Motor fault detection method and device, unmanned aerial vehicle and computer readable storage medium

Abstract

This application provides a motor fault detection method, apparatus, unmanned aerial vehicle (UAV), and computer-readable storage medium. The method includes: obtaining the target motor speed corresponding to the speed adjustment command received by the target motor in the target UAV; acquiring the track tilt angle and airspeed of the target UAV; and determining the motor fault detection result of the target motor based on the target motor speed, track tilt angle, and airspeed. The motor fault detection method proposed in this application utilizes the change in UAV airspeed, the track tilt angle used to describe the gravity acting on the UAV, and the target motor speed obtained from the motor speed command received by the target motor to describe the thrust acting on the UAV. This allows for motor fault detection without the need for an additional speed measurement device, avoiding additional weight and cost, and facilitating the miniaturization and lightweighting of UAVs.

Description

Technical Field

[0001] This application relates to the field of unmanned aerial vehicle (UAV) technology, specifically to a motor fault detection method, device, UAV, and computer-readable storage medium. Background Technology

[0002] Fault detection of drone motors is crucial for ensuring stable flight. Currently, common methods for detecting motor faults primarily rely on motor speed or drone airspeed.

[0003] However, detecting motor speed requires an additional speed measurement device, which increases the weight and cost of the drone, hindering its miniaturization and weight reduction. Furthermore, due to inertia, fault detection of the motor based on rotational speed or airspeed will have a certain lag. Summary of the Invention

[0004] This application provides a motor fault detection method, device, drone, and computer-readable storage medium, aiming to solve the technical problem that existing drone motor fault detection methods require additional speed measurement devices, which is not conducive to the miniaturization and lightweighting of drones.

[0005] On one hand, embodiments of this application provide a method for detecting motor faults, including:

[0006] Based on the speed adjustment command received by the target motor in the target UAV, the target motor speed corresponding to the speed adjustment command is obtained;

[0007] Obtain the trajectory tilt angle and airspeed of the target UAV;

[0008] The motor fault detection result of the target motor is determined based on the target motor speed, the track inclination angle, and the airspeed.

[0009] As an optional embodiment of this application, the motor fault detection method is applied in an extended state observer;

[0010] The step of determining the motor fault detection result of the target motor based on the target motor speed, the flight path inclination angle, and the airspeed includes:

[0011] Query the preset database to obtain the motor thrust corresponding to the target motor speed;

[0012] The motor thrust, the flight path inclination angle, and the airspeed are input into the extended state observer, and the observed flight drag is output.

[0013] The motor fault detection result of the target motor is determined based on the observed flight resistance.

[0014] As an optional embodiment of this application, before inputting the motor thrust, the trajectory inclination angle, and the airspeed into the extended state observer and outputting the observed flight drag, the method further includes:

[0015] Obtain the model information of the target drone;

[0016] Query the preset database to obtain the observer intrinsic parameters corresponding to the model information;

[0017] The observer's intrinsic parameters are set to the internal parameters of a preset initial observer to obtain the extended state observer.

[0018] As an optional embodiment of this application, determining the motor fault detection result of the target motor based on the observed flight resistance includes:

[0019] The observed flight resistance is compared with a preset flight resistance threshold.

[0020] If the observed flight resistance is greater than the flight force threshold, then the target motor is determined to have a motor fault.

[0021] As an optional embodiment of this application, before comparing the observed flight resistance with a preset flight resistance threshold, the method further includes:

[0022] Acquire the test track tilt angle, test airspeed, and test motor speed corresponding to the test speed adjustment command;

[0023] Query the preset database to obtain the test motor thrust corresponding to the test motor speed.

[0024] The test motor thrust, the test track inclination angle, and the test airspeed are input into the extended state observer, and the test flight drag is output.

[0025] The flight resistance threshold is set based on the statistical characteristics of the test flight resistance data.

[0026] As an optional embodiment of this application, determining the motor fault detection result of the target motor based on the observed flight resistance includes:

[0027] The flight drag coefficient of the target UAV is calculated based on air density, airspeed, and observed flight drag.

[0028] The flight drag coefficient is compared with a preset flight drag coefficient threshold.

[0029] If the flight drag coefficient is greater than the flight drag coefficient threshold, then the target motor is determined to have a motor fault.

[0030] As an optional embodiment of this application, before obtaining the trajectory tilt angle and airspeed of the target UAV, the method further includes:

[0031] Obtain the vertical velocity and ground speed of the target drone;

[0032] The ratio of the vertical velocity to the ground velocity is set as the trajectory tilt angle of the target UAV.

[0033] On the other hand, embodiments of this application also provide a motor fault detection device, including:

[0034] The rotation speed determination module is used to obtain the target motor rotation speed corresponding to the rotation speed adjustment command received by the target motor in the target UAV.

[0035] The acquisition module is used to acquire the trajectory tilt angle and airspeed of the target UAV;

[0036] The fault detection module is used to determine the motor fault detection result of the target motor based on the target motor speed, the track inclination angle, and the airspeed.

[0037] On the other hand, this application also provides a drone, which includes a processor, a memory, and a motor fault detection program stored in the memory and executable on the processor. The processor executes the motor fault detection program to implement the steps in the above-described motor fault detection method.

[0038] On the other hand, embodiments of this application also provide a computer-readable storage medium storing a motor fault detection program, which is executed by a processor to implement the steps in the above-described motor fault detection method.

[0039] The motor fault detection method proposed in this application takes into account that when a motor fails, the actual thrust of the UAV, derived from the change in UAV airspeed and the track tilt angle which can be used to describe the gravity of the UAV, does not match the thrust of the UAV determined by the motor speed command. Therefore, by using the UAV airspeed, track tilt angle, and the target motor speed which can be used to describe the thrust of the UAV, obtained from the motor speed command received from the target motor, it is possible to quickly and accurately determine whether the motor is faulty. The entire fault judgment process does not require the additional setting of a speed measuring device to measure the speed, avoiding additional weight and cost, and facilitating the realization of UAV miniaturization and lightweighting. Attached Figure Description

[0040] To more clearly illustrate the technical solutions in the embodiments of this application, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0041] Figure 1 This is a schematic diagram of a scenario for the motor fault detection method provided in an embodiment of this application;

[0042] Figure 2 This is a flowchart illustrating the first embodiment of the motor fault detection method provided in this application.

[0043] Figure 3 This is a flowchart illustrating the second embodiment of the motor fault detection method provided in this application.

[0044] Figure 4 This is a schematic flowchart of the third embodiment of the motor fault detection method provided in this application.

[0045] Figure 5 This is a flowchart illustrating the fourth embodiment of the motor fault detection method provided in this application.

[0046] Figure 6 This is a flowchart illustrating the fifth embodiment of the motor fault detection method provided in this application.

[0047] Figure 7 This is a flowchart of the sixth embodiment of the motor fault detection method provided in this application.

[0048] Figure 8 This is a flowchart of the seventh embodiment of the motor fault detection method provided in this application.

[0049] Figure 9 This is a schematic diagram of a functional module of the motor fault detection device provided in the embodiments of this application;

[0050] Figure 10 This is a schematic diagram of the motor fault detection device provided in the embodiments of this application. Detailed Implementation

[0051] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of the present invention.

[0052] In this application, the term "exemplary" is used to mean "used as an example, illustration, or description." Any embodiment described as "exemplary" in this application is not necessarily to be construed as being more preferred or advantageous than other embodiments. The following description is provided to enable any person skilled in the art to implement and use the invention. Details are set forth in the following description for purposes of explanation. It should be understood that those skilled in the art will recognize that the invention can be implemented without using these specific details. In other instances, well-known structures and processes will not be described in detail to avoid obscuring the description of the invention with unnecessary detail. Therefore, the invention is not intended to be limited to the embodiments shown, but is consistent with the broadest scope of the principles and features disclosed in the embodiments of this application.

[0053] This application provides a method, apparatus, drone, and computer-readable storage medium for detecting motor faults, which will be described in detail below.

[0054] First, the relevant technologies of this application's technical solution will be explained. Specifically, the airspeed differential equation of the UAV can be described by the following formula:

[0055]

[0056] in, It is airspeed V a The differential form of the equation can also be simply understood as the change in airspeed, i.e., acceleration. m is the mass of the UAV, T represents the component of the thrust provided by the motor along the x-axis of the airframe, α is the angle of attack, β is the sideslip angle, D represents the drag, and G... xa This represents the component of gravity along the x-axis in the airflow coordinate system. For conventionally designed UAVs, the angle of attack α remains within a small range during flight, typically [-5, 15] degrees. Furthermore, most UAVs employ no-sideslip flight, with the sideslip angle generally varying around 0 degrees. Therefore, a simplified form of the airspeed differential equation can be obtained:

[0057]

[0058] When the sideslip angle is ignored, G xa ≈-mgsinγ, where γ represents the flight path inclination angle, which is the angle between the UAV's airspeed vector and the ground plane, specifically obtained by subtracting the angle of attack from the UAV's pitch angle. Therefore, the above differential equation regarding airspeed can be simplified to:

[0059]

[0060] This application proposes a method for detecting motor faults based on the differential equation of this airspeed.

[0061] like Figure 1 As shown, Figure 1 This is a schematic diagram of a scenario for a motor fault detection method in this application embodiment. Specifically, it includes a motor fault detection device 100 and an information acquisition device 200. The information acquisition device 200 is mainly used to collect information data for realizing motor fault detection, including but not limited to the track inclination angle, airspeed, and motor speed adjustment commands provided in this application embodiment. The collected information data is then transmitted to the motor fault detection device 100. The motor fault detection device 100 runs a computer-readable storage medium corresponding to the motor fault detection method to execute the steps of motor fault detection, thereby realizing the detection of motor faults.

[0062] It should be noted that the information acquisition device 200 can be understood as a general term for a type of information acquisition unit, meaning that the specific unit that performs the acquisition function differs for different types of information data. For example, the flight path tilt angle of a UAV can be obtained by subtracting the angle of attack obtained by the angle of attack sensor from the pitch angle obtained by the attitude sensor installed on the UAV. The airspeed of the UAV can be obtained by the pitot tube installed on the UAV. The speed adjustment command received by the motor is usually issued by the flight control unit of the UAV. Therefore, by parsing the command issued by the flight control unit, the speed adjustment command received by the motor can be obtained.

[0063] It should be noted that, Figure 1 The schematic diagram of the motor fault detection scenario shown is merely an example. The motor fault detection scenario described in this embodiment of the invention is intended to more clearly illustrate the technical solution of this embodiment and does not constitute a limitation on the technical solution provided by this embodiment of the invention.

[0064] Based on the above-mentioned scenarios for motor fault detection, an embodiment of a motor fault detection method is proposed.

[0065] like Figure 2 As shown, Figure 2 This is a flowchart illustrating the first embodiment of the motor fault detection method provided in this application. The motor fault detection method in this embodiment includes steps 201-203:

[0066] 201. Based on the speed adjustment command received by the target motor in the target UAV, the target motor speed corresponding to the speed adjustment command is obtained.

[0067] In this embodiment, the speed adjustment command is a command issued by the flight controller (or flight controller) during the flight of the target UAV to adjust the speed of the target motor according to actual flight requirements. Specifically, the speed adjustment command includes, but is not limited to, the form of "adjust the motor speed to A" or "increase / decrease the motor speed by B". Regardless of the form used, the required motor speed in the speed adjustment command can be determined based on the current set speed of the target motor, and this motor speed is the target motor speed.

[0068] It should be noted that the target motor speed is determined based on the speed adjustment command issued by the flight control unit on the UAV, which can also be understood as the speed requirement received by the target motor, rather than the actual motor speed measured by an additional speed measuring device. In other words, the parameters required for motor fault detection in this embodiment, including the target motor speed and the subsequently acquired flight path tilt angle and airspeed, can be obtained using existing information acquisition devices installed on the UAV. This enables motor fault detection in the context of miniaturized and lightweight UAVs. Furthermore, since the UAV is driven by the power generated by the rotation of the motor, the motor operating at the target motor speed can generate a certain motor thrust, which is the flight power that drives the UAV to fly as required. That is, in the aforementioned airspeed differential equation, T has a corresponding relationship with the target motor speed.

[0069] 202, Obtain the trajectory tilt angle and airspeed of the target UAV.

[0070] In this embodiment, the track tilt angle refers to the angle between the UAV's airspeed vector and the ground plane, which can be obtained by subtracting the angle of attack from the UAV's pitch angle. Specifically, the UAV's tilt angle can usually be obtained by an attitude sensor, while the angle of attack can usually be obtained by an angle of attack sensor. Airspeed can be understood as the speed of the UAV relative to the air, and this value can usually be obtained by an airspeed indicator. Of course, it is also feasible to obtain the track tilt angle and airspeed through other methods. For those skilled in the art, the definition and acquisition method of the track tilt angle and airspeed are conventional solutions, and this application will not elaborate on them here.

[0071] Considering that small fixed-wing UAVs generally do not have angle-of-attack sensors, it is impossible to directly obtain the UAV's angle of attack. Therefore, it is also impossible to obtain the UAV's track tilt angle by subtracting the angle of attack from the pitch angle. As an optional embodiment of this application, a scheme for obtaining the track tilt angle based on the UAV's vertical velocity and ground speed is proposed. Please refer to the following sections for details. Figure 8 And its explanations and descriptions.

[0072] Furthermore, combining the aforementioned airspeed differential equation, by differentiating the collected airspeed, the differential form of the airspeed differential equation can be obtained. The flight path tilt angle is related to the component of the gravity acting on the UAV along the x-axis in the airflow coordinate system. 203, the motor fault detection result of the target motor is determined based on the target motor speed, the flight path tilt angle, and the airspeed.

[0073] In this embodiment, the drag experienced by the UAV is related to airspeed and air density. During stable flight, the drag experienced by the UAV is usually within a relatively stable range. Therefore, based on the aforementioned airspeed differential equation, when the motor is in normal working condition (i.e., when the theoretical thrust provided by the motor at the target motor speed is equal to the actual thrust provided), the obtained target motor speed, flight path angle, airspeed, and drag experienced by the UAV satisfy the aforementioned airspeed differential equation. However, when the motor is in abnormal working condition, the actual thrust provided by the motor will not be equal to the theoretical thrust provided at the target motor speed, meaning the obtained target motor speed, flight path angle, airspeed, and drag experienced by the UAV will not satisfy the airspeed differential equation. Therefore, the motor fault detection result of the target motor can be determined based on the target motor speed, flight path angle, and airspeed.

[0074] As an optional embodiment of this application, since the drag experienced by the UAV is related to airspeed and air density, an extended state observer can be constructed based on the airspeed differential equation and the relationship between UAV drag and airspeed. When the actual thrust decreases rapidly due to a motor failure, the observed drag output by the extended state observer will rapidly increase, thereby facilitating the detection of motor failures. The specific framework and principle of the extended state observer can be found in subsequent articles. Figure 3 And its explanations and descriptions.

[0075] Furthermore, motors are prone to burnout when operating at full throttle for extended periods. Before complete motor failure, the drone typically experiences a thrust reduction process. Compared to conventional motor fault detection methods, the motor fault detection method provided in this application can quickly detect motor faults during this thrust reduction process, i.e., before complete motor failure. In other words, when a motor abnormality is detected, certain safety strategies can be implemented to prevent complete motor burnout. Additionally, for drones with power redundancy, such as those detecting motor abnormalities during the fixed-wing phase, switching to rotor motor control can effectively ensure flight safety and improve drone flight stability. The motor fault detection method proposed in this application takes into account that when a motor fails, the actual thrust of the UAV derived from the change in UAV airspeed and the track tilt angle that can be used to describe the gravity of the UAV does not match the thrust of the UAV determined by the motor speed command. Therefore, the target motor speed that can be used to describe the thrust of the UAV, obtained by UAV airspeed, track tilt angle and motor speed command received from the target motor, can further quickly and accurately determine whether the motor is faulty. At the same time, the entire fault judgment process does not require the additional setting of a speed measuring device to measure the speed, avoiding additional weight and cost, and facilitating the realization of UAV miniaturization and lightweighting.

[0076] like Figure 3 As shown, Figure 3 This is a flowchart illustrating the second embodiment of the motor fault detection method provided in this application.

[0077] This application embodiment provides a flowchart of steps for implementing a motor fault detection method based on an extended state observer, specifically including steps 301 to 303:

[0078] 301. Query the preset database to obtain the motor thrust corresponding to the target motor speed.

[0079] In this embodiment of the application, as described above, the motor operating at the target motor speed can generate a certain motor thrust. That is, there is a corresponding mapping relationship between T in the airspeed differential equation and the target motor speed. Specifically, this mapping relationship can be described by a function, i.e., T = f(T sp ), where T sp This is the target motor speed.

[0080] In this embodiment of the application, the function describing the mapping relationship between thrust and speed can usually be found directly on the official website of the motor. Therefore, the function can be pre-stored in the database. When the motor fault detection device obtains the target motor speed, it can directly query the corresponding motor thrust from the database.

[0081] 302, The motor thrust, the track inclination angle and the airspeed are input into the extended state observer, and the observed flight drag is output.

[0082] In this embodiment of the application, the contents of the extended state observer are as follows:

[0083]

[0084]

[0085] The extended state observer consists of two parts, outputting observed flight drag and observed airspeed. It's important to note that the observed airspeed is the output of the extended state observer, not the input airspeed. Specifically, V... a It is the input airspeed. It is the observed airspeed output by the extended state observer, while This is the differential form of the observed airspeed output by the extended state observer. The observed airspeed output by the extended state observer can be obtained by integration. It is the observed flight drag output by the extended state observer. It is the differential form of observing flight drag, Integrating the data yields the observed flight drag output by the extended state observer. Furthermore, T represents the input motor thrust, m is the UAV mass (a fixed preset value), g is the gravitational acceleration (a fixed preset value), γ is the input track tilt angle, and k1 and k2 are pre-trained observer parameters used to adjust the observer's performance, ensuring rapid tracking of signal changes and output of the changing results even when the input data changes rapidly.

[0086] As described above, the differences between k1 and k2 affect the performance of the extended state observer. Therefore, in practice, k1 and k2 are set based on the UAV's past flight data. As an optional embodiment of this application, considering that UAVs of the same model typically have similar configuration parameters, the optimal parameters k1 and k2 for different UAV models can be predetermined and stored in a database. In subsequent practical applications, the motor fault detection device only needs to query the corresponding optimal parameters k1 and k2 based on the UAV model and configure the observer's internal parameters accordingly to obtain the optimal extended state observer. Please refer to the following for details. Figure 4 And its explanations and descriptions.

[0087] In this embodiment of the application, it can be seen that the observed airspeed output by the extended state observer is not only related to the input motor thrust, track inclination angle and airspeed, but also to the observed flight drag. Conversely, the observed flight drag is affected by the observed airspeed. Therefore, by continuously inputting the motor thrust, track inclination angle and airspeed into the extended state observer, the extended state observer can continuously and synchronously output the observed flight drag and observed airspeed.

[0088] 303, Determine the motor fault detection result of the target motor based on the observed flight resistance.

[0089] In this embodiment, when the motor is in normal operating condition, meaning the actual thrust of the motor matches the theoretical input thrust, the observed flight drag output by the extended state observer will fluctuate within a certain range, which is typically related to the actual resistance experienced by the UAV. However, when the motor stops operating, the actual thrust generated by the motor decreases rapidly, but the motor thrust input to the observer is assumed to be normal. At this time, the observed flight drag output by the extended state observer will increase rapidly to compensate for the thrust error. Therefore, by observing the observed flight drag, motor faults can be quickly detected.

[0090] As an optional embodiment of this application, considering that the observed flight resistance will fluctuate within a certain range, the motor fault detection result can be directly determined based on the relationship between the observed flight resistance and the preset flight resistance threshold. Please refer to the following for details. Figure 5 The content of the explanation is as follows. Furthermore, as another optional embodiment of this application, to eliminate misjudgments caused by changes in airspeed and air density, the output observed flight drag can be preprocessed to obtain a dimensionless drag coefficient. Similar to the observed flight drag, the drag coefficient also fluctuates within a certain range and becomes more stable. Therefore, a more accurate motor fault detection result can be obtained based on the drag coefficient. For details, please refer to the following... Figure 7 And its explanations and descriptions.

[0091] This application presents a flowchart illustrating the steps of a method for detecting motor faults based on an extended state observer. An extended state observer is constructed by pre-calculating the differential equation based on airspeed and the relationship between drag and airspeed. When motor thrust, flight path tilt angle, and airspeed are input into this extended state observer, it can simultaneously output observed flight drag and observed airspeed. When the UAV motor is operating normally, the observed flight drag output by the extended state observer will fluctuate normally within a certain range. However, when the UAV motor malfunctions, the observed flight drag will rapidly increase to compensate for the error caused by the rapid decrease in thrust. Therefore, based on the observed flight drag, motor fault detection can be achieved quickly and accurately.

[0092] like Figure 4 As shown, Figure 4 This is a flowchart illustrating the third embodiment of the motor fault detection method provided in this application.

[0093] In this embodiment of the application, an implementation scheme for configuring an extended state observer according to the UAV model is provided, specifically including steps 401 to 403:

[0094] 401, Obtain the model information of the target drone.

[0095] In this embodiment, considering that UAVs of the same model have basically the same configuration and their flight performance is often quite similar, the same extended state observer can be used. That is, when it is necessary to configure an extended state observer for a UAV to detect motor faults, the model information of the target UAV can be obtained first, so that the corresponding extended state observer can be configured according to the model information of the target UAV.

[0096] 402. Query the preset database to obtain the observer intrinsic parameters corresponding to the model information.

[0097] In this embodiment, by pre-compiling statistics on past flight data of various UAV models, the optimal observer parameters k1 and k2 for each UAV model can be set. When the observer parameters k1 and k2 are associated with the UAV model information and stored in the database, the subsequent motor fault detection device can directly query the corresponding parameters through the UAV model information.

[0098] 403. Set the observer's intrinsic parameters to the preset initial observer's intrinsic parameters to obtain the extended state observer.

[0099] In this embodiment of the application, the parameters obtained by query are respectively used as k1 and k2 in the preset initial observer. The observer obtained at this time is the extended state observer that can effectively detect motor faults of the target UAV.

[0100] In this embodiment, by pre-determining the optimal extended state observer for each UAV model based on its past flight data, extracting the observer parameters, and storing them in the database in association with the UAV model, the corresponding observer parameters can be quickly obtained and set by querying the UAV model data. This ensures the detection effect while enabling rapid configuration of the extended state observer for the UAV.

[0101] like Figure 5 As shown, Figure 5This is a flowchart illustrating the fourth embodiment of the motor fault detection method provided in this application.

[0102] In this embodiment of the application, a scheme for determining motor faults based on observed flight resistance and a preset flight resistance threshold is provided, specifically including steps 501 to 502:

[0103] 501, compare the observed flight resistance with the preset flight resistance threshold.

[0104] In this embodiment, as described above, when the UAV is in normal operating condition, the observed flight drag output by the extended state observer will be close to the actual flight drag experienced by the UAV, meaning it fluctuates normally within a range near the actual flight drag. However, when the motor malfunctions, meaning it cannot provide the required thrust (i.e., the actual thrust provided is less than the theoretical thrust provided by the set motor speed), the thrust input to the observer remains at a normal value. Therefore, to compensate for the error caused by the rapid decrease in thrust, the observed flight drag output by the observer will increase rapidly and cannot maintain fluctuation within a certain range. Therefore, a flight drag threshold can be set, and the observed flight drag can be compared with the flight drag threshold to determine if the motor is malfunctioning.

[0105] Specifically, since the observed flight drag output by the extended state observer is relatively stable when the motor is operating normally, the flight drag threshold can be set using historical operating data of the motor during normal operation to ensure good motor fault detection results. See subsequent sections for details. Figure 6 And its explanations and descriptions.

[0106] 502. If the observed flight resistance is greater than the flight resistance threshold, then it is determined that the target motor has a motor fault.

[0107] In this embodiment, when the observed flight drag exceeds the set flight drag threshold, meaning the observed flight drag can no longer be maintained within a stable fluctuation range, it indicates that the target motor is faulty. Therefore, it can be determined that the target motor is faulty, and subsequent operations after the motor failure can be performed, such as activating the backup motor or adopting other pre-set safety strategies, which will not be elaborated upon here.

[0108] This application provides a scheme for determining whether a motor is faulty by utilizing the relationship between observed flight resistance and a preset flight resistance threshold. Specifically, when the motor is working normally, the observed flight resistance output by the extended state observer will remain within a stable fluctuation range. However, when the motor is faulty, the observed flight resistance output by the extended state observer cannot continue to remain within this stable fluctuation range and will expand rapidly. When it exceeds the preset flight resistance threshold, the target motor can be considered to have a motor fault.

[0109] like Figure 6 As shown, Figure 6 This is a flowchart illustrating the fifth embodiment of the motor fault detection method provided in this application.

[0110] In this embodiment of the application, a scheme for setting a flight drag threshold is provided, specifically including steps 601 to 604:

[0111] 601, obtain the test track tilt angle, test airspeed, and test motor speed corresponding to the test speed adjustment command.

[0112] In this embodiment of the application, the test track tilt angle, test airspeed, and test speed adjustment commands are obtained under normal operating conditions of the UAV motor, and can also be understood as the UAV's past flight data.

[0113] In this embodiment, the specific implementation scheme for obtaining the test track tilt angle, test airspeed, test speed adjustment command, and obtaining the test motor speed based on the test speed adjustment command is the same as that in steps 201 and 202 above. For example, the test track tilt angle is obtained through an attitude sensor, and the test airspeed is obtained through an airspeed tube; these will not be elaborated upon here. Please refer to the explanations of steps 201 and 202 above for details.

[0114] 602. Query the preset database to obtain the test motor thrust corresponding to the test motor speed.

[0115] In this embodiment, similar to step 301 described above, since the mapping relationship between motor thrust and motor speed is stored in a database beforehand, the subsequent motor fault detection device can directly query the database to obtain the test motor thrust corresponding to the test motor speed. This application will not elaborate further here; please refer to the explanation of step 301 above for details.

[0116] 603, input the test motor thrust, the test track inclination angle and the test airspeed into the extended state observer, and output the test flight drag.

[0117] In this embodiment, similar to step 302 described above, the test motor thrust, test track inclination angle, and test airspeed are input into the extended state observer to obtain the test flight drag. It should be noted that the observer parameters in this extended state observer are already set. This will not be elaborated upon here; please refer to the explanation of step 302 above for details.

[0118] 604. Set the flight resistance threshold based on the statistical characteristics of the test flight resistance data.

[0119] In this embodiment, when continuously acquiring test track tilt angle, test airspeed, and test speed adjustment commands, and continuously inputting the corresponding test motor thrust, test track tilt angle, and test airspeed into the extended state observer for processing, the extended state observer will continuously output test flight drag. Since the motor is in normal working condition at this time, the obtained test flight drag will fluctuate within a range similar to the actual flight drag experienced by the UAV. At this point, data statistics on the test flight drag can be performed. For example, the maximum value of the fluctuation can be determined first, and then a certain amount of redundancy can be added to the maximum value to obtain the flight drag threshold. For example, when the test flight drag output by the extended state observer fluctuates between 0.5 and 1.5, a flight drag threshold of 1.8 or 2.0 can be set. Subsequently, when it is detected that the test flight drag output by the extended state observer exceeds 1.8 or 2.0, that is, it cannot continue to fluctuate within the range of 0.5 to 1.5, it can be determined that the motor has malfunctioned.

[0120] It should be noted that the actual setting of the flight resistance threshold can be selected according to user needs. Specifically, the larger the set flight resistance threshold, for example, when the set flight resistance threshold is 2.0, the higher the lag in determining whether a motor fault has occurred. That is, after a motor fault occurs, it takes longer for the observed flight resistance output by the extended state observer to exceed the flight resistance threshold before a motor fault can be determined. However, correspondingly, the probability of false positives is lower, because when the motor is operating normally, the observed flight resistance output by the extended state observer is less likely to reach the flight resistance threshold. Conversely, the smaller the flight resistance threshold setting, for example, when the set flight resistance threshold is 1.8, the faster the motor fault can be determined, but there will be a certain degree of false positives. Therefore, the flight resistance threshold can be set according to the user's requirements for lag and stability.

[0121] This application proposes a scheme for setting a flight drag threshold. Specifically, it utilizes an extended state observer to process data from the UAV and motor under normal operating conditions to obtain the test flight drag. When the motor is in normal operating condition, the statistical characteristics of the test flight drag data output by the extended state observer are used to determine the fluctuation range of the drag normally experienced by the UAV. A certain redundancy is added to the maximum fluctuation value to obtain the set flight drag threshold. This allows the relationship between the test flight drag output by the extended state observer and the flight drag threshold to effectively detect motor faults.

[0122] like Figure 7 As shown, Figure 7 This is a schematic flowchart of the sixth embodiment of the motor fault detection method provided in this application.

[0123] In this embodiment of the application, another implementation scheme for judging motor faults based on observed flight drag is provided, specifically including steps 701 to 703:

[0124] 701. The flight drag coefficient of the target UAV is calculated based on air density, airspeed, and observed flight drag.

[0125] In this embodiment, air density can be measured by an air density measuring instrument or calculated from the flight altitude of the drone. Of course, considering that the change in air density is not significant, a fixed air density can also be preset.

[0126] In this embodiment of the application, the air resistance D experienced by the UAV during flight satisfies the following relationship:

[0127]

[0128] Where ρ is the air density, V a The airspeed is the airspeed measured by airspeed measurement, S is the wingspan of the UAV, and C is the airspeed measured by airspeed measurement. D k is the damping factor. d This is a combination of wingspan S and damping factor C. D The composite drag coefficient is the flight drag coefficient of the target UAV. Therefore, let the observed flight drag be D, the air density be ρ, and the airspeed be V. a By inputting the formula above, the flight drag coefficient k of the target UAV can be obtained. d .

[0129] Compared to the observed flight drag output by the extended state observer, the flight drag coefficient k of the target UAV is... d It is not affected by changes in airspeed and air density. Therefore, when the motor is working normally, the flight drag coefficient k dIt will remain within a more stable range, but when the motor malfunctions, the observed flight drag output by the extended state observer suddenly increases. At this time, the calculated flight drag coefficient will also be difficult to maintain within a stable range and will increase sharply.

[0130] 702, compare the flight drag coefficient with the preset flight drag coefficient threshold.

[0131] In this embodiment, the principle is the same as that of using observed flight drag to determine motor faults. When the motor malfunctions, the observed flight drag output by the extended state observer suddenly increases. At this time, the calculated flight drag coefficient is also difficult to maintain within a stable range and will increase sharply. Therefore, the flight drag coefficient can be compared with a preset flight drag coefficient threshold, and the presence of a motor fault can be determined based on the relationship between the two thresholds.

[0132] As an optional embodiment of this application, similar to setting a flight drag threshold, the flight drag coefficient threshold can also be set using test data from the normal operating state of the UAV motor. Specifically, the test data from the normal operating state of the UAV motor is input into the extended state observer to obtain the test flight drag, and the corresponding test flight drag coefficient is obtained based on the test flight drag. Then, the statistical characteristics of the test flight drag coefficient are used to set the flight drag coefficient threshold. The specific implementation method of the flight drag coefficient threshold is not described in detail here, but can be referred to the foregoing. Figure 6 The implementation scheme for setting the flight drag threshold is shown.

[0133] 703. If the flight drag coefficient is greater than the flight drag coefficient threshold, then it is determined that the target motor has a motor fault.

[0134] In this embodiment, similar to flight drag, when the motor is working normally, the obtained flight drag coefficient will fluctuate within a stable range. When the flight drag coefficient exceeds the set flight drag coefficient threshold, that is, when the flight drag coefficient can no longer be maintained within a stable fluctuation range, it indicates that the target motor is faulty. Therefore, it can be determined that the target motor is faulty, and subsequent operations after the motor failure can be performed, such as activating the backup motor or taking other pre-set safety strategies, which will not be elaborated here.

[0135] In this embodiment, by processing the observed flight drag output by the extended state observer and filtering out variables related to air density and airspeed, the judgment of observed flight drag is transformed into the judgment of flight drag coefficient. This can effectively avoid interference from changes in air density and airspeed. Compared with observed flight drag, the flight drag coefficient of the UAV is more stable. Therefore, when used to determine whether there is a motor fault, a more accurate motor diagnosis result can be obtained.

[0136] like Figure 8 As shown, Figure 8 This is a schematic flowchart of the seventh embodiment of the motor fault detection method provided in this application.

[0137] This application embodiment provides another implementation scheme for obtaining the trajectory tilt angle of a UAV, specifically including steps 801-802:

[0138] 801, obtain the vertical speed and ground speed of the target drone.

[0139] In this embodiment of the application, the flight path tilt angle γ of the UAV satisfies:

[0140]

[0141] Among them, V z This refers to the vertical velocity of the target drone, V. g The ground speed and vertical speed of the target drone can be obtained through displacement sensors or GPS positioning systems.

[0142] 802, The ratio of the vertical velocity to the ground velocity is set as the track tilt angle of the target UAV.

[0143] In this embodiment of the application, as can be seen from the foregoing description, since the differential equation of airspeed uses gsinγ to describe the gravity acting on the UAV, and sinγ can be roughly obtained through the ratio of vertical velocity to ground velocity, the ratio of vertical velocity to ground velocity can be set as the trajectory tilt angle of the target UAV and substituted into the differential equation of airspeed for processing.

[0144] Compared to directly obtaining the track tilt angle by setting up additional pose sensors, the embodiments of this application estimate the track tilt angle by using the vertical velocity and ground speed of the target UAV, which are simpler and easier to obtain, making the motor fault detection method proposed in this application more practical.

[0145] To better implement the motor fault detection method in the embodiments of this application, based on the motor fault detection method, the embodiments of this application also provide a motor fault detection device, such as... Figure 9 As shown, Figure 9This is a schematic diagram of a functional module of a motor fault detection device. Specifically, it includes:

[0146] The rotation speed determination module 901 is used to obtain the target motor rotation speed corresponding to the rotation speed adjustment command based on the rotation speed adjustment command received by the target motor in the target UAV.

[0147] The acquisition module 902 is used to acquire the trajectory tilt angle and airspeed of the target UAV;

[0148] The fault detection module 903 is used to determine the motor fault detection result of the target motor based on the target motor speed, the track inclination angle, and the airspeed.

[0149] In some embodiments of this application, the fault detection module includes:

[0150] The thrust acquisition sub-module is used to query a preset database to obtain the motor thrust corresponding to the target motor speed.

[0151] The observer processing submodule is used to input the motor thrust, the track inclination angle and the airspeed into the extended state observer and output the observed flight drag.

[0152] The fault detection sub-module is used to determine the motor fault detection result of the target motor based on the observed flight resistance.

[0153] In some embodiments of this application, the fault detection module further includes:

[0154] The model acquisition module is used to acquire the model information of the target UAV.

[0155] The parameter acquisition module is used to query a preset database to obtain the observer intrinsic parameters corresponding to the model information;

[0156] The parameter setting submodule is used to set the observer's intrinsic parameters to the preset initial observer's intrinsic parameters to obtain the extended state observer.

[0157] In some embodiments of this application, the above-mentioned fault detection sub-module includes:

[0158] The first comparison unit is used to compare the observed flight resistance with a preset flight resistance threshold.

[0159] The first fault determination unit is used to determine that the target motor has a motor fault if the observed flight resistance is greater than the flight resistance threshold.

[0160] In some embodiments of this application, the above-mentioned fault detection sub-module further includes:

[0161] The test data acquisition unit is used to acquire the test track tilt angle, test airspeed, and test motor speed corresponding to the test speed adjustment command;

[0162] The test thrust acquisition unit is used to query a preset database to obtain the test motor thrust corresponding to the test motor speed.

[0163] The observer test processing unit is used to input the test motor thrust, the test track inclination angle and the test airspeed into the extended state observer, and output the test flight drag.

[0164] The drag threshold setting unit is used to set the flight drag threshold based on the statistical characteristics of the test flight drag data.

[0165] In some embodiments of this application, the above-mentioned fault detection sub-module includes:

[0166] The drag coefficient calculation unit is used to calculate the flight drag coefficient of the target UAV based on air density, airspeed, and observed flight drag.

[0167] The second comparison unit is used to compare the flight drag coefficient with a preset flight drag coefficient threshold.

[0168] The second fault determination unit is used to determine that the target motor has a motor fault if the flight drag coefficient is greater than the flight drag coefficient threshold.

[0169] In some embodiments of this application, the above-mentioned acquisition module includes:

[0170] The speed acquisition submodule is used to acquire the vertical speed and ground speed of the target UAV.

[0171] The track tilt angle calculation module is used to set the ratio of the vertical velocity to the ground speed as the track tilt angle of the target UAV.

[0172] This invention also provides a motor fault detection device, such as... Figure 10 As shown, Figure 10 This is a schematic diagram of the motor fault detection device provided in the embodiments of this application.

[0173] Specifically, the motor fault detection device may include components such as a processor 1001 with one or more processing cores, a memory 1002 with one or more storage media, a power supply 1003, and an input unit 1004. Those skilled in the art will understand that... Figure 10 The structure of the motor fault detection device shown does not constitute a limitation on the motor fault detection device. It may include more or fewer components than shown, or combine certain components, or have different component arrangements. Wherein:

[0174] The processor 1001 is the control center of the motor fault detection device. It connects various parts of the device via interfaces and lines, and executes software programs and / or modules stored in the memory 1002, as well as calling data stored in the memory 1002, to perform various functions and process data, thereby providing overall monitoring of the motor fault detection device. Optionally, the processor 1001 may include one or more processing cores; preferably, the processor 1001 may integrate an application processor and a modem processor, wherein the application processor mainly handles the operating system, user interface, and applications, while the modem processor mainly handles wireless communication. It is understood that the modem processor may not be integrated into the processor 1001.

[0175] The memory 1002 can be used to store software programs and modules. The processor 1001 executes various functional applications and data processing by running the software programs and modules stored in the memory 1002. The memory 1002 may mainly include a program storage area and a data storage area. The program storage area may store the operating system, application programs required for at least one function (such as sound playback function, image playback function, etc.), etc.; the data storage area may store data created based on the use of the motor fault detection device, etc. In addition, the memory 1002 may include high-speed random access memory, and may also include non-volatile memory, such as at least one disk storage device, flash memory device, or other volatile solid-state storage device. Accordingly, the memory 1002 may also include a memory controller to provide the processor 1001 with access to the memory 1002.

[0176] The motor fault detection device also includes a power supply 1003 that supplies power to various components. Preferably, the power supply 1003 can be logically connected to the processor 1001 through a power management system, thereby enabling functions such as charging, discharging, and power consumption management through the power management system. The power supply 1003 may also include one or more DC or AC power supplies, recharging systems, power fault detection circuits, power converters or inverters, power status indicators, and other arbitrary components.

[0177] The motor fault detection device may also include an input unit 1004, which can be used to receive input digital or character information, and generate keyboard, mouse, joystick, optical or trackball signal inputs related to user settings and function control.

[0178] Although not shown, the motor fault detection device may also include a display unit, etc., which will not be described in detail here. Specifically, in this embodiment, the processor 1001 in the motor fault detection device loads the executable files corresponding to the processes of one or more application programs into the memory 1002 according to the following instructions, and the processor 1001 runs the application programs stored in the memory 1002, thereby implementing the steps in any of the motor fault detection methods provided in the embodiments of the present invention.

[0179] Furthermore, embodiments of the present invention also provide a drone, which includes a processor, a memory, and a motor fault detection program stored in the memory and executable on the processor. The processor executes the motor fault detection program to implement the steps in any of the motor fault detection methods provided in the embodiments of the present invention. In addition, the drone also includes other necessary components such as a fuselage, wings, and motors, which will not be described in detail here.

[0180] Furthermore, embodiments of the present invention provide a computer-readable storage medium, which may include: read-only memory (ROM), random access memory (RAM), a magnetic disk, or an optical disk, etc. The computer-readable storage medium stores a motor fault detection program, which, when executed by a processor, implements the steps of any of the motor fault detection methods provided in the embodiments of the present invention.

[0181] In the above embodiments, the descriptions of each embodiment have different focuses. For parts not described in detail in a certain embodiment, please refer to the detailed descriptions of other embodiments above, which will not be repeated here.

[0182] In practice, each of the above units or structures can be implemented as an independent entity or can be arbitrarily combined to be implemented as the same or several entities. For the specific implementation of each of the above units or structures, please refer to the previous method embodiments, which will not be repeated here.

[0183] For details on the implementation of each of the above operations, please refer to the previous examples, which will not be repeated here.

[0184] The above provides a detailed description of a motor fault detection method provided by the embodiments of this application. Specific examples have been used to illustrate the principles and implementation methods of the present invention. The description of the above embodiments is only for the purpose of helping to understand the method and core ideas of the present invention. At the same time, for those skilled in the art, there will be changes in the specific implementation methods and application scope based on the ideas of the present invention. Therefore, the content of this specification should not be construed as a limitation of the present invention.

Claims

1. A method of detecting a fault in an electric machine, characterized by, include: Based on the speed adjustment command received by the target motor in the target UAV, the target motor speed corresponding to the speed adjustment command is obtained; Obtain the trajectory tilt angle and airspeed of the target UAV; The motor fault detection result of the target motor is determined based on the target motor speed, the track inclination angle, and the airspeed; The motor fault detection method is applied to the extended state observer; The step of determining the motor fault detection result of the target motor based on the target motor speed, the flight path inclination angle, and the airspeed includes: Query the preset database to obtain the motor thrust corresponding to the target motor speed; The motor thrust, the flight path inclination angle, and the airspeed are input into the extended state observer, and the observed flight drag is output. The motor fault detection result of the target motor is determined based on the observed flight resistance.

2. The motor fault detection method of claim 1, wherein, Before inputting the motor thrust, the trajectory inclination angle, and the airspeed into the extended state observer and outputting the observed flight drag, the method further includes: Obtain the model information of the target drone; Query the preset database to obtain the observer intrinsic parameters corresponding to the model information; The observer's intrinsic parameters are set to the internal parameters of a preset initial observer to obtain the extended state observer.

3. The motor fault detection method of claim 1, wherein The determination of the target motor's motor fault detection result based on the observed flight drag includes: The observed flight resistance is compared with a preset flight resistance threshold. If the observed flight resistance is greater than the flight resistance threshold, then it is determined that the target motor has a motor fault.

4. The method of claim 3, wherein, Before comparing the observed flight drag with a preset flight drag threshold, the method further includes: Acquire the test track tilt angle, test airspeed, and test motor speed corresponding to the test speed adjustment command; Query the preset database to obtain the test motor thrust corresponding to the test motor speed; The test motor thrust, the test track inclination angle, and the test airspeed are input into the extended state observer, and the test flight drag is output. The flight resistance threshold is set based on the statistical characteristics of the test flight resistance data.

5. The method of claim 1, wherein, The determination of the target motor's motor fault detection result based on the observed flight drag includes: The flight drag coefficient of the target UAV is calculated based on air density, airspeed, and observed flight drag. The flight drag coefficient is compared with a preset flight drag coefficient threshold. If the flight drag coefficient is greater than the flight drag coefficient threshold, then the target motor is determined to have a motor fault.

6. The method of claim 1-5, wherein Before acquiring the flight path tilt angle and airspeed of the target UAV, the method further includes: Obtain the vertical velocity and ground speed of the target drone; The ratio of the vertical velocity to the ground velocity is set as the trajectory tilt angle of the target UAV.

7. An electric machine fault detection apparatus, characterized by, include: The rotation speed determination module is used to obtain the target motor rotation speed corresponding to the rotation speed adjustment command received by the target motor in the target UAV. The acquisition module is used to acquire the trajectory tilt angle and airspeed of the target UAV; The fault detection module is used to determine the motor fault detection result of the target motor based on the target motor speed, the track tilt angle, and the airspeed. The motor fault detection device is used in the extended state observer; The step of determining the motor fault detection result of the target motor based on the target motor speed, the flight path inclination angle, and the airspeed includes: Query the preset database to obtain the motor thrust corresponding to the target motor speed; The motor thrust, the flight path inclination angle, and the airspeed are input into the extended state observer, and the observed flight drag is output. The motor fault detection result of the target motor is determined based on the observed flight resistance.

8. A drone, characterized in that, The drone includes a processor, a memory, and a motor fault detection program stored in the memory and executable on the processor. The processor executes the motor fault detection program to implement the steps of the motor fault detection method according to any one of claims 1 to 6.

9. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a motor fault detection program, which is executed by a processor to implement the steps of the motor fault detection method according to any one of claims 1 to 6.

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