KR102736683B1 - Aircraft health management method and aircraft health management pparatus - Google Patents

Abstract

An aircraft health management method according to an embodiment of the present invention may include a process of searching for abnormal operation data among component-specific operation data collected in each of the states before takeoff, during flight after takeoff, and after landing of the aircraft, and a process of deriving a component that has generated the abnormal operation data as a risk factor among the components of the aircraft.
Therefore, according to embodiments of the present invention, risk factors of an aircraft can be derived predictively. That is, operational data for each situation before takeoff, during flight, and after landing of an aircraft are collected, and abnormal operational data is derived from the collected operational data, thereby predictively deriving risk factors. Accordingly, components that have already developed defects or are likely to develop defects in the future can be derived as risk factors. Therefore, reliability of risk factors can be improved. In addition, by replacing or inspecting the derived risk factors after the operation of the aircraft is terminated, the health of the aircraft can be managed more stably.

Description

{AIRCRAFT HEALTH MANAGEMENT METHOD AND AIRCRAFT HEALTH MANAGEMENT PPARATUS}

The present invention relates to an aircraft health management method and an aircraft health management device, and more specifically, to an aircraft health management method and an aircraft health management device capable of stably managing the health of an aircraft.

The main causes of aircraft accidents are aircraft defects, human factors, and natural disasters. In order to prevent aircraft accidents caused by aircraft defects, aircraft must maintain airworthiness.

Traditionally, regular replacement work on aircraft components was performed before and after the aircraft was put into service according to a program approved by the airworthiness authority. This is called preventive health management.

However, even if the components are managed using the preventive health management method, problems may occur in the operation of the aircraft. This is because defects in the components may occur due to other factors such as unexpected situations even before the replacement cycle has been reached. In addition, even if the components have not yet developed defects, abnormal operation may occur temporarily. Therefore, there is a limit to the stable management of the aircraft using the preventive health management method.

Korean Patent Publication No. 10-2020-0008550

The present invention provides an aircraft health management method and an aircraft health management device capable of stably managing the health of an aircraft.

The present invention provides an aircraft health management method and an aircraft health management device capable of predictively deriving risk factors of an aircraft.

An aircraft health management method according to an embodiment of the present invention may include a process of searching for abnormal operation data among component-specific operation data collected in each of the states before takeoff, during flight after takeoff, and after landing of the aircraft; and a risk factor derivation process including a process of deriving a component that has generated the abnormal operation data as a risk factor among the components of the aircraft.

An aircraft health management method according to an embodiment of the present invention includes: a process of initiating operation of an aircraft; a process of taking off an aircraft whose operation has been initiated; a process of flying an aircraft that has taken off; a process of landing an aircraft whose flight has ended; a process of terminating operation of an aircraft that has landed; and a process of collecting component-specific operation data in each of the states of the aircraft before takeoff, during flight after takeoff, and after landing; wherein the component-specific operation data before takeoff of the aircraft is collected after initiating operation of the aircraft and before taking off, the component-specific operation data during flight is performed after taking off and before landing the aircraft, the component-specific operation data after landing is performed after landing the aircraft, and the risk factor derivation process can be performed after terminating operation of the aircraft.

The process of exploring the above abnormal operation data may include: a process of exploring first abnormal operation data, which is abnormal operation data, among component-specific operation data collected before takeoff of the aircraft; a process of exploring second abnormal operation data, which is abnormal operation data, among component-specific operation data collected during the flight of the aircraft; and a process of exploring third abnormal operation data, which is abnormal operation data, among component-specific operation data collected after landing the aircraft; and the process of deriving a component that has generated the abnormal operation data as a risk factor may include a process of deriving a component that has generated at least one of the first to third abnormal operation data as a risk factor, among a plurality of components of the aircraft.

The method may include a process of searching for a component that has generated the above abnormal operating data, and the process of searching for a component that has generated the above abnormal operating data may include: a process of searching for a component that has generated the first abnormal operating data among a plurality of components of the aircraft when the first abnormal operating data is searched; a process of searching for a component that has generated the second abnormal operating data among a plurality of components of the aircraft when the second abnormal operating data is searched; and a process of searching for a component that has generated the third abnormal operating data among a plurality of components of the aircraft when the third abnormal operating data is searched.

The process of searching for a component that generated the first abnormal operation data is performed during at least one of a time before the aircraft takes off, a time while the aircraft is taking off, a time while the aircraft is flying after taking off, a time while the aircraft is landing, and a time after the landing of the aircraft is completed; the process of searching for a component that generated the second abnormal operation data is performed during at least one of a time while the aircraft is flying after taking off, a time while the aircraft is landing, and a time after the landing of the aircraft is completed; and the process of searching for a component that generated the third abnormal operation data can be performed during a time after the landing of the aircraft is completed.

In deriving a component that has generated at least one of the first to third abnormal operation data as a risk factor, the component that has generated at least one of the first to third abnormal operation data is derived as a first risk factor, and the risk factor derivation process may include a process of additionally deriving a second risk factor depending on whether there is a component that operates in conjunction with the first risk factor when at least one of the first to third abnormal operation data is generated after deriving the first risk factor.

The process of additionally deriving the second risk factor may include: a process of deriving, as a second risk factor, a component that operates in conjunction with the first risk factor when at least one of the first to third abnormal operation data is generated, when there is a component that operates in conjunction with the first risk factor when at least one of the first to third abnormal operation data is generated by the first risk factor.

An aircraft health management method according to an embodiment of the present invention may include a process of performing maintenance on the identified risk factors after the operation of the aircraft is terminated.

An aircraft health management method according to an embodiment of the present invention may include a process of providing information on risk factors derived in the risk factor derivation process so that a worker can confirm it; and a process of performing maintenance on the risk factors using the provided information after the operation of the aircraft is terminated.

At least one of the process of exploring the above abnormal operation data, the process of exploring the component that generated the abnormal operation data, and the process of deriving risk factors can be performed using artificial intelligence.

An aircraft health management device according to an embodiment of the present invention may include a search unit capable of searching for abnormal operation data among component-specific operation data collected in each of the states before takeoff, during flight after takeoff, and after landing of the aircraft; and a derivation unit capable of deriving a component that has generated the abnormal operation data as a risk factor among the components of the aircraft.

The above-described exploration unit can explore first abnormal operation data, which is abnormal data, among the first operation data for each component collected before takeoff of the aircraft, explore second abnormal operation data, which is abnormal data, among the second operation data for each component collected during flight after takeoff of the aircraft, and explore third abnormal operation data, which is abnormal data, among the third operation data for each component collected after landing of the aircraft.

The above exploration unit and derivation unit can transmit and receive data using wireless communication.

An aircraft health management device according to an embodiment of the present invention includes a first abnormal operation data section storing the first abnormal operation data for each component; a second abnormal operation data section storing the second abnormal operation data for each component; and a first abnormal operation data section storing the third abnormal operation data for each component; wherein the first to third abnormal operation data sections and the derivation section can transmit and receive data using wireless communication.

In deriving risk factors in the above derivation section, among multiple components of the aircraft, a component that has generated at least one of the first to third abnormal operating data can be derived as a risk factor.

In deriving a component that generates at least one of the first to third abnormal operation data as a risk factor in the above derivation unit, the component that generates at least one of the first to third abnormal operation data is derived as a first risk factor, and the derivation unit can additionally derive a second risk factor depending on whether there is a component that operates in conjunction with the first risk factor when at least one of the first to third abnormal operation data is generated after deriving the first risk factor.

An aircraft health management device according to an embodiment of the present invention may include an information providing unit that provides information on risk factors derived from the derivation unit so that a worker can confirm the information.

The above search and extraction units can be installed in at least one of the control stations located on the aircraft and the ground.

The first to third abnormal operation data units may be installed in at least one of the control stations located on the aircraft and the ground.

The above information providing unit may be installed in at least one of the aircraft and the control station located on the aircraft and the ground.

According to embodiments of the present invention, risk factors of an aircraft can be derived predictively. That is, operational data for each situation before takeoff, during flight, and after landing of an aircraft are collected, and abnormal operational data is derived from the collected operational data, thereby predictively deriving risk factors. Accordingly, components that have already developed defects or are likely to develop defects in the future can be derived as risk factors. Therefore, reliability of risk factors can be improved. In addition, by replacing or inspecting the derived risk factors after the operation of the aircraft is terminated, the health of the aircraft can be managed more stably.

In addition, when conducting an inspection of an aircraft after it has landed, there is an effect of shortening the inspection time by conducting an inspection of identified risk factors rather than inspecting all components.

FIG. 1 is a block diagram illustrating a main part of an aircraft health management device according to an embodiment of the present invention.
FIGS. 2 and 3 are flowcharts illustrating an aircraft health management method according to an embodiment of the present invention.

Hereinafter, embodiments of the present invention will be described in more detail with reference to the attached drawings. However, the present invention is not limited to the embodiments disclosed below, but may be implemented in various different forms, and these embodiments are provided only to make the disclosure of the present invention complete and to fully inform a person having ordinary skill in the art of the scope of the invention. In order to explain the embodiments of the present invention, the drawings may be exaggerated, and the same reference numerals in the drawings represent the same components.

First, let me briefly explain the aircraft. The aircraft may include a fuselage, wings, engines, landing gear, a pressure regulator that controls the pressure of the fuselage, a temperature regulator that controls the temperature of the fuselage, and various sensors installed on the fuselage. In addition, the aircraft may include electronic devices that are equipped for the operation of the aircraft, such as takeoff and landing, flight, and mission execution.

The sensor may include an altitude sensor capable of detecting the altitude of the aircraft, a pressure sensor capable of measuring pressure of at least one of the inside and the outside of the fuselage, a temperature sensor capable of measuring temperature of at least one of the inside and the outside of the fuselage, and a vibration sensor capable of detecting vibration or shock generated from the fuselage. Of course, the aircraft is not limited to the above-described examples and may further be equipped with various sensors for takeoff, landing, and flight of the aircraft.

The electronic device may include an electronic device for operating or operating an aircraft and an electronic device for performing a mission. In addition, a plurality of electronic devices may be provided, and at least some of the plurality of electronic devices may transmit or receive data to at least one of other electronic devices and a control center. At this time, the electronic devices may transmit and receive at least one of the data and signals using at least one of a wired network and a wireless network. In addition, the electronic device and the control center may transmit and receive data using a wireless network.

Hereinafter, devices, parts, etc. equipped in or constituting an aircraft are referred to as 'components'. For example, components of an aircraft may include a fuselage, wings, engines, landing gear, pressure regulators, temperature regulators, sensors, and electronic devices. Of course, components of an aircraft are not limited to the examples described above, and may refer to all elements equipped in or constituting an aircraft.

FIG. 1 is a block diagram illustrating a main part of an aircraft health management device according to an embodiment of the present invention.

Referring to FIG. 1, the aircraft health management device may include an exploration unit (100) capable of exploring abnormal operation data among aircraft operation data, an abnormal operation data unit (200) that collects and stores abnormal operation data, and a derivation unit (300) capable of deriving risk factors using abnormal operation data stored in the abnormal operation data unit (200) and deriving a component that generated the abnormal operation data as a risk factor.

In addition, the aircraft health management device may include an information provision unit (400) that provides information on risk factors derived from the derivation unit (300) so that the worker can check them.

In addition, the aircraft health management device may include a first operation data unit (500a) capable of collecting and storing operation data of an aircraft prior to takeoff (operation data prior to takeoff), a second operation data unit (500b) capable of collecting and storing operation data of an aircraft in flight (operation data during flight), and a third operation data unit (500c) capable of collecting and storing operation data of a landed aircraft (operation data after landing).

For convenience of explanation, below, ‘pre-takeoff operational data’ is named ‘first operational data’, ‘in-flight operational data’ is named ‘second operational data’, and ‘post-landing operational data’ is named ‘third operational data’.

An aircraft is driven to take off. Accordingly, the aircraft may be in a driven state before taking off. That is, some of the plurality of components included in the aircraft are in a driven state for takeoff before takeoff. In addition, the aircraft is driven for a predetermined period of time after landing the aircraft. For example, at least some of the components of the aircraft are in a driven state for a predetermined period of time, such as when the aircraft moves along the runway after landing on the runway. Accordingly, operational data (first and third operational data) of the aircraft can be provided and collected before taking off and after landing the aircraft. In addition, the aircraft is in flight after taking off and before landing. Accordingly, operational data (second operational data) of the aircraft in flight can be provided and collected.

In an embodiment of the present invention, operational data for components in operation are collected before takeoff, during flight, and after landing. At this time, the operational data for the components may include at least one of the operating status of the components, signal output, received signals, measured values, etc., and the operational data may be different for each component. In addition, the operational data acquired for each component may be in at least one of the following forms: numerical, image, and message.

The first operational data (pre-takeoff operational data) collected before the aircraft takes off can be stored in the first operational data section (500a), the second operational data (in-flight operational data) collected during the flight can be stored in the second operational data section (500b), and the third operational data (post-landing operational data) collected after landing can be stored in the third operational data section (500c). In addition, the first to third operational data are stored in each of the first operational data section (500a), the second operational data section (500b), and the third operational data section (500c), and can be classified and stored by component.

The exploration unit (100) explores abnormal operation data (abnormal operation data before takeoff) among the first operation data, explores abnormal operation data (abnormal operation data during flight) among the second operation data, and explores abnormal operation data (abnormal operation data after landing) among the third operation data.

For convenience of explanation, below, ‘abnormal operation data before takeoff’ is named ‘first abnormal operation data’, ‘abnormal operation data during flight’ is named ‘second abnormal operation data’, and ‘abnormal operation data after landing’ is named ‘third abnormal operation data’.

In searching for the first to third abnormal operation data in the search unit (100), the search can be performed using preset reference operation data. To this end, the search unit (100) can store 'reference operation data' for each component. The 'reference operation data' for each component can be the same or different. In addition, the 'reference operation data' for each component can be classified and stored as 'pre-takeoff reference operation data', 'in-flight reference operation data', and 'post-landing reference operation data'.

For convenience of explanation, below, ‘pre-takeoff reference operational data’ is named ‘first reference operational data’, ‘in-flight reference operational data’ is named ‘second reference operational data’, and ‘post-landing reference operational data’ is named ‘third reference operational data’.

In searching for the first abnormal operation data, the search unit (100) compares the first operation data with the first reference operation data and selects data that deviates from or does not match the first reference operation data as the first abnormal operation data. At this time, the first operation data and the first reference operation data are compared for each component.

In addition, when searching for the second abnormal operation data, the search unit (100) compares the second operation data with the second reference operation data and selects data that deviates from or does not match the second reference operation data as the second abnormal operation data. At this time, the second operation data and the second reference operation data are compared for each component.

In addition, when searching for the third abnormal operation data, the search unit (100) compares the third operation data with the third reference operation data and selects data that deviates from or does not match the third reference operation data as the third abnormal operation data. At this time, the third operation data and the third reference operation data are compared for each component.

The search unit (100) searches for a component that has generated at least one of the first to third abnormal operation data. To explain more specifically, the process of searching for a component that has generated the first abnormal operation data will be explained with an example. In the first operation data unit (500a), first operation data is collected and stored for each component. Accordingly, the search unit (100) searches for the first operation data stored in the first operation data unit (500a) to select and find a component that has generated the abnormal operation data (the first abnormal operation data). Of course, there may be no abnormal operation data (the first abnormal operation data) among the first operation data. In this case, the component that has generated the abnormal operation data is not searched for.

In the same way, the exploration unit (100) can explore the component that generated the second abnormal operation data, and can explore the component that generated the third abnormal operation data. Of course, there may be no abnormal operation data (the second abnormal operation data) among the second operation data, or there may be no abnormal operation data (the third abnormal operation data) among the third operation data. In such a case, the component that generated the abnormal operation data (the second and third abnormal operation data) is not explored.

In this way, searching for the first to third abnormal operation data using the reference operation data in the exploration unit (100) or searching for the component that generated the abnormal operation data can be performed using pre-learned artificial intelligence.

The process of searching for the presence of the first abnormal operation data among the first operation data in the search unit (100) and the process of searching for the component that generated the first abnormal operation data may be performed before the takeoff of the aircraft. Of course, the process of searching for the presence of the first abnormal operation data and the process of searching for the component that generated the first abnormal operation data may be performed at least one of the times when the aircraft is taking off, the time when the aircraft is flying after taking off, the time when the aircraft is landing, and the time after the landing of the aircraft is completed.

In addition, the process of searching for the second abnormal operation data among the second operation data in the search unit (100) and the process of searching for the component that generated the second abnormal operation data can be performed during the flight of the aircraft. Of course, the process of searching for the second abnormal operation data and the process of searching for the component that generated the second abnormal operation data can be performed during at least one of the times during which the aircraft is landing and the time after the landing of the aircraft is completed.

And, the process of searching for the third abnormal operation data among the third operation data in the search unit (100) and searching for the component that generated the third abnormal operation data can be performed after the aircraft lands. Of course, the process of searching for the third abnormal operation data and searching for the component that generated the third abnormal operation data can also be performed after the aircraft lands and stops operating.

The first abnormal operation data section (200a) stores the first abnormal operation data (abnormal operation data before takeoff) searched by the search section (100) and the components that generated the first abnormal operation data. In addition, the second abnormal operation data section (200b) stores the second abnormal operation data (abnormal operation data during flight) searched by the search section (100) and the components that generated the second abnormal operation data. In addition, the third abnormal operation data section (200c) stores the third abnormal operation data (abnormal operation data after landing) searched by the search section (100) and the third abnormal operation data. At this time, the first to third abnormal operation data sections (200a to 200c) and the search section (100) can transmit and receive data using wireless communication.

The first to third abnormal operation data units (200a to 200c) may be a means including a memory chip or memory element capable of storing data. As another example, the first to third abnormal operation data units (200a to 200c) may be network-based cloud computing.

The derivation unit (300) derives risk factors using abnormal operation data. That is, the derivation unit (300) derives a component that causes at least one of the first abnormal operation data, the second abnormal operation data, and the third abnormal operation data as a risk factor.

To explain with a more specific example, assume as follows. Among n components, the first component did not generate any abnormal operation data among the operational data collected before takeoff (the first operational data), the operational data collected during the flight (the second operational data), and the operational data collected after landing (the third operational data). In other words, the first to third abnormal operation data were not generated by the first component.

However, the second component did not generate abnormal operation data among the operational data collected before takeoff (the first operational data) and the operational data collected after landing (the third operational data), but generated abnormal operation data among the operational data collected during the flight (the second operational data). In other words, the second abnormal operation data was generated by the second component, and the first and third abnormal operation data were not generated.

As another example, the third component did not generate any abnormal operational data among the operational data collected during the flight (second operational data), but generated abnormal operational data among the operational data collected before takeoff (first operational data) and the operational data collected after landing (third operational data). In other words, the third component generated the first and third abnormal operational data, but did not generate the second abnormal operational data.

As another example, the fourth component generated abnormal operational data from each of the operational data collected before takeoff (the first operational data), the operational data collected during flight (the second operational data), and the operational data collected after landing (the third operational data). In other words, the first to third abnormal operational data were generated by the fourth component.

In this case, the derivation unit (300) derives the second component, the third component, and the fourth component as risk factors (first risk factors). That is, the derivation unit (300) determines that there is a possibility of a defect occurring in the second to fourth components, and that they are components that have a risk of causing an accident during flight.

Meanwhile, the reason why abnormal operation data is generated from a component may be due to a problem with the component itself or another component linked to the component. In addition, the reason why abnormal operation data is generated from the component may be due to the component itself and another component linked to the component. In addition, the other components that operate in connection with the component may be the same or different depending on the state before takeoff, during flight, and after landing.

For example, the first component can operate in conjunction with the second component in all of the pre-takeoff state, the in-flight state, and the post-landing state. As another example, the third component can operate in conjunction with the fourth component in the pre-takeoff state and the post-landing state, and in conjunction with the fifth component in the in-flight state. As another example, the sixth component can operate in conjunction with the seventh component in the pre-takeoff state, in conjunction with the eighth component in the in-flight state, and in conjunction with the ninth component in the post-landing state. As another example, the tenth component can operate independently without being associated with any component in all of the pre-takeoff state, the in-flight state, and the post-landing state.

Accordingly, the derivation unit (300) additionally derives a risk factor (second risk factor) depending on whether there is a component that operates in conjunction with the first risk factor. In addition, the components that operate in conjunction with the first risk factor before takeoff, during flight, and after landing may be the same or different. Accordingly, the derivation unit (300) can extract a component that operates in conjunction with the first risk factor depending on which abnormal operation data occurred among the first to third abnormal operation data, and derive the extracted component as the second risk factor. At this time, the additionally derived second risk factor may be one or more.

The following is an explanation of the case in which the first component is derived as the first risk factor among the examples described above. The first component operates in conjunction with the second component before takeoff, during flight, and after landing. Accordingly, in the case in which the first component is derived as the first risk factor, the derivation unit (300) derives the second component as the second risk factor.

As another example, if abnormal operation data (first and third abnormal operation data) are generated by the third component before takeoff and after landing, and abnormal operation data (second abnormal operation data) are not generated by the third component during the flight, the derivation unit (300) derives the third component as the first risk factor. The third component operates in conjunction with the fourth component before takeoff and after landing, and operates in conjunction with the fifth component during the flight. Accordingly, the derivation unit (300) derives the fourth component, which operates in conjunction with the third component (first risk factor) before takeoff and after landing, as the second risk factor.

As another example, abnormal operation data (second abnormal operation data) was generated by the 10th component during the flight, and abnormal operation data (first and third abnormal operation data) were not generated by the 10th component before takeoff and after landing. In addition, the 10th component operates independently without being linked to any other component before takeoff, during the flight, and after landing. Accordingly, the derivation unit (300) derives the 10th component as the first risk factor. In addition, since there is no component that operates in connection with the 10th component (first risk factor) before takeoff, during the flight, and after landing, the derivation unit (300) does not derive the second risk factor.

As described above, in deriving risk factors in the derivation unit (300), the derivation unit (300) can derive risk factors using pre-learned artificial intelligence. To this end, the derivation unit (300) can include an artificial intelligence-based analysis system.

The derivation unit (300) can transmit information on the derived risk factors to the information provision unit (400). At this time, the information on the risk factors provided by the derivation unit (300) can include at least one of the name and installation location of the component corresponding to the risk factor. In addition, when multiple risk factors are derived from the derivation unit (300), information on each of the multiple risk factors is transmitted to the information provision unit (400).

The information providing unit (400) may include a display unit that displays or displays information on risk factors derived from the derivation unit (300) so that a worker can check them. The display unit may include, for example, a monitor device that displays information so that a worker can check it with the naked eye.

At least one of the search unit (100), the first to third abnormal operation data units (200a to 200c), the derivation unit (300), and the information provision unit (400) described above may be installed in at least one of the control stations located on the aircraft and the ground.

Figures 2 and 3 are flowcharts illustrating an aircraft health management method according to an embodiment of the present invention. Figures 2 and 3 are separate flowcharts, and Figure 3 illustrates the process after 'S50'.

Hereinafter, an aircraft health management method according to an embodiment of the present invention will be described with reference to FIGS. 2 and 3. At this time, any content that overlaps with the previously described content will be omitted or briefly described.

First, as illustrated in FIG. 2, the aircraft is driven for takeoff (S10). That is, the operation of the aircraft is initiated (S10). Then, the operational data (first operational data) is collected for each component (S21) and stored in the first operational data unit (500a). At this time, the first operational data collected for each component is data collected before takeoff and is 'pre-takeoff operational data'. Next, the search unit (100) determines or searches for abnormal operational data (first abnormal operational data) among the first operational data (S22). At this time, if there is no abnormal operational data (first abnormal operational data) among the first operational data (S22-->No), the search unit (100) does not search for the component that generated the abnormal operational data (first abnormal operational data). However, if abnormal operation data (first abnormal operation data) exists among the first operation data (S22-->Yes), the search unit (100) searches for the component that generated the abnormal operation data (first abnormal operation data) (S23).

Thereafter, the aircraft is taken off (S30) and flown. Next, operational data (second operational data) is collected for each component (S41) and stored in the second operational data unit (500b). The second operational data collected at this time is data collected during the flight and is 'in-flight operational data'. Thereafter, the exploration unit (100) determines or explores whether there is abnormal operational data (second abnormal operational data) among the second operational data (S42). At this time, if there is no abnormal operational data (second abnormal operational data) among the second operational data (S42-->No), the exploration unit (100) does not explore the component that generated the abnormal operational data (second abnormal operational data). Conversely, if there is abnormal operational data (second abnormal operational data) among the second operational data (S42-->Yes), the exploration unit (100) explores the component that generated the abnormal operational data (second abnormal operational data) (S43).

Next, when the aircraft lands (S50), operational data (third operational data) is collected for each component (S61) and stored in the third operational data unit (500c). The third operational data collected at this time is data collected after landing and is 'post-landing operational data'. Next, the exploration unit (100) determines or explores whether there is abnormal operational data (third abnormal operational data) among the third operational data (S62). At this time, if there is no abnormal operational data (third abnormal operational data) among the third operational data (S62-->No), the exploration unit (100) does not explore the component that generated the abnormal operational data (third abnormal operational data). However, if there is abnormal operational data (third abnormal operational data) among the third operational data (S62-->Yes), the exploration unit (100) explores the component that generated the abnormal operational data (third abnormal operational data) (S63).

After that, the operation of the landed aircraft is terminated (S70). That is, the operation of the aircraft is terminated (S70). When the operation of the aircraft is terminated (S70), the derivation unit (300) derives a risk factor (S81). That is, the derivation unit (300) derives a component that has generated at least one of the first abnormal operation data, the second abnormal operation data, and the third abnormal operation data as the first risk factor (S81).

In addition, depending on whether the abnormal operation data generated by the first risk factor is among the first to third abnormal operation data, a second risk factor is additionally derived or not derived. To this end, first, when at least one of the first to third abnormal operation data is generated, it is determined whether there is a component that operates in conjunction with the first risk factor (S82). Then, when at least one of the first to third abnormal operation data is generated, and there is no component that operates in conjunction with the first risk factor (S82-->No), the derivation unit (300) does not derive the second risk factor (S83). However, when at least one of the first to third abnormal operation data is generated, and there is a component that operates in conjunction with the first risk factor (S82-->Yes), the derivation unit (300) derives the second risk factor (S84). That is, the derivation unit (300) derives a component that operates in conjunction with the first risk factor when at least one of the first to third abnormal operation data occurs as a second risk factor.

When a risk factor is derived from the derivation unit (300), information about the risk factor is transmitted to the information provision unit (400). That is, information including at least one of the name and installation location of the derived risk factor is transmitted to the information provision unit (400). Accordingly, the information provision unit (400) displays information about the risk factor derived from the derivation unit (300) so that the worker can confirm it (S85).

Then, the worker checks the information provided by the information provider (400) and performs maintenance on the components identified as risk factors (S90). That is, after inspecting the components identified as risk factors, the components are replaced or repaired according to the inspection results.

In the above, it has been described that the process of determining whether there is first abnormal operation data among the first operation data (S22) and searching for a component that generated the first abnormal operation data (S23) are performed before the takeoff of the aircraft. However, the process of searching for whether there is first abnormal operation data (S22) and searching for a component that generated the first abnormal operation data (S23) may be performed during at least one of the times when the aircraft is taking off, the time when the taken-off aircraft is flying, the time when the aircraft is landing, and the time after the landing of the aircraft is completed.

In addition, the above described process of searching for second abnormal operation data among the second operation data (S42) and searching for a component that generated the second abnormal operation data (S43) is performed during the flight of the aircraft. However, it is not limited thereto, and the process of searching for second abnormal operation data (S42) and searching for a component that generated the second abnormal operation data (S43) may be performed during at least one of the time when the aircraft is landing and the time after the landing of the aircraft is completed.

And, in the above, it was explained that the process of searching for third abnormal operation data among the third operation data (S62) and searching for a component that generated the third abnormal operation data (S63) is performed after landing the aircraft. However, it is not limited thereto, and the process of searching for third abnormal operation data among the third operation data (S62) and searching for a component that generated the third abnormal operation data (S63) may be performed after landing the aircraft and terminating operation (S70).

According to embodiments of the present invention, risk factors of an aircraft can be derived predictively. That is, operational data for each situation before takeoff, during flight, and after landing of an aircraft are collected, and abnormal operational data is derived from the collected operational data, thereby predictively deriving risk factors. Accordingly, components that have already developed defects or are likely to develop defects in the future can be derived as risk factors. Therefore, reliability of risk factors can be improved. In addition, by replacing or inspecting the derived risk factors after the operation of the aircraft is terminated, the health of the aircraft can be managed more stably.

In addition, when conducting an inspection of an aircraft after it has landed, there is an effect of shortening the inspection time by conducting an inspection of identified risk factors rather than inspecting all components.

100: Exploration Department
200a: 1st Abnormal Operation Data Section
200b: 2nd Abnormal Operation Data Section
200c: 3rd Abnormal Operation Data Section
300: Derivative
400: Information Department

Claims (20)

The process of searching for abnormal operation data among the component-specific operational data collected in each state of the aircraft before takeoff, during flight after takeoff, and after landing; and
A risk factor derivation process including a process of deriving a risk factor from among the components of an aircraft, the component that generated the above abnormal operation data;
Including,
The above risk factor derivation process is:
The process of deriving the component that caused the above abnormal operation data as the first risk factor; and
An aircraft health management method, comprising: a process of additionally deriving a second risk factor, after deriving the first risk factor, depending on whether there is a component that operates in conjunction with the first risk factor when the abnormal operation data is generated; In claim 1,
The process of initiating operation of an aircraft;
The process of taking off an aircraft whose operation has been initiated;
The process of flying an aircraft that has taken off;
The process of landing an aircraft whose flight has ended;
The process of terminating the operation of a landed aircraft; and
A process of collecting operational data for each component in each state, before takeoff, during flight after takeoff, and after landing of the aircraft;
The pre-takeoff component operation data of the above aircraft is collected after the aircraft is put into operation and before the aircraft takes off.
The above in-flight component operation data is conducted after takeoff and before landing of the aircraft.
The above post-landing component-specific operational data is conducted after landing the aircraft.
The above risk factor derivation process is an aircraft health management method implemented after the operation of the aircraft is terminated. In claim 2,
The process of exploring the above abnormal operation data is:
The process of exploring the first abnormal operation data, which is abnormal operation data among the component-specific operation data collected before taking off the aircraft;
The process of exploring the second abnormal operation data, which is abnormal operation data among the component-specific operation data collected during the flight of the aircraft; and
It includes a process of searching for the third abnormal operation data, which is abnormal operation data, among the component-specific operation data collected after landing the aircraft;
The process of deriving the component that caused the above abnormal operation data as the first risk factor is as follows:
An aircraft health management method, comprising: a process of deriving a component that has generated at least one of the first to third abnormal operation data among a plurality of aircraft components as a first risk factor; In claim 3,
Including a process of exploring the component that caused the above abnormal operating data,
The process of exploring the component that caused the above abnormal operating data is as follows:
When the above first abnormal operation data is detected, a process of detecting a component among multiple components of the aircraft that generated the first abnormal operation data;
When the above second abnormal operation data is searched, a process of searching for a component among multiple components of the aircraft that generated the second abnormal operation data; and
An aircraft health management method, comprising: a process of searching for a component among multiple components of the aircraft that generated the third abnormal operation data when the third abnormal operation data is detected; In claim 4,
The process of searching for the component that generated the above first abnormal operation data is performed during at least one of the following times: the time before the aircraft takes off, the time the aircraft is taking off, the time the aircraft is flying after taking off, the time the aircraft is landing, and the time after the landing of the aircraft is completed.
The process of searching for the component that generated the above second abnormal operation data is performed during at least one of the time when the aircraft is flying after takeoff, the time when the aircraft is landing, and the time after the landing of the aircraft is completed.
The process of searching for the component that caused the above third abnormal operation data is an aircraft health management method implemented at a time after the aircraft landing is completed. In claim 3,
An aircraft health management method for additionally deriving the second risk factor, wherein, when at least one of the first to third abnormal operation data occurs, the second risk factor is additionally derived depending on whether there is a component that operates in conjunction with the first risk factor. In claim 6,
The process of additionally deriving the second risk factor is as follows:
In the event that at least one of the first to third abnormal operation data occurs, if there is a component that operates in conjunction with the first risk factor,
An aircraft health management method, comprising: a process of deriving a component that operates in conjunction with the first risk factor as a second risk factor, wherein at least one of the first to third abnormal operation data is generated by the first risk factor. In any one of claims 1 to 7,
An aircraft health management method, comprising: a process of performing maintenance on the identified risk factors after the operation of the aircraft has ended; In any one of claims 1 to 7,
A process for providing information on risk factors derived from the above risk factor derivation process so that workers can check it; and
An aircraft health management method, comprising: a process of performing maintenance on the risk factors using the provided information after the operation of the aircraft is terminated; In any one of claims 4 to 7,
An aircraft health management method, wherein at least one of the processes of exploring the above abnormal operation data, the process of exploring the component that caused the abnormal operation data, and the process of deriving risk factors is performed using artificial intelligence. An exploration unit capable of exploring abnormal operation data among the component-specific operational data collected in each state of the aircraft before takeoff, during flight after takeoff, and after landing; and
Among the components of the aircraft, a derivation unit capable of deriving the component that generated the above abnormal operation data as the first risk factor;
The above-mentioned derivation unit is an aircraft health management device that, after deriving the first risk factor, additionally derives a second risk factor depending on whether there is a component that operates in conjunction with the first risk factor when at least one of the abnormal operation data occurs. In claim 11,
The above exploration section,
Among the first operational data collected by each component before takeoff of the aircraft, the first abnormal operational data, which is abnormal data, is explored.
After the aircraft takes off, we explore the second abnormal operation data, which is abnormal data among the second operation data collected during the flight by component.
An aircraft health management device that searches for abnormal third-order operation data, which is abnormal data among the third-order operation data collected by each component after the landing of the aircraft. In claim 12,
The above-mentioned exploration unit and derivation unit are aircraft health management devices capable of transmitting and receiving data using wireless communication. In claim 12,
A first abnormal operation data section storing the first abnormal operation data for each component;
A second abnormal operation data section storing the second abnormal operation data for each component; and
Includes a third abnormal operation data section storing the third abnormal operation data by component;
An aircraft health management device capable of transmitting and receiving data using wireless communication, the first to third abnormal operation data sections and the derivation section. In claim 12,
In deriving the first risk factor from the above derivation section,
An aircraft health management device that derives a component that has generated at least one of the first to third abnormal operation data among multiple components of the aircraft as a first risk factor. In claim 15,
In the above derivation section, in deriving a component that has generated at least one of the first to third abnormal operation data as a risk factor, the component that has generated at least one of the first to third abnormal operation data is derived as a first risk factor, and
An aircraft health management device that additionally derives a second risk factor in the above-described derivation section, wherein at least one of the first to third abnormal operation data occurs, and depending on whether there is a component that operates in conjunction with the first risk factor, a second risk factor is additionally derived. In any one of claims 11 to 16,
An aircraft health management device including an information provision unit that provides information on risk factors derived from the above-mentioned derivation unit so that a worker can check them. In claim 11,
The above search and extraction units are aircraft health management devices installed in at least one of the control stations located on the aircraft and the ground. In claim 14,
The first to third abnormal operation data units are aircraft health management devices installed in at least one of the control stations located on the aircraft and the ground. In claim 17,
The above information providing unit is an aircraft health management device installed in at least one of the aircraft and the control stations located on the aircraft and the ground.

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