CN116147928A - Method, device and equipment for determining health state of aeroengine thermal jet device - Google Patents
Method, device and equipment for determining health state of aeroengine thermal jet device Download PDFInfo
- Publication number
- CN116147928A CN116147928A CN202310424392.3A CN202310424392A CN116147928A CN 116147928 A CN116147928 A CN 116147928A CN 202310424392 A CN202310424392 A CN 202310424392A CN 116147928 A CN116147928 A CN 116147928A
- Authority
- CN
- China
- Prior art keywords
- jet device
- thermal jet
- state information
- operation data
- determining
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Granted
Links
Images
Classifications
-
- G—PHYSICS
- G07—CHECKING-DEVICES
- G07C—TIME OR ATTENDANCE REGISTERS; REGISTERING OR INDICATING THE WORKING OF MACHINES; GENERATING RANDOM NUMBERS; VOTING OR LOTTERY APPARATUS; ARRANGEMENTS, SYSTEMS OR APPARATUS FOR CHECKING NOT PROVIDED FOR ELSEWHERE
- G07C5/00—Registering or indicating the working of vehicles
- G07C5/08—Registering or indicating performance data other than driving, working, idle, or waiting time, with or without registering driving, working, idle or waiting time
- G07C5/0808—Diagnosing performance data
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01M—TESTING STATIC OR DYNAMIC BALANCE OF MACHINES OR STRUCTURES; TESTING OF STRUCTURES OR APPARATUS, NOT OTHERWISE PROVIDED FOR
- G01M15/00—Testing of engines
- G01M15/14—Testing gas-turbine engines or jet-propulsion engines
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computing arrangements based on biological models
- G06N3/02—Neural networks
- G06N3/08—Learning methods
-
- G—PHYSICS
- G07—CHECKING-DEVICES
- G07C—TIME OR ATTENDANCE REGISTERS; REGISTERING OR INDICATING THE WORKING OF MACHINES; GENERATING RANDOM NUMBERS; VOTING OR LOTTERY APPARATUS; ARRANGEMENTS, SYSTEMS OR APPARATUS FOR CHECKING NOT PROVIDED FOR ELSEWHERE
- G07C5/00—Registering or indicating the working of vehicles
- G07C5/006—Indicating maintenance
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02T—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO TRANSPORTATION
- Y02T90/00—Enabling technologies or technologies with a potential or indirect contribution to GHG emissions mitigation
Landscapes
- Physics & Mathematics (AREA)
- Engineering & Computer Science (AREA)
- General Physics & Mathematics (AREA)
- Theoretical Computer Science (AREA)
- Evolutionary Computation (AREA)
- Computing Systems (AREA)
- Biomedical Technology (AREA)
- Biophysics (AREA)
- Computational Linguistics (AREA)
- Data Mining & Analysis (AREA)
- Life Sciences & Earth Sciences (AREA)
- General Health & Medical Sciences (AREA)
- Molecular Biology (AREA)
- Artificial Intelligence (AREA)
- General Engineering & Computer Science (AREA)
- Mathematical Physics (AREA)
- Software Systems (AREA)
- Health & Medical Sciences (AREA)
- Chemical & Material Sciences (AREA)
- Combustion & Propulsion (AREA)
- Testing And Monitoring For Control Systems (AREA)
Abstract
The application relates to a method, a device and equipment for determining the health state of a thermal jet device of an aeroengine. The method comprises the following steps: acquiring operation data of a thermal jet device of the aeroengine; inputting the operation data into a preset neural network model, and determining the operation state information of a thermal jet device of the aeroengine; the running state information represents whether the attenuation rate of the thermal jet device is abnormal or not; and under the condition that the operation state information represents that the attenuation rate of the thermal jet device is abnormal, determining the residual service cycle of the thermal jet device according to the operation data and the operation state information. The method can accurately determine the residual service period of the thermal jet device, so that the health state of the thermal jet device can be accurately estimated according to the residual service period of the thermal jet device, and the health state of the thermal jet device can be estimated.
Description
Technical Field
The application relates to the technical field of aeroengines, in particular to a method, a device and equipment for determining the health state of a thermal jet device of an aeroengine.
Background
The afterburner of an aeroengine has high airflow speed, low oxygen content and complex combustion conditions, and in general, a thermal jet device can be used for solving the ignition problem of the afterburner. However, the thermal jet device has a short life cycle in the process of solving the afterburner ignition problem, and therefore, when the aircraft engine is subjected to ground maintenance, the health state of the thermal jet device needs to be checked.
However, because the thermal jet device has a complex structure, the thermal jet device has a plurality of factors influencing the service cycle of the thermal jet device, and the health state of the thermal jet device is difficult to evaluate in the traditional technology, so that the problem that the health state of the thermal jet device cannot be accurately determined exists.
Disclosure of Invention
In view of the foregoing, it is desirable to provide a method, apparatus, and device for determining the health status of an aircraft engine thermal jet device that can accurately determine the health status of the thermal jet device.
In a first aspect, the present application provides a method of determining a health status of an aircraft engine thermal jet device. The method comprises the following steps:
acquiring operation data of a thermal jet device of the aeroengine;
inputting the operation data into a preset neural network model, and determining the operation state information of a thermal jet device of the aeroengine; the running state information represents whether the attenuation rate of the thermal jet device is abnormal or not;
And under the condition that the operation state information represents that the attenuation rate of the thermal jet device is abnormal, determining the residual service cycle of the thermal jet device according to the operation data and the operation state information.
In one embodiment, the method further comprises:
determining standard operation state information corresponding to historical operation data of the thermal jet device according to the standard attenuation rate of the thermal jet device;
inputting the historical operation data into an initial neural network model to obtain sample operation state information corresponding to the historical operation data;
and training the initial neural network model according to the standard running state information and the sample running state information to obtain the preset neural network model.
In one embodiment, the determining, according to the standard attenuation rate of the thermal jet device, standard operation state information corresponding to historical operation data of the thermal jet device includes:
acquiring a sample attenuation rate corresponding to the historical operation data;
if the sample attenuation rate is greater than the standard attenuation rate, determining that the standard operation state information corresponding to the historical operation data is abnormal attenuation rate of the thermal jet device;
And if the sample attenuation rate is smaller than or equal to the standard attenuation rate, determining that the standard operation state information corresponding to the historical operation data is that the attenuation rate of the thermal jet device is normal.
In one embodiment, the method further comprises:
based on the working principle and maintenance period of the thermal jet device, acquiring characteristic information of operation data of the thermal jet device;
and acquiring the standard attenuation rate of the thermal jet device according to the characteristic information.
In one embodiment, the determining the remaining usage period of the thermal jet device according to the operation data and the operation state information includes:
according to a preset empirical formula
Determining the remaining usage period; wherein P is the remaining service cycle of the thermal jet device, t is the current time, n is the number of sample thermal jet devices, y is the operation state information of the thermal jet device, and x is the operation data of the thermal jet device.
In one embodiment, the method further comprises:
outputting prompt information when the residual using period is smaller than a preset using period threshold value; the prompt message is used for indicating maintenance personnel to replace or maintain the thermal jet device.
In one embodiment, the outputting the prompt information includes:
outputting the prompt information through a prompt interface; or outputting the prompt information through voice.
In a second aspect, the present application also provides a health status determination device for an aeroengine thermal jet device. The device comprises:
the acquisition module is used for acquiring the operation data of the thermal jet device of the aeroengine;
the first determining module is used for inputting the operation data into a preset neural network model and determining the operation state information of the thermal jet device of the aeroengine; the running state information represents whether the attenuation rate of the thermal jet device is abnormal or not;
and the second determining module is used for determining the residual service cycle of the thermal jet device according to the operation data and the operation state information under the condition that the operation state information represents that the attenuation rate of the thermal jet device is abnormal.
In a third aspect, the present application also provides a computer device. The computer device comprises a memory storing a computer program and a processor implementing the method of the first aspect described above when executing the computer program.
In a fourth aspect, the present application also provides a computer-readable storage medium. The computer readable storage medium has stored thereon a computer program which, when executed by a processor, implements the method of the first aspect described above.
According to the method, the device and the equipment for determining the health state of the aeroengine thermal jet device, the operation data of the aeroengine thermal jet device are acquired, the acquired operation data of the thermal jet device are input into the preset neural network model, so that the operation state information of the aeroengine thermal jet device can be accurately determined based on the preset neural network model trained by the model, whether the attenuation rate of the thermal jet device is abnormal or not can be accurately determined through the operation state information of the thermal jet device, and accordingly, the residual service cycle of the thermal jet device can be accurately determined according to the operation data and the accurate operation state information under the condition that the attenuation rate of the thermal jet device is abnormal, and the health state of the thermal jet device can be accurately estimated according to the residual service cycle of the thermal jet device, so that the health state of the thermal jet device can be estimated.
Drawings
FIG. 1 is a diagram of an application environment for a method of determining the health of an aircraft engine thermal jet device in one embodiment;
FIG. 2 is a flow diagram of a method of determining the health of an aircraft engine thermal jet device in one embodiment;
FIG. 3 is a flowchart illustrating a pre-configured neural network model generation step according to another embodiment;
FIG. 4 is a schematic diagram of a preset fault tree in one embodiment;
FIG. 5 is a flow chart illustrating the standard operating state information determination steps in one embodiment;
FIG. 6 is a flow chart of a standard attenuation deceleration rate acquisition step in another embodiment;
FIG. 7 is a flow chart of a method of determining the health of an aircraft engine thermal jet device in an alternative embodiment;
FIG. 8 is a block diagram of a health determination device of an aircraft engine thermal jet device in one embodiment;
fig. 9 is an internal structural diagram of a computer device in one embodiment.
Detailed Description
In order to make the objects, technical solutions and advantages of the present application more apparent, the present application will be further described in detail with reference to the accompanying drawings and examples. It should be understood that the specific embodiments described herein are for purposes of illustration only and are not intended to limit the present application.
The method for determining the health state of the aeroengine thermal jet device, provided by the embodiment of the application, can be applied to an application environment shown in fig. 1. The data storage system may store data that the server 100 needs to process. The data storage system may be integrated on the server 100 or may be located on a cloud or other network server. The method comprises the steps that a server 100 obtains operation data of a thermal jet device of an aeroengine, the server 100 inputs the operation data into a preset neural network model, and operation state information of the thermal jet device of the aeroengine is determined, wherein the operation state information represents whether attenuation rate of the thermal jet device is abnormal or not; in the event that the operational state information characterizes an abnormal decay rate of the thermal fluidic device, the server 100 determines a remaining life cycle of the thermal fluidic device based on the operational data and the operational state information. The server 100 may be implemented as a stand-alone server or as a server cluster including a plurality of servers.
In one embodiment, as shown in fig. 2, a method for determining the health status of an aeroengine thermal jet device is provided, and the method is applied to the server in fig. 1 for illustration, and includes the following steps:
S201, operation data of a thermal jet device of the aero-engine are obtained.
The aeroengine is an engine for providing power required by flight for an aerocraft. Aeroengines can be divided into three categories, piston aeroengines, ramjet engines, and gas turbine engines. The aeroengine comprises a thermal jet device, wherein the thermal jet device is used for effectively solving the ignition problem of the afterburner, and is a part which needs to be checked in a major way in the ground overhaul process of the aeroengine. The operation data of the thermal jet device refers to measurement data of the thermal jet device during operation, for example, the operation data of the thermal jet device can be measurement data such as temperature, rotation speed, turbine outlet measurement value, forced fuel flow and the like of the thermal jet device during operation.
Optionally, the server may acquire, in real time, operation data of the thermal jet device of the aeroengine through a measurement device installed on the aeroengine, the aircraft or the ground detection device; alternatively, the server may also periodically acquire operational data of the thermal jet device acquiring the aeroengine through a measurement device mounted on the aeroengine, the aircraft or the ground detection device; alternatively, the operation data of the thermal jet device of the aeroengine may be obtained by performing a secondary calculation based on the data obtained by the direct measurement. The measuring device can include, but is not limited to, a temperature sensor, a pressure tester, a current transformer and other testing instruments.
S202, inputting operation data into a preset neural network model, and determining operation state information of a thermal jet device of the aeroengine; the operating state information characterizes whether the decay rate of the thermal fluidic device is abnormal.
Optionally, the server may directly input the operation data of the thermal jet device of the aero-engine into a preset neural network model, and determine the operation state information of the thermal jet device of the aero-engine. Alternatively, the server may also pre-process the operation data of the thermal jet device of the aeroengine, for example, screen the operation data of the thermal jet device; and inputting the preprocessed operation data into a preset neural network model, and determining the operation state information of the thermal jet device of the aeroengine.
The preset neural network model can be any one of the neural network models with model training completed. For example, the predetermined neural network model includes, but is not limited to, a dense neural network model, a convolutional neural network model, a cyclic neural network model, and the like. The operational status information of the thermal jet device is used to characterize whether the decay rate of the thermal jet device is abnormal. For example, when the operating state information of the thermal jet device is less than or equal to a preset threshold, the decay rate of the thermal jet device is characterized as normal; and when the operation state information of the thermal jet device is larger than a preset threshold value, characterizing that the attenuation rate of the thermal jet device is abnormal. Of course, the preset threshold may be set according to actual situations or historical experiences, and the embodiment of the present application does not limit the preset threshold.
S203, determining the residual service period of the thermal jet device according to the operation data and the operation state information under the condition that the operation state information represents that the attenuation rate of the thermal jet device is abnormal.
Optionally, when the operation state information of the thermal jet device is greater than a preset threshold, that is, when the operation state information represents that the attenuation rate of the thermal jet device is abnormal, the remaining service cycle of the thermal jet device can be predicted according to historical experience, the operation state information of the thermal jet device and the operation data of the thermal jet device; or, fitting calculation can be performed on the operation state information of the thermal jet device and the operation data of the thermal jet device, so as to determine the residual period of the thermal jet device. The fitting method may include, but is not limited to, least square method, least residual method, probability adjustment method, and the like. The remaining life cycle of the thermal jet device is used to characterize the remaining time that the thermal jet device can be used before repair or replacement.
According to the method for determining the health state of the aeroengine thermal jet device, the operation data of the aeroengine thermal jet device is acquired and is input into the preset neural network model, so that the operation state information of the aeroengine thermal jet device can be accurately determined based on the preset neural network model trained by the model, whether the attenuation rate of the thermal jet device is abnormal or not can be accurately determined through the operation state information of the thermal jet device, and therefore, the residual service cycle of the thermal jet device can be accurately determined according to the operation data and the accurate operation state information under the condition that the attenuation rate of the thermal jet device is accurately determined, and the health state of the thermal jet device can be accurately estimated according to the residual service cycle of the thermal jet device, so that the health state of the thermal jet device is estimated.
Before the operation data is input into the preset neural network model, model training is further needed to generate the preset neural network model. In one embodiment, as shown in fig. 3, the method for determining the health status of the aeroengine thermal jet device further includes:
s301, determining standard operation state information corresponding to historical operation data of the thermal jet device according to the standard decay rate of the thermal jet device.
Optionally, the server may obtain historical operation data of the thermal jet device based on at least one of test bed data of the aeroengine in the factory and maintenance data of the aeroengine in the external factory through multiple tests, multiple simulations, and expert experience. The historical operation data of the thermal jet device refers to measurement data of the thermal jet device in the whole life operation process. The test bed data of the aeroengine in the factory and the maintenance data of the aeroengine in the external factory comprise normal operation data, fault state data and the like of a preset number of thermal jet devices. The preset number may be, for example, more than 50. The server can obtain the standard decay rate of the thermal jet device according to the historical operation data of the thermal jet device, the working principle of the thermal jet device and the maintenance period of the thermal jet device. Wherein the standard decay rate of the thermal fluidic device is used to characterize the decay rate of the thermal fluidic device under normal operating conditions.
Optionally, the server may determine, according to historical operating data of the thermal jet device, a decay rate corresponding to the historical operating data. And determining standard operation state information corresponding to the historical operation data of the thermal jet device according to the attenuation rate corresponding to the historical operation data and the standard attenuation rate of the thermal jet device.
S302, inputting the historical operation data into an initial neural network model to obtain sample operation state information corresponding to the historical operation data.
In this embodiment, a neural network model based on a preset fault tree structure may be used as the initial neural network model. As shown in fig. 4, fig. 4 is a schematic structural diagram of a preset fault tree in one embodiment. The preset fault tree is a summary of the thermal jet device during long-term operation of the thermal jet device. Therefore, in the process of training the initial neural network model, the output result of the sample operation state information corresponding to the historical operation data may represent whether the node in the preset fault tree is faulty, which may be understood that whether the historical operation data is abnormal may be determined according to the preset fault tree.
Optionally, the historical operation data of the thermal jet device may be used as an input variable of the initial neural network model, the sample operation state information corresponding to the historical operation data may be used as an output variable of the initial neural network model, and the server may input the historical operation data of the thermal jet device into the initial neural network model to obtain the sample operation state information corresponding to the historical operation data.
And S303, training the initial neural network model according to the standard running state information and the sample running state information to obtain a preset neural network model.
In this embodiment, the server may calculate according to the standard running state information and the sample running state information, to obtain the value of the loss function. According to the calculated value of the loss function, the initial model parameters of the initial neural network model can be adjusted, so that the intermediate model parameters and the intermediate neural network model corresponding to the intermediate model parameters are obtained. And inputting the historical operation data into the intermediate neural network model to obtain new sample operation state information. And calculating the value of the new loss function again according to the standard running state information and the new sample running state information until the value of the new loss function reaches the minimum value, and taking the intermediate model parameter corresponding to the value of the loss function at the moment as the target model parameter. Updating initial model parameters of the initial neural network model based on the target model parameters to generate a preset neural network model.
In this embodiment, according to the standard decay rate of the thermal jet device, standard operation state information corresponding to the historical operation data of the thermal jet device can be accurately determined. And training the initial neural network model according to the accurate standard running state information and the historical running data, so as to obtain a trained preset neural network model. Because the preset neural network model is obtained by using accurate standard running state information and historical running data through multiple rounds of training, the preset neural network model has higher accuracy.
According to the standard decay rate of the thermal jet device, standard operation state information corresponding to historical operation data of the thermal jet device can be determined. In one embodiment, as shown in fig. 5, S301 includes:
s501, acquiring a sample attenuation rate corresponding to historical operation data.
Alternatively, a mapping relationship between the historical operation data of the thermal jet device and the sample attenuation rate may be pre-established, so that the sample attenuation rate corresponding to the historical operation data is determined according to the mapping relationship. Wherein the sample decay rate corresponding to the historical operational data characterizes a decay rate of the thermal fluidic device during operation using the historical operational data. The mapping relationship characterizes different historical operating data corresponding to different sample decay rates. Alternatively, a functional relationship between the historical operation data of the thermal jet device and the sample decay rate may be pre-established, so that the sample decay rate corresponding to the historical operation data is determined according to the functional relationship. Wherein the functional relationship characterizes different historical operating data as corresponding to different sample decay rates.
S502, if the sample attenuation rate is greater than the standard attenuation rate, determining that the standard operation state information corresponding to the historical operation data is abnormal attenuation rate of the thermal jet device.
S503, if the sample attenuation rate is smaller than or equal to the standard attenuation rate, determining that the standard operation state information corresponding to the historical operation data is that the attenuation rate of the thermal jet device is normal.
In this embodiment, the server may determine a magnitude relationship between the sample decay rate corresponding to the historical operating data and the standard decay rate of the thermal fluidic device. For example, it is assumed that the standard operation state information corresponding to the historical operation data includes 0 and 1, wherein 0 indicates that the decay rate of the thermal jet device is normal and 1 indicates that the decay rate of the thermal jet device is abnormal. Standard operating state information indicative of an abnormal decay rate may be annotated. If the sample attenuation rate is judged to be larger than the standard attenuation rate, the fact that the attenuation rate of the thermal jet device is too high is indicated, and the server can determine that the standard operation state information corresponding to the historical operation data is abnormal in the attenuation rate of the thermal jet device, namely the standard operation state information corresponding to the historical operation data is 1. If the sample attenuation rate is less than or equal to the standard attenuation rate, the attenuation rate of the thermal jet device is in a normal range, and the standard operation state information corresponding to the historical operation data can be determined to be the normal attenuation rate of the thermal jet device, namely the standard operation state information corresponding to the historical operation data is 0.
In this embodiment, the magnitude relation between the sample attenuation rate and the standard attenuation rate can be accurately obtained by acquiring the sample attenuation rate corresponding to the historical operation data and comparing the sample attenuation rate with the standard attenuation rate. When the sample attenuation rate is higher than the standard attenuation rate, the attenuation rate of the thermal jet device is too high, and the standard running state information can be accurately determined to be abnormal. When the sample attenuation rate is smaller than or equal to the standard attenuation rate, the attenuation rate of the thermal jet device is in a normal range, and the standard running state information can be accurately determined to be that the attenuation rate of the thermal jet device is normal.
The standard operation state information corresponding to the historical operation data of the thermal jet device can be determined according to the standard attenuation rate of the thermal jet device. Thus, a standard decay rate of the thermal jet device can be obtained. In one embodiment, as shown in fig. 6, the method for determining the health status of an aeroengine thermal jet device further comprises:
s601, based on the working principle and the maintenance period of the thermal jet device, acquiring characteristic information of historical operation data of the thermal jet device.
The working principle of the thermal jet device may be that gas (such as air or nitrogen) is blown into the fluid at a high speed, so that vortex flow occurs in the fluid, and a vacuum area is formed, so that the fluid is sucked into the high-pressure area from the low-pressure area. The maintenance period of the thermal jet device may be 1 year, 3 years, 5 years, etc., and of course, the maintenance period is not limited in the embodiments of the present application. The characteristic information of the historical operating data of the thermal jet device may be a characteristic curve of the historical operating data of the thermal jet device during operation over the lifetime.
Optionally, fitting calculation may be performed on the historical operation data of the thermal jet device according to the working principle of the thermal jet device and the maintenance period of the thermal jet device, so as to obtain the characteristic information of the historical operation data of the thermal jet device. Or, based on the working principle of the thermal jet device and the maintenance period of the thermal jet device, the historical operation data of the thermal jet device is input into preset software to be simulated, and the characteristic information of the historical operation data of the thermal jet device is generated. The preset software may be any software capable of drawing a graph.
S602, according to the characteristic information, acquiring the standard attenuation rate of the thermal jet device.
Alternatively, a mapping relationship between the characteristic information of the historical operation data of the thermal jet device and the standard attenuation rate of the thermal jet device may be pre-established, so that the standard attenuation rate of the thermal jet device is determined according to the mapping relationship. Wherein the standard decay rate of the thermal fluidic device is used to characterize the decay rate of the thermal fluidic device under normal operating conditions. The mapping relation characterizes different characteristic information to correspond to different standard attenuation rates. Alternatively, a functional relationship between the characteristic information of the historical operation data of the thermal jet device and the standard decay rate of the thermal jet device may be pre-established, so that the standard decay rate of the thermal jet device is determined according to the functional relationship. Wherein, the functional relation characterizes different characteristic information corresponding to different standard attenuation rates.
In this embodiment, based on the working principle and the maintenance period of the thermal jet device, the historical operation data of the thermal jet device can be accurately analyzed, so that the characteristic information of the historical operation data of the thermal jet device can be accurately obtained. And then according to the accurate characteristic information, the standard attenuation rate of the thermal jet device can be accurately determined.
Under the condition that the operation state information represents that the attenuation rate of the thermal jet device is abnormal, the residual service cycle of the thermal jet device can be determined according to the operation data and the operation state information. In one embodiment, the step S203 includes:
According to a preset empirical formula
Determining the remaining use period; wherein P is the remaining service cycle of the thermal jet device, t is the current time, n is the number of sample thermal jet devices, y is the operation state information of the thermal jet device, and x is the operation data of the thermal jet device.
In this embodiment, the server may obtain a preset empirical formula in advance, where the preset empirical formula is shown in the following formula (1):
wherein P is the remaining use period of the thermal jet device, and the smaller the value is, the longer the remaining use period is; t is the current moment, and n is the number of sample thermal jet devices; y is the operation state information of the thermal jet device, and x is the operation data of the thermal jet device; a and B are correction coefficients of the empirical formula.
After the operation data and the operation state information of the thermal jet device are obtained, the operation data and the operation state information of the thermal jet device can be substituted into a preset empirical formula, so that the residual service cycle of the thermal jet device is obtained in real time.
It should be noted that, generally, if it is determined that the remaining service period (P value) exceeds 1, it indicates that the service life of the thermal jet device has exceeded, and at this time, repair or replacement of the thermal jet device is required by a serviceman.
In this embodiment, according to the operation data and the accurate operation state information, a preset empirical formula can be used to accurately and in real time determine the remaining service period of the thermal jet device, so that the health state of the thermal jet device can be accurately and in real time estimated according to the remaining service period of the thermal jet device.
After the remaining service period of the thermal jet device is determined, prompt information can be output according to the remaining service period of the thermal jet device. In one embodiment, the method of determining the health of an aeroengine thermal jet device further comprises:
outputting prompt information when the residual service period is smaller than a preset service period threshold value; the prompt message is used for indicating maintenance personnel to replace or maintain the thermal jet device.
In this embodiment, after determining the remaining usage period of the thermal jet device, the server may determine a magnitude relationship between the remaining usage period and a preset usage period threshold. The preset usage period threshold may be specifically set according to actual situations or historical experiences, which is not limited in the embodiment of the present application. When the remaining usage period is less than a preset usage period threshold, the server may output a prompt message, where the prompt message is used to instruct a serviceman to replace or repair the thermal jet device.
In one embodiment, optionally, the prompt information may be output through a prompt interface on the server; alternatively, the server may output the prompt message by voice.
In this embodiment, when the remaining service period is smaller than the preset service period threshold, a prompt message may be output to instruct a serviceman to replace or repair the thermal jet device in time, so that the operation safety problem caused by not replacing or repairing the thermal jet device in time can be avoided.
For the convenience of understanding of those skilled in the art, the following describes in detail the method for determining the health status of the aeroengine thermal jet device provided in the present application, as shown in fig. 7, the method may include:
s1, acquiring characteristic information of historical operation data of a thermal jet device based on the working principle and maintenance period of the thermal jet device, and acquiring a standard decay rate of the thermal jet device according to the characteristic information;
s2, acquiring a sample attenuation rate corresponding to historical operation data; if the sample attenuation rate is greater than the standard attenuation rate, determining that the standard operation state information corresponding to the historical operation data is abnormal attenuation rate of the thermal jet device; if the sample attenuation rate is smaller than or equal to the standard attenuation rate, determining that the standard operation state information corresponding to the historical operation data is that the attenuation rate of the thermal jet device is normal;
S3, inputting the historical operation data into an initial neural network model to obtain sample operation state information corresponding to the historical operation data;
s4, training the initial neural network model according to the standard running state information and the sample running state information to obtain a preset neural network model;
s5, acquiring operation data of a thermal jet device of the aero-engine;
s6, inputting the operation data into a preset neural network model, and determining the operation state information of a thermal jet device of the aeroengine; the running state information represents whether the attenuation rate of the thermal jet device is abnormal or not;
s7, under the condition that the running state information represents that the attenuation rate of the thermal jet device is abnormal, determining the residual service period according to a preset empirical formula;
s8, outputting prompt information through a prompt interface when the residual service period is smaller than a preset service period threshold value; or outputting prompt information through voice; the prompt message is used for indicating maintenance personnel to replace or maintain the thermal jet device.
According to the method for determining the health state of the aeroengine thermal jet device, the operation data of the aeroengine thermal jet device is acquired, and the acquired operation data of the thermal jet device is input into the preset neural network model, so that the operation state information of the aeroengine thermal jet device can be accurately determined based on the preset neural network model which is trained by the model. Whether the decay rate of the thermal jet device is abnormal or not can be accurately determined through the operation state information of the thermal jet device, so that the remaining service cycle of the thermal jet device can be accurately determined according to the operation data and the accurate operation state information under the condition that the decay rate of the thermal jet device is accurately determined, and the health state of the thermal jet device can be accurately estimated according to the remaining service cycle of the thermal jet device. During practical application, the fault treatment efficiency of the thermal jet device is improved by 36%, the fault problem positioning accuracy is improved by 19%, and the overhaul cost of the aeroengine is reduced.
It should be understood that, although the steps in the flowcharts related to the above embodiments are sequentially shown as indicated by arrows, these steps are not necessarily sequentially performed in the order indicated by the arrows. The steps are not strictly limited to the order of execution unless explicitly recited herein, and the steps may be executed in other orders. Moreover, at least some of the steps in the flowcharts described in the above embodiments may include a plurality of steps or a plurality of stages, which are not necessarily performed at the same time, but may be performed at different times, and the order of the steps or stages is not necessarily performed sequentially, but may be performed alternately or alternately with at least some of the other steps or stages.
Based on the same inventive concept, the embodiment of the application also provides a health state determining device of the aero-engine thermal jet device for realizing the health state determining method of the aero-engine thermal jet device. The implementation of the solution provided by the device is similar to the implementation described in the above method, so the specific limitation in the embodiments of the health status determining device of one or more aero-engine thermal jet devices provided below may be referred to the limitation of the health status determining method of the aero-engine thermal jet device hereinabove, and will not be repeated here.
In one embodiment, as shown in fig. 8, there is provided a health status determining apparatus of an aeroengine thermal jet apparatus, comprising: an acquisition module 10, a first determination module 11 and a second determination module 12, wherein:
an acquisition module 10 for acquiring operational data of a thermal jet device of the aircraft engine.
The first determining module 11 is used for inputting the operation data into a preset neural network model and determining the operation state information of the thermal jet device of the aero-engine; the operating state information characterizes whether the decay rate of the thermal fluidic device is abnormal.
A second determination module 12 is configured to determine a remaining usage period of the thermal jet device based on the operation data and the operation status information, in a case where the operation status information characterizes an abnormality in a decay rate of the thermal jet device.
In one embodiment, the state of health determination device of the aeroengine thermal jet device further comprises:
and the third determining module is used for determining standard operation state information corresponding to the historical operation data of the thermal jet device according to the standard attenuation rate of the thermal jet device.
And the fourth determining module is used for inputting the historical operation data into the initial neural network model to obtain sample operation state information corresponding to the historical operation data.
The preset neural network model generation module is used for training the initial neural network model according to the standard running state information and the sample running state information to obtain a preset neural network model.
In one embodiment, the third determination module includes:
and the sample attenuation rate acquisition unit is used for acquiring the sample attenuation rate corresponding to the historical operation data.
The attenuation rate abnormality determining unit is used for determining that the standard operation state information corresponding to the historical operation data is abnormal attenuation rate of the thermal jet device if the attenuation rate of the sample is larger than the standard attenuation rate.
And the decay rate normal determining unit is used for determining that the standard operation state information corresponding to the historical operation data is that the decay rate of the thermal jet device is normal if the sample decay rate is smaller than or equal to the standard decay rate.
In one embodiment, the state of health determination device of the aeroengine thermal jet device further comprises:
the characteristic information acquisition module is used for acquiring characteristic information of operation data of the thermal jet device based on the working principle and the maintenance period of the thermal jet device.
And the standard attenuation rate acquisition module is used for acquiring the standard attenuation rate of the thermal jet device according to the characteristic information.
In one embodiment, the second determination module 12 includes:
a remaining use period determining unit for determining a remaining use period according to a preset empirical formula
Determining the remaining use period; wherein P is the remaining service cycle of the thermal jet device, t is the current time, n is the number of sample thermal jet devices, y is the operation state information of the thermal jet device, and x is the operation data of the thermal jet device.
In one embodiment, the state of health determination device of the aeroengine thermal jet device further comprises:
the prompt information output module is used for outputting prompt information when the residual service period is smaller than a preset service period threshold value; the prompt message is used for indicating maintenance personnel to replace or maintain the thermal jet device.
In one embodiment, the hint information output module includes:
the prompt information output unit is used for outputting prompt information through a prompt interface; or outputting the prompt information through voice.
The device for determining the health state of the aeroengine thermal jet device provided by the embodiment can execute the method embodiment, and the implementation principle and the technical effect are similar and are not repeated here.
The individual modules in the above-described state of health determination device for an aircraft engine thermal jet device may be implemented in whole or in part by software, hardware, and combinations thereof. The above modules may be embedded in hardware or may be independent of a processor in the computer device, or may be stored in software in a memory in the computer device, so that the processor may call and execute operations corresponding to the above modules.
In one embodiment, a computer device is provided, which may be a server, and the internal structure of which may be as shown in fig. 9. The computer device includes a processor, a memory, and a network interface connected by a system bus. Wherein the processor of the computer device is configured to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, computer programs, and a database. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage media. The database of the computer device is used for storing health status determination data of the thermal jet device. The network interface of the computer device is used for communicating with an external terminal through a network connection. The computer program is executed by a processor to implement a method of determining a health status of an aircraft engine thermal jet device.
It will be appreciated by those skilled in the art that the structure shown in fig. 9 is merely a block diagram of a portion of the structure associated with the present application and is not limiting of the computer device to which the present application applies, and that a particular computer device may include more or fewer components than shown, or may combine some of the components, or have a different arrangement of components.
In one embodiment, a computer device is provided comprising a memory and a processor, the memory having stored therein a computer program, the processor when executing the computer program performing the steps of:
acquiring operation data of a thermal jet device of the aeroengine;
inputting the operation data into a preset neural network model, and determining the operation state information of a thermal jet device of the aeroengine; the running state information represents whether the attenuation rate of the thermal jet device is abnormal or not;
and under the condition that the operation state information represents that the attenuation rate of the thermal jet device is abnormal, determining the residual service cycle of the thermal jet device according to the operation data and the operation state information.
In one embodiment, the processor when executing the computer program further performs the steps of:
determining standard operation state information corresponding to historical operation data of the thermal jet device according to the standard attenuation rate of the thermal jet device;
inputting the historical operation data into an initial neural network model to obtain sample operation state information corresponding to the historical operation data;
training the initial neural network model according to the standard running state information and the sample running state information to obtain a preset neural network model.
In one embodiment, the method further comprises determining standard operating state information corresponding to historical operating data of the thermal jet device according to a standard decay rate of the thermal jet device, the processor executing the computer program to further perform the steps of:
acquiring a sample attenuation rate corresponding to historical operation data;
if the sample attenuation rate is greater than the standard attenuation rate, determining that the standard operation state information corresponding to the historical operation data is abnormal attenuation rate of the thermal jet device;
if the sample decay rate is smaller than or equal to the standard decay rate, determining that the standard operation state information corresponding to the historical operation data is that the decay rate of the thermal jet device is normal.
In one embodiment, the processor when executing the computer program further performs the steps of:
based on the working principle and maintenance period of the thermal jet device, acquiring characteristic information of operation data of the thermal jet device;
and obtaining the standard attenuation rate of the thermal jet device according to the characteristic information.
In one embodiment, the remaining life cycle of the thermal jet device is determined based on the operational data and the operational status information, the processor when executing the computer program further performing the steps of:
according to a preset empirical formula 
Determining the remaining use period; wherein P is the residual service period of the thermal jet device, t is the current moment, n is the number of sample thermal jet devices, y is the running state information of the thermal jet devices,x is the operating data of the thermal jet device.
In one embodiment, the processor when executing the computer program further performs the steps of:
outputting prompt information when the residual service period is smaller than a preset service period threshold value; the prompt message is used for indicating maintenance personnel to replace or maintain the thermal jet device.
In one embodiment, outputting the hint information includes:
outputting prompt information through a prompt interface; or outputting the prompt information through voice.
In one embodiment, a computer readable storage medium is provided having a computer program stored thereon, which when executed by a processor, performs the steps of:
acquiring operation data of a thermal jet device of the aeroengine;
inputting the operation data into a preset neural network model, and determining the operation state information of a thermal jet device of the aeroengine; the running state information represents whether the attenuation rate of the thermal jet device is abnormal or not;
and under the condition that the operation state information represents that the attenuation rate of the thermal jet device is abnormal, determining the residual service cycle of the thermal jet device according to the operation data and the operation state information.
In one embodiment, the computer program when executed by the processor further performs the steps of:
determining standard operation state information corresponding to historical operation data of the thermal jet device according to the standard attenuation rate of the thermal jet device;
inputting the historical operation data into an initial neural network model to obtain sample operation state information corresponding to the historical operation data;
training the initial neural network model according to the standard running state information and the sample running state information to obtain a preset neural network model.
In one embodiment, the method further comprises determining, based on the standard decay rate of the thermal fluidic device, standard operational state information corresponding to historical operational data of the thermal fluidic device, the computer program when executed by the processor further implementing the steps of:
acquiring a sample attenuation rate corresponding to historical operation data;
if the sample attenuation rate is greater than the standard attenuation rate, determining that the standard operation state information corresponding to the historical operation data is abnormal attenuation rate of the thermal jet device;
if the sample decay rate is smaller than or equal to the standard decay rate, determining that the standard operation state information corresponding to the historical operation data is that the decay rate of the thermal jet device is normal.
In one embodiment, the computer program when executed by the processor further performs the steps of:
based on the working principle and maintenance period of the thermal jet device, acquiring characteristic information of operation data of the thermal jet device;
and obtaining the standard attenuation rate of the thermal jet device according to the characteristic information.
In one embodiment, the remaining life cycle of the thermal jet device is determined based on the operational data and the operational status information, the computer program when executed by the processor further performing the steps of:
according to a preset empirical formula
Determining the remaining use period; wherein P is the remaining service cycle of the thermal jet device, t is the current time, n is the number of sample thermal jet devices, y is the operation state information of the thermal jet device, and x is the operation data of the thermal jet device.
In one embodiment, the computer program when executed by the processor further performs the steps of:
outputting prompt information when the residual service period is smaller than a preset service period threshold value; the prompt message is used for indicating maintenance personnel to replace or maintain the thermal jet device.
In one embodiment, outputting the hint information includes:
outputting prompt information through a prompt interface; or outputting the prompt information through voice.
It should be noted that, user information (including but not limited to user equipment information, user personal information, etc.) and data (including but not limited to data for analysis, stored data, presented data, etc.) referred to in the present application are information and data authorized by the user or sufficiently authorized by each party.
Those skilled in the art will appreciate that implementing all or part of the above-described methods in accordance with the embodiments may be accomplished by way of a computer program stored on a non-transitory computer readable storage medium, which when executed may comprise the steps of the embodiments of the methods described above. Any reference to memory, database, or other medium used in the various embodiments provided herein may include at least one of non-volatile and volatile memory. The nonvolatile Memory may include Read-Only Memory (ROM), magnetic tape, floppy disk, flash Memory, optical Memory, high density embedded nonvolatile Memory, resistive random access Memory (ReRAM), magnetic random access Memory (Magnetoresistive Random Access Memory, MRAM), ferroelectric Memory (Ferroelectric Random Access Memory, FRAM), phase change Memory (Phase Change Memory, PCM), graphene Memory, and the like. Volatile memory can include random access memory (Random Access Memory, RAM) or external cache memory, and the like. By way of illustration, and not limitation, RAM can be in the form of a variety of forms, such as static random access memory (Static Random Access Memory, SRAM) or dynamic random access memory (Dynamic Random Access Memory, DRAM), and the like. The databases referred to in the various embodiments provided herein may include at least one of relational databases and non-relational databases. The non-relational database may include, but is not limited to, a blockchain-based distributed database, and the like. The processors referred to in the embodiments provided herein may be general purpose processors, central processing units, graphics processors, digital signal processors, programmable logic units, quantum computing-based data processing logic units, etc., without being limited thereto.
The technical features of the above embodiments may be arbitrarily combined, and all possible combinations of the technical features in the above embodiments are not described for brevity of description, however, as long as there is no contradiction between the combinations of the technical features, they should be considered as the scope of the description.
The foregoing examples represent only a few embodiments of the present application, which are described in more detail and are not thereby to be construed as limiting the scope of the present application. It should be noted that it would be apparent to those skilled in the art that various modifications and improvements could be made without departing from the spirit of the present application, which would be within the scope of the present application. Accordingly, the scope of protection of the present application shall be subject to the appended claims.
Claims (10)
1. A method of determining the health of an aircraft engine thermal jet device, the method comprising:
acquiring operation data of a thermal jet device of the aeroengine;
inputting the operation data into a preset neural network model, and determining the operation state information of a thermal jet device of the aeroengine; the running state information represents whether the attenuation rate of the thermal jet device is abnormal or not;
And under the condition that the operation state information represents that the attenuation rate of the thermal jet device is abnormal, determining the residual service cycle of the thermal jet device according to the operation data and the operation state information.
2. The method according to claim 1, wherein the method further comprises:
determining standard operation state information corresponding to historical operation data of the thermal jet device according to the standard attenuation rate of the thermal jet device;
inputting the historical operation data into an initial neural network model to obtain sample operation state information corresponding to the historical operation data;
and training the initial neural network model according to the standard running state information and the sample running state information to obtain the preset neural network model.
3. The method of claim 2, wherein determining, based on the standard decay rate of the thermal jet device, standard operational state information corresponding to historical operational data of the thermal jet device comprises:
acquiring a sample attenuation rate corresponding to the historical operation data;
if the sample attenuation rate is greater than the standard attenuation rate, determining that the standard operation state information corresponding to the historical operation data is abnormal attenuation rate of the thermal jet device;
And if the sample attenuation rate is smaller than or equal to the standard attenuation rate, determining that the standard operation state information corresponding to the historical operation data is that the attenuation rate of the thermal jet device is normal.
4. A method according to claim 2 or 3, characterized in that the method further comprises:
based on the working principle and maintenance period of the thermal jet device, acquiring characteristic information of historical operation data of the thermal jet device;
and acquiring the standard attenuation rate of the thermal jet device according to the characteristic information.
5. The method of claim 1, wherein determining a remaining life cycle of the thermal jet device based on the operational data and the operational status information comprises:
according to a preset empirical formula
Determining the remaining usage period; wherein P is the remaining service cycle of the thermal jet device, t is the current time, n is the number of sample thermal jet devices, y is the operation state information of the thermal jet device, and x is the operation data of the thermal jet device.
6. The method of claim 5, wherein the method further comprises:
outputting prompt information when the residual using period is smaller than a preset using period threshold value; the prompt message is used for indicating maintenance personnel to replace or maintain the thermal jet device.
7. The method of claim 6, wherein outputting the hint information comprises:
outputting the prompt information through a prompt interface; or outputting the prompt information through voice.
8. A health status determination device for an aeroengine thermal jet device, the device comprising:
the acquisition module is used for acquiring the operation data of the thermal jet device of the aeroengine;
the first determining module is used for inputting the operation data into a preset neural network model and determining the operation state information of the thermal jet device of the aeroengine; the running state information represents whether the attenuation rate of the thermal jet device is abnormal or not;
and the second determining module is used for determining the residual service cycle of the thermal jet device according to the operation data and the operation state information under the condition that the operation state information represents that the attenuation rate of the thermal jet device is abnormal.
9. A computer device comprising a memory and a processor, the memory storing a computer program, characterized in that the processor implements the steps of the method of any of claims 1 to 7 when the computer program is executed.
10. A computer readable storage medium, on which a computer program is stored, characterized in that the computer program, when being executed by a processor, implements the steps of the method of any of claims 1 to 7.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202310424392.3A CN116147928B (en) | 2023-04-20 | 2023-04-20 | Method, device and equipment for determining health state of aeroengine thermal jet device |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202310424392.3A CN116147928B (en) | 2023-04-20 | 2023-04-20 | Method, device and equipment for determining health state of aeroengine thermal jet device |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN116147928A true CN116147928A (en) | 2023-05-23 |
| CN116147928B CN116147928B (en) | 2023-07-14 |
Family
ID=86339242
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202310424392.3A Active CN116147928B (en) | 2023-04-20 | 2023-04-20 | Method, device and equipment for determining health state of aeroengine thermal jet device |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN116147928B (en) |
Citations (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109472110A (en) * | 2018-11-29 | 2019-03-15 | 南京航空航天大学 | A kind of aero-engine remaining life prediction technique based on LSTM network and ARIMA model |
| CN110276125A (en) * | 2019-06-20 | 2019-09-24 | 中国航空发动机研究院 | Aero-engine overall performance slump evaluations and prediction technique based on data |
| WO2020114622A1 (en) * | 2018-12-06 | 2020-06-11 | Deutz Aktiengesellschaft | Method for determining and predicting the individual oil change interval of an internal combustion engine |
| CN112559305A (en) * | 2020-11-10 | 2021-03-26 | 深圳前海有电物联科技有限公司 | Method, device and equipment for predicting performance of key module in uninterruptible power supply system |
| CN112580153A (en) * | 2020-12-29 | 2021-03-30 | 成都运达科技股份有限公司 | Health state management system and method for vehicle running gear monitoring component |
| CN113722985A (en) * | 2021-08-12 | 2021-11-30 | 武汉科技大学 | Method and system for evaluating health state and predicting residual life of aircraft engine |
| CN114993373A (en) * | 2022-04-11 | 2022-09-02 | 中国电子产品可靠性与环境试验研究所（（工业和信息化部电子第五研究所）（中国赛宝实验室）） | Aviation glass state monitoring method and device, computer equipment and medium |
| CN114997051A (en) * | 2022-05-30 | 2022-09-02 | 浙大城市学院 | Aero-engine service life prediction and health assessment method based on transfer learning |
-
2023
- 2023-04-20 CN CN202310424392.3A patent/CN116147928B/en active Active
Patent Citations (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109472110A (en) * | 2018-11-29 | 2019-03-15 | 南京航空航天大学 | A kind of aero-engine remaining life prediction technique based on LSTM network and ARIMA model |
| WO2020114622A1 (en) * | 2018-12-06 | 2020-06-11 | Deutz Aktiengesellschaft | Method for determining and predicting the individual oil change interval of an internal combustion engine |
| CN110276125A (en) * | 2019-06-20 | 2019-09-24 | 中国航空发动机研究院 | Aero-engine overall performance slump evaluations and prediction technique based on data |
| CN112559305A (en) * | 2020-11-10 | 2021-03-26 | 深圳前海有电物联科技有限公司 | Method, device and equipment for predicting performance of key module in uninterruptible power supply system |
| CN112580153A (en) * | 2020-12-29 | 2021-03-30 | 成都运达科技股份有限公司 | Health state management system and method for vehicle running gear monitoring component |
| CN113722985A (en) * | 2021-08-12 | 2021-11-30 | 武汉科技大学 | Method and system for evaluating health state and predicting residual life of aircraft engine |
| CN114993373A (en) * | 2022-04-11 | 2022-09-02 | 中国电子产品可靠性与环境试验研究所（（工业和信息化部电子第五研究所）（中国赛宝实验室）） | Aviation glass state monitoring method and device, computer equipment and medium |
| CN114997051A (en) * | 2022-05-30 | 2022-09-02 | 浙大城市学院 | Aero-engine service life prediction and health assessment method based on transfer learning |
Non-Patent Citations (1)
| Title |
|---|
| 彭宅铭;程龙生;詹君;姚启峰;: "基于改进马田系统的复杂系统健康状态评估", 系统工程与电子技术, no. 04 * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN116147928B (en) | 2023-07-14 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| JP6069498B2 (en) | Method and system for determining lifetime consumption of machine parts | |
| JP5844978B2 (en) | System and method for monitoring a gas turbine | |
| US8793004B2 (en) | Virtual sensor system and method for generating output parameters | |
| JP2010530179A (en) | Virtual sensor system and method | |
| JP6610987B2 (en) | Abnormality diagnosis method and abnormality diagnosis system | |
| EP2388672A1 (en) | Identifying of turbomachine faults | |
| Simon | Propulsion diagnostic method evaluation strategy (ProDiMES) user's guide | |
| EP2256641A2 (en) | Real-time scheduling of linear models for control and estimation | |
| Liao et al. | Uncertainties in gas-path diagnosis of gas turbines: Representation and impact analysis | |
| JP6219945B2 (en) | Method for detecting turbomachine degradation by monitoring turbomachine performance | |
| CN116147928B (en) | Method, device and equipment for determining health state of aeroengine thermal jet device | |
| WO2019124367A1 (en) | Unit space generating device, plant diagnosing system, unit space generating method, plant diagnosing method, and program | |
| Zaccaria et al. | A model-based solution for gas turbine diagnostics: Simulations and experimental verification | |
| Tang et al. | Extending engine gas path analysis using full flight data | |
| JP2022547193A (en) | Nuclear control system with neural network | |
| JP6554162B2 (en) | Power plant performance evaluation method and power plant performance evaluation program | |
| JP6379089B2 (en) | How to determine the machine state | |
| Abbasfard et al. | Fault diagnosis of gas turbine engines: A symbolic multiple model approach | |
| CN116147929B (en) | Method, device and equipment for determining current value of stress-applied flame detector of aero-engine | |
| CN112906237A (en) | Engine component fault analysis method and system | |
| CN116148600B (en) | Method, device and equipment for correcting stress application line fault positioning model of aero-engine | |
| Castillo et al. | Data-Driven Models for Gas Turbine Online Diagnosis. Machines 2021, 9, 372 | |
| CN113077105B (en) | Long-false-day load prediction method and device | |
| Wang et al. | Investigation of unmeasured parameters estimation for distributed control systems | |
| CN114638030B (en) | Thermal hydraulic model correction method and device based on expert knowledge base |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |