CN109447401A - Ground health control method, system and device based on adaptive resource allotment - Google Patents
Ground health control method, system and device based on adaptive resource allotment Download PDFInfo
- Publication number
- CN109447401A CN109447401A CN201811090388.3A CN201811090388A CN109447401A CN 109447401 A CN109447401 A CN 109447401A CN 201811090388 A CN201811090388 A CN 201811090388A CN 109447401 A CN109447401 A CN 109447401A
- Authority
- CN
- China
- Prior art keywords
- resource
- failure symptom
- aircraft
- target aircraft
- data
- 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.)
- Pending
Links
- 230000003044 adaptive effect Effects 0.000 title claims abstract description 57
- 230000036541 health Effects 0.000 title claims abstract description 35
- 238000000034 method Methods 0.000 title claims abstract description 31
- 238000012360 testing method Methods 0.000 claims abstract description 142
- 208000024891 symptom Diseases 0.000 claims abstract description 80
- 238000012549 training Methods 0.000 claims description 42
- 238000001514 detection method Methods 0.000 claims description 13
- 238000010801 machine learning Methods 0.000 claims description 12
- 238000007726 management method Methods 0.000 claims description 12
- 238000012544 monitoring process Methods 0.000 claims description 12
- 238000003745 diagnosis Methods 0.000 claims description 5
- 238000003062 neural network model Methods 0.000 claims description 5
- 238000013468 resource allocation Methods 0.000 claims description 5
- 238000002955 isolation Methods 0.000 abstract description 3
- 230000006870 function Effects 0.000 description 4
- 238000012423 maintenance Methods 0.000 description 4
- 238000005516 engineering process Methods 0.000 description 3
- 238000013528 artificial neural network Methods 0.000 description 2
- 230000008859 change Effects 0.000 description 2
- 238000010586 diagram Methods 0.000 description 2
- 230000004927 fusion Effects 0.000 description 2
- 238000012706 support-vector machine Methods 0.000 description 2
- 230000004888 barrier function Effects 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 230000008878 coupling Effects 0.000 description 1
- 238000010168 coupling process Methods 0.000 description 1
- 238000005859 coupling reaction Methods 0.000 description 1
- 230000001419 dependent effect Effects 0.000 description 1
- 238000000605 extraction Methods 0.000 description 1
- 239000000446 fuel Substances 0.000 description 1
- 238000012545 processing Methods 0.000 description 1
- 230000009467 reduction Effects 0.000 description 1
- 238000004171 remote diagnosis Methods 0.000 description 1
- 230000008439 repair process Effects 0.000 description 1
- 238000012384 transportation and delivery Methods 0.000 description 1
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
- G06Q10/063—Operations research, analysis or management
- G06Q10/0631—Resource planning, allocation, distributing or scheduling for enterprises or organisations
- G06Q10/06315—Needs-based resource requirements planning or analysis
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/20—Administration of product repair or maintenance
Landscapes
- Business, Economics & Management (AREA)
- Human Resources & Organizations (AREA)
- Engineering & Computer Science (AREA)
- Strategic Management (AREA)
- Entrepreneurship & Innovation (AREA)
- Economics (AREA)
- Physics & Mathematics (AREA)
- Marketing (AREA)
- Operations Research (AREA)
- Quality & Reliability (AREA)
- Tourism & Hospitality (AREA)
- General Business, Economics & Management (AREA)
- General Physics & Mathematics (AREA)
- Theoretical Computer Science (AREA)
- Game Theory and Decision Science (AREA)
- Development Economics (AREA)
- Educational Administration (AREA)
- Management, Administration, Business Operations System, And Electronic Commerce (AREA)
Abstract
The present invention relates to Aircraft Health administrative skill fields, and in particular to a kind of ground health control method, system and device based on adaptive resource allotment.It aims to solve the problem that and how accurately to test airplane fault information, fast implement the fault location and Fault Isolation to target aircraft.This method comprises: step S1: obtaining the failure symptom of target aircraft according to the current test data of target aircraft;Step S2: it is matched based on failure symptom to the corresponding relationship of fault mode and according to the failure symptom of target aircraft: if not matching relevant fault mode, going to step S3;Step S3: based on adaptive resource scheduling model and according to failure symptom, relevant test resource and aircraft testing requirement are obtained;Step S4: based on aircraft testing requirement and target aircraft is detected using test resource to obtain resource testing data, then switches into step S1.This method can rapidly and accurately provide the fault message of target aircraft for ground is health management system arranged.
Description
Technical field
The present invention relates to Aircraft Health administrative skill fields, and in particular to a kind of ground based on adaptive resource allotment is strong
Health management method, system and device.
Background technique
Failure predication and health control technology are in safety, reduction life cycle cost and the raising dimension for improving aircraft equipment
It repairs and ensures that efficiency etc. has played important function, aircraft support system just ensures towards aircraft autonomous type and develops at present.With
The use of high integrity and new and high technology, so that the degree of coupling and the degree of association inside aircraft system and between system are increasingly
Height, test and diagnosis become increasingly complex, but also the fault diagnosis of aircraft, maintenance and guarantee management disconnect mutually, therefore efficiency
Low, maintenance cost is high.
Summary of the invention
In order to solve the above problem in the prior art, in order to solve how accurately to test airplane fault information, fastly
Speed realizes that the fault location and Fault Isolation to target aircraft, the first aspect of the present invention provide a kind of based on adaptive money
The ground health control method of source allotment, comprising:
Step S1: the failure symptom of the target aircraft is obtained according to the current test data of target aircraft;
Step S2: the corresponding relationship based on preset failure symptom and preset fault mode and flown according to the target
The failure symptom of machine is matched: if not matching relevant fault mode, going to step S3;
Step S3: based on preset adaptive resource scheduling model and according to the failure symptom, relevant survey is obtained
Try resource and aircraft testing requirement;
Step S4: the target aircraft detect based on the aircraft testing requirement and using the test resource
To resource testing data, step S1 is then switched into;
Wherein, the test data includes the on-board data and optional resource testing data of the target aircraft, institute
State adaptive resource scheduling model be based on preset failure symptom and its corresponding test resource and aircraft testing requirement, and
The neural network model constructed using machine learning algorithm.
Further, the present invention provides an optimal technical schemes are as follows:
The step of " failure symptom of the target aircraft is obtained according to the current test data of target aircraft " includes:
Step S101: the data characteristics of the current test data is extracted;
Step S102: feature carries out status monitoring to the target aircraft based on the data, and is based on status monitoring
As a result judge whether the target aircraft flies normally: if so, going to step S101;If it is not, then going to step S103;
Step S103: the corresponding relationship based on preset test data and preset failure symptom and according to described current
Test data matches relevant failure symptom.
Further, the present invention provides an optimal technical schemes are as follows:
" based on preset adaptive resource scheduling model and according to the failure symptom, obtaining relevant test money
After the step of source and aircraft testing requirement ", the method also includes:
Judge the failure symptom and the corresponding test resource of the failure symptom and whether aircraft testing requirement is default
Training sample in training set;
If it is not, then according to the training set, the failure symptom and the corresponding test resource of the failure symptom and aircraft
Testing requirement, and network training is carried out to the adaptive resource scheduling model again using machine learning algorithm.
The second aspect of the present invention, additionally provide it is a kind of based on adaptive resource allotment ground it is health management system arranged, packet
It includes:
Fault detection module is configured to obtain the failure sign of the target aircraft according to the current test data of target aircraft
Million;
Fault diagnosis module is configured to the corresponding relationship and root of preset failure symptom Yu preset fault mode
It is matched according to the failure symptom of the target aircraft: if not matching relevant fault mode, the failure symptom is defeated
It send to resource allocation module;
Resource allocation module, is configured to preset adaptive resource scheduling model and according to the failure symptom,
Obtain relevant test resource and aircraft testing requirement;
Module of target detection is configured to the aircraft testing requirement and is flown using the test resource to the target
Machine is detected to obtain resource testing data, and the resource testing data are then delivered to fault detection module;
Wherein, the test data includes the on-board data and optional resource testing data of the target aircraft, institute
State adaptive resource scheduling model be based on preset failure symptom and its corresponding test resource and aircraft testing requirement, and
The neural network model constructed using machine learning algorithm.
Further, the present invention provides an optimal technical schemes are as follows:
The fault detection module is further configured to perform the following operations:
Step S101: the data characteristics of the current test data is extracted;
Step S102: feature carries out status monitoring to the target aircraft based on the data, and is based on status monitoring
As a result judge whether the target aircraft flies normally: if so, going to step S101;If it is not, then going to step S103;
Step S103: the corresponding relationship based on preset test data and preset failure symptom and according to described current
Test data matches relevant failure symptom.
Further, the present invention provides an optimal technical schemes are as follows:
The system also includes model training module, the model training module is configured to perform the following operations:
Judge the failure symptom and the corresponding test resource of the failure symptom and whether aircraft testing requirement is default
Training sample in training set;
If it is not, then according to the training set, the failure symptom and the corresponding test resource of the failure symptom and aircraft
Testing requirement, and network training is carried out to the adaptive resource scheduling model again using machine learning algorithm.
Third invention of the invention, additionally provides a kind of storage device, wherein being stored with a plurality of program, described program is suitable for
It is loaded by processor and is executed to realize the above-mentioned ground health control method based on adaptive resource allotment.
The fourth aspect of the present invention additionally provides a kind of control device, comprising:
Processor is adapted for carrying out each program;
Equipment is stored, is suitable for storing a plurality of program;
It is characterized in that, described program is above-mentioned based on adaptive resource tune to realize suitable for being loaded by processor and being executed
The ground health control method matched.
Compared with the immediate prior art, above-mentioned technical proposal is at least had the following beneficial effects:
Ground health control method based on adaptive resource allotment of the invention includes: step S1: according to target aircraft
Current test data obtain target aircraft failure symptom;Step S2: based on preset failure symptom and preset failure mould
It the corresponding relationship of formula and is matched according to the failure symptom of target aircraft: if not matching relevant fault mode, being turned
To step S3;Step S3: based on preset adaptive resource scheduling model and according to failure symptom, relevant test money is obtained
Source and aircraft testing requirement;Step S4: target aircraft is detected to obtain based on aircraft testing requirement and using test resource
Resource testing data then switch into step S1;But the adaptive allotment to test resource is realized based on the above method, is ground
Information is accurately tested in health management system arranged offer, to fast implement the fault location and Fault Isolation to target aircraft.
Further, by judging whether are failure symptom and its corresponding test resource and aircraft testing requirement in the present invention
For the training sample in training set, to determine whether the training to adaptive resource scheduling model, with the increase of frequency of training,
Adaptive resource scheduling model accuracy is also continuously improved.
Detailed description of the invention
Fig. 1 is a kind of key step of the ground health control method based on adaptive resource allotment in the embodiment of the present invention
Schematic diagram;
Fig. 2 is the main flow signal that a kind of pair of adaptive resource scheduling model carries out model training in the embodiment of the present invention
Figure;
Fig. 3 is a kind of primary structure that the ground based on adaptive resource allotment is health management system arranged in the embodiment of the present invention
Schematic diagram.
Specific embodiment
The preferred embodiment of the present invention described with reference to the accompanying drawings.It will be apparent to a skilled person that this
A little embodiments are used only for explaining technical principle of the invention, it is not intended that limit the scope of the invention.
Refering to attached drawing 1, Fig. 1 illustrates a kind of master of ground health control method based on adaptive resource allotment
Step is wanted, as shown in Figure 1, the ground health control method based on adaptive resource allotment may include steps of:
Step S1: the failure symptom of target aircraft is obtained according to the current test data of target aircraft.
Specifically, " according to the current test data of target aircraft obtain target aircraft failure symptom " the step of include:
Step S101: the data characteristics of current test data is extracted;
Step S102: status monitoring is carried out to target aircraft based on data characteristics, and status monitoring result judges target
Whether aircraft flies normally: if so, going to step S101;If it is not, then going to step S103;
Step S103: the corresponding relationship based on preset test data and preset failure symptom and according to current test
Data match relevant failure symptom.
It should be noted that test data include the target aircraft on-board data and optional resource testing number
According to, test data is the parameter of each key subsystem of aircraft, such as: flying height, engine speed, is started at flight Mach number
Machine delivery temperature, pitch angle, angular velocity in roll, engine luggine, atmospheric pressure, fuel flow etc..Extract data characteristics
Method can carry out data characteristics extraction using the methods of time domain, frequency domain, time-frequency domain.Dependent failure sign is matched according to test data
Million method can be using intelligent algorithms such as neural network, support vector machine, information fusions.
Step S2: the corresponding relationship based on preset failure symptom and preset fault mode and according to target aircraft
Failure symptom is matched: if not matching relevant fault mode, going to step S3.
Further, the corresponding relationship based on preset failure symptom and preset fault mode and according to target aircraft
Failure symptom matched, if the fault mode first closed can be matched, target aircraft is repaired based on the fault mode
Or maintenance.Carrying out matched algorithm can be the intelligent algorithms such as neural network, support vector machine, information fusion.
Step S3: based on preset adaptive resource scheduling model and according to failure symptom, relevant test money is obtained
Source and aircraft testing requirement.
Further, test resource mainly include common test equipment, it is automatic test equipment, extraordinary test equipment, auxiliary
Help maintenance assistant, remote diagnosis expert etc., the test equipment for aircraft key subsystem.Adaptive resource scheduling model is base
In preset failure symptom and its corresponding test resource and aircraft testing requirement, and utilize the mind of machine learning algorithm building
Through network model.It further include that mould is dispatched to adaptive resource after the network training to adaptive resource scheduling model is completed
Type re-starts the step of network training, is continued to optimize with realizing to adaptive resource scheduling model, to improve resource tune
The accuracy of degree.
Refering to attached drawing 2, Fig. 2 illustrates the main flow that model training is carried out to adaptive resource scheduling model,
As shown in Fig. 2, the training sample in training set includes failure symptom and its corresponding test resource and aircraft testing requirement, it is based on
The training set carries out model training, obtains adaptive resource scheduling model.In the application stage of model, will be obtained according to test data
The failure symptom input adaptive resource dispatching model arrived, obtains corresponding test resource and aircraft testing requirement, judges the event
Whether barrier sign and its corresponding test resource and aircraft testing requirement are training sample in training set: if it is not, then according to should
Training set, the failure symptom and its corresponding test resource and aircraft testing requirement, and it is again right using machine learning algorithm
Adaptive resource scheduling model carries out network training.
Step S4: based on aircraft testing requirement and target aircraft is detected using test resource to obtain resource testing number
According to then switching into step S1.
Specifically, it after being detected using test resource and according to the testing requirement of aircraft to target aircraft, obtains
To resource testing data, step S1 is executed based on the resource testing data.
Further, it is based on above-mentioned ground health control method embodiment, the embodiment of the invention also provides one kind to be based on
The ground of adaptive resource allotment is health management system arranged.
Refering to attached drawing 3, it is health management system arranged that attached drawing 3 illustrates a kind of ground based on adaptive resource allotment
Primary structure.As shown in figure 3, health management system arranged based on the ground that adaptive resource is deployed may include:
Fault detection module 11 is configured to obtain the failure sign of target aircraft according to the current test data of target aircraft
Million;
Fault diagnosis module 12, be configured to the corresponding relationship of preset failure symptom and preset fault mode and
It is matched according to the failure symptom of target aircraft: if not matching relevant fault mode, failure symptom being delivered to money
Source scheduling module 13;
Resource allocation module 13, is configured to preset adaptive resource scheduling model and according to failure symptom, obtains
Take relevant test resource and aircraft testing requirement;
Module of target detection 14 is configured to aircraft testing requirement and is detected using test resource to target aircraft
Resource testing data are obtained, resource testing data are then delivered to fault detection module 11;
Wherein, test data includes the on-board data and optional resource testing data of target aircraft, adaptive resource
Scheduling model is to be based on preset failure symptom and its corresponding test resource and aircraft testing requirement, and utilize machine learning
The neural network model of algorithm building.
Further, fault detection module is further configured to perform the following operations:
Step S101: the data characteristics of current test data is extracted;
Step S102: status monitoring is carried out to the target aircraft based on data characteristics, and is based on status monitoring result
Judge whether target aircraft flies normally: if so, going to step S101;If it is not, then going to step S103;
Step S103: the corresponding relationship based on preset test data and preset failure symptom and according to current test
Data match relevant failure symptom.
Further, it further includes model training module that ground is health management system arranged, and model training module is configured to execute such as
Lower operation:
Judge whether failure symptom and its corresponding test resource and aircraft testing requirement are the training preset in training set
Sample: if it is not, then according to the training set, the failure symptom and its corresponding test resource and aircraft testing requirement, and utilize
Machine learning algorithm carries out network training to adaptive resource scheduling model again.
Further, it is based on above-mentioned ground health control method embodiment, the present invention also provides a kind of storage devices, should
A plurality of program is can store in storage device, program is suitable for being loaded by processor and being executed as above-mentioned based on adaptive resource
The ground health control method of allotment.
Still further, based on the above-mentioned ground health control method embodiment based on adaptive resource allotment, the present invention
A kind of processing unit is additionally provided, which may include processor, storage equipment;Processor is adapted for carrying out each journey
Sequence;Equipment is stored, is suitable for storing a plurality of program;Program is suitable for being loaded by processor and being executed as above-mentioned based on adaptive resource
The ground health control method of allotment.
Those skilled in the art should be able to recognize that, side described in conjunction with the examples disclosed in the embodiments of the present disclosure
Method step, system and device, can be realized with electronic hardware, computer software, or a combination of the two, in order to clearly say
The interchangeability of bright electronic hardware and software generally describes each exemplary composition according to function in the above description
And step.These functions are executed actually with electronic hardware or software mode, depending on technical solution specific application and set
Count constraint condition.Those skilled in the art can realize described function using distinct methods to each specific application
Can, but such implementation should not be considered as beyond the scope of the present invention.
So far, it has been combined preferred embodiment shown in the drawings and describes technical solution of the present invention, still, this field
Technical staff is it is easily understood that protection scope of the present invention is expressly not limited to these specific embodiments.Without departing from this
Under the premise of the principle of invention, those skilled in the art can make equivalent change or replacement to the relevant technologies feature, these
Technical solution after change or replacement will fall within the scope of protection of the present invention.
Claims (8)
1. a kind of ground health control method based on adaptive resource allotment, characterized by comprising:
Step S1: the failure symptom of the target aircraft is obtained according to the current test data of target aircraft;
Step S2: the corresponding relationship based on preset failure symptom and preset fault mode and according to the target aircraft
Failure symptom is matched: if not matching relevant fault mode, going to step S3;
Step S3: based on preset adaptive resource scheduling model and according to the failure symptom, relevant test money is obtained
Source and aircraft testing requirement;
Step S4: it is provided based on the aircraft testing requirement and being detected using the test resource to the target aircraft
Source test data then switches into step S1;
Wherein, the test data includes the on-board data and optional resource testing data of the target aircraft, it is described from
Adapting to resource dispatching model is to be based on preset failure symptom and its corresponding test resource and aircraft testing requirement, and utilize
The neural network model of machine learning algorithm building.
2. the ground health control method according to claim 1 based on adaptive resource allotment, which is characterized in that " root
The failure symptom of the target aircraft is obtained according to the current test data of target aircraft " the step of include:
Step S101: the data characteristics of the current test data is extracted;
Step S102: feature carries out status monitoring to the target aircraft based on the data, and is based on status monitoring result
Judge whether the target aircraft flies normally: if so, going to step S101;If it is not, then going to step S103;
Step S103: the corresponding relationship based on preset test data and preset failure symptom and according to the current test
Data match relevant failure symptom.
3. the ground health control method according to claim 2 based on adaptive resource allotment, which is characterized in that
It " based on preset adaptive resource scheduling model and according to the failure symptom, obtains relevant test resource and aircraft is surveyed
After the step of examination demand ", the method also includes:
Judge whether the failure symptom and the corresponding test resource of the failure symptom and aircraft testing requirement are default training
The training sample of concentration;
If it is not, then being tested according to the training set, the failure symptom and the corresponding test resource of the failure symptom and aircraft
Demand, and network training is carried out to the adaptive resource scheduling model again using machine learning algorithm.
4. a kind of ground based on adaptive resource allotment is health management system arranged, characterized by comprising:
Fault detection module is configured to obtain the failure symptom of the target aircraft according to the current test data of target aircraft;
Fault diagnosis module is configured to the corresponding relationship of preset failure symptom and preset fault mode and according to institute
The failure symptom for stating target aircraft is matched: if not matching relevant fault mode, the failure symptom being delivered to
Resource allocation module;
Resource allocation module is configured to preset adaptive resource scheduling model and according to the failure symptom, acquisition
Relevant test resource and aircraft testing requirement;
Airplane detection module, be configured to the aircraft testing requirement and using the test resource to the target aircraft into
Row detection obtains resource testing data, and the resource testing data are then delivered to fault detection module;
Wherein, the test data includes the on-board data and optional resource testing data of the target aircraft, it is described from
Adapting to resource dispatching model is to be based on preset failure symptom and its corresponding test resource and aircraft testing requirement, and utilize
The neural network model of machine learning algorithm building.
5. the ground according to claim 4 based on adaptive resource allotment is health management system arranged, which is characterized in that described
Fault detection module is further configured to perform the following operations:
Step S101: the data characteristics of the current test data is extracted;
Step S102: feature carries out status monitoring to the target aircraft based on the data, and is based on status monitoring result
Judge whether the target aircraft flies normally: if so, going to step S101;If it is not, then going to step S103;
Step S103: the corresponding relationship based on preset test data and preset failure symptom and according to the current test
Data match relevant failure symptom.
6. the ground according to claim 5 based on adaptive resource allotment is health management system arranged, which is characterized in that described
System further includes model training module, and the model training module is configured to perform the following operations:
Judge whether the failure symptom and the corresponding test resource of the failure symptom and aircraft testing requirement are default training
The training sample of concentration;
If it is not, then being tested according to the training set, the failure symptom and the corresponding test resource of the failure symptom and aircraft
Demand, and network training is carried out to the adaptive resource scheduling model again using machine learning algorithm.
7. a kind of storage device, wherein being stored with a plurality of program, which is characterized in that described program is suitable for being loaded and being held by processor
Row is to realize the ground health control method of any of claims 1-3 based on adaptive resource allotment.
8. a kind of control device, comprising:
Processor is adapted for carrying out each program;
Equipment is stored, is suitable for storing a plurality of program;
It is characterized in that, described program is of any of claims 1-3 to realize suitable for being loaded by processor and being executed
Ground health control method based on adaptive resource allotment.
Priority Applications (1)
Application Number | Priority Date | Filing Date | Title |
---|---|---|---|
CN201811090388.3A CN109447401A (en) | 2018-09-18 | 2018-09-18 | Ground health control method, system and device based on adaptive resource allotment |
Applications Claiming Priority (1)
Application Number | Priority Date | Filing Date | Title |
---|---|---|---|
CN201811090388.3A CN109447401A (en) | 2018-09-18 | 2018-09-18 | Ground health control method, system and device based on adaptive resource allotment |
Publications (1)
Publication Number | Publication Date |
---|---|
CN109447401A true CN109447401A (en) | 2019-03-08 |
Family
ID=65530526
Family Applications (1)
Application Number | Title | Priority Date | Filing Date |
---|---|---|---|
CN201811090388.3A Pending CN109447401A (en) | 2018-09-18 | 2018-09-18 | Ground health control method, system and device based on adaptive resource allotment |
Country Status (1)
Country | Link |
---|---|
CN (1) | CN109447401A (en) |
Citations (7)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN103353752A (en) * | 2013-04-12 | 2013-10-16 | 北京航空航天大学 | Aircraft environment control system control component fault diagnosis method based on four-level RBF neural network |
US20130318018A1 (en) * | 2012-05-23 | 2013-11-28 | General Electric Company | Neural network-based turbine monitoring system |
CN103926526A (en) * | 2014-05-05 | 2014-07-16 | 重庆大学 | Analog circuit fault diagnosis method based on improved RBF neural network |
CN104635081A (en) * | 2015-01-29 | 2015-05-20 | 西北工业大学 | Adaptive fault diagnosis method of aircraft generator rectifier |
CN105955243A (en) * | 2016-06-08 | 2016-09-21 | 中国商用飞机有限责任公司 | Fault-case-based plane diagnosis and maintenance method and apparatus |
CN108226662A (en) * | 2016-12-14 | 2018-06-29 | 中国航空工业集团公司西安航空计算技术研究所 | A kind of airborne computer failure prediction method |
CN108427400A (en) * | 2018-03-27 | 2018-08-21 | 西北工业大学 | A kind of aircraft airspeed pipe method for diagnosing faults based on neural network Analysis design |
-
2018
- 2018-09-18 CN CN201811090388.3A patent/CN109447401A/en active Pending
Patent Citations (7)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
US20130318018A1 (en) * | 2012-05-23 | 2013-11-28 | General Electric Company | Neural network-based turbine monitoring system |
CN103353752A (en) * | 2013-04-12 | 2013-10-16 | 北京航空航天大学 | Aircraft environment control system control component fault diagnosis method based on four-level RBF neural network |
CN103926526A (en) * | 2014-05-05 | 2014-07-16 | 重庆大学 | Analog circuit fault diagnosis method based on improved RBF neural network |
CN104635081A (en) * | 2015-01-29 | 2015-05-20 | 西北工业大学 | Adaptive fault diagnosis method of aircraft generator rectifier |
CN105955243A (en) * | 2016-06-08 | 2016-09-21 | 中国商用飞机有限责任公司 | Fault-case-based plane diagnosis and maintenance method and apparatus |
CN108226662A (en) * | 2016-12-14 | 2018-06-29 | 中国航空工业集团公司西安航空计算技术研究所 | A kind of airborne computer failure prediction method |
CN108427400A (en) * | 2018-03-27 | 2018-08-21 | 西北工业大学 | A kind of aircraft airspeed pipe method for diagnosing faults based on neural network Analysis design |
Similar Documents
Publication | Publication Date | Title |
---|---|---|
CA2781029C (en) | Improved diagnostics for aircraft | |
CN107085744A (en) | Utilize the enhanced aircraft maintenance of data analysis and inspection | |
CN104865949B (en) | The test of initiation is health management system arranged and method | |
CN106066184A (en) | For detecting the system and method for communication tool system fault | |
CN105741381B (en) | A kind of method that determining aircraft flies ginseng recording parameters set | |
KR102124990B1 (en) | Elevator remote maintenance support system, and elevator remote maintenance support method | |
CN107462425B (en) | A kind of failure prediction method and failure predication platform of car kits system | |
CN109632355B (en) | Fault prediction method and system based on electromechanical device state data drift | |
BR102013002439A2 (en) | Aircraft maintenance system, method of providing maintenance for an aircraft and method of operating an aircraft | |
CN107544458B (en) | Airborne detection device and detection method for air-to-air missile | |
CN108475445A (en) | The method and system of the automation fusion and analysis of multi-source aircraft data | |
CN106447107B (en) | Maintenance method based on aircraft structure health monitoring | |
CN108454879B (en) | Airplane fault processing system and method and computer equipment | |
US20110004369A1 (en) | Method and System for Generating Electronic Documentation for Maintenance | |
CN106155035A (en) | Method for diagnosing faults based on maintenance class data and fault diagnosis system | |
CN110378273A (en) | A kind of method and apparatus of monitoring results process | |
CN106462158A (en) | Methods and apparatus for assisting in maintenance of aircraft and other mobile platforms using occurrence probabilities of potential causes of detected event | |
CN106779294B (en) | aircraft operation error detection method and system | |
US8219276B2 (en) | Method and device for aiding the maintenance of a system | |
CN109189044A (en) | A kind of method that pure electric automobile remotely monitors failure formation database | |
CN110634329A (en) | Test method of air traffic management electronic system, related electronic equipment and platform | |
US8989951B1 (en) | Maintaining the airworthiness configuration of aircraft | |
CN109447401A (en) | Ground health control method, system and device based on adaptive resource allotment | |
US10861259B2 (en) | Method for testing the integrity of the avionics of an aircraft, associated device and computer program product | |
CN101359430A (en) | Open type flight delay early-warning system and processing method thereof |
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 | ||
RJ01 | Rejection of invention patent application after publication | ||
RJ01 | Rejection of invention patent application after publication |
Application publication date: 20190308 |