Aircraft body state analysis method and system based on aero-engine maintenance big data

Abstract

The invention discloses an aircraft body state analysis method and system based on aero-engine maintenance big data, and the method comprises the steps: completing the collection and obtaining of various parameters in an aircraft through implanting a sensor in an aircraft body at an airborne side, an embedded health management model and an airborne computer are used for responding to various kinds of collected parameter data according to the priority of the data, and meanwhile data transmission is conducted to the ground side through a communication network; and the ground side receives airborne flight performance data, performs state monitoring and trend analysis on the aircraft by using maintenance data of the ground side, and performs evaluation and fault diagnosis prediction on the health state of the aircraft by using an algorithm model. According to the method, indexes such as representative performance and reliability of data obtained by a sensor implanted in the equipment or an external detector are monitored in real time or near real time, abnormity is found and alarmed, fault diagnosis is carried out, the health state is evaluated, and the trend and possible faults are predicted.

Description

Technical Field

[0001] The present application belongs to the field of engine detection technology, and in particular, relates to an airframe status analysis method and system based on aviation engine maintenance big data. Background Art

[0002] Generally speaking, aviation maintenance is divided into four categories: engine maintenance, fuselage maintenance, accessory maintenance and line maintenance. Labor costs account for 20%, 80%, 45% and 85% respectively. In addition, the structure of modern aircraft is becoming more sophisticated and complex, and the time cost of aviation maintenance is also rising.

[0003] For traditional aviation maintenance, the main method used is "more, more often, and more carefully" regular maintenance to reduce the failure rate, but it consumes a lot of resources, is inefficient, and is costly, seriously affecting the equipment's integrity and attendance rate.

[0004] The traditional manual maintenance mode can no longer meet the modernization needs of the aviation industry. Therefore, the use of intelligent and automated maintenance strategies has become an inevitable trend in the development of the industry. The use of cutting-edge monitoring technology and intelligent tools can not only significantly improve maintenance efficiency and reduce operating costs, but also ensure the safety and reliability of aircraft. Summary of the invention

[0005] The purpose of this application is to overcome the problems of the prior art and disclose a method and system for analyzing the state of an aircraft engine based on big data of aircraft engine maintenance. This application monitors the representative performance, reliability and other indicators of the equipment in real time or near real time through data obtained by sensors implanted inside the equipment or external detectors, detects anomalies and issues alarms, performs fault diagnosis, evaluates health status, and predicts trends and possible faults.

[0006] On the one hand, the purpose of this application is achieved through the following technical solutions:

[0007] A method for analyzing an aircraft body state based on aircraft engine maintenance big data, the method comprising:

[0008] On the airborne side, sensors are implanted in the fuselage to collect and acquire various parameters in the aircraft, and the embedded health management model and the airborne computer are used to respond to the collected parameter data according to the priority of the data, and the data is transmitted to the ground side through the communication network;

[0009] The ground side receives the onboard flight performance data and uses the maintenance data on the ground side to monitor the status and analyze trends of the aircraft, and uses algorithm models to evaluate the health status of the aircraft and predict fault diagnosis.

[0010] According to a preferred embodiment, the data collected by the sensor from the aircraft includes first priority data and second priority data.

[0011] Among them, the first priority data includes engine vibration and wear data; the second priority data includes: current, voltage, temperature, pressure, and speed data.

[0012] According to a preferred embodiment, the first priority data is collected by a high-speed collector; and the second priority data is collected by a low-speed collector.

[0013] According to a preferred embodiment, a portable maintenance auxiliary device is used to complete aircraft detection beside the aircraft, and to perform maintenance on the aircraft before takeoff or after landing.

[0014] According to a preferred embodiment, maintenance personnel use portable maintenance auxiliary equipment to obtain / detect aircraft-related equipment data, and perform data analysis to obtain corresponding maintenance guidance opinions.

[0015] According to a preferred embodiment, the machine-side supplements the original detection data and maintenance data to the database on the ground side through the communication network.

[0016] According to a preferred embodiment, the ground side completes the iterative upgrade of the expert system, decision support and prediction analysis algorithm model based on the data continuously input from the airborne side and the aircraft side.

[0017] On the other hand, the present application also discloses:

[0018] An airframe state analysis system based on aircraft engine maintenance big data, the airframe state analysis system uses the above method to complete the airframe state analysis, and the airframe state analysis system includes:

[0019] The data layer includes various sensors used to collect and acquire various parameters within the aircraft;

[0020] The collection layer includes a low-speed collector and a high-speed collector, and is used to collect data of the first priority data and the second priority data;

[0021] The real-time processing layer includes embedded health management models and onboard computers;

[0022] The storage layer stores data from each stage of the organism status analysis process;

[0023] The processing layer includes various algorithm models for aircraft health status assessment and fault diagnosis and prediction.

[0024] The aforementioned main scheme of the present application and its further options can be freely combined to form multiple schemes, all of which are schemes that can be adopted and claimed for protection in the present application. After understanding the scheme of the present application, those skilled in the art can understand that there are multiple combinations based on the prior art and common knowledge, all of which are technical schemes to be protected by the present application, and they are not exhaustively listed here.

[0025] Beneficial effects of this application:

[0026] This application is based on the big data obtained by engine sensors and air traffic control big data platforms. It establishes a mathematical model about aircraft engines through message servers and background management systems. It obtains various indicators of aircraft aerospace engines such as speed, temperature, pressure, etc. in real time. While monitoring the real-time operation of aerospace engines, it can also predict engine faults through background calculations, reduce the risk of problems during operation, and reduce aircraft operating costs. BRIEF DESCRIPTION OF THE DRAWINGS

[0027] Figure 1 It is a schematic diagram of the body state analysis system of this application. DETAILED DESCRIPTION

[0028] The following describes the embodiments of the present application through specific examples, and those skilled in the art can easily understand other advantages and effects of the present application from the contents disclosed in this specification. The present application can also be implemented or applied through other different specific embodiments, and the details in this specification can also be modified or changed in various ways based on different viewpoints and applications without departing from the spirit of the present application. It should be noted that the following embodiments and features in the embodiments can be combined with each other without conflict.

[0029] It should be noted that similar reference numerals and letters denote similar items in the following drawings, and therefore, once an item is defined in one drawing, further definition and explanation thereof is not required in subsequent drawings.

[0030] In the description of the present application, it should be noted that the terms "center", "upper", "lower", "left", "right", "vertical", "horizontal", "inner", "outer", etc. indicate positions or positional relationships based on the positions or positional relationships shown in the accompanying drawings, or the positions or positional relationships in which the product of the application is usually placed when in use. They are only for the convenience of describing the present application and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific position, be constructed and operated in a specific position, and therefore cannot be understood as a limitation on the present application. In addition, the terms "first", "second", "third", etc. are only used to distinguish the description, and cannot be understood as indicating or implying relative importance.

[0031] In addition, the terms "horizontal", "vertical", "overhanging" and the like do not mean that the components are required to be absolutely horizontal or overhanging, but can be slightly tilted. For example, "horizontal" only means that its direction is more horizontal than "vertical", and does not mean that the structure must be completely horizontal, but can be slightly tilted.

[0032] In the description of this application, it should also be noted that, unless otherwise clearly specified and limited, the terms "set", "install", "connect", and "connect" should be understood in a broad sense, for example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection, or it can be indirectly connected through an intermediate medium, or it can be the internal communication of two elements. For ordinary technicians in this field, the specific meanings of the above terms in this application can be understood according to specific circumstances.

[0033] In addition, the present application would like to point out that, in the present application, unless the specific structure, connection relationship, positional relationship, power source relationship, etc. are specifically written out, the structure, connection relationship, positional relationship, power source relationship, etc. involved in the present application are all known by those skilled in the art on the basis of the prior art without creative work.

[0034] Example 1

[0035] The present application discloses an airframe status analysis method based on aviation engine maintenance big data. The airframe status analysis method can be divided into an airborne side, an aircraft side, and a ground side according to the user role.

[0036] The body state analysis method of the present application includes the following process.

[0037] Preferably, on the airborne side, various parameters in the aircraft are collected and acquired by implanting sensors in the fuselage, and an embedded health management model and an airborne computer are used to respond to the collected various parameter data according to the priority of the data, and the data is transmitted to the ground side through a communication network.

[0038] Furthermore, the data collected by the sensor on the aircraft includes first priority data and second priority data, wherein the first priority data includes vibration and wear data of the engine; the second priority data includes: current, voltage, temperature, pressure, and speed data.

[0039] Furthermore, the first priority data is collected by a high-speed collector; and the second priority data is collected by a low-speed collector.

[0040] Preferably, portable maintenance assistance equipment is used to complete aircraft detection at the side of the aircraft, and to perform maintenance before takeoff or after landing. Maintenance personnel use portable maintenance assistance equipment to obtain / detect aircraft-related equipment data, analyze the data, and obtain corresponding maintenance guidance opinions.

[0041] Furthermore, the machine side supplements the original detection data and maintenance data to the database on the ground side through the communication network.

[0042] Preferably, the ground side receives the onboard flight performance data and uses the maintenance data on the ground side to perform status monitoring and trend analysis on the aircraft, and uses algorithm models to evaluate the aircraft health status and predict fault diagnosis.

[0043] Furthermore, the ground side completes the iterative upgrade of the expert system, decision support and prediction analysis algorithm model based on the data continuously input from the airborne side and the aircraft side.

[0044] Example 2

[0045] On the basis of Example 1, this embodiment discloses an airframe status analysis system based on aircraft engine maintenance big data, and the airframe status analysis system uses the method described in Example 1 to complete the airframe status analysis.

[0046] Preferably, the body state analysis system comprises:

[0047] The data layer includes various sensors used to collect and acquire various parameters within the aircraft;

[0048] The collection layer includes a low-speed collector and a high-speed collector, and is used to collect data of the first priority data and the second priority data;

[0049] The real-time processing layer includes embedded health management models and onboard computers;

[0050] The storage layer stores data from each stage of the organism status analysis process;

[0051] The processing layer includes various algorithm models for aircraft health status assessment and fault diagnosis and prediction.

[0052] This application is based on the big data obtained by engine sensors and air traffic control big data platforms. It establishes a mathematical model about aircraft engines through message servers and background management systems. It obtains various indicators of aircraft aerospace engines such as speed, temperature, pressure, etc. in real time. While monitoring the real-time operation of aerospace engines, it can also predict engine faults through background calculations, reduce the risk of problems during operation, and reduce aircraft operating costs.

[0053] The above description is only a preferred embodiment of the present application and is not intended to limit the present application. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present application should be included in the protection scope of the present application.

Claims

1. A method for analyzing the state of an aircraft engine based on big data of aircraft engine maintenance, characterized in that: The body state analysis method comprises: On the airborne side, sensors are implanted in the fuselage to collect and acquire various parameters in the aircraft, and the embedded health management model and the airborne computer are used to respond to the collected parameter data according to the priority of the data, and the data is transmitted to the ground side through the communication network; The ground side receives the onboard flight performance data and uses the maintenance data on the ground side to monitor the status and analyze trends of the aircraft, and uses algorithm models to evaluate the health status of the aircraft and predict fault diagnosis.

2. The method for analyzing body state according to claim 1, characterized in that: The data collected by the sensor from the aircraft includes first priority data and second priority data, Among them, the first priority data includes engine vibration and wear data; the second priority data includes: current, voltage, temperature, pressure, and speed data.

3. The method for analyzing body state according to claim 2, characterized in that: The first priority data is collected by a high-speed collector; the second priority data is collected by a low-speed collector.

4. The method for analyzing body state according to claim 1, characterized in that: At the side of the aircraft, portable maintenance assistance equipment is used to complete aircraft detection and perform maintenance before the aircraft takes off or after landing.

5. The method for analyzing body state according to claim 4, characterized in that: Maintenance personnel use portable maintenance auxiliary equipment to obtain / detect aircraft-related equipment data, analyze the data, and obtain corresponding maintenance instructions.

6. The method for analyzing body state according to claim 4, characterized in that: The machine side supplements the original detection data and maintenance data to the database on the ground side through the communication network.

7. The method for analyzing body state according to claim 1, characterized in that: The ground side completes the iterative upgrade of the expert system, decision support and prediction analysis algorithm model based on the data continuously input from the airborne side and the aircraft side.

8. An airframe status analysis system based on aircraft engine maintenance big data, characterized in that: The body state analysis system uses the method according to claims 1 to 7 to complete the body state analysis, and the body state analysis system includes: The data layer includes various sensors used to collect and acquire various parameters within the aircraft; The collection layer includes a low-speed collector and a high-speed collector, and is used to collect data of the first priority data and the second priority data; The real-time processing layer includes embedded health management models and onboard computers; The storage layer stores data from each stage of the organism status analysis process; The processing layer includes various algorithm models for aircraft health status assessment and fault diagnosis and prediction.

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