KR102671666B1 - Method and device for predicting failure of onboard equipments of military unmanned aerial vehicle - Google Patents

Abstract

A method and device for predicting failure of on-board equipment of a military unmanned aerial vehicle are provided, which can predict the occurrence of failures in multiple on-board equipment of a military unmanned aerial vehicle in flight. The failure prediction method predicts the occurrence of a failure of at least one mounted equipment according to status information at multiple points in time for each of the plurality of mounted equipment of each military unmanned aerial vehicle received from a plurality of military unmanned aerial vehicles in flight and outputs a prediction result. It includes steps to:

Description

Method and device for predicting failure of onboard equipment of military unmanned aerial vehicle {METHOD AND DEVICE FOR PREDICTING FAILURE OF ONBOARD EQUIPMENTS OF MILITARY UNMANNED AERIAL VEHICLE}

The present invention relates to a method and device for predicting failure of onboard equipment of an unmanned aerial vehicle, which can predict the occurrence of failures in a plurality of onboard equipment of a military unmanned aerial vehicle in flight to perform a mission.

In general, unmanned aerial vehicles are equipped with various equipment based on high-reliability designs such as redundant designs for flight safety. Accordingly, the unmanned aerial vehicle operating system requires rapid failure detection of equipment mounted on the unmanned aerial vehicle.

Equipment mounted on unmanned aerial vehicles has various operating characteristics and design environments, so separate equipment is required to check the condition of the equipment before and after flight.

In particular, in order to determine whether the equipment mounted on a military unmanned aerial vehicle is normal, conventionally, a separate inspection device must be designed and used for each mounted equipment, and an arbitrary value is entered into each mounted equipment to determine whether a normal response is received. Methods are being used to check, collect and display BIT (Built In Test) results, or have a mechanic manually inspect the mounted equipment according to the procedures displayed on the screen of the inspection device.

To solve this problem, a technology to perform and manage integrated inspection of the on-board equipment of unmanned aerial vehicles has been proposed. However, this technology is used to inspect on-board equipment before and after the flight of an unmanned aerial vehicle, and it is very difficult for a mechanic to quickly respond to a breakdown of on-board equipment that occurs while the unmanned aerial vehicle is performing its mission.

Korean Patent No. 2124501 (2020.06.23.)

The purpose of the present invention is to provide a method and device for predicting failure of onboard equipment of an unmanned aerial vehicle, which can predict the occurrence of failures in a plurality of onboard equipment of a military unmanned aerial vehicle in flight to perform a mission.

A method for predicting failure of on-board equipment of a military unmanned aerial vehicle according to an embodiment of the present invention includes the steps of receiving status information at a plurality of points in time for each of a plurality of on-board equipment of each military unmanned aerial vehicle from a plurality of military unmanned aerial vehicles in flight; Diagnosing the status of each of the plurality of onboard equipment for each military unmanned aerial vehicle based on the status information for each of the plurality of viewpoints and predicting the occurrence of a failure of at least one of the plurality of onboard equipment; and outputting the diagnosis result for each of the plurality of mounted devices and a failure prediction result for the at least one mounted device.

Predicting the occurrence of a failure may include predicting a state information value at a future time based on a plurality of state information at adjacent times among the state information for each of the plurality of times; and predicting the occurrence of a failure of the at least one mounted equipment based on the predicted status information value at the future time.

In addition, the step of predicting the occurrence of a failure may include determining whether the current state information among the plurality of state information for each time point is included in a preset range; If the current status information is within the set range, calculating a change amount based on the current status information and the previous status information; predicting a future state information value from the current state information based on the change amount; and predicting the occurrence of a failure of the at least one mounted equipment at the future time by comparing a preset reference value with the status information value at the future time.

In addition, the step of predicting the occurrence of a failure may include, if the current status information is not within the set range, comparing the current status information with the set reference value to determine if a failure of the at least one mounted device occurs at the current status. It further includes the step of diagnosing.

The onboard equipment failure prediction method of this embodiment includes determining the validity of the received status information for each of the plurality of time points; and classifying the status information for each of the plurality of points in time, the validity of which has been determined, for each of the plurality of military unmanned aerial vehicles.

An onboard equipment failure prediction device according to an embodiment of the present invention includes a memory storing a failure prediction program; And executing the failure prediction program, and based on the status information at a plurality of points in time for each of the plurality of on-board equipment of each military unmanned aerial vehicle received from the plurality of military unmanned aerial vehicles in flight through an input/output unit, Diagnose the status of each of the plurality of mounted devices to predict the occurrence of a failure of at least one mounted device among the plurality of mounted devices, and determine the diagnosis result for each of the plurality of mounted devices and a failure of the at least one mounted device. Includes a processor that outputs occurrence prediction results.

The processor predicts a state information value at a future time based on a plurality of state information at adjacent times among the state information for each of the plurality of times, and performs the at least one mounting operation based on the predicted state information value at the future time. Predict the occurrence of equipment failure.

In addition, the processor determines whether the current status information among the plurality of status information for each viewpoint is included in a preset range, and if the current status information is included in the set range, the current status information and the immediately previous status information are included in the current status information. Calculate the change amount based on the time state information, predict the future state information value from the current state information based on the change amount, and compare the future state information value with a preset reference value to determine the at least Predict the occurrence of a failure of a single piece of equipment.

In addition, if the current status information is not within the set range, the processor compares the current status information with the set reference value to diagnose a failure of the at least one onboard device at the current status.

The processor determines the validity of the received status information for each of the plurality of viewpoints, classifies the status information for each of the plurality of viewpoints whose validity has been determined for each of the plurality of military unmanned aerial vehicles, and prevents the occurrence of a failure of the at least one onboard equipment. predict

The status information for each of the plurality of time points includes information on Power-on BIT (PBIT) and Continuous BIT (CBIT) results for each of the plurality of mounted devices.

In addition, the status information for each of the plurality of viewpoints includes status information on one or more mounted equipment of each of the plurality of systems of each military unmanned aerial vehicle.

The present invention can predict the occurrence of a failure by receiving status information about on-board equipment at multiple times from a plurality of military unmanned aerial vehicles in flight to perform a mission.

Accordingly, the present invention allows an aircraft mechanic to identify in real time the occurrence of a malfunction in the on-board equipment of an unmanned aerial vehicle performing a mission through an unmanned aerial vehicle inspection device, and thus promptly inspects and repairs the on-board equipment of an unmanned aerial vehicle whose mission has ended. processing, it is possible to increase the efficiency of unmanned aerial vehicle operation and maintenance.

1 is a diagram showing a device for predicting equipment failure in a military unmanned aerial vehicle according to an embodiment of the present invention.
FIG. 2 is a diagram conceptually showing the function of the failure prediction program of FIG. 1.
Figure 3 is a diagram showing a method for predicting equipment failure in a military unmanned aerial vehicle according to an embodiment of the present invention.
FIG. 4 is a diagram illustrating in detail the steps for predicting the occurrence of a failure of the equipment shown in FIG. 3.

The advantages and features of the present invention and methods for achieving them will become clear by referring to the embodiments described in detail below along with the accompanying drawings. However, the present invention is not limited to the embodiments disclosed below and may be implemented in various different forms. The present embodiments are merely provided to ensure that the disclosure of the present invention is complete and to be understood by those skilled in the art in the technical field to which the present invention pertains. It is provided to fully inform those who have the scope of the invention, and the present invention is only defined by the scope of the claims.

In describing embodiments of the present invention, if a detailed description of a known function or configuration is judged to unnecessarily obscure the gist of the present invention, the detailed description will be omitted. The terms described below are defined in consideration of functions in the embodiments of the present invention, and may vary depending on the intention or custom of the user or operator. Therefore, the definition should be made based on the contents throughout this specification.

Hereinafter, preferred embodiments of the present invention will be described in detail with reference to the attached drawings.

Figure 1 is a diagram showing a device for predicting equipment failure in a military unmanned aerial vehicle according to an embodiment of the present invention.

Referring to FIG. 1, the equipment failure prediction device (hereinafter referred to as the failure prediction device) 100 of the military unmanned aerial vehicle of this embodiment is configured to detect the corresponding unmanned aerial vehicle 200 from each of a plurality of military unmanned aerial vehicles 200 in flight due to mission performance. It is possible to receive status information for each of a plurality of devices mounted on a , and predict the occurrence of a failure of the corresponding mounted equipment based on the received status information of the mounted equipment.

This failure prediction device 100 may include an input/output unit 110, a processor 120, and a memory 130.

The input/output unit 110 may receive status information about a plurality of mounted equipment of each unmanned aerial vehicle (200) from each of the plurality of unmanned aerial vehicles (200). In addition, the input/output unit 110 outputs the results predicted by the processor 120, which will be described later, such as a diagnosis result and a failure prediction result of at least one of the plurality of mounted equipment of each unmanned aerial vehicle 200, to the unmanned aerial vehicle inspection device 300. ) can be output.

Here, each of the plurality of unmanned aerial vehicles 200 may include a plurality of onboard equipment related to a plurality of systems of the unmanned aerial vehicle 200.

For example, each of the plurality of unmanned aerial vehicles 200 includes a plurality of systems such as a flight control system, propulsion/fuel system, landing system, avionics system, electrical system, communication link system, environmental system, and mission system, and each system It may contain one or more payloads.

For example, the flight control system of the unmanned aerial vehicle 200 may include on-board equipment such as a flight control computer, complex navigation device, atmospheric measurement device, radio altimeter, flight data storage device, and precision position measurement device, and propulsion/ The fuel system may include on-board equipment such as an engine, fuel pump, and fuel valve, and the landing system may include on-board equipment such as brakes, steering, and landing gear. In addition, the avionics system of the unmanned aerial vehicle 200 may include on-board equipment such as a mission computer and enemy identification device, the electrical system may include on-board equipment such as a generator and battery, and the communication link system may include on-board equipment such as a transmission and reception device, etc. It may include onboard equipment. Additionally, the environmental system of the unmanned aerial vehicle 200 may include on-board equipment such as a temperature sensor, pressure sensor, fan, heater, etc., and the mission system may include on-board equipment such as a SAR and IR camera.

Accordingly, each unmanned aerial vehicle 200 can transmit status information, such as BIT information, about a plurality of mounted equipment according to each system to the input/output unit 110. Here, each unmanned aerial vehicle 200 may be in flight to perform a mission, and may transmit status information at multiple time points at preset intervals. In addition, status information, that is, BIT information, transmitted from each unmanned aerial vehicle 200 may include information about the results of Power-on BIT (PBIT) and Continuous BIT (CBIT) of each mounted equipment.

The processor 120 receives status information at a plurality of time points from each of the plurality of unmanned aerial vehicles 200 through the input/output unit 110, and uses the failure prediction program 140 stored in the memory 130 to detect each unmanned aerial vehicle ( 200), a plurality of mounted equipment can be diagnosed and the occurrence of a failure in at least one of them can be predicted.

The processor 120 may output diagnosis results and failure prediction results for a plurality of mounted equipment of each unmanned aerial vehicle 200 to the unmanned aerial vehicle inspection device 300 through the input/output unit 110.

The memory 130 may store the failure prediction program 140 and information necessary for its execution. The failure prediction program 140 performs diagnosis on a plurality of mounted equipment of each unmanned aerial vehicle 200 based on the status information of each of the plurality of unmanned aerial vehicles 200 provided through the input/output unit 110, at least one of them. It may be software that includes instructions to predict the occurrence of a failure of a piece of on-board equipment.

Accordingly, when the processor 120 receives status information for each of the plurality of unmanned aerial vehicles 200 in the input/output unit 110, it executes the failure prediction program 140 of the memory 130 and uses this to detect each unmanned aerial vehicle ( 200) It is possible to diagnose and predict failures for multiple mounted equipment and output diagnosis and prediction results.

FIG. 2 is a diagram conceptually showing the function of the failure prediction program of FIG. 1.

Referring to FIG. 2, the failure prediction program 140 of this embodiment includes a communication link maintenance unit 141, a validity determination unit 142, a status information classification unit 143, an onboard equipment diagnosis unit 144, and a diagnosis result management unit. It may include (145).

However, the present invention is not limited to this, and the communication link maintenance unit 141, validity determination unit 142, status information classification unit 143, on-board equipment diagnosis unit 144, and diagnosis result of the failure prediction program 140 The functions of the management unit 145 may be merged or separated, and may be implemented as a series of instructions included in one program.

The communication link maintenance unit 141 may establish a communication link between a plurality of unmanned aerial vehicles 200 and the failure prediction device 100 or between the failure prediction device 100 and the unmanned aerial vehicle inspection device 300.

For example, the communication link maintenance unit 141 can be connected to each of the plurality of unmanned aerial vehicles 200 through wireless communication to establish a communication link between them. In addition, the communication link maintenance unit 141 can be connected to the unmanned aerial vehicle inspection device 300 through wired or wireless communication to establish a communication link between them.

At this time, the communication link maintenance unit 141 may check the authorization information of each of the plurality of unmanned aerial vehicles 200 and establish a communication link with each of the plurality of unmanned aerial vehicles 200 according to the confirmation result. Additionally, the communication link maintenance unit 141 may check the authorization information of the unmanned aerial vehicle inspection device 300 and establish a communication link with the unmanned aerial vehicle inspection device 300 according to the results.

Here, the authorization information for each of the plurality of unmanned aerial vehicles 200 may include identification information for each unmanned aerial vehicle 200, and the unmanned aerial vehicle inspection device 300 may receive input from a user operating the device, such as a mechanic. It may include identification information, such as ID and password information.

In addition, the communication link maintenance unit 141 maintains the communication link established between the plurality of unmanned aerial vehicles 200 and the failure prediction device 100 for a predetermined time to transmit a plurality of communication links from each unmanned aerial vehicle 200 to the input/output unit 110. Status information can be transmitted at each time point.

For example, the plurality of unmanned aerial vehicles 200 of this embodiment may be flying to perform a mission. Therefore, after establishing a communication link with each of the plurality of unmanned aerial vehicles 200 in flight, the communication link maintenance unit 141 can maintain the established communication link until the end of the mission of each unmanned aerial vehicle 200. . Accordingly, each of the plurality of unmanned aerial vehicles 200 in flight maintains an established communication link with the failure prediction device 100 until the flight ends, that is, until the unmanned aerial vehicle 200 lands on the ground at the end of the mission. Through this, a plurality of status information corresponding to each of a plurality of viewpoints can be transmitted.

The validity determination unit 142 may determine the validity of signals transmitted from each of the plurality of unmanned aerial vehicles 200, that is, a plurality of status information.

For example, status information transmitted from each of the plurality of unmanned aerial vehicles 200 through a communication link at each of a plurality of viewpoints may include the unit identification information and on-board equipment identification information of each unmanned aerial vehicle 200.

Accordingly, the validity determination unit 142 can determine the validity of the status information at each time point according to whether at least one of the unit identification information and the mounted equipment identification information is missing from the status information at each of the plurality of time points received. there is.

For example, when status information of one unmanned aerial vehicle 200 is received through the input/output unit 110 at a specific time, the validity determination unit 142 determines whether the vehicle identification information or on-board equipment identification information in the status information at that time is missing. If at least one of the two pieces of identification information is missing, it can be determined that the status information at that time is not valid.

The status information classification unit 143 may classify the status information at each of the plurality of viewpoints, the validity of which has been determined by the validity determination unit 142, for each of the plurality of unmanned aerial vehicles 200.

For example, the status information classification unit 143 may classify and arrange the status information at each of the plurality of viewpoints for each of the plurality of unmanned aerial vehicles 200 based on the device identification information of the status information. At this time, the status information classification unit 143 may sort the status information for each unmanned aerial vehicle 200 in chronological order, that is, in the order of the time the status information was received.

The on-board equipment diagnosis unit 144 receives status information for each of the plurality of unmanned aerial vehicles (200) at multiple points in time from the status information classification unit 143 and diagnoses the status of the plurality of on-board equipment of each unmanned aerial vehicle (200). can do. Additionally, the onboard equipment diagnosis unit 144 can predict the occurrence of a failure in at least one of the plurality of onboard equipment of each unmanned aerial vehicle 200 according to the diagnosis result.

For example, the on-board equipment diagnosis unit 144 may diagnose the current status of a plurality of on-board equipment based on status information for each unmanned aerial vehicle (200) at multiple points in time.

In addition, the on-board equipment diagnosis unit 144 can predict the status information value of a future perspective based on the status information of at least two adjacent viewpoints among the status information for each of the plurality of viewpoints of each unmanned aerial vehicle 200.

For example, the onboard equipment diagnosis unit 144 may determine whether the current status information among the status information for each unmanned aerial vehicle 200 at a plurality of viewpoints is within a preset range. Here, the range may be set by two preset reference values, for example, a first reference value and a second reference value.

At this time, if the current status information is within the set range, the on-board equipment diagnosis unit 144 calculates the amount of change in the status information based on the current status information and the status information at the previous time, and based on the calculated amount of change, the onboard equipment diagnosis unit 144 calculates the current status information. The state information value at a future point in time can be predicted from the point in time state information.

Accordingly, the on-board equipment diagnosis unit 144 can predict the occurrence of a failure in the future for at least one on-board equipment among the plurality of on-board equipment of each unmanned aerial vehicle 200 based on the predicted status information value.

Here, the on-board equipment diagnosis unit 144 may include a plurality of diagnostic units (not shown) that perform diagnosis and failure occurrence prediction for each of the plurality of on-board equipment in correspondence with each of the status information at a plurality of time points.

For example, as described above, a plurality of mounted equipment includes a plurality of devices such as the flight control system, propulsion/fuel system, landing system, avionics system, electrical system, communication link system, environmental system, and mission system of each unmanned aerial vehicle (200). It can be mounted on each system.

In addition, each unmanned aerial vehicle 200 may transmit status information for each time point for each of the plurality of mounted equipment mounted on each of the plurality of systems to the failure prediction device 100 at each of the plurality of times.

Accordingly, the onboard equipment diagnostic unit 144 of this embodiment can diagnose and predict the occurrence of a failure according to status information at each point in time of each onboard equipment through a plurality of diagnostic units corresponding to each of a plurality of onboard devices.

Here, the plurality of diagnostic units of the onboard equipment diagnostic unit 144 include a flight control system diagnostic unit, a propulsion/fuel system diagnostic unit, a landing system diagnostic unit, an avionics system diagnostic unit, an electrical system diagnostic unit, a communication link system diagnostic unit, and an environmental system. It may include a diagnosis department, mission system diagnosis department, etc.

The diagnosis result management unit 145 may store the diagnosis results and failure prediction results for a plurality of onboard equipment of each unmanned aerial vehicle 200 by the onboard equipment diagnosis unit 144. In addition, the diagnosis result management unit 145 may generate the stored diagnosis results and failure prediction results in the form of a report and output them to the unmanned aerial vehicle inspection device 300 through the processor 120.

Meanwhile, the diagnosis result management unit 145 may receive pre-flight inspection results for a plurality of mounted equipment of each of the plurality of unmanned aerial vehicles 200 from the unmanned aerial vehicle inspection device 300 through the input/output unit 110.

Accordingly, the diagnosis result management unit 145 determines each of the plurality of onboard equipment of each unmanned aerial vehicle 200 based on the pre-flight inspection results of each unmanned aerial vehicle 200 and the failure occurrence prediction result provided by the onboard equipment diagnosis unit 144. The frequency of failure occurrence can be calculated and the results can be included and output in the aforementioned report.

As described above, the failure prediction device 100 of this embodiment receives status information about the mounted equipment at a plurality of time points from each of the plurality of unmanned aerial vehicles 200 in flight to perform the mission, and loads the device from the received status information. The occurrence of equipment failure can be predicted and output to the unmanned aerial vehicle inspection device 300.

Accordingly, the aircraft mechanic determines in real time the occurrence of a failure of the on-board equipment of the unmanned aerial vehicle (200) through the unmanned aerial vehicle inspection device 300, and accordingly inspects and repairs the on-board equipment of the unmanned aerial vehicle (200) whose mission has ended. It can be processed quickly, increasing the efficiency of unmanned aerial vehicle operation and maintenance.

Figure 3 is a diagram showing a method for predicting failure of onboard equipment of an unmanned aerial vehicle according to an embodiment of the present invention.

Referring to FIG. 3, the failure prediction device 100 of this embodiment connects a communication link with each of a plurality of unmanned aerial vehicles 200 in flight to perform a mission, and receives multiple signals from each unmanned aerial vehicle 200 through the connected communication link. It is possible to receive status information at multiple points in time for each of the mounted equipment (S10).

Accordingly, the processor 120 uses the failure prediction program 140 stored in the memory 130 to detect the occurrence of a failure of the mounted equipment based on status information at multiple points in time for the plurality of mounted equipment of each unmanned aerial vehicle 200. It is predictable.

The validity determination unit 142 may determine the validity of the status information for each unmanned aerial vehicle 200 provided through the input/output unit 110 at a plurality of time points (S20).

Here, the validity determination unit 142 may determine the validity of the status information at each time point according to whether at least one of the unit identification information and the onboard equipment identification information included in each status information is missing.

Next, the status information classification unit 143 may classify the status information for each plurality of time points whose validity has been determined for each unmanned aerial vehicle 200 (S30).

Here, the status information classification unit 143 may classify and arrange the status information for each plurality of viewpoints for each of the plurality of unmanned aerial vehicles 200 based on the vehicle identification information included in each status information.

Subsequently, the on-board equipment diagnosis unit 144 diagnoses the status of each of the plurality of on-board equipment of each unmanned aerial vehicle 200 according to the status information at a plurality of viewpoints, and determines the plurality of on-board equipment of each unmanned aerial vehicle 200 according to the diagnosis result. It is possible to predict the occurrence of a failure in at least one of the mounted equipment (S40).

Here, the on-board equipment diagnostic unit 144 can diagnose the current status of a plurality of on-board equipment based on status information for each unmanned aerial vehicle (200) at multiple points in time.

In addition, the on-board equipment diagnosis unit 144 can predict the occurrence of a failure of at least one on-board equipment according to state information at a future time point predicted from the state information at a plurality of time points of each unmanned aerial vehicle 200.

FIG. 4 is a diagram illustrating in detail the steps for predicting the occurrence of a failure of the equipment shown in FIG. 3.

Hereinafter, for convenience of explanation, prediction of the occurrence of a failure of one of the plurality of onboard equipment of the first unmanned aerial vehicle 200 among the plurality of unmanned aerial vehicles 200 will be described, but the present invention is not limited thereto.

Referring to FIG. 4, the on-board equipment diagnosis unit 144 includes first viewpoint status information (BIT(t)) among status information for each plurality of viewpoints for one on-board equipment of the first unmanned aerial vehicle, for example, the first mounted equipment; For example, current status information can be obtained (S110).

Next, the on-board equipment diagnosis unit 144 may determine whether the first viewpoint status information (BIT(t)) is within a preset range (S120).

Here, the range can be set between the first reference value (Ref1) and the second reference value (Ref2), where the first reference value (Ref1) may be a normal operation allowable value of the first mounted equipment, and the second reference value (Ref2) may be the first reference value (Ref2). 1This may be the normal operating limit value of the mounted equipment.

If the first viewpoint status information (BIT(t)) is within the set range (Y), the on-board equipment diagnosis unit 144 provides one or more status information at a time immediately prior to the first viewpoint, for example, the past viewpoint, and the first viewpoint status information. The amount of change according to the difference in values between (BIT(t)) can be calculated (S130).

Next, the on-board equipment diagnosis unit 144 can predict the status information value at a second time point, for example, a future time point after the first time point, by reflecting the calculated amount of change in the first time point status information (BIT(t)) (S140 ).

Continuing, the onboard equipment diagnosis unit 144 may compare the predicted second time state information (BIT(t+1)) with the preset second reference value (Ref2) (S150).

As a result of the comparison, if the second time state information (BIT(t+1)) is greater than or equal to the second reference value (Ref2) (Y), the mounted equipment diagnosis unit 144 can predict the occurrence of a failure of the first mounted equipment at the second time point. (S160).

In addition, as a result of the comparison, if the second time state information (BIT(t+1)) is less than the second reference value (Ref2) (N), the mounted equipment diagnosis unit 144 diagnoses the normal operation of the first mounted equipment and 1Diagnosis results regarding the current status of the mounted equipment can be output.

Meanwhile, if the first viewpoint status information (BIT(t)) of the first mounted equipment is not included in the set range (N), the mounted equipment diagnosis unit 144 determines the first viewpoint status information (BIT(t)) and the 2 Reference values (Ref2) can be compared (S170).

As a result of the comparison, if the first time status information (BIT(t)) is greater than or equal to the second reference value (Ref2) (Y), the mounted equipment diagnosis unit 144 determines that a failure of the first mounted equipment occurs at the first time point, that is, at the current time. can be diagnosed and the diagnosis results can be output (S180).

Referring again to FIG. 3, the diagnosis result management unit 145 reports the diagnosis results and failure prediction results for the plurality of onboard equipment of each unmanned aerial vehicle 200 provided by the onboard equipment diagnosis unit 144 to the unmanned aerial vehicle inspection device 300. ) can be output (S50).

Here, the diagnosis result management unit 145 can generate and output the diagnosis result and failure occurrence prediction result in the form of a report. Accordingly, the mechanic uses the unmanned aerial vehicle inspection device 300 to determine the current status and occurrence of failures of the on-board equipment of each unmanned aerial vehicle 200 in real time, and accordingly inspects the on-board equipment of the unmanned aerial vehicle 200 whose mission has ended. Rapid maintenance can be performed.

Combinations of each block of the block diagram of the present invention and each step of the flowchart described above may be performed by computer program instructions. Since these computer program instructions can be mounted on the encoding processor of a general-purpose computer, special-purpose computer, or other programmable data processing equipment, the instructions performed through the encoding processor of the computer or other programmable data processing equipment are included in each block or block of the block diagram. Each step of the flowchart creates a means to perform the functions described. These computer program instructions may also be stored in computer-usable or computer-readable memory that can be directed to a computer or other programmable data processing equipment to implement a function in a particular way, so that the computer-usable or computer-readable memory The instructions stored in can also produce manufactured items containing instruction means that perform the functions described in each block of the block diagram or each step of the flowchart. Computer program instructions can also be mounted on a computer or other programmable data processing equipment, so that a series of operational steps are performed on the computer or other programmable data processing equipment to create a process that is executed by the computer, thereby generating a process that is executed by the computer or other programmable data processing equipment. Instructions that perform processing equipment may also provide steps for executing functions described in each block of the block diagram and each step of the flowchart.

Additionally, each block or each step may represent a module, segment, or portion of code that includes one or more executable instructions for executing specified logical function(s). Additionally, it should be noted that in some alternative embodiments it is possible for the functions mentioned in the blocks or steps to occur out of order. For example, two blocks or steps shown in succession may in fact be performed substantially simultaneously, or the blocks or steps may sometimes be performed in reverse order depending on the corresponding function.

The above description is merely an illustrative explanation of the technical idea of the present invention, and those skilled in the art will be able to make various modifications and variations without departing from the essential quality of the present invention. Accordingly, the embodiments disclosed in the present invention are not intended to limit the technical idea of the present invention, but are for illustrative purposes, and the scope of the technical idea of the present invention is not limited by these embodiments. The scope of protection of the present invention shall be interpreted in accordance with the claims below, and all technical ideas within the scope equivalent thereto shall be construed as being included in the scope of rights of the present invention.

100: Failure prediction device
110: input/output unit
120: processor
130: memory
141: Communication link maintenance unit
142: Validity judgment unit
143: Status information classification unit
144: Onboard equipment diagnostic unit
145: Diagnosis result management department
200: Unmanned aerial vehicle
300: Unmanned aerial vehicle inspection device

Claims (14)

Receiving status information at a plurality of points in time for each of the plurality of mounted equipment of each military unmanned aerial vehicle from a plurality of military unmanned aerial vehicles in flight;
determining the validity of the received status information for each of the plurality of time points;
Classifying the status information for each of the plurality of time points whose validity has been determined for each of the plurality of military unmanned aerial vehicles;
Diagnosing the status of each of the plurality of onboard equipment for each military unmanned aerial vehicle based on the classified status information for each of the plurality of time points and predicting the occurrence of a failure of at least one of the plurality of onboard equipment; and
A method for predicting a failure of on-board equipment for a military unmanned aerial vehicle, comprising the step of outputting a diagnosis result for each of the plurality of on-board equipment and a failure prediction result for the at least one on-board equipment.
According to paragraph 1,
The step of predicting the occurrence of a failure is,
predicting a state information value at a future point in time based on a plurality of state information at adjacent times among the state information for each of the plurality of points in time; and
A method for predicting a failure of onboard equipment for a military unmanned aerial vehicle, comprising predicting the occurrence of a failure of the at least one onboard equipment based on the predicted state information value of the future time.
Receiving status information at a plurality of points in time for each of the plurality of mounted equipment of each military unmanned aerial vehicle from a plurality of military unmanned aerial vehicles in flight;
determining whether current-time status information among the plurality of status-specific status information is included in a preset range;
If the current status information is within the set range, calculating a change amount based on the current status information and the previous status information;
predicting a future state information value from the current state information based on the change amount;
Comparing a preset reference value with the status information value at the future time to predict the occurrence of a failure of at least one mounted equipment at the future time; and
A method for predicting a failure of on-board equipment for a military unmanned aerial vehicle, comprising the step of outputting a diagnosis result for each of the plurality of on-board equipment and a failure prediction result for the at least one on-board equipment.
According to paragraph 3,
The step of predicting the occurrence of a failure is,
If the current status information is not within the set range, comparing the current status information with the set reference value to diagnose a failure of the at least one onboard equipment at the current status. Method for predicting onboard equipment failure.
delete Memory in which a failure prediction program is stored; and
Executes the failure prediction program, receives status information for each of the plurality of mounted equipment of each military unmanned aerial vehicle received from the plurality of military unmanned aerial vehicles in flight through an input/output unit, and receives status information for each of the plurality of viewpoints received for each of the plurality of viewpoints. Determine the validity of the status information, classify the status information for each of the plurality of viewpoints whose validity has been determined for each of the plurality of military unmanned aerial vehicles, and classify the status information for each of the plurality of viewpoints based on the classified status information for each of the plurality of viewpoints. Diagnose the status of each of the plurality of mounted devices to predict the occurrence of a failure of at least one mounted device among the plurality of mounted devices, and predict the occurrence of a failure of the at least one mounted device and the diagnosis result for each of the plurality of mounted devices. A failure prediction device for on-board equipment of a military unmanned aerial vehicle that includes a processor that outputs results.
According to clause 6,
The processor,
Predicting a state information value at a future time based on a plurality of state information at adjacent times among the state information for each of the plurality of times, and occurring a failure of the at least one mounted equipment based on the predicted state information value at the future time. A equipment failure prediction device for military unmanned aerial vehicles.
Memory in which a failure prediction program is stored; and
Executes the failure prediction program, receives status information for each of the plurality of mounted equipment of each military unmanned aerial vehicle received from a plurality of military unmanned aerial vehicles in flight through an input/output unit, and receives status information for each of the plurality of viewpoints. Determine whether the current status information is included in the preset range, and if the current status information is included in the set range, calculate the amount of change based on the current status information and the previous status information, and calculate the amount of change. Based on the current status information, predict a future status information value, and compare the future status information value with a preset reference value to predict the occurrence of a failure of at least one on-board equipment in the future. Equipment failure prediction device for military unmanned aerial vehicles.
According to clause 8,
The processor,
If the current status information is not within the set range, the current status information is compared with the set reference value to predict a failure of the at least one onboard equipment at the current time. Device.
delete According to clause 6,
The status information for each of the plurality of points in time is,
A device for predicting onboard equipment failure of a military unmanned aerial vehicle including information on PBIT (Power-on BIT) and CBIT (Continuous BIT) results for each of the plurality of onboard equipment.
According to clause 6,
The status information for each of the plurality of points in time is,
A device for predicting onboard equipment failure of a military unmanned aerial vehicle, including status information on one or more onboard equipment of each of the plurality of systems of each military unmanned aerial vehicle.
A computer-readable recording medium storing a computer program,
The computer program is,
Receiving status information at a plurality of points in time for each of the plurality of mounted equipment of each military unmanned aerial vehicle from a plurality of military unmanned aerial vehicles in flight;
determining the validity of the received status information for each of the plurality of time points;
Classifying the status information for each of the plurality of time points whose validity has been determined for each of the plurality of military unmanned aerial vehicles;
Diagnosing the status of each of the plurality of onboard equipment for each military unmanned aerial vehicle based on the classified status information for each of the plurality of time points and predicting the occurrence of a failure of at least one of the plurality of onboard equipment; and
A computer including instructions for a processor to perform a method for predicting a failure of on-board equipment for a military unmanned aerial vehicle, including outputting a diagnosis result for each of the plurality of on-board equipment and a failure prediction result for the at least one on-board equipment. Readable recording medium.
A computer program stored on a computer-readable recording medium,
The computer program is,
Receiving status information at a plurality of points in time for each of the plurality of mounted equipment of each military unmanned aerial vehicle from a plurality of military unmanned aerial vehicles in flight;
determining the validity of the received status information for each of the plurality of time points;
Classifying the status information for each of the plurality of time points whose validity has been determined for each of the plurality of military unmanned aerial vehicles;
Diagnosing the status of each of the plurality of onboard equipment for each military unmanned aerial vehicle based on the classified status information for each of the plurality of time points and predicting the occurrence of a failure of at least one of the plurality of onboard equipment; and
A record containing instructions for a processor to perform a method for predicting a failure of on-board equipment for a military unmanned aerial vehicle, including outputting a diagnosis result for each of the plurality of on-board equipment and a failure prediction result for the at least one on-board equipment. A computer program stored on media.