[go: up one dir, main page]

CN114237110A - Multimodal data-driven reliability monitoring and early warning system for general aviation fleet - Google Patents

Multimodal data-driven reliability monitoring and early warning system for general aviation fleet Download PDF

Info

Publication number
CN114237110A
CN114237110A CN202111523385.6A CN202111523385A CN114237110A CN 114237110 A CN114237110 A CN 114237110A CN 202111523385 A CN202111523385 A CN 202111523385A CN 114237110 A CN114237110 A CN 114237110A
Authority
CN
China
Prior art keywords
data
reliability
early warning
fleet
probability
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
Application number
CN202111523385.6A
Other languages
Chinese (zh)
Inventor
陈农田
马婷
宁威峰
满永政
李俊辉
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Civil Aviation Flight University of China
Original Assignee
Civil Aviation Flight University of China
Priority date (The priority date 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 date listed.)
Filing date
Publication date
Application filed by Civil Aviation Flight University of China filed Critical Civil Aviation Flight University of China
Priority to CN202111523385.6A priority Critical patent/CN114237110A/en
Publication of CN114237110A publication Critical patent/CN114237110A/en
Pending legal-status Critical Current

Links

Images

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
    • G05B19/00—Programme-control systems
    • G05B19/02—Programme-control systems electric
    • G05B19/04—Programme control other than numerical control, i.e. in sequence controllers or logic controllers
    • G05B19/042—Programme control other than numerical control, i.e. in sequence controllers or logic controllers using digital processors
    • G05B19/0428—Safety, monitoring
    • 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
    • G05B2219/00—Program-control systems
    • G05B2219/20—Pc systems
    • G05B2219/24—Pc safety
    • G05B2219/24024—Safety, surveillance

Landscapes

  • Physics & Mathematics (AREA)
  • General Physics & Mathematics (AREA)
  • Engineering & Computer Science (AREA)
  • Automation & Control Theory (AREA)
  • Testing And Monitoring For Control Systems (AREA)

Abstract

The invention discloses a multi-mode data-driven universal aviation fleet reliability monitoring and early warning system, relates to the technical field of air traffic service, and solves the problems that operation data of an aviation fleet are relatively independent and difficult to interact.

Description

Multi-mode data driving-based general aviation fleet reliability monitoring and early warning system
Technical Field
The invention relates to the technical field of air traffic service, in particular to a multi-mode data-driven universal aviation fleet reliability monitoring and early warning system.
Background
General aviation and transportation aviation are two wings of the civil aviation industry, and with the gradual opening of the low-altitude airspace in China, the general aviation will be in the rapid development period, and the scale of the general aviation fleet will obtain unprecedented accelerated growth situation. In recent years, the total number of aircrafts in the volume of general aviation enterprises increases by more than 10% in each year. The problem of managing the reliability of the equipment of the general aviation fleet is further highlighted. The reliability of the equipment of the general aviation fleet not only relates to the safety and the economy of the navigation enterprise in the operation process, but also is an important factor for determining the efficiency and the life cycle cost of the navigation aircraft, and is an important guarantee for the continuous sustainable and healthy development of the navigation. However, the reliability management work of the general aviation fleet is not clearly specified in the national civil aviation regulations, most of the reliability management works are executed by referring to transport aviation, a maintenance data management mode taking reliability as a center is not formed aiming at the characteristics of general aviation, and the reliability state analysis technical means of the equipment of the general aviation fleet is weak. The reliability state identification of the general aviation fleet equipment is a key technology for realizing dynamic monitoring and systematic analysis of the reliability state, and provides scientific basis for developing reliability management work and eliminating safety hidden dangers and defects of the fleet equipment for the aviation department. At present, the reliability research of domestic and foreign aviation equipment mainly focuses on: statistical analysis of reliability data, such as avionics product reliability BAYES statistical inference; analyzing the reliability of the product component and the system thereof, such as a non-parameter statistical analysis method for the reliability of the aircraft component and the like; the reliability and the maintainability, such as the determination and the distribution of the reliability and the maintainability requirements of the aviation equipment, the prediction of the reliability and the maintainability, the test and the verification and other theories; fourthly, reliability quality standards, such as the operation reliability reference of the complex general aircraft; and fifthly, reliability evaluation methods and models, such as an aircraft engine reliability index evaluation method, an airline fleet equipment reliability nonlinear dynamic evaluation model and an aviation equipment field data reliability evaluation method, are less in effectiveness analysis, and a fleet equipment reliability dynamic identification method for the navigation operation characteristics of a set of aircraft is not formed. Therefore, in order to meet the requirement of reliability state evaluation of the general aviation fleet equipment, further intensive research on the reliability identification method is urgently needed.
Disclosure of Invention
In view of the technical defects, the invention provides a general aviation fleet reliability monitoring and early warning system based on multi-modal data driving.
In order to achieve the purpose, the technical scheme of the invention is as follows:
the general aviation fleet reliability monitoring and early warning method based on multi-modal data driving comprises the following steps:
step 1: acquiring fleet data according to a set time interval to form a data set, and preprocessing the data set to form single-mode information;
step 2: traversing the reliability probability table, and screening the reliability probability for each single-mode information;
and step 3: calculating corresponding multi-modal information fusion probability for each fleet data according to the reliability probability and a Bayesian inference model;
and 4, step 4: and (4) estimating the early warning information of the type to which the fleet data finally belongs according to the multi-mode information fusion probability.
The multi-modal data-driven-based general aviation fleet reliability monitoring and early warning method according to claim 1, wherein the fleet data comprises communication data, power data, flight control data, fuel data, landing gear data, light data, navigation data, power plant data, engine fuel control data, ignition data, and engine indication data.
Preferably, in the step 2, there are n types of single-mode information, each single-mode information corresponds to m reliability probability tables, and there are m × n reliability probability tables in total, where the reliability probability table EijThe reliability probability of the jth fleet data expressed as the ith single-mode information is stored in the reliability probability table EijThe reliability probability P (M) of the j-th fleet data expressed as the measured value can be inquired according to the measured value of the i-th monomodal informationi|Nj)。
Preferably, the reliability data in the reliability probability table is obtained from the reliability data of the past year aviation fleet, and the reliability probability table can be updated in real time.
Preferably, in step 3, the j-th fleet data corresponds to multiple modesState information fusion probability P (N)j|M1,Mi,...,Mn) Calculated according to the following formula:
P(Nj|M1，Mi，...，Mn)＝||ΔP(Nj)·Φ||j
wherein, P (N)j) Representing the prior probability of the fleet data in the j; phi epsilon to Rj×i(K < i < j) is an observation matrix; the parameter lambda is greater than 0.
The general aviation fleet reliability monitoring and early warning system based on multi-modal data driving comprises a multi-modal information acquisition system, a computer, an early warning presumption module and a polling module, wherein the computer is provided with a data acquisition module, a single-modal information preprocessing module, a multi-modal information fusion module and a condition probability table;
the data acquisition module is used for acquiring various data to form a data set;
the single-mode information preprocessing module is used for preprocessing the data set to form single-mode information;
the multi-modal information fusion module is used for calculating multi-modal information fusion probability through a Bayesian inference model according to the single-modal information and the reliability probability table;
the early warning presumption module is used for predicting early warning information according to the multi-mode information fusion probability;
the polling module is used for parallelly polling the aviation fleet according to a set time interval.
Preferably, the system further comprises an early warning terminal, and the early warning terminal is used for receiving early warning information sent by the early warning presumption module.
Preferably, the multi-modal information fusion module comprises a weighted naive bayes classifier.
The invention has the beneficial effects that:
the problem that the operation data of the aviation fleet are relatively independent and difficult to interact is solved, based on the objectively obtained operation data, the operation data of the aviation fleet are reconstructed and integrated in a multi-mode information fusion driving mode, the association barrier among the multi-azimuth operation data is broken through, the relation among the operation data can be effectively reflected, the early warning condition of the currently obtained data is effectively judged according to the operation data through the training calculation of a Bayesian inference model, an early warning signal is sent out, and the flight safety of the aviation fleet is guaranteed.
Drawings
FIG. 1 is a schematic flow diagram of a multi-modal data-driven general aviation fleet reliability monitoring and early warning system;
Detailed Description
The technical solutions in the embodiments of the present invention are clearly and completely described below with reference to the accompanying drawings, and other advantages and effects of the present invention will be readily apparent to those skilled in the art from the disclosure of the present specification. The invention is capable of other and different embodiments and of being practiced or of being carried out in various ways, and its several details are capable of modification in various respects, all without departing from the spirit and scope of the present invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
As shown in fig. 1, the method for monitoring and warning the reliability of a general aviation fleet based on multi-modal data driving comprises the following steps:
step 1: acquiring fleet data according to a set time interval to form a data set, and preprocessing the data set to form single-mode information;
step 2: traversing the reliability probability table, and screening the reliability probability for each single-mode information;
and step 3: calculating corresponding multi-modal information fusion probability for each fleet data according to the reliability probability and a Bayesian inference model;
and 4, step 4: and (4) estimating the early warning information of the type to which the fleet data finally belongs according to the multi-mode information fusion probability.
The multi-modal data-driven-based general aviation fleet reliability monitoring and early warning method according to claim 1, wherein the fleet data comprises communication data, power data, flight control data, fuel data, landing gear data, light data, navigation data, power plant data, engine fuel control data, ignition data, and engine indication data.
Specifically, in step 2, there are n types of single-mode information, each single-mode information corresponds to m reliability probability tables, and there are m × n reliability probability tables in total, where the reliability probability table EijThe reliability probability of the jth fleet data expressed as the ith single-mode information is stored in the reliability probability table EijThe reliability probability P (M) of the j-th fleet data expressed as the measured value can be inquired according to the measured value of the i-th monomodal informationi|Nj)。
Specifically, the reliability data in the reliability probability table is obtained from the reliability data of the past year aviation fleet, and the reliability probability table can be updated in real time.
Specifically, in step 3, the multimodal information fusion probability P (N) corresponding to the jth fleet dataj|M1,Mi,...,Mn) Calculated according to the following formula:
P(Nj|M1，Mi，...，Mn)＝||ΔP(Nj)·Φ||j
wherein, P (N)j) Representing the prior probability of the fleet data in the j; phi epsilon to Rj×i(K < i < j) is an observation matrix; the parameter lambda is greater than 0.
The general aviation fleet reliability monitoring and early warning system based on multi-modal data driving comprises a multi-modal information acquisition system, a computer, an early warning presumption module and a polling module, wherein the computer is provided with a data acquisition module, a single-modal information preprocessing module, a multi-modal information fusion module and a condition probability table;
the data acquisition module is used for acquiring various data to form a data set;
the single-mode information preprocessing module is used for preprocessing the data set to form single-mode information;
the multi-modal information fusion module is used for calculating multi-modal information fusion probability through a Bayesian inference model according to the single-modal information and the reliability probability table;
the early warning presumption module is used for predicting early warning information according to the multi-mode information fusion probability;
the polling module is used for parallelly polling the aviation fleet according to a set time interval.
The early warning system comprises an early warning terminal and an early warning module, wherein the early warning terminal is used for receiving early warning information sent by the early warning presumption module.
Specifically, the multi-modal information fusion module comprises a weighted naive Bayesian classifier.

Claims (8)

1.基于多模态数据驱动的通用航空机队可靠性监测预警方法，其特征在于，包括以下步骤：1. The general aviation fleet reliability monitoring and early warning method driven by multimodal data is characterized in that, comprising the following steps: 步骤1：根据设定的时间间隔采集机队数据，形成数据集，对数据集进行预处理，形成单模态信息；Step 1: Collect fleet data according to the set time interval to form a data set, and preprocess the data set to form single-modal information; 步骤2：遍历可靠性概率表，为各单模态信息筛查可靠性概率；Step 2: Traverse the reliability probability table to screen the reliability probability for each single-modal information; 步骤3：根据可靠性概率以及贝叶斯推理模型为每种机队数据计算对应的多模态信息融合概率；Step 3: Calculate the corresponding multimodal information fusion probability for each fleet data according to the reliability probability and the Bayesian inference model; 步骤4：根据多模态信息融合概率推测出机队数据最终所属种类的预警信息。Step 4: According to the multimodal information fusion probability, the early warning information of the final category of the fleet data is inferred. 2.根据权利要求1所述的基于多模态数据驱动的通用航空机队可靠性监测预警方法，其特征在于，所述机队数据包括有通讯数据、电源数据、飞行操控数据、燃油数据、起落架数据、灯光数据、导航数据、动力装置数据、发动机燃油控制数据、点火数据、发动机指示数据。2. The general aviation fleet reliability monitoring and early warning method based on multimodal data drive according to claim 1, wherein the fleet data includes communication data, power data, flight control data, fuel data, Landing gear data, light data, navigation data, powerplant data, engine fuel control data, ignition data, engine indication data. 3.根据权利要求1所述的基于多模态数据驱动的通用航空机队可靠性监测预警方法，其特征在于，所述步骤2中，有n种单模态信息，每个单模态信息均对应有m个可靠性概率表，总共有m×n张可靠性概率表，其中，可靠性概率表Eij中存储有第j种机队数据表现为第i种单模态信息的可靠性概率，从而通过可靠性概率表Eij能够根据第i种单模态信息的测量值查询到第j种机队数据表现为该测量值的可靠性概率P(Mi|Nj)。3. The general aviation fleet reliability monitoring and early warning method based on multi-modal data drive according to claim 1, is characterized in that, in described step 2, there are n kinds of single-modal information, each single-modal information Each corresponds to m reliability probability tables, and there are m×n reliability probability tables in total. Among them, the reliability probability table E ij stores the reliability of the jth type of fleet data, which represents the reliability of the ith type of unimodal information. Therefore, through the reliability probability table E ij , the reliability probability P(M i |N j ) that the jth type of fleet data is represented by the measurement value can be queried according to the measurement value of the i-th type of unimodal information. 4.根据权利要求3所述的基于多模态数据驱动的通用航空机队可靠性监测预警系统，其特征在于，所述可靠性概率表中的可靠性数据由往年航空机队可靠性数据获得，并且可靠性概率表能够实时更新。4. The general aviation fleet reliability monitoring and early warning system driven by multimodal data according to claim 3, wherein the reliability data in the reliability probability table is obtained from the aviation fleet reliability data in previous years , and the reliability probability table can be updated in real time. 5.根据权利要求1所述的基于多模态数据驱动的通用航空机队可靠性监测预警系统，其特征在于，所述步骤3中，第j种机队数据所对应的多模态信息融合概率P(Nj|M1,Mi,...,Mn)按如下公式计算：5. The general aviation fleet reliability monitoring and early warning system based on multimodal data drive according to claim 1, is characterized in that, in described step 3, the multimodal information fusion corresponding to the jth kind of fleet data The probability P(N j |M 1 ,M i ,...,M n ) is calculated as follows: P(Nj|M1，Mi，...，Mn)＝||ΔP(Nj)·Φ||j P(N j |M 1 , M i , . . . , Mn )=||ΔP(N j )·Φ|| j 其中，P(Nj)表示第j中机队数据的先验概率；Φ∈Rj×i(K＜i＜＜j)为观测矩阵；参数λ＞0。Among them, P(N j ) represents the prior probability of the jth fleet data; Φ∈R j×i (K<i<<j) is the observation matrix; the parameter λ>0. 6.基于多模态数据驱动的通用航空机队可靠性监测预警系统，其特征在于，包含权利要求1-5所述的基于多模态数据驱动的通用航空机队可靠性监测预警方法，包括多模态信息采集系统以及配置有数据采集模块、单模态信息预处理模块、多模态信息融合模块和条件概率表的计算机、预警推测模块、轮询；6. The general aviation fleet reliability monitoring and early warning system based on multimodal data driving is characterized in that, comprising the general aviation fleet reliability monitoring and early warning method based on multimodal data driving described in claims 1-5, including A multi-modal information acquisition system, and a computer equipped with a data acquisition module, a single-modal information preprocessing module, a multi-modal information fusion module and a conditional probability table, an early warning inference module, and polling; 数据采集模块用于采集多种数据，形成数据集；The data acquisition module is used to collect a variety of data to form a data set; 单模态信息预处理模块用于将数据集进行预处理，形成单模态信息；The unimodal information preprocessing module is used to preprocess the data set to form unimodal information; 所述多模态信息融合模块用于根据单模态信息与可靠性概率表通过贝叶斯推理模型计算多模态信息融合概率；The multi-modal information fusion module is used to calculate the multi-modal information fusion probability through the Bayesian inference model according to the single-modal information and the reliability probability table; 所述预警推测模块用于根据多模态信息融合概率进行预警信息的预测；The early warning inference module is used to predict early warning information according to the multimodal information fusion probability; 所述轮询模块用于根据设定时间间隔并行轮询航空机队。The polling module is used to poll the aviation fleet in parallel according to the set time interval. 7.根据权利要求6所述的基于多模态数据驱动的通用航空机队可靠性监测预警系统，其特征在于，还包括预警终端，所述预警终端用于接收预警推测模块发出的预警信息。7 . The general aviation fleet reliability monitoring and early warning system based on multimodal data drive according to claim 6 , further comprising an early warning terminal, wherein the early warning terminal is used to receive early warning information sent by the early warning speculation module. 8 . 8.根据权利要求6所述的基于多模态数据驱动的通用航空机队可靠性监测预警系统，其特征在于，所述多模态信息融合模块包括有权重阶梯朴素贝叶斯分类器。8 . The general aviation fleet reliability monitoring and early warning system driven by multimodal data according to claim 6 , wherein the multimodal information fusion module comprises a weighted ladder naive Bayes classifier. 9 .
CN202111523385.6A 2021-12-13 2021-12-13 Multimodal data-driven reliability monitoring and early warning system for general aviation fleet Pending CN114237110A (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN202111523385.6A CN114237110A (en) 2021-12-13 2021-12-13 Multimodal data-driven reliability monitoring and early warning system for general aviation fleet

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN202111523385.6A CN114237110A (en) 2021-12-13 2021-12-13 Multimodal data-driven reliability monitoring and early warning system for general aviation fleet

Publications (1)

Publication Number Publication Date
CN114237110A true CN114237110A (en) 2022-03-25

Family

ID=80755629

Family Applications (1)

Application Number Title Priority Date Filing Date
CN202111523385.6A Pending CN114237110A (en) 2021-12-13 2021-12-13 Multimodal data-driven reliability monitoring and early warning system for general aviation fleet

Country Status (1)

Country Link
CN (1) CN114237110A (en)

Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN116881850A (en) * 2023-09-04 2023-10-13 山东航天九通车联网有限公司 Safety early warning system based on multi-mode data fusion
CN117351257A (en) * 2023-08-24 2024-01-05 长江水上交通监测与应急处置中心 Multi-mode information-based shipping data extraction method and system

Citations (12)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN102519733A (en) * 2011-12-02 2012-06-27 南京航空航天大学 Method for assessing flying reliability of aircraft engine on basis of monitoring information fusion
CN205281183U (en) * 2015-12-30 2016-06-01 南京信息工程大学 A low-altitude environmental monitoring UAV system
BR112012033543A2 (en) * 2010-06-30 2016-11-29 Antonio Pujante Cuadrupani method for data processing performed by a device equipped with a navigation receiver
US20170259942A1 (en) * 2016-03-08 2017-09-14 Harris Corporation Wireless engine monitoring system for environmental emission control and aircraft networking
CN107966992A (en) * 2018-01-11 2018-04-27 中国运载火箭技术研究院 A kind of Reusable Launch Vehicles control reconfiguration method and system
CN109044283A (en) * 2018-08-31 2018-12-21 重庆高铂瑞骐科技开发有限公司 A kind of esophagus functional disease diagnostic system based on multi-modal information
CN109192304A (en) * 2018-08-31 2019-01-11 重庆高铂瑞骐科技开发有限公司 A kind of multimodal information fusion system for esophagus functional disease diagnostic system
CN110953082A (en) * 2019-12-18 2020-04-03 中国民用航空飞行学院 Method for eliminating airplane slow-vehicle parking fault with high reliability
CN111461176A (en) * 2020-03-09 2020-07-28 华南理工大学 Multi-mode fusion method, device, medium and equipment based on normalized mutual information
CN111582510A (en) * 2020-05-13 2020-08-25 中国民用航空飞行学院 Intelligent identification method and system based on support vector machine and civil aircraft engine
CN112487592A (en) * 2020-12-16 2021-03-12 北京航空航天大学 Bayesian network-based task reliability modeling analysis method
CN113255777A (en) * 2021-05-28 2021-08-13 郑州轻工业大学 Equipment fault early warning method and system based on multi-mode sensitive feature selection fusion

Patent Citations (12)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
BR112012033543A2 (en) * 2010-06-30 2016-11-29 Antonio Pujante Cuadrupani method for data processing performed by a device equipped with a navigation receiver
CN102519733A (en) * 2011-12-02 2012-06-27 南京航空航天大学 Method for assessing flying reliability of aircraft engine on basis of monitoring information fusion
CN205281183U (en) * 2015-12-30 2016-06-01 南京信息工程大学 A low-altitude environmental monitoring UAV system
US20170259942A1 (en) * 2016-03-08 2017-09-14 Harris Corporation Wireless engine monitoring system for environmental emission control and aircraft networking
CN107966992A (en) * 2018-01-11 2018-04-27 中国运载火箭技术研究院 A kind of Reusable Launch Vehicles control reconfiguration method and system
CN109044283A (en) * 2018-08-31 2018-12-21 重庆高铂瑞骐科技开发有限公司 A kind of esophagus functional disease diagnostic system based on multi-modal information
CN109192304A (en) * 2018-08-31 2019-01-11 重庆高铂瑞骐科技开发有限公司 A kind of multimodal information fusion system for esophagus functional disease diagnostic system
CN110953082A (en) * 2019-12-18 2020-04-03 中国民用航空飞行学院 Method for eliminating airplane slow-vehicle parking fault with high reliability
CN111461176A (en) * 2020-03-09 2020-07-28 华南理工大学 Multi-mode fusion method, device, medium and equipment based on normalized mutual information
CN111582510A (en) * 2020-05-13 2020-08-25 中国民用航空飞行学院 Intelligent identification method and system based on support vector machine and civil aircraft engine
CN112487592A (en) * 2020-12-16 2021-03-12 北京航空航天大学 Bayesian network-based task reliability modeling analysis method
CN113255777A (en) * 2021-05-28 2021-08-13 郑州轻工业大学 Equipment fault early warning method and system based on multi-mode sensitive feature selection fusion

Non-Patent Citations (1)

* Cited by examiner, † Cited by third party
Title
陈勇刚: "通用航空机队设备可靠性动态识别模型", 《航空学报》 *

Cited By (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN117351257A (en) * 2023-08-24 2024-01-05 长江水上交通监测与应急处置中心 Multi-mode information-based shipping data extraction method and system
CN117351257B (en) * 2023-08-24 2024-04-02 长江水上交通监测与应急处置中心 Multi-mode information-based shipping data extraction method and system
CN116881850A (en) * 2023-09-04 2023-10-13 山东航天九通车联网有限公司 Safety early warning system based on multi-mode data fusion
CN116881850B (en) * 2023-09-04 2023-12-08 山东航天九通车联网有限公司 Safety early warning system based on multi-mode data fusion

Similar Documents

Publication Publication Date Title
US12026440B1 (en) Optimizing aircraft flows at airports using data driven predicted capabilities
CA2780380C (en) Method of and system for evaluating the health status of a system using groups of vibration data comprising images of the vibrations of the system
US8914149B2 (en) Platform health monitoring system
CA2771401C (en) Platform health monitoring system
CN103530704B (en) A kind of air dynamic traffic volume in terminal airspace prognoses system and method thereof
CN107085744A (en) Utilize the enhanced aircraft maintenance of data analysis and inspection
CN112381406A (en) Ship energy efficiency management big data system and method based on ship-shore cooperation
EP3336636A1 (en) Machine fault modelling
CN114237110A (en) Multimodal data-driven reliability monitoring and early warning system for general aviation fleet
CN113344425B (en) Flight quality monitoring method and system based on QAR data
CN113344423A (en) Pilot scene applicability diagnosis method and system based on machine learning
CN113298431A (en) Aviation QAR big data-based pilot competence portrayal method and system
CN110796315B (en) Departure flight delay prediction method based on aging information and deep learning
CN114282792B (en) A flight landing quality monitoring and evaluation method and system
CN111914217A (en) A method for evaluating and predicting the environmental carrying capacity of airports
KR102118748B1 (en) Platform health monitoring system
CN117455365A (en) Dangerous chemical storage early warning method and system based on improved artificial neural network
CN113344408A (en) Processing method for multi-scale situation perception process of civil aviation traffic control operation
CN119047842A (en) Operation safety management and control system and method based on risk classification
CN118211034A (en) Multi-dimensional civil aviation passenger flow prediction method based on KNN regression model
Li et al. Overview of application in data mining techniques to qar data ansys
Luxhøj et al. Integrated decision support for aviation safety inspectors
Li et al. Method for predicting failure rate of airborne equipment based on optimal combination model
Vidović et al. Aircraft noise monitoring in function of flight safety and aircraft model determination
Dong et al. Evaluation for Trainee Pilot Workload Management Competency During Approach Phase Based on Flight Training Data

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