation of each component in subsequent flight sorties. Monitoring data from subsequent flight sorties is input into the pre-trained digital engineering model for field engine performance evaluation to obtain the performance degradation of engine components at various times during subsequent sorties. Figure 2 The black triangle in the middle represents the performance degradation of the field engine compressor calculated based on this model.

[0059] S5: Compare the performance degradation of each component: Compare the component degradation at various times in the field with the performance degradation curves of each engine component established in step S2. Based on the difference between these curves and a set threshold, determine the degradation status of each component and pinpoint which component(s) is primarily responsible for the severe degradation of the overall engine performance. (Reference) Figure 2 ,exist Figure 2 At the current moment, the black triangle represents the performance degradation of the field engine compressor. The performance degradation of the field engine compressor is much higher than the performance degradation curve of the compressor in the factory at the corresponding moment (black curve), and exceeds the set threshold for the difference in degradation. This threshold can be set manually, and it can be determined that the compressor has an abnormality at this moment and needs to be repaired.

[0060] The present invention also provides an electronic device, comprising:

[0061] One or more processors;

[0062] Storage device for storing one or more programs;

[0063] When the one or more programs are executed by the one or more processors, the one or more processors implement the method for intelligently locating faulty components of an aero-engine as described above.

[0064] The present invention also provides a computer-readable medium storing a computer program that, when executed by a processor, implements the method for intelligently locating faulty components in an aero-engine as described above.

[0065] The embodiments described above are merely preferred embodiments of the present invention and are not intended to limit the scope of the present invention. Any modifications, alterations, substitutions, or variations made by those skilled in the art to the technical solutions of the present invention without departing from the spirit of the present invention shall fall within the protection scope defined by the claims of the present invention.

Claims

1. A method of intelligently locating a failed component of an aeroengine, characterized by, Comprise the following steps: S1: Establish the performance evaluation digital engineering model of each component in the factory test: according to the data of the aero-engine test in the factory, select the environmental parameters and state parameters as the model input, and the performance parameters of each component as the model output, train the performance evaluation digital engineering model of each component in the factory test; the selected environmental parameters and state parameters are the data that can be monitored on the engine in the factory and the engine in the field; the performance parameters of each component are the parameters that can reflect the performance state of each component, which can be directly measured or obtained by conversion during the factory test stage; S2: Construct the performance degradation curve of each component in the factory test: simulate the field task and input the monitored test data into the performance evaluation digital engineering model of each component trained in step S1, real-time evaluate the performance degradation of each component, and fit the performance degradation curve of the aero-engine with time; S3: Establish the performance evaluation digital engineering model of the field engine: take the environmental parameters and state parameters in the field engine test flight monitoring data as the model input, and the real-time performance parameters of each component of the engine as the output, train the performance evaluation digital engineering model of the field engine; The real-time performance parameters of each component of the engine are obtained by establishing the real-time performance prediction digital engineering model of the engine performance parameters, and the specific method is: according to the data of the aero-engine test in the factory, select the environmental parameters, state parameters and whole machine performance parameters as the model input, and the performance parameters of each component as the model output, train the real-time performance prediction digital engineering model of the engine component performance parameters; according to the real-time performance prediction digital engineering model of the engine component performance parameters, and combining the field monitoring data, the real-time performance parameters of each component of the engine are obtained; The selected environmental parameters, state parameters and whole machine performance parameters are the data that can be monitored on the engine in the factory and the engine in the field; the performance parameters of each component are the parameters that can reflect the performance state of each component, which can be directly measured or obtained by conversion during the factory test stage S4: Calculate the performance degradation of each component in the field: input the environmental parameters and state parameters in the field engine evaluation flight monitoring data into the field engine performance evaluation digital engineering model in step S3, and obtain the performance degradation of each component at each time of the subsequent flight; S5: Compare the performance degradation of each component: compare the actual performance degradation of each component of the field engine at each time in step S4 with the performance degradation curve in step S2, and judge the degradation of each component according to the difference and the set threshold.

2. The method of claim 1, wherein: The real-time performance parameters of each component of the engine are obtained by inputting the corresponding characteristic parameters selected from the data monitored by the field engine sensor into the real-time performance prediction digital engineering model of the engine performance parameters.

3. The method of claim 1, wherein: The whole machine performance parameter in step S3 is the temperature of the gas after the low-pressure turbine; and the component performance parameters include the total pressure recovery coefficient of the air inlet, the fan pressure ratio, the high-pressure compressor pressure ratio, the fuel-air ratio or the combustion efficiency of the combustion chamber, the low-pressure turbine pressure ratio, and the flow coefficient or the thrust coefficient of the tail nozzle.

4. The method of claim 1, wherein: The performance degradation is calculated by the following formula: where D i represents the percentage of component performance parameter degradation at the i-th time point, p i represents the component performance parameter value at the i-th time point, P0represents the component performance parameter value exhibited by the engine in the same operating condition at the i-th time point, which is in the initial state, i.e., the engine whose performance has not degraded.

5. The method of claim 1, wherein: In step S3, the field engine test cycles are the first several flight cycles of the field engine; and in step S4, the field engine evaluation cycles are the subsequent flight cycles of the field engine except the first several flight cycles.

6. The method of intelligently locating a faulty component of a gas turbine engine of claim 1, wherein: The data of the in-plant test in step S1 include the inlet temperature of the air inlet, the compressor pressure ratio, the fuel flow, the turbine pressure ratio, the throat diameter of the tail nozzle, the rotating speed, the ambient temperature, the ambient pressure, the temperature and pressure of each section of the engine internal flow passage.

7. An electronic device, comprising: The method comprises the steps of: one or more processors; a memory device for storing one or more programs; when the one or more programs are executed by the one or more processors, the one or more processors implement the method for intelligently locating the faulty component of the aero-engine according to any one of claims 1-6.

8. A computer readable medium storing a computer program, characterized in that: The computer program is executed by the processor to implement the method for intelligently locating the faulty component of the aero-engine according to any one of claims 1-6.

Images