CN114556069A - State detection method, device and system for movable platform and movable platform - Google Patents

State detection method, device and system for movable platform and movable platform Download PDF

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Publication number
CN114556069A
CN114556069A CN202080070657.9A CN202080070657A CN114556069A CN 114556069 A CN114556069 A CN 114556069A CN 202080070657 A CN202080070657 A CN 202080070657A CN 114556069 A CN114556069 A CN 114556069A
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movable platform
state
response signal
key point
characteristic information
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赵阳
夏颖
张根垒
吴利鑫
龚鼎
何国俊
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SZ DJI Technology Co Ltd
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SZ DJI Technology Co Ltd
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    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01M—TESTING STATIC OR DYNAMIC BALANCE OF MACHINES OR STRUCTURES; TESTING OF STRUCTURES OR APPARATUS, NOT OTHERWISE PROVIDED FOR
    • G01M13/00—Testing of machine parts
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B64—AIRCRAFT; AVIATION; COSMONAUTICS
    • B64F—GROUND OR AIRCRAFT-CARRIER-DECK INSTALLATIONS SPECIALLY ADAPTED FOR USE IN CONNECTION WITH AIRCRAFT; DESIGNING, MANUFACTURING, ASSEMBLING, CLEANING, MAINTAINING OR REPAIRING AIRCRAFT, NOT OTHERWISE PROVIDED FOR; HANDLING, TRANSPORTING, TESTING OR INSPECTING AIRCRAFT COMPONENTS, NOT OTHERWISE PROVIDED FOR
    • B64F5/00—Designing, manufacturing, assembling, cleaning, maintaining or repairing aircraft, not otherwise provided for; Handling, transporting, testing or inspecting aircraft components, not otherwise provided for
    • B64F5/60—Testing or inspecting aircraft components or systems
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01H—MEASUREMENT OF MECHANICAL VIBRATIONS OR ULTRASONIC, SONIC OR INFRASONIC WAVES
    • G01H13/00—Measuring resonant frequency
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01M—TESTING STATIC OR DYNAMIC BALANCE OF MACHINES OR STRUCTURES; TESTING OF STRUCTURES OR APPARATUS, NOT OTHERWISE PROVIDED FOR
    • G01M7/00—Vibration-testing of structures; Shock-testing of structures
    • G01M7/02—Vibration-testing by means of a shake table
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01M—TESTING STATIC OR DYNAMIC BALANCE OF MACHINES OR STRUCTURES; TESTING OF STRUCTURES OR APPARATUS, NOT OTHERWISE PROVIDED FOR
    • G01M7/00—Vibration-testing of structures; Shock-testing of structures
    • G01M7/02—Vibration-testing by means of a shake table
    • G01M7/025—Measuring arrangements

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  • Physics & Mathematics (AREA)
  • General Physics & Mathematics (AREA)
  • Engineering & Computer Science (AREA)
  • Manufacturing & Machinery (AREA)
  • Transportation (AREA)
  • Aviation & Aerospace Engineering (AREA)
  • Testing Of Devices, Machine Parts, Or Other Structures Thereof (AREA)

Abstract

A movable platform state detection method, device and system, and a movable platform, acquiring a response signal (101) generated by a key point on the movable platform for a stimulus output by a stimulus source; acquiring characteristic information (102) of response signals of the key points; and detecting the state of the movable platform based on the characteristic information of the response signal of the key point (103).

Description

State detection method, device and system for movable platform and movable platform
Technical Field
The present disclosure relates to the field of movable platform technologies, and in particular, to a method, an apparatus, and a system for detecting a state of a movable platform, and a movable platform.
Background
Because the movable platform can carry various loads to execute different tasks, the movable platform is rapidly developed and widely applied in many fields. In the use process of the movable platform, abnormal conditions such as aging and damage of structural components inevitably occur, and in order to guarantee the safety of the movable platform, the state of the movable platform needs to be detected to determine whether the abnormal conditions exist. One detection method is to use a flaw detection device to detect an abnormality, however, the flaw detection device is expensive, resulting in a high cost for detecting the state of the movable platform.
Disclosure of Invention
The disclosure provides a method, a device and a system for detecting the state of a movable platform and the movable platform, which can reduce the cost of detecting the state of the movable platform.
In a first aspect, an embodiment of the present disclosure provides a method for detecting a state of a movable platform, where the method includes: acquiring a response signal generated by a key point on the movable platform for the excitation output by an excitation source; acquiring characteristic information of a response signal of the key point; and detecting the state of the movable platform based on the characteristic information of the response signal of the key point.
In a second aspect, an embodiment of the present disclosure provides a state detection apparatus for a movable platform, including a processor, configured to perform the following method: acquiring a response signal generated by a key point on the movable platform for the excitation output by an excitation source; acquiring characteristic information of a response signal of the key point; and detecting the state of the movable platform based on the characteristic information of the response signal of the key point.
In a third aspect, an embodiment of the present disclosure provides a system for detecting a state of a movable platform, where the system includes: a motor for outputting a stimulus; the inertial measurement unit is arranged near a key point of the movable platform and used for acquiring a response signal generated by the key point aiming at the excitation output by the excitation source; and a processor, communicatively connected with the inertial measurement unit, for performing the method according to any embodiment of the disclosure.
In a fourth aspect, embodiments of the present disclosure provide a movable platform, comprising: the state detection device according to any one of the embodiments of the present disclosure, or the state detection system according to any one of the embodiments of the present disclosure.
In a fifth aspect, embodiments of the present disclosure provide a computer-readable storage medium including instructions that, when executed on a computer, cause the computer to perform the method according to any one of the embodiments of the present disclosure.
The technical scheme provided by the embodiment of the disclosure can have the following beneficial effects:
in the embodiment of the disclosure, the excitation is output to the key point on the movable platform, the response signal generated by the key point for the excitation is obtained, and the state detection can be performed on the movable platform based on the characteristic information of the response signal only by obtaining the characteristic information of the response signal without using special flaw detection equipment, so that the detection cost of performing the state detection on the movable platform is reduced.
Drawings
Fig. 1 is a flowchart of a method of detecting a state of a movable platform according to an embodiment of the present disclosure.
Fig. 2 is a schematic diagram of a response signal of an embodiment of the disclosure.
Fig. 3 is a schematic diagram of a frequency spectrum of a response signal of an embodiment of the disclosure.
Fig. 4 is a schematic diagram of a resonance state detection process of an embodiment of the present disclosure.
Fig. 5 is a schematic diagram of a fatigue state detection process of an embodiment of the disclosure.
Fig. 6 is a schematic diagram of a mechanical condition detection process of an embodiment of the present disclosure.
Fig. 7 is a schematic diagram of a state detection device of an embodiment of the present disclosure.
Fig. 8 is a schematic diagram of a condition detection system of an embodiment of the present disclosure.
Detailed Description
Reference will now be made in detail to the exemplary embodiments, examples of which are illustrated in the accompanying drawings. When the following description refers to the accompanying drawings, like numbers in different drawings represent the same or similar elements unless otherwise indicated. The implementations described in the exemplary embodiments below are not intended to represent all implementations consistent with the present disclosure. Rather, they are merely examples of apparatus and methods consistent with certain aspects of the present disclosure, as detailed in the appended claims.
The terminology used in the disclosure is for the purpose of describing particular embodiments only and is not intended to be limiting of the disclosure. As used in this disclosure and the appended claims, the singular forms "a", "an", and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise. It should also be understood that the term "and/or" as used herein refers to and encompasses any and all possible combinations of one or more of the associated listed items.
It is to be understood that although the terms first, second, third, etc. may be used herein to describe various information, such information should not be limited to these terms. These terms are only used to distinguish one type of information from another. For example, first information may also be referred to as second information, and similarly, second information may also be referred to as first information, without departing from the scope of the present disclosure. The word "if" as used herein may be interpreted as "at … …" or "when … …" or "in response to a determination", depending on the context.
The movable platform can carry various loads to perform different tasks, and therefore, the movable platform is rapidly developed and widely applied to many fields. By taking the movable platform as the unmanned aerial vehicle as an example, the unmanned aerial vehicle has good stability, strong anti-interference capability, can hover actively and has relatively low requirements on take-off and landing conditions, and can carry imaging loads through the unmanned aerial vehicle to execute an image shooting task.
However, on the one hand, when the blades on the unmanned aerial vehicle rotate, vibration excitation related to the rotating speed frequency of the blades can be generated due to the production and installation deviation of the blades, and vibration excitation related to the rotating speed of the blades and the blade number of the blades can be generated due to the relative movement of the blades and air. Above-mentioned excitation belongs to alternating load, and the structure on the unmanned aerial vehicle easily produces the crackle under alternating load's effect, and then takes place fatigue damage. On the other hand, the rotating frequency of the paddle coincides with the natural modal frequency point of the unmanned aerial vehicle. A typical unmanned aerial vehicle's paddle rotational speed is 3000 at every minute and is changeed 5000 between changeing, if the coincidence appears in the natural mode frequency point that will avoid unmanned aerial vehicle and the rotational frequency of unmanned aerial vehicle paddle, and one mode is to improve the natural mode frequency point, makes the natural mode frequency point be higher than the highest rotational frequency of paddle. However, this approach can significantly increase the weight and cost of the drone. The other mode is to reduce the natural mode frequency point, so that the natural mode frequency point is lower than the lowest rotation frequency of the blade. However, this method may cause the natural mode frequency point to couple with the frequency band of the flight control of the drone, resulting in an abnormal flight control. Therefore, the coincidence between the rotating frequency of the paddle and the natural mode frequency point of the unmanned aerial vehicle is difficult to avoid, so that the rotating frequency of the paddle and the natural mode frequency point can resonate, and the structural part fails. Besides, the unmanned aerial vehicle can also have various flying conditions in the flying process, so that structural members on the unmanned aerial vehicle can be abraded or cracked in different degrees.
In various abnormal situations including the above situations such as fatigue damage and wear, it is generally difficult for users to directly perceive the partially fine and internal cracks through their senses, and the changes due to fatigue damage and wear cannot be quantitatively evaluated, which results in the risk of inaccurate problem exposure. In order to determine whether the unmanned aerial vehicle has the above abnormal situation, a common detection method is to perform abnormal detection by using a flaw detection device. Yet another way is to periodically return the drone to the factory for detection. However, the flaw detection equipment is expensive, the time cost (including the material circulation time, the detection time and the replacement time) and the maintenance cost caused by the mode of returning to the factory for detection are high, and the loss caused by the fact that the unmanned aerial vehicle cannot be used during the period of returning to the factory is also accompanied. To sum up, the detection cost of traditional unmanned aerial vehicle state detection mode is higher. It should be noted that, in the above embodiment, the unmanned aerial vehicle is taken as an example to exemplify the problem existing in the state detection process of the movable platform, and other types of movable platforms (for example, a movable robot, an unmanned vehicle, and an unmanned ship) may have abnormal situations such as fatigue damage and structural wear, and also have a problem of high state detection cost.
Based on the above, the present disclosure provides a method for detecting a state of a movable platform, so as to solve the long-standing problem that it is difficult to accurately detect the state of the movable platform and a load in actual use, thereby affecting the working effect of the load and the driving safety of the movable platform. As shown in fig. 1, the method includes:
step 101: acquiring a response signal generated by a key point on the movable platform for the excitation output by an excitation source;
step 102: acquiring characteristic information of a response signal of the key point;
step 103: and detecting the state of the movable platform based on the characteristic information of the response signal of the key point.
The movable platform of the embodiments of the present disclosure includes, but is not limited to, unmanned aerial vehicle, unmanned ship, movable robot, and the like, and the key points on the movable platform include, but are not limited to, at least one of a horn, a blade, a horn connector, a body center, a shock absorbing ball, a horn shaft, a motor base, and a load. The state detection of the movable platform in the embodiments of the present disclosure includes state detection of the movable platform itself, and state detection of a load (e.g., a camera) on the movable platform. The state of the load and the state of the movable platform can be detected synchronously, asynchronously or separately. The method of the embodiments of the present disclosure may be performed by an onboard processor on the movable platform, for example, by a flight Control Unit on the drone, or by an Electronic Control Unit (ECU) on the drone. Alternatively, the method may be performed by a cloud server or a terminal device (e.g., a dedicated remote controller or a smart terminal such as a mobile phone) in communication with the mobile platform.
In step 101, an excitation signal (abbreviated as excitation) may be output by an excitation source, and a response signal of one or more keypoints for the excitation is obtained. The excitation may be a signal of a particular form, e.g. a frequency sweep signal, a pulse signal, etc. The response signal may include, but is not limited to, at least one of an acceleration response signal, an angular velocity response signal, a displacement response signal, a stress response signal, a strain response signal. Unless otherwise specified, the response signal in the present disclosure refers to a response signal in a time domain, and the response signal in a frequency domain refers to a response signal in a frequency domain. The acceleration response signal and the angular velocity response signal may be obtained by an Inertial Measurement Unit (IMU) on the movable platform or by Measurement of an acceleration sensor and an angular velocity sensor, respectively, and the displacement response signal, the stress response signal, and the strain response signal may be obtained by acquisition of a displacement sensor, a stress sensor, and a strain sensor, respectively. Any one of the above-mentioned collecting devices for collecting the response signal may be a device carried by the movable platform itself, or may be a device additionally mounted on the movable platform.
The acquisition device may further perform at least one of filtering, amplifying, adjusting, analog differential processing, and analog-to-digital conversion processing on the response signal after acquiring the response signal. The response signals of a plurality of key points can be collected by one collecting device on the movable platform, or a multi-point collecting system can be formed by a plurality of collecting devices arranged on the movable platform, each collecting device in the multi-point collecting system can be respectively arranged near one key point, and the response signals of each key point can be collected by one or more collecting devices in the multi-point collecting system. The position and number of acquisition devices in a multi-point acquisition system can be determined based on the accuracy requirements of state detection, the size and weight limitations of the movable platform, and the like. When one acquisition device is close to the position of the key point, the reliability of the acquisition device for acquiring the response signal of the key point is higher. For a key point, the confidence of the acquisition device closer to the key point may be set to be higher, the confidence of the acquisition device farther from the key point may be set to be lower, and the response signals acquired by the acquisition devices are weighted and averaged based on the confidence to obtain a final response signal, thereby improving the reliability of the acquired response signals.
In steps 102 and 103, feature information of the response signal of the key point may be acquired, and the movable platform may be subjected to state detection based on the feature information of the response signal of the key point. For example, the state detection may be performed by a processor on the movable platform, or the characteristic information may be sent to the control terminal, so that the control terminal performs the state detection on the movable platform based on the characteristic information of the response signal of the key point. Wherein, the characteristic information of a response signal may include, but is not limited to, at least one of amplitude, frequency, phase, frequency spectrum, etc. of the response signal. The characteristic information of the response signal of the key point on the movable platform is related to the state of the movable platform, and when the state of the movable platform changes, the characteristic information of the response signal also changes correspondingly. For example, the response signal of a structure on the movable platform when the structure has a crack may have different characteristic information than the response signal of the structure when the structure has not been cracked. Accordingly, the state of the movable platform can be determined based on the characteristic information of the response signal of the key point.
In some embodiments, the feature information of the response signal of the key point may be compared with the feature information of the eigen model of the movable platform to obtain a comparison result, and based on the comparison result, the state detection may be performed on the movable platform. The eigen model is obtained by modeling in a Computer Aided Engineering (CAE) mode or the like, or by measurement. The mode is called model identification, and the state of the movable platform can be determined by comparing the characteristic information of the response signal of the key point with the characteristic information of the intrinsic model through the model identification, so that the mode is simple to realize, the detection efficiency is high, special detection equipment is not needed, and the detection cost is reduced. In addition, the detection mode can detect various structural components inside the movable platform, the problems that the internal damage of the movable platform is difficult to observe or the state of the movable platform with a large machine type is inconvenient to detect are solved, and the movable platform is suitable for movable platforms with various structural forms and various sizes and has a wide application range.
The excitation in the embodiments of the present disclosure may be output by a power system (e.g., a motor and/or a blade) on the movable platform, or may be output by an external device of the movable platform, and the manner of outputting the excitation may be determined based on the state to be detected. Wherein the condition to be detected may include, but is not limited to, at least one of a resonance condition, a fatigue condition, and a mechanical condition of the movable platform. The resonance state detection is used for determining whether the frequency of a power system providing power for the running of the movable platform and the natural modal frequency point of the movable platform resonate or not in the running process of the movable platform, for example, determining whether the rotating frequency of blades of the unmanned aerial vehicle and the natural modal frequency point of the unmanned aerial vehicle resonate or not. And the fatigue state detection is used for determining the fatigue damage degree of the structural member on the movable platform, so that the fatigue life of the movable platform is controlled. Mechanical condition detection is used to determine the mechanical condition of the structure on the movable platform, e.g. degree of wear, mechanical snapping forces, presence or absence of cracks, etc.
In the case of detecting a resonance state or a fatigue state, the power generated for driving the movable platform to travel may be used as the excitation, which may be generated by a motor on the movable platform. When the number of the motors is multiple, the control program can send out control commands to control the motors independently. For example, when the movable platform is an unmanned aerial vehicle, the motors of the three shafts of the roll shaft (roll), the course shaft (yaw) and the pitch shaft (pitch) can be respectively controlled through control instructions, so that the rotating speed of the corresponding blade is controlled, the control of the motion of the unmanned aerial vehicle is effectively realized, and the stability of the unmanned aerial vehicle is higher. The detection of the resonance state or the fatigue state may be performed during the traveling of the movable platform. Because the power generated by driving the movable platform to run is directly used as the excitation, no additional excitation needs to be input. The movable platform is the unmanned aerial vehicle, and the unmanned aerial vehicle is not restricted by the ground in the flying process, so that the detection of the resonance state and the fatigue state is carried out in the flying process of the unmanned aerial vehicle, the error caused by the ground restriction can be reduced, and the detection accuracy is improved.
When the resonance state is detected, the response signal is collected as shown in fig. 2. Amplitude information of the response signal may be determined as characteristic information of the response signal. For example, if the amplitude of the response signal is greater than a first preset value, it is determined that the resonance state of the movable platform is an abnormal state, i.e., resonance occurs. For another example, if the amplitude of the response signal is less than or equal to the first preset value, it is determined that the resonance state of the movable platform is a normal state. By detecting the resonance state, it is possible to determine whether the movable platform is in a dangerous state. The first preset value corresponding to the response signal of a key point may be determined based on the amplitude of the same key point in the eigen model, for example, K times the amplitude of the same key point in the eigen model, where K is a number greater than 1.
Further, when the resonance state is detected, the frequency spectrum of the response signal may also be determined as the characteristic information of the response signal. The frequency spectrum may be obtained by performing a Fast Fourier Transform (FFT) on the response signal, one specific form of which is shown in fig. 3. For example, if the frequency spectrum includes a point where the frequency is within the natural frequency range of the movable platform and the amplitude is greater than a second preset value, it is determined that the resonance state of the movable platform is an abnormal state. For another example, if the frequency spectrum does not include a point where the frequency is within the range of the natural frequency band of the movable platform and the amplitude is greater than a second preset value, it is determined that the resonance state of the movable platform is a normal state. If the frequency spectrum of the response signal comprises the inherent frequency range (namely the inherent modal frequency point) of the movable platform, the resonance danger is indicated, and at the moment, whether the resonance occurs can be determined by further judging the amplitude of the frequency spectrum. If the amplitude exceeds a second preset value, the resonance is generated, and if the amplitude is positive, the resonance is not generated. The second preset value corresponding to the response signal of one key point may be determined based on the amplitude of the spectrum of the same key point in the eigen model, for example, may be determined to be P times the amplitude of the spectrum of the same key point in the eigen model, where P is a number greater than 1.
As shown in fig. 4, it is a schematic diagram of a resonance state detection process, and in this embodiment, the movable platform is an unmanned aerial vehicle as an example for description. In step 401, an abnormal protection mode of the drone may be set through an Application (APP) on a server or a control terminal in communication with the drone, for example, when a slight abnormal situation is detected, the drone is controlled to continue flying; when a serious abnormal condition is detected, the unmanned aerial vehicle is controlled to return. The resonant state detection process can also be triggered and started through APP. In step 402, response signals (e.g., acceleration responses 403) generated by key points on the drone with respect to the excitation signals may be acquired by a data acquisition device such as an IMU. In step 404, if the magnitude A of the acceleration response is greater than the maximum allowable magnitude AmaxThen step 410 is performed to record data information including the magnitude a of the acceleration response, the magnitude a exceeding the maximum magnitude amaxOne or more of the information of the ratio of (b), the state detection result (e.g., whether the state is abnormal, the degree of abnormality, etc.), the current time, etc. After step 410 is performed, step 402 may also be returned to, so that status detection continues.
If the amplitude A of the acceleration response is less than or equal to the maximum amplitude A allowedmaxThen, step 405 is executed to perform FFT on the acceleration response to obtain the frequency spectrum of the acceleration response. In step 406, it is determined whether the frequency of the frequency spectrum of the acceleration response matches the natural mode frequency point of the drone (i.e., whether there is an overlap between the two). If there is a match, indicating that there is an overlap between the two, it is further determined in step 407 whether the magnitude of the spectrum of the acceleration response matches the magnitude of the eigen-model of the drone. If matched, i.e. frequency spectrum of acceleration responseThe amplitude of the signal is far larger than that of the intrinsic model, resonance is considered to occur, and therefore the state of the unmanned aerial vehicle is judged to be abnormal. When the amplitude or frequency of the frequency spectrum of the acceleration response does not match the eigenmode, step 410 may be performed to record data information, including one or more information of the amplitude of the frequency spectrum, the amplitude and frequency of the frequency spectrum, the amplitude of the eigenmode and the frequency point of the eigenmode, and the like. Under the condition that the posture of the unmanned aerial vehicle is abnormal, step 408 can be executed, an alarm is given through the APP, and step 409 is executed, and the unmanned aerial vehicle is subjected to abnormal protection based on the abnormal protection mode.
In the case of detecting a fatigue state, amplitude information and/or phase information of the response signal may be used as characteristic information of the response signal. The fatigue damage of a key point can be determined based on the characteristic information of the response signal of the key point, and the state of the movable platform can be detected based on the fatigue damage of the key point. The fatigue damage of a key point in a certain time period can be counted by a rain flow counting method. The fatigue damage of a plurality of key points on the movable platform can be respectively obtained, the total fatigue damage of the movable platform is determined based on the fatigue damage of the key points, and the fatigue state of the movable platform is detected based on the total fatigue damage of the movable platform. And if the total fatigue damage is larger than a third preset value, judging that the fatigue state of the movable platform is an abnormal state. And if the total fatigue damage is less than or equal to the third preset value, judging that the fatigue state of the movable platform is a normal state.
Alternatively, the fatigue state of the movable platform may be determined based on the fatigue damage at each of the critical points, respectively. For example, when there is a critical point where the fatigue damage exceeds a preset value, the fatigue state of the movable platform is determined to be an abnormal state, and when there is no critical point where the fatigue damage exceeds a preset value, the fatigue state of the movable platform is determined to be a normal state. Suppose the fatigue damage of the ith key point in the jth time period is dijThe preset value may be 1/s, where s is a safety margin, that is, when the fatigue damage of the ith key point meets the following condition, it is determined that the fatigue state of the movable platform is normalThe state is as follows:
Di*s＜1。
wherein D isi＝∑jdij。
Fig. 5 is a schematic diagram of the fatigue state detection process. In step 501, an abnormal protection mode of the drone may be set through an APP on a server or control terminal in communication with the drone. In step 502, data acquisition may be performed by an IMU or the like to obtain an acceleration response 503, and in step 505, data statistics is performed by a rainflow counting method by combining the acceleration response 503 and an eigen model 504 of the movable platform, that is, fatigue damage of one or more key points on the movable platform is counted. In step 506, a total fatigue damage D of the movable platform is calculated based on the counted fatigue damages of the respective key points, and in step 507, a fatigue state of the movable platform is determined based on the total fatigue damage D. If the fatigue state is normal, that is, the total fatigue damage D does not exceed the fatigue damage threshold (i.e., the third preset value), step 508 is executed to record data information, including the fatigue damage of each key point, the total fatigue damage, the current time, and the like. If the fatigue state is abnormal, that is, the total fatigue damage D exceeds the fatigue damage threshold, step 509 and step 510 are executed, and the drone protection is performed through APP alarm and based on a preset abnormal protection mode.
The above embodiment collects the response signal at the designated key point, uses the rain flow counting method in combination with the time domain data information to count the fatigue damage at the key point according to the eigen model of the movable platform, and determines the frequency and amplitude of the characteristic point (e.g., the extreme point) in the frequency domain response signal according to the time domain information and using the fast fourier transform to determine whether the movable platform is in a dangerous state. The obtained fatigue damage information and the dangerous state information can be provided for the user through the interactive module. In addition, the user can also assist in judging whether maintenance is needed or the structural part needs to be replaced by himself or herself according to the state of the key point, and the problem can be more accurately located and help is provided for liability determination by using the result recorded by the method after sale.
Using unmanned aerial vehicle as an example, unmanned aerial vehicle's paddle is under the high-speed rotatory operating mode of self, because the deviation of production and installation and aerodynamic's coupling, can produce with the reciprocal excitation that paddle rotational speed and paddle number are relevant, and each horn root of unmanned aerial vehicle is comparatively abominable because arm of force reason atress condition, has great fatigue failure risk. Vibration data (being the above-mentioned response signal) that gather through IMU combines unmanned aerial vehicle self eigen-model, can the backstepping obtain the concrete atress condition of each horn root, utilizes the rain flow counting method can obtain the fatigue damage condition of each horn root, also can obtain the estimation result of the fatigue state of other key points of unmanned aerial vehicle in the same way, has improved unmanned aerial vehicle's reliability.
Unmanned aerial vehicle need satisfy various flight operating modes, leads to paddle rotational speed distribution range broad usually, and then the frequency band scope of the vibration excitation that leads to by the paddle is also broad, overlaps with certain natural frequency of unmanned aerial vehicle takes place easily to arouse resonance and lead to the flight accident. According to the time domain data information (namely the amplitude of the time domain response signal) acquired by the IMU and the frequency and amplitude of the frequency domain response signal determined by the FFT, whether the unmanned aerial vehicle is in a dangerous state or not can be judged by combining the natural frequency of the intrinsic model of the unmanned aerial vehicle, if the dangerous state occurs, corresponding operation is executed according to a dangerous state protection strategy, and the reliability of the unmanned aerial vehicle is improved.
In the case of detecting a mechanical state, the excitation may be output by a motor and/or a blade or an external excitation source on the movable platform as the excitation source, wherein the motor and/or the blade or the external excitation source may output the excitation in response to the received input signal in the hovering state of the movable platform. For example, for an unmanned aerial vehicle, if mechanical state detection is triggered, a frequency sweep signal can be injected into motor control signals of the unmanned aerial vehicle and a load, and effective excitation input is implemented on the basis of not borrowing other additional equipment by utilizing imbalance of blades and various shaft arms.
Wherein the input signal may be generated in case a preset condition is satisfied. The preset condition may be that a time interval between the current time and the last time of performing the state detection on the movable platform reaches a preset duration, or may be that a detection trigger instruction for performing the state detection on the movable platform is received, or the movable platform executes a specified task (for example, a shooting task), or may be other conditions, which is not limited by the present disclosure.
At the time of detecting the mechanical state, at least one of amplitude information and phase information of the response signal may be used as characteristic information of the response signal. The feature information of the response signal of the key point may be compared with the feature information of the eigen model of the movable platform, and the state detection of the movable platform may be performed based on a difference between the feature information of the response signal of the key point and the feature information of the same key point in the eigen model. For example, for a certain key point, a magnitude spectrum curve of a response signal of the key point may be obtained, an extreme point in the magnitude spectrum curve may be determined, then a difference between the extreme point and a position of the extreme point in the magnitude spectrum curve of the key point in the eigen model and a difference between values of the extreme points in the two curves may be determined, and the state of the movable platform may be detected according to at least one of the two differences. For another example, a phase spectrum curve of the response signal of the key point may be obtained, an extreme point in the phase spectrum curve may be determined, then a difference between the position of the extreme point and the extreme point in the phase spectrum curve of the key point in the eigen model and a difference between values of the extreme points in the two curves may be determined, and the movable platform may be subjected to state detection according to at least one of the two differences.
In some embodiments, if the difference is smaller than a fourth predetermined value, it is determined that the mechanical state of the movable platform is a normal state. In some embodiments, if the difference is greater than the fourth predetermined value, it is determined that the mechanical state of the movable platform is an abnormal state.
Fig. 6 is a schematic diagram of the mechanical state detection process. In step 601, a mechanical state detection process may be triggered through the APP, and in step 602, a decision control module on the drone outputs a decision control instruction for controlling the motion execution module to output excitation in step 603. In step 604, the drone or a load on the drone may generate a response signal for the excitation. In steps 605 and 606, the response signal may be acquired by an IMU or the like, and the response signal may be subjected to data processing such as filtering and amplification. In step 607, it can be determined whether there is a characteristic point anomaly, such as an amplitude anomaly or a phase anomaly of an extreme point, at each key point based on the processed response signal and the eigen model of the movable platform. If the characteristic point is abnormal, an alarm prompt is output in step 609, and if the characteristic point is not abnormal, in step 608, the state detection result is fed back to the unmanned aerial vehicle and the user through the APP. And the unmanned aerial vehicle can be subjected to abnormal protection based on the state detection result. The response signal processed in step 606 can also be fed back to step 603, so as to adjust the amplitude or change rate of the excitation signal according to the response signal.
In the embodiment, the mechanical state detection is completed by combining the active control excitation source with the collected response signal, model identification and calculation processing, so that intrinsic models describing the mechanical states of the movable platform and the load on the movable platform can be obtained respectively, and the mechanical states of the movable platform and the load are judged by utilizing the difference between the characteristic information of the intrinsic models, so that whether the mechanical structure is damaged or not and whether the abrasion degree influences the safe use or not are evaluated. If the difference exceeds the allowable range, a replacement, maintenance or danger prompt is triggered. In addition, the user can also assist according to the testing result of unmanned aerial vehicle and load and judge whether need maintain or change by oneself, can utilize testing result more accurately location problem after sale to provide help to deciding to blame.
In some embodiments, at least one of the first preset value, the second preset value, the third preset value, the fourth preset value and the frequency of the feature point in the response signal may be further modified based on the feature information of the response signal of the key point. For example, the feature information of the response signals of the key points obtained multiple times may be averaged, and any one or more preset values may be modified according to the average value.
In some embodiments, the driving state of the movable platform may be controlled based on the state detection result to improve safety during driving of the movable platform. The driving state may include at least one of a speed, a position, and an attitude. For example, a safety margin for the movable platform may be obtained; and controlling the driving state of the movable platform based on the safety margin under the condition that the state detection result indicates that the movable platform is in an abnormal state. The safety margin is used for representing the requirement on safety in the driving process of the movable platform, and the larger the safety margin is, the higher the requirement on safety is. The safety margin may be expressed in numerical values, e.g., a numerical value of "1" for a looser safety margin, a numerical value of "2" for a standard safety margin, a numerical value of "3" for a strict safety margin, etc.
When the safety margin is a first margin, the movable platform can be controlled to continue to run until a running state control instruction is received; when the safety margin is a second margin, controlling the movable platform to stop running; when the safety margin is a third margin, controlling the movable platform to return to a specified place; wherein the first margin is less than the second margin, which is less than the third margin. For example, the first, second, and third margins may be 1, 2, and 3, respectively. Of course, the above description is only an exemplary illustration, the representation and number of the safety margins and the execution logic under each safety margin are not limited thereto, and the safety margins and the execution logic under each safety margin may be set according to the actual application scenario and the user requirement.
For example, in the case that the movable platform is an unmanned aerial vehicle, when the safety margin is 1, the unmanned aerial vehicle may be controlled to continue flying until a new specific instruction given by the interaction module is received; when the safety margin is 2, controlling the unmanned aerial vehicle to immediately switch to a hovering mode and waiting for the interaction module to give a new specific instruction; when the safety margin is 3, controlling the unmanned aerial vehicle to automatically fine-tune the flight speed according to the current flight state and waiting for the interaction module to give a new specific instruction; and when the safety margin is 4, controlling the unmanned aerial vehicle to return to the home immediately.
After the movable platform is subjected to state detection, the state detection result can be sent to the control terminal, and the control terminal can display the detection result or output prompt information based on the detection result. The state detection result may also be stored, for example, directly in a storage unit on the mobile platform, or stored in a cloud server or a control terminal in communication with the mobile platform. The control unit on the movable platform can also output prompt information directly based on the state detection result. The prompt message may include, but is not limited to, at least any of: the information used for representing the current state of the movable platform is a normal state or an abnormal state, the information used for representing the degree of abnormality (for example, attention is needed, replacement is needed, danger is needed, and the like), the identification information of an abnormal key point, the current time, the time of last state detection, and the like.
The embodiment of the present disclosure further provides a state detection apparatus for a movable platform, including a processor, where the processor is configured to execute the following method:
acquiring a response signal generated by a key point on the movable platform for the excitation output by an excitation source;
acquiring characteristic information of a response signal of the key point;
and detecting the state of the movable platform based on the characteristic information of the response signal of the key point.
In some embodiments, the state detection comprises resonance state detection.
In some embodiments, the characteristic information of the response signal includes an amplitude of the response signal; the processor is configured to: if the amplitude of the response signal is larger than a first preset value, judging that the resonance state of the movable platform is an abnormal state; and/or if the amplitude of the response signal is smaller than or equal to the first preset value, determining that the resonance state of the movable platform is a normal state.
In some embodiments, the characteristic information of the response signal comprises a frequency spectrum of the response signal; the processor is configured to: if the frequency spectrum comprises a point of which the frequency is within the range of the natural frequency band of the movable platform and the amplitude is greater than a second preset value, judging that the resonance state of the movable platform is an abnormal state; and if the frequency spectrum does not include a point of which the frequency is within the range of the natural frequency band of the movable platform and the amplitude is greater than a second preset value, judging that the resonance state of the movable platform is a normal state.
In some embodiments, the state detection comprises fatigue state detection.
In some embodiments, the processor is configured to: determining fatigue damage of the key points based on the characteristic information of the response signals of the key points; and detecting the state of the movable platform based on the fatigue damage of the key points.
In some embodiments, the number of keypoints is greater than 1; the processor is configured to: determining a total fatigue damage of the movable platform based on the fatigue damage of the plurality of key points; and detecting the state of the movable platform based on the total fatigue damage of the movable platform.
In some embodiments, the processor is configured to: if the total fatigue damage is larger than a third preset value, judging that the fatigue state of the movable platform is an abnormal state; and/or if the total fatigue damage is less than or equal to the third preset value, judging that the fatigue state of the movable platform is a normal state.
In some embodiments, the state detection comprises at least one of resonance state detection and fatigue detection; the excitation source is a motor on the movable platform; the excitation output by the excitation source is power generated by driving the movable platform to run.
In some embodiments, the condition detection comprises mechanical condition detection.
In some embodiments, the processor is configured to: comparing feature information of the response signals of the key points with feature information of the key points in an eigen model of the movable platform; and detecting the state of the movable platform based on the difference between the characteristic information of the response signal of the key point and the characteristic information of the key point in the intrinsic model of the movable platform.
In some embodiments, the processor is configured to: if the difference is smaller than a fourth preset value, judging that the mechanical state of the movable platform is a normal state; and/or if the difference is larger than the fourth preset value, judging that the mechanical state of the movable platform is an abnormal state.
In some embodiments, the characteristic information comprises amplitude information and/or phase information of the response signal.
In some embodiments, the processor is further configured to: and correcting at least one of the first preset value, the second preset value, the third preset value, the fourth preset value and the frequency of the characteristic point in the response signal based on the characteristic information of the response signal of the key point.
In some embodiments, the excitation source is a motor and/or a blade on the movable platform or an external excitation source; the stimulus source outputs a stimulus in response to the received input signal in the movable platform hover state.
In some embodiments, the input signal is generated if a preset condition is satisfied.
In some embodiments, the preset condition comprises at least any one of: and when the time interval between the current time and the last time of carrying out state detection on the movable platform reaches the preset time length, receiving a detection trigger instruction of carrying out state detection on the movable platform, and executing the specified task by the movable platform.
In some embodiments, the processor is configured to: comparing the characteristic information of the response signal of the key point with the characteristic information of the intrinsic model of the movable platform to obtain a comparison result; and detecting the state of the movable platform based on the comparison result.
In some embodiments, the eigenmodel is obtained by modeling, or by measurement.
In some embodiments, the excitation is a swept frequency signal or a pulsed signal.
In some embodiments, the apparatus further comprises: and controlling the driving state of the movable platform based on the state detection result.
In some embodiments, the processor is configured to: acquiring a safety margin of the movable platform; and controlling the driving state of the movable platform based on the safety margin under the condition that the state detection result indicates that the movable platform is in an abnormal state.
In some embodiments, the processor is configured to: when the safety margin is a first margin, controlling the movable platform to continue to run until a running state control instruction is received; when the safety margin is a second margin, controlling the movable platform to stop running; when the safety margin is a third margin, controlling the movable platform to return to a specified place; wherein the first margin is less than the second margin, which is less than the third margin.
In some embodiments, the response signal comprises at least one of an acceleration response signal, an angular velocity response signal, a displacement response signal, a stress response signal, a strain response signal.
In some embodiments, the response signal is acquired by at least one of an inertial measurement unit, an acceleration sensor, an angular velocity sensor, a displacement sensor, a stress sensor, and a strain sensor on the movable platform.
In some embodiments, the processor is further configured to: sending the state detection result to a control terminal; and/or storing the state detection result; and/or outputting prompt information based on the state detection result.
In some embodiments, the processor is configured to: and sending the information of the characteristic points to a control terminal so that the control terminal can detect the state of the movable platform based on the characteristic information of the response signals of the key points.
In some embodiments, the keypoints comprise at least any one of: the aircraft comprises an aircraft arm, a paddle, an aircraft arm connecting piece, an aircraft body middle frame, a damping ball, an aircraft arm rotating shaft, a motor base and a load.
Fig. 7 is a schematic diagram illustrating a hardware structure of a more specific state detection apparatus provided in an embodiment of the present specification, where the apparatus may include: a processor 701, a memory 702, an input/output interface 703, a communication interface 704, and a bus 705. Wherein the processor 701, the memory 702, the input/output interface 703 and the communication interface 704 are communicatively connected to each other within the device via a bus 705. When the clock synchronization apparatus is used to execute the method applied to the first subsystem, the processor 701 is a first processor, and the communication interface 704 is a first communication interface. When the clock synchronization apparatus is used to execute the method applied to the second subsystem, the processor 701 is a second processor, and the communication interface 704 is a second communication interface.
The processor 701 may be implemented by a general-purpose CPU (Central Processing Unit), a microprocessor, an Application Specific Integrated Circuit (ASIC), or one or more Integrated circuits, and is configured to execute related programs to implement the technical solutions provided in the embodiments of the present specification.
The Memory 702 may be implemented in the form of a ROM (Read Only Memory), a RAM (Random Access Memory), a static storage device, a dynamic storage device, or the like. The memory 702 may store an operating system and other application programs, and when the technical solution provided by the embodiments of the present specification is implemented by software or firmware, the relevant program codes are stored in the memory 702 and called to be executed by the processor 701.
The input/output interface 703 is used for connecting an input/output module to realize information input and output. The i/o module may be configured as a component in a device (not shown) or may be external to the device to provide a corresponding function. The input devices may include a keyboard, a mouse, a touch screen, a microphone, various sensors, etc., and the output devices may include a display, a speaker, a vibrator, an indicator light, etc.
The communication interface 704 is used for connecting a communication module (not shown in the figure) to realize communication interaction between the device and other devices. The communication module can realize communication in a wired mode (such as USB, network cable and the like) and also can realize communication in a wireless mode (such as mobile network, WIFI, Bluetooth and the like).
Bus 705 includes a pathway for communicating information between various components of the device, such as processor 701, memory 702, input/output interface 703, and communication interface 704.
It should be noted that although the above-mentioned device only shows the processor 701, the memory 702, the input/output interface 703, the communication interface 704 and the bus 705, in a specific implementation, the device may also include other components necessary for normal operation. In addition, those skilled in the art will appreciate that the above-described apparatus may also include only those components necessary to implement the embodiments of the present description, and not necessarily all of the components shown in the figures.
As shown in fig. 8, an embodiment of the present disclosure further provides a status detection system, where the system includes:
a motor 801 for outputting excitation;
the inertial measurement unit 802 is arranged near a key point of the movable platform and is used for acquiring a response signal generated by the key point aiming at the excitation output by the excitation source; and
a processor 803, communicatively coupled to the inertial measurement unit, for performing the method according to any of the embodiments of the present disclosure.
In some embodiments, the system further comprises: a control terminal 804 for performing at least any one of the following operations: triggering the motor to output excitation; receiving the detection result output by the processor 803; displaying and/or storing the detection result output by the processor 803; controlling a driving state of the movable platform based on the detection result; prompt information is displayed based on the detection result output by the processor 803.
The control terminal 804 may communicate with the processor 803, and the control terminal 804 may include an interaction module (e.g., a touch screen, a button), so that a user may send a detection result downloading instruction to the processor 803 through the touch screen and receive a detection result sent by the processor 803. Alternatively, the processor 803 may also actively push the detection result to the control terminal 804 after acquiring the detection result. The control terminal 804 may further include a display module for displaying the detection result. The control terminal 804 may further include a storage module for storing the detection result. The control terminal 804 may also communicate with the movable platform to control the traveling state (e.g., speed, pose, etc.) of the movable platform based on the detection result. The control terminal 804 may also output prompt information based on the detection result, for example, output voice prompt information through a voice playing module, or output text prompt information through a display screen. The prompt information may be used to prompt a user of the current state of the movable platform.
The embodiment of the present disclosure further provides a movable platform, including the state detection device of the movable platform of any embodiment of the present disclosure, or including the state detection system of the movable platform of any embodiment of the present disclosure.
The embodiments of the present disclosure also provide a computer-readable storage medium, which includes instructions that, when executed on a computer, cause the computer to perform the method of any of the preceding embodiments.
Computer-readable media, including both non-transitory and non-transitory, removable and non-removable media, may implement information storage by any method or technology. The information may be computer readable instructions, data structures, modules of a program, or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), Static Random Access Memory (SRAM), Dynamic Random Access Memory (DRAM), other types of Random Access Memory (RAM), Read Only Memory (ROM), Electrically Erasable Programmable Read Only Memory (EEPROM), flash memory or other memory technology, compact disc read only memory (CD-ROM), Digital Versatile Discs (DVD) or other optical storage, magnetic cassettes, magnetic tape magnetic disk storage or other magnetic storage devices, or any other non-transmission medium that can be used to store information that can be accessed by a computing device. As defined herein, a computer readable medium does not include a transitory computer readable medium such as a modulated data signal and a carrier wave.
From the above description of the embodiments, it is clear to those skilled in the art that the embodiments of the present disclosure can be implemented by software plus necessary general hardware platform. Based on such understanding, the technical solutions of the embodiments of the present specification may be essentially or partially implemented in the form of a software product, which may be stored in a storage medium, such as a ROM/RAM, a magnetic disk, an optical disk, etc., and includes several instructions for enabling a computer device (which may be a personal computer, a server, or a network device, etc.) to execute the methods described in the embodiments or some parts of the embodiments of the present specification.
The systems, devices, modules or units illustrated in the above embodiments may be implemented by a computer chip or an entity, or by a product with certain functions. A typical implementation device is a computer, which may take the form of a personal computer, laptop computer, cellular telephone, camera phone, smart phone, personal digital assistant, media player, navigation device, email messaging device, game console, tablet computer, wearable device, or a combination of any of these devices.
Any combination of the various features in the above embodiments may be combined as long as there is no conflict or contradiction between the combinations of the features, but the combination is not limited by space and is not described one by one, so that any combination of the various features in the above embodiments also falls within the scope of the present disclosure.

Claims (60)

1. A method of detecting a condition of a movable platform, the method comprising:
acquiring a response signal generated by a key point on the movable platform for the excitation output by an excitation source;
acquiring characteristic information of a response signal of the key point;
and detecting the state of the movable platform based on the characteristic information of the response signal of the key point.
2. The method of claim 1, wherein the state detection comprises resonance state detection.
3. The method of claim 2, wherein the characteristic information of the response signal includes an amplitude of the response signal; the detecting the state of the movable platform based on the characteristic information of the response signal of the key point comprises the following steps:
if the amplitude of the response signal is larger than a first preset value, the resonance state of the movable platform is judged to be an abnormal state; and/or
And if the amplitude of the response signal is less than or equal to the first preset value, judging that the resonance state of the movable platform is a normal state.
4. The method of claim 2, wherein the characteristic information of the response signal includes a frequency spectrum of the response signal; the state detection of the movable platform based on the characteristic information of the response signal of the key point comprises the following steps:
if the frequency spectrum comprises a point of which the frequency is within the range of the natural frequency band of the movable platform and the amplitude is greater than a second preset value, judging that the resonance state of the movable platform is an abnormal state;
and if the frequency spectrum does not include a point of which the frequency is within the range of the natural frequency band of the movable platform and the amplitude is greater than a second preset value, judging that the resonance state of the movable platform is a normal state.
5. The method of claim 1, wherein the state detection comprises fatigue state detection.
6. The method of claim 5, wherein the performing the state detection on the movable platform based on the feature information of the response signal of the key point comprises:
determining fatigue damage of the key points based on characteristic information of response signals of the key points;
and detecting the state of the movable platform based on the fatigue damage of the key points.
7. The method of claim 6, wherein the number of keypoints is greater than 1; the detecting the state of the movable platform based on the fatigue damage of the key points comprises:
determining a total fatigue damage of the movable platform based on the fatigue damage of the plurality of key points;
and detecting the state of the movable platform based on the total fatigue damage of the movable platform.
8. The method of claim 7, wherein the performing state detection for the movable platform based on the total fatigue damage of the movable platform comprises:
if the total fatigue damage is larger than a third preset value, judging that the fatigue state of the movable platform is an abnormal state; and/or
And if the total fatigue damage is less than or equal to the third preset value, judging that the fatigue state of the movable platform is a normal state.
9. The method of claim 1, wherein the condition detection comprises at least one of resonance condition detection and fatigue detection; the excitation source is a motor on the movable platform; the excitation output by the excitation source is power generated by driving the movable platform to run.
10. The method of claim 1, wherein the condition detection comprises mechanical condition detection.
11. The method of claim 10, wherein the performing the state detection on the movable platform based on the feature information of the response signal of the key point comprises:
comparing feature information of the response signals of the key points with feature information of the key points in an eigen model of the movable platform;
and detecting the state of the movable platform based on the difference between the characteristic information of the response signal of the key point and the characteristic information of the key point in the intrinsic model of the movable platform.
12. The method of claim 11, wherein the performing state detection on the movable platform based on the difference between the feature information of the response signal of the key point and the feature information of the key point in the eigen model of the movable platform comprises:
if the difference is smaller than a fourth preset value, judging that the mechanical state of the movable platform is a normal state; and/or
And if the difference is larger than the fourth preset value, judging that the mechanical state of the movable platform is an abnormal state.
13. The method of claim 11, wherein the characteristic information comprises amplitude information and/or phase information of the response signal.
14. The method of claim 11, further comprising:
and correcting the fourth preset value based on the characteristic information of the response signal of the key point.
15. The method of claim 10, wherein the excitation source is a motor and/or paddle on the movable platform or an external excitation source; the stimulus source outputs a stimulus in response to the received input signal in the movable platform hover state.
16. The method of claim 15, wherein the input signal is generated if a preset condition is met.
17. The method of claim 16, wherein the preset condition comprises at least any one of:
the time interval between the current time and the last time of the state detection of the movable platform reaches a preset time length,
receiving a detection trigger instruction for detecting the state of the movable platform,
the movable platform performs specified tasks.
18. The method of claim 1, wherein the performing the state detection on the movable platform based on the feature information of the response signal of the key point comprises:
comparing the characteristic information of the response signal of the key point with the characteristic information of the intrinsic model of the movable platform to obtain a comparison result;
and detecting the state of the movable platform based on the comparison result.
19. The method of claim 18, wherein the eigenmodel is obtained by modeling or by measurement.
20. The method of claim 1, wherein the excitation is a swept frequency signal or a pulsed signal.
21. The method of claim 1, further comprising:
and controlling the driving state of the movable platform based on the state detection result.
22. The method of claim 21, wherein controlling the travel state of the movable platform based on the state detection result comprises:
acquiring a safety margin of the movable platform;
and controlling the driving state of the movable platform based on the safety margin under the condition that the state detection result indicates that the movable platform is in an abnormal state.
23. The method of claim 22, wherein the controlling the driving state of the movable platform based on the safety margin in the case that the state detection result indicates that the movable platform is in an abnormal state comprises:
when the safety margin is a first margin, controlling the movable platform to continue to run until a running state control instruction is received;
when the safety margin is a second margin, controlling the movable platform to stop running;
when the safety margin is a third margin, controlling the movable platform to return to a specified place;
wherein the first margin is less than the second margin, which is less than the third margin.
24. The method of claim 1, wherein the response signal comprises at least one of an acceleration response signal, an angular velocity response signal, a displacement response signal, a stress response signal, a strain response signal.
25. The method of claim 24, wherein the response signal is acquired by at least one of an inertial measurement unit, an acceleration sensor, an angular velocity sensor, a displacement sensor, a stress sensor, and a strain sensor on the movable platform.
26. The method of claim 1, wherein after performing the state detection of the movable platform based on the feature information of the response signal of the key point, the method further comprises:
sending the state detection result to a control terminal; and/or
Storing the state detection result; and/or
And outputting prompt information based on the state detection result.
27. The method of claim 1, wherein the performing the state detection on the movable platform based on the feature information of the response signal of the key point comprises:
and sending the information of the characteristic points to a control terminal so that the control terminal can detect the state of the movable platform based on the characteristic information of the response signals of the key points.
28. The method of claim 1, wherein the keypoints comprise at least any one of: the aircraft comprises an aircraft arm, a paddle, an aircraft arm connecting piece, an aircraft body middle frame, a damping ball, an aircraft arm rotating shaft, a motor base and a load.
29. A movable platform condition detection apparatus comprising a processor, wherein the processor is configured to perform the following method:
acquiring a response signal generated by a key point on the movable platform for the excitation output by an excitation source;
acquiring characteristic information of the response signal of the key point;
and detecting the state of the movable platform based on the characteristic information of the response signal of the key point.
30. The apparatus of claim 29, wherein the state detection comprises resonance state detection.
31. The apparatus of claim 30, wherein the characteristic information of the response signal comprises an amplitude of the response signal; the processor is configured to:
if the amplitude of the response signal is larger than a first preset value, the resonance state of the movable platform is judged to be an abnormal state; and/or
And if the amplitude of the response signal is smaller than or equal to the first preset value, judging that the resonance state of the movable platform is a normal state.
32. The apparatus of claim 30, wherein the characteristic information of the response signal comprises a frequency spectrum of the response signal; the processor is configured to:
if the frequency spectrum comprises a point of which the frequency is within the range of the natural frequency band of the movable platform and the amplitude is greater than a second preset value, judging that the resonance state of the movable platform is an abnormal state;
and if the frequency spectrum does not include a point of which the frequency is within the range of the natural frequency band of the movable platform and the amplitude is greater than a second preset value, judging that the resonance state of the movable platform is a normal state.
33. The apparatus of claim 29, wherein the state detection comprises fatigue state detection.
34. The apparatus of claim 33, wherein the processor is configured to:
determining fatigue damage of the key points based on characteristic information of response signals of the key points;
and detecting the state of the movable platform based on the fatigue damage of the key points.
35. The apparatus of claim 34, wherein the number of keypoints is greater than 1; the processor is configured to:
determining a total fatigue damage of the movable platform based on the fatigue damage of the plurality of key points;
and detecting the state of the movable platform based on the total fatigue damage of the movable platform.
36. The apparatus of claim 35, wherein the processor is configured to:
if the total fatigue damage is larger than a third preset value, judging that the fatigue state of the movable platform is an abnormal state; and/or
And if the total fatigue damage is less than or equal to the third preset value, judging that the fatigue state of the movable platform is a normal state.
37. The apparatus of claim 29, wherein the condition detection comprises at least one of resonance condition detection and fatigue detection; the excitation source is a motor on the movable platform; the excitation output by the excitation source is power generated by driving the movable platform to run.
38. The apparatus of claim 29, wherein the condition detection comprises mechanical condition detection.
39. The apparatus of claim 38, wherein the processor is configured to:
comparing feature information of the response signals of the key points with feature information of the key points in an eigen model of the movable platform;
and detecting the state of the movable platform based on the difference between the characteristic information of the response signal of the key point and the characteristic information of the key point in the intrinsic model of the movable platform.
40. The apparatus of claim 39, wherein the processor is configured to:
if the difference is smaller than a fourth preset value, judging that the mechanical state of the movable platform is a normal state; and/or
And if the difference is larger than the fourth preset value, judging that the mechanical state of the movable platform is an abnormal state.
41. The apparatus of claim 39, wherein the characteristic information comprises amplitude information and/or phase information of the response signal.
42. The apparatus of claim 39, wherein the processor is further configured to:
and correcting the fourth preset value based on the characteristic information of the response signal of the key point.
43. The apparatus of claim 38, wherein the excitation source is a motor and/or a paddle on the movable platform or an external excitation source; the stimulus source outputs a stimulus in response to the received input signal in the movable platform hover state.
44. The apparatus of claim 43, wherein the input signal is generated if a preset condition is met.
45. The apparatus of claim 44, wherein the preset condition comprises at least any one of:
the time interval between the current time and the last time of the state detection of the movable platform reaches a preset time length,
receiving a detection trigger instruction for detecting the state of the movable platform,
the movable platform performs specified tasks.
46. The apparatus of claim 29, wherein the processor is configured to:
comparing the characteristic information of the response signal of the key point with the characteristic information of the intrinsic model of the movable platform to obtain a comparison result;
and detecting the state of the movable platform based on the comparison result.
47. The apparatus of claim 46, wherein the eigenmodel is obtained by modeling or by measurement.
48. The apparatus of claim 29, wherein the excitation is a swept frequency signal or a pulsed signal.
49. The apparatus of claim 29, further comprising:
and controlling the driving state of the movable platform based on the state detection result.
50. The apparatus of claim 49, wherein the processor is configured to:
acquiring a safety margin of the movable platform;
and controlling the driving state of the movable platform based on the safety margin under the condition that the state detection result indicates that the movable platform is in an abnormal state.
51. The apparatus of claim 50, wherein the processor is configured to:
when the safety margin is a first margin, controlling the movable platform to continue to run until a running state control instruction is received;
when the safety margin is a second margin, controlling the movable platform to stop running;
when the safety margin is a third margin, controlling the movable platform to return to a specified place;
wherein the first margin is less than the second margin, which is less than the third margin.
52. The apparatus of claim 29, wherein the response signal comprises at least one of an acceleration response signal, an angular velocity response signal, a displacement response signal, a stress response signal, a strain response signal.
53. The apparatus of claim 52, wherein the response signal is collected by at least one of an inertial measurement unit, an acceleration sensor, an angular velocity sensor, a displacement sensor, a stress sensor, and a strain sensor on the movable platform.
54. The apparatus of claim 29, wherein the processor is further configured to:
sending the state detection result to a control terminal; and/or
Storing the state detection result; and/or
And outputting prompt information based on the state detection result.
55. The apparatus of claim 29, wherein the processor is configured to:
and sending the information of the characteristic points to a control terminal so that the control terminal can detect the state of the movable platform based on the characteristic information of the response signals of the key points.
56. The apparatus of claim 29, wherein the keypoints comprise at least any one of: the aircraft comprises an aircraft arm, a paddle, an aircraft arm connecting piece, an aircraft body middle frame, a damping ball, an aircraft arm rotating shaft, a motor base and a load.
57. A system for detecting the condition of a movable platform, the system comprising:
a motor for outputting a stimulus;
the inertial measurement unit is arranged near a key point of the movable platform and used for acquiring a response signal generated by the key point aiming at the excitation output by the excitation source; and
a processor, communicatively coupled to the inertial measurement unit, for performing the method of any of claims 1 to 28.
58. The system of claim 57, further comprising:
the control terminal is used for executing at least any one of the following operations:
triggering the motor to output excitation;
receiving a detection result output by the processor;
displaying and/or storing the detection result output by the processor;
controlling a driving state of the movable platform based on the detection result;
and displaying prompt information based on the detection result output by the processor.
59. A movable platform, comprising:
the movable platform status detection apparatus of any one of claims 29 to 56, or the movable platform status detection system of claim 57 or 58.
60. A computer-readable storage medium comprising instructions that, when executed on a computer, cause the computer to perform the method of any one of claims 1 to 28.
CN202080070657.9A 2020-11-25 2020-11-25 State detection method, device and system for movable platform and movable platform Pending CN114556069A (en)

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Citations (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN104200122A (en) * 2014-09-22 2014-12-10 大连交通大学 Fatigue life forecasting method for complicated welding structure in random vibration condition
CN109000872A (en) * 2018-05-31 2018-12-14 东北大学 Unmanned plane wing damage detection device and method under the dust storm of laser scanning-heat is shaken
CN109000866A (en) * 2018-06-07 2018-12-14 东北大学 Unmanned plane answers material wing dynamic characteristic and damage check equipment under dust storm-thermal environment
CN110160767A (en) * 2019-06-14 2019-08-23 安徽智寰科技有限公司 Impulse period automatic identification and extracting method and system based on Envelope Analysis
CN110332080A (en) * 2019-08-01 2019-10-15 内蒙古工业大学 A kind of fan blade health method of real-time based on resonance response
CN110542525A (en) * 2019-06-25 2019-12-06 上海航空材料结构检测股份有限公司 Method for testing vibration fatigue performance of metal in axial resonance state

Family Cites Families (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN105466672A (en) * 2014-09-12 2016-04-06 中航惠腾风电设备股份有限公司 Fatigue test method of wind wheel blade and application of method in full-size fatigue test
CN108195537A (en) * 2018-02-06 2018-06-22 东南大学 A kind of blade of aviation engine vibration fatigue test method based on amplitude controlling
US11175491B2 (en) * 2019-04-25 2021-11-16 Microsoft Technology Licensing, Llc Non-resonant microelectromechanical systems scanner with piezoelectric actuators
CN210971596U (en) * 2019-11-05 2020-07-10 深圳市赛为智能股份有限公司 Unmanned aerial vehicle horn fatigue strength measuring device

Patent Citations (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN104200122A (en) * 2014-09-22 2014-12-10 大连交通大学 Fatigue life forecasting method for complicated welding structure in random vibration condition
CN109000872A (en) * 2018-05-31 2018-12-14 东北大学 Unmanned plane wing damage detection device and method under the dust storm of laser scanning-heat is shaken
CN109000866A (en) * 2018-06-07 2018-12-14 东北大学 Unmanned plane answers material wing dynamic characteristic and damage check equipment under dust storm-thermal environment
CN110160767A (en) * 2019-06-14 2019-08-23 安徽智寰科技有限公司 Impulse period automatic identification and extracting method and system based on Envelope Analysis
CN110542525A (en) * 2019-06-25 2019-12-06 上海航空材料结构检测股份有限公司 Method for testing vibration fatigue performance of metal in axial resonance state
CN110332080A (en) * 2019-08-01 2019-10-15 内蒙古工业大学 A kind of fan blade health method of real-time based on resonance response

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