AU2021105377A4 - A fault prediction system of civil aviation aircraft based on neural network - Google Patents
A fault prediction system of civil aviation aircraft based on neural network Download PDFInfo
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- AU2021105377A4 AU2021105377A4 AU2021105377A AU2021105377A AU2021105377A4 AU 2021105377 A4 AU2021105377 A4 AU 2021105377A4 AU 2021105377 A AU2021105377 A AU 2021105377A AU 2021105377 A AU2021105377 A AU 2021105377A AU 2021105377 A4 AU2021105377 A4 AU 2021105377A4
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- 238000013528 artificial neural network Methods 0.000 title claims abstract description 21
- 230000008676 import Effects 0.000 claims abstract description 23
- 238000004364 calculation method Methods 0.000 claims abstract description 14
- 238000004088 simulation Methods 0.000 claims abstract description 7
- 238000012544 monitoring process Methods 0.000 claims description 23
- 230000017525 heat dissipation Effects 0.000 claims description 7
- 238000013500 data storage Methods 0.000 claims description 6
- 230000002159 abnormal effect Effects 0.000 claims description 4
- 238000001514 detection method Methods 0.000 claims description 4
- 238000005057 refrigeration Methods 0.000 claims description 3
- 238000000034 method Methods 0.000 abstract description 8
- 238000003672 processing method Methods 0.000 abstract description 4
- 238000005457 optimization Methods 0.000 description 7
- 238000012986 modification Methods 0.000 description 2
- 230000004048 modification Effects 0.000 description 2
- 230000001537 neural effect Effects 0.000 description 2
- 230000009286 beneficial effect Effects 0.000 description 1
- 238000010586 diagram Methods 0.000 description 1
- 238000006467 substitution reaction Methods 0.000 description 1
Classifications
-
- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
- G05B23/00—Testing or monitoring of control systems or parts thereof
- G05B23/02—Electric testing or monitoring
- G05B23/0205—Electric testing or monitoring by means of a monitoring system capable of detecting and responding to faults
- G05B23/0259—Electric testing or monitoring by means of a monitoring system capable of detecting and responding to faults characterized by the response to fault detection
- G05B23/0275—Fault isolation and identification, e.g. classify fault; estimate cause or root of failure
- G05B23/0281—Quantitative, e.g. mathematical distance; Clustering; Neural networks; Statistical analysis
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- Physics & Mathematics (AREA)
- Mathematical Analysis (AREA)
- Evolutionary Computation (AREA)
- Artificial Intelligence (AREA)
- Algebra (AREA)
- Mathematical Optimization (AREA)
- Mathematical Physics (AREA)
- Probability & Statistics with Applications (AREA)
- Pure & Applied Mathematics (AREA)
- Automation & Control Theory (AREA)
- Testing And Monitoring For Control Systems (AREA)
Abstract
The invention discloses a civil aviation aircraft fault prediction system based on neural
network, which comprises a preprocessor, an input end of which is electrically connected with
an introduction unit, and the introduction unit comprises a previous fault processing data
introduction module, an aircraft normal data introduction module, a previous fault data
introduction module and a real-time aircraft sensor data introduction module. According to the
invention, each data is imported into a processor by an introduction unit through a preprocessor,
the fault data classification module and the error calculation module calculate and simulate the
fault probability through the probability prediction module and the deep neural network
calculation simulation module, the recommendation instruction module exports the
corresponding previous processing method for users to watch through the previous fault
processing data import module, therefore, it has the advantage of rapid prediction and prompt,
and solves the problems that the existing fault prediction system can't accurately judge the fault
parameters and can't simulate and prompt the fault information in the use process.
FIGURES
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Piektve daftssodver
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figurel
Description
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Akfcnh &clts PM.O Peoufait &= Mpwf
Pxobsbdn atv ecmdl Dsaa raemodle
FaultdItc31mMstOnmdleEfmaclsnmdl
Piektve daftssodver
Dizp1adevic 1kofccigalcono Dnenpoort
twdtectmwimodule fla vicoe Ennnorigd & EnMDta"xrpbxmx&k
figurel
A Fault Prediction System of Civil Aviation Aircraft Based on Neural
Network
The invention relates to the technical field of aircraft fault prediction, in
particular to a civil aviation aircraft fault prediction system based on a neural network.
In the process of aircraft operation, various data will be predicted by the fault
prediction system, but the existing fault prediction system can not accurately judge
the fault parameters, and can not simulate and prompt the fault information, which
easily leads to users missing the best time to deal with faults.
To solve the problems raised in the above background art, the invention aims at
providing a civil aviation aircraft fault prediction system based on a neural network,
the invention has the advantage of rapid prediction and prompt, and solves the
problem that the existing fault prediction system can not accurately judge the fault
parameters, and can not simulate and prompt the fault information at the same time,
which easily leads to the user missing the best time to deal with the fault.
To achieve the above objectives, the invention provides the following technical
proposal: a civil aviation aircraft fault prediction system based on neural network,
Including a preprocessor, the input end of the preprocessor is electrically connected
with an introduction unit, the lead-in unit comprises a previous fault processing data
lead-in module, an aircraft normal data lead-in module, a previous fault data lead-in module and a real-time aircraft sensor data lead-in module, the previous fault processing data import module, the aircraft normal data import module, the previous fault data import module and the real-time aircraft sensor data import module are all electrically connected with the input end of the preprocessor;
The output end of the preprocessor is electrically connected with a processor, the
input end of the processor is bidirectionally electrically connected with data storage,
the input end of the processor is bidirectionally electrically connected with a
probability prediction module, the input end of the processor is bidirectionally
electrically connected with a fault data classification module, the input end of the
processor is bidirectionally electrically connected with a deep neural network
calculation simulation module, the input end of the processor is bidirectionally
electrically connected with an error calculation module, the input end of the processor
is bidirectionally electrically connected with data storage, the output end of the
processor is electrically connected with a display module, the input end of the
processor is bidirectionally electrically connected with a prediction data transceiving
module, the output end of the Prediction Data Transceiving Module 1 is
bidirectionally electrically connected with the Prediction Data Transceiving Module
2, the input end of the prediction data transceiver module 2 is bidirectionally
electrically connected with a monitoring command center, the output end of the
display module and the output end of the monitoring command center are electrically
connected with a data warning module, and the input end of the processor is
electrically connected with a recommendation instruction module.
As the invention is preferred, the display module is a waterproof display screen.
As the invention is preferred, the input end of the processor is electrically
connected with a heat dissipation module, and the heat dissipation module is a
refrigeration fan.
Preferably, the storage module is a hard disk.
As the invention is preferred, the data warning module is an acousto-optic alarm.
As a preference of the invention, the input end of the processor is bidirectionally
electrically connected with a fault location module.
Preferably, the probability prediction module is a probability calculator.
As preferred in the present invention, the input end of the processor is
bidirectionally electrically connected with a monitoring module, the monitoring
module is a monitoring camera, the input end of the processor is bidirectionally
electrically connected with an abnormal report generating module, the input end of
the processor is electrically connected with a standby power supply module, the input
end of the standby power supply module is electrically connected with a power supply
detection module, the input end of the monitoring command center is electrically
connected with a data encryption module, and the input end of the data encryption
module is electrically connected with a data decryption module.
Compared with the prior art, the invention has the following beneficial effects:
1. According to the invention, each data is introduced into the processor by the
introduction unit through the preprocessor, the fault data classification module and the
error calculation module calculate and simulate the fault probability through the probability prediction module and the deep neural network calculation simulation module, at the same time, the prediction data transceiving module 1 and the prediction data transceiving module 2 warn the user through the data warning module, the display device and the monitoring command center, the recommendation instruction module exports the corresponding previous processing method for users to watch through the previous fault processing data import module, therefore, the invention has the advantage of rapid prediction and prompt, and solves the problem that the existing fault prediction system can not accurately judge the fault parameters and can not simulate and prompt the fault information during the use process, which easily leads to the user missing the best time to deal with the fault.
2. By setting the display module as a waterproof display screen, the invention
can quickly display data and improve the waterproof performance of the display
module.
3. The invention can dissipate heat to the processor by arranging the heat
dissipation module, thus prolonging the service life of the processor.
4. By setting the storage module as a hard disk, the invention can store the data,
which is convenient for users to call up and view the data.
5. The invention can quickly remind the user by setting the data warning module
as an acousto-optic alarm.
6. The invention can detect and locate the fault of the processor and other
modules by setting the fault location module, so that the user can quickly understand
the fault location.
7. By setting the probability prediction module as a probability calculator, the
invention can quickly calculate the probability.
8. The invention can observe and monitor the operation of the user by setting the
monitoring module, which is convenient for remote command.
Fig. 1 is a system diagram of the present invention;
A clear and complete description of the technical aspects of the embodiments of
the invention will be given below in conjunction with the accompanying drawings in
which the embodiments of the invention are described, and it will be apparent that the
described embodiments are only part of the embodiments of the invention, not all of
them. Based on the embodiments in the present invention, all other embodiments
obtained by those of ordinary skill in the art without making creative efforts are
within the scope of protection of the present invention.
As shown in Figure 1, the invention provides a civil aviation aircraft fault
prediction system based on a neural network, Including a preprocessor, the input end
of the preprocessor is electrically connected with an introduction unit, the lead-in unit
comprises a previous fault processing data lead-in module, an aircraft normal data
lead-in module, a previous fault data lead-in module and a real-time aircraft sensor
data lead-in module, the previous fault processing data import module, the aircraft
normal data import module, the previous fault data import module and the real-time aircraft sensor data import module are electrically connected with the input end of the preprocessor;
The output end of the preprocessor is electrically connected with the processor,
the input end of the processor is electrically connected with data storage in both
directions, the input end of the processor is bidirectionally electrically connected with
a probability prediction module, the input end of the processor is bidirectionally
electrically connected with a fault data classification module, the input end of the
processor is bidirectionally electrically connected with a deep neural network
calculation simulation module, the input end of the processor is bidirectionally
electrically connected with an error calculation module, the input end of the processor
is electrically connected with data storage in both directions, the output end of the
processor is electrically connected with a display module, the input end of the
processor is bidirectionally electrically connected with a prediction data transceiving
module, the output end of the prediction data transceiver module one is bidirectionally
electrically connected with the prediction data transceiver module two, the input end
of the prediction data transceiver module 2 is bidirectionally electrically connected
with a monitoring command center, the output end of the display module and the
output end of the monitoring command center are electrically connected with a data
warning module, and the input end of the processor is electrically connected with a
recommendation instruction module.
Referring to Fig. 1, the display module is a waterproof display screen.
As a technical optimization scheme of the invention, by setting the display
module as a waterproof display screen, data can be quickly displayed, and the
waterproof performance of the display module is improved at the same time.
Referring to Fig. 1, the input end of the processor is electrically connected with a
heat dissipation module, which is a refrigeration fan.
As a technical optimization scheme of the invention, heat dissipation can be
carried out on the processor by arranging a heat dissipation module, thus prolonging
the service life of the processor.
Referring to Fig. 1, the storage module is a hard disk.
As a technical optimization scheme of the invention, the data can be stored by
setting the storage module as a hard disk, which is convenient for users to call out and
view the data.
Referring to Fig. 1, the data warning module is an acousto-optic alarm.
As a technical optimization scheme of the invention, the data warning module is
set as an acousto-optic alarm, which can quickly remind users.
Referring to fig. 1, a fault location module is electrically connected to the input
of the processor in both directions.
As a technical optimization scheme of the invention, the fault detection and
location of the processor and other modules can be carried out by setting the fault
location module, so that the user can quickly understand the fault location.
Referring to fig. 1, the probability prediction module is a probability calculator.
As a technical optimization scheme of the invention, the probability prediction
module is set as a probability calculator, so that the probability can be quickly
calculated.
Referring to Figure 1, the input end of the processor is bidirectionally electrically
connected with a monitoring module, the monitoring module is a monitoring camera,
the input end of the processor is bidirectionally electrically connected with an
abnormal report generation module, the input end of the processor is electrically
connected with a standby power supply module, the input end of the standby power
supply module is electrically connected with a power supply detection module, the
input end of the monitoring command center is electrically connected with a data
encryption module, and the input end of the data encryption module is electrically
connected with a data decryption module.
As a technical optimization scheme of the invention, the monitoring module can
observe and monitor the operation of the user, which is convenient for remote
command.
The working principle and use flow of the invention are as follows: When in use,
the previous fault data import module and the previous fault processing data import
module import the previous data and the processing data into the processor through
the preprocessor, at the same time, the aircraft normal data import module and the
real-time aircraft sensor data import module import the normal data of the aircraft and
the real-time data of various sensors into the processor through the preprocessor, the
fault data classification module classifies various data, at the same time, the probability prediction module calculates and predicts the fault probability through data, the deep neural network calculation and simulation module can calculate and simulate the fault probability, the error calculation module calculates the error of the calculated prediction data, the processor will derive the failure probability and probability error through the display screen, convenient for users to watch, at the same time, the prediction data transceiving module 1 leads the fault probability prediction data into the monitoring command center through the prediction data transceiving module 2, when the probability of fault data is high, the data warning module can warn users, at the same time, the recommendation instruction module can export the corresponding previous processing method for users to watch through the previous fault processing data import module, the standby power supply module can supply power when power is cut off, while the abnormal report generation module can display the equipment fault information generation report, and the data encryption module and the data decryption module can carry out encryption protection, thus achieving the advantages of rapid prediction and prompt.
To sum up: the civil aviation aircraft fault prediction system based on neural
network, the data are imported into the processor by the import unit through the
preprocessor, the fault data classification module and the error calculation module
calculate and simulate the fault probability through the probability prediction module
and the deep neural network calculation simulation module, at the same time, the
prediction data transceiving module 1 and the prediction data transceiving module 2
warn the user through the data warning module, the display device and the monitoring command center, the recommendation instruction module exports the corresponding previous processing method for users to watch through the previous fault processing data import module, therefore, the invention has the advantage of rapid prediction and prompt, and solves the problem that the existing fault prediction system can not accurately judge the fault parameters and can not simulate and prompt the fault information during the use process, which easily leads to the user missing the best time to deal with the fault.
It should be noted that relational terms such as first and second are used herein
only to distinguish one entity or operation from another and do not necessarily require
or imply any such actual relationship or order between these entities or operations.
Moreover, the terms "including", "including" or any other variation thereof are
intended to encompass non-exclusive inclusion, so that a process, method, article or
equipment that includes a set of elements includes not only those elements but also
other elements that are not explicitly listed or are inherent to such a process, method,
article or equipment.
Although embodiments of the invention have been shown and described, it will
be understood to those of ordinary skill in the art that various variations,
modifications, substitutions and modifications may be made to these embodiments
without departing from the principle and spirit of the invention, the scope of which is
defined by the appended claims and their equivalents.
Claims (8)
1. A fault prediction system of civil aviation aircraft based on neural network,
including a preprocessor, Characterized by: the input end of the preprocessor is
electrically connected with an introduction unit, the lead-in unit comprises a previous
fault processing data lead-in module, an aircraft normal data lead-in module, a
previous fault data lead-in module and a real-time aircraft sensor data lead-in module,
the previous fault processing data import module, the aircraft normal data import
module, the previous fault data import module and the real-time aircraft sensor data
import module are all electrically connected with the input end of the preprocessor;
The output end of the preprocessor is electrically connected with a processor, the
input end of the processor is bidirectionally electrically connected with data storage,
the input end of the processor is bidirectionally electrically connected with a
probability prediction module, the input end of the processor is bidirectionally
electrically connected with a fault data classification module, the input end of the
processor is bidirectionally electrically connected with a deep neural network
calculation simulation module, the input end of the processor is bidirectionally
electrically connected with an error calculation module, the input end of the processor
is bidirectionally electrically connected with data storage, the output end of the
processor is electrically connected with a display module, the input end of the
processor is bidirectionally electrically connected with a prediction data transceiving
module, the output end of the Prediction Data Transceiving Module 1 is
bidirectionally electrically connected with the Prediction Data Transceiving Module
2, the input end of the prediction data transceiver module 2 is bidirectionally
electrically connected with a monitoring command center, the output end of the
display module and the output end of the monitoring command center are electrically
connected with a data warning module, and the input end of the processor is
electrically connected with a recommendation instruction module.
2. A civil aviation aircraft fault prediction system based on neural network
according to Claim 1, which is characterized in that the display module is a
waterproof display screen.
3. A civil aviation aircraft fault prediction system based on neural network
according to Claim 1, which is characterized in that: the input end of the processor is
electrically connected with a heat dissipation module, and the heat dissipation module
is a refrigeration fan.
4. A civil aviation aircraft fault prediction system based on neural network
according to Claim 1, which is characterized in that the storage module is a hard disk.
5. A civil aviation aircraft fault prediction system based on neural network
according to Claim 1, which is characterized in that the data warning module is an
acousto-optic alarm.
6. A civil aviation aircraft fault prediction system based on neural network
according to Claim 1, which is characterized in that the input end of the processor is
electrically connected with a fault location module in both directions.
7. A civil aviation aircraft fault prediction system based on neural network
according to Claim 1, characterized in that the probability prediction module is a
probability calculator.
8. A civil aviation aircraft fault prediction system based on a neural network
according to Claim 1, Characterized by: the input end of the processor is
bidirectionally electrically connected with a monitoring module, the monitoring
module is a monitoring camera, the input end of the processor is bidirectionally
electrically connected with an abnormal report generating module, the input end of
the processor is electrically connected with a standby power supply module, the input
end of the standby power supply module is electrically connected with a power supply
detection module, the input end of the monitoring command center is electrically
connected with a data encryption module, and the input end of the data encryption
module is electrically connected with a data decryption module.
FIGURES 1/1
figure1
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| AU2021105377A AU2021105377A4 (en) | 2021-08-12 | 2021-08-12 | A fault prediction system of civil aviation aircraft based on neural network |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| AU2021105377A AU2021105377A4 (en) | 2021-08-12 | 2021-08-12 | A fault prediction system of civil aviation aircraft based on neural network |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| AU2021105377A4 true AU2021105377A4 (en) | 2021-10-14 |
Family
ID=78007507
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| AU2021105377A Ceased AU2021105377A4 (en) | 2021-08-12 | 2021-08-12 | A fault prediction system of civil aviation aircraft based on neural network |
Country Status (1)
| Country | Link |
|---|---|
| AU (1) | AU2021105377A4 (en) |
-
2021
- 2021-08-12 AU AU2021105377A patent/AU2021105377A4/en not_active Ceased
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