WO2021017013A1

WO2021017013A1 - Motor state monitoring method and apparatus, and computer device

Info

Publication number: WO2021017013A1
Application number: PCT/CN2019/098942
Authority: WO
Inventors: 简华; 郑勇; 许仕哲; 王声平; 王辉
Original assignee: 深圳市无限动力发展有限公司
Priority date: 2019-08-01
Filing date: 2019-08-01
Publication date: 2021-02-04

Abstract

A motor state monitoring method and apparatus, and a computer device. Said method comprises: respectively acquiring current rotation speed information of motors; respectively acquiring and parsing target noise signals generated by the motors, so as to obtain corresponding target noise parameter values; and determining, according to the target noise parameter values, whether the current state of the motors is a fault state. In the present application, the states of the motors can be monitored respectively, and a faulty motor can be detected in time, thereby facilitating maintenance and repair of a smart device.

Description

Motor state monitoring method, device and computer equipment

Technical field

This application relates to the field of smart home appliances, and in particular to a method, device and computer equipment for monitoring the state of a motor.

Background technique

With the rapid development of artificial intelligence, more and more smart home appliances are widely used in people's daily lives. Smart sweeping robots are currently highly sought after smart home appliances. The motor is an important component of the intelligent sweeping robot. The intelligent sweeping robot contains multiple motors, which are distributed in the intelligent sweeping robot to achieve different functions. The intelligent sweeping robot is driven by the wheel to move, and the side brush motor and the roller brush motor are used. To achieve cleaning, dust is collected by a fan. Since each motor of the intelligent sweeping robot is running at a high speed, the frequency of motor failure is very high. As long as one motor fails, the sweeping robot cannot operate normally. When repairing, it is necessary to perform manual troubleshooting for each motor one by one, and the repair efficiency is low. Therefore, it is very necessary to monitor the state of each motor of the intelligent sweeping robot. The current intelligent sweeping robot does not have the function of monitoring the motor status.

technical problem

The purpose of this application is to provide a motor state monitoring method, device, and computer equipment, aiming to solve the problem that the faulty motor of the intelligent sweeping robot cannot be automatically detected in the prior art.

Technical solutions

This application proposes a motor status monitoring method, which is applied to a smart device, where multiple motors and microphone arrays are installed, and the relative position of each motor and the microphone array is fixed, and the motor status The monitoring method includes: obtaining the current rotational speed information of each of the motors of the smart device respectively; obtaining the target noise signal generated by each of the motors through the microphone array; respectively analyzing each target noise signal to obtain Corresponding target noise parameter value; compare the target noise parameter value corresponding to each motor with the preset noise parameter value at the current rotation speed corresponding to each motor, and determine each of the Whether the current state of the motor is a fault state.

The application also proposes a motor state monitoring device, which is set in a smart device, where multiple motors and microphone arrays are installed, and the relative position of each motor and the microphone array is fixed. The motor state monitoring device includes: a first acquiring unit, configured to separately acquire the current rotational speed information of each of the motors of the smart device; a second acquiring unit, configured to separately acquire the generation of each of the motors through the microphone array The target noise signal; the signal analysis unit is used to analyze each of the target noise signals to obtain the corresponding target noise parameter value; the fault judgment unit is used to compare the target noise parameter value corresponding to each of the motors with The preset noise parameter values at the current rotation speed corresponding to each of the motors are compared, and the current state of each of the motors is determined according to the comparison result as a fault state.

This application also proposes a computer device, which includes a processor, a memory, and a computer program stored on the memory and capable of running on the processor. The processor implements the above-mentioned motor when the computer program is executed. Condition monitoring method.

Beneficial effect

In the motor state monitoring method, device and computer equipment of the present application, the current speed information generated by each motor of the smart device is obtained separately; then the target noise signal of each motor is separately collected through the microphone array; and then the target noise signal is measured Analyze to obtain the corresponding target noise parameter value; then compare the target noise parameter value corresponding to each motor with the preset noise parameter value at the current speed corresponding to each motor, and determine the current state of each motor according to the comparison result Whether it is a fault state; thus, the state of each motor can be monitored separately, and the faulty motor can be detected in time, which is convenient for the maintenance and repair of the intelligent equipment.

Description of the drawings

FIG. 1 is a schematic flowchart of a method for monitoring a motor state according to an embodiment of the present application;

2 is a schematic block diagram of the structure of a motor state monitoring device according to an embodiment of the present application;

3 is a schematic block diagram of the structure of a motor state monitoring device according to another embodiment of the present application;

4 is a schematic block diagram of the structure of the second acquisition unit in FIG. 2;

FIG. 5 is a schematic block diagram of the structure of the signal acquisition subunit in FIG. 4;

6 is a schematic block diagram of the structure of a motor state monitoring device according to another embodiment of the present application;

7 is a schematic block diagram of the structure of a motor state monitoring device according to another embodiment of the present application;

8 is a schematic block diagram of the structure of a motor state monitoring device according to another embodiment of the present application;

Fig. 9 is a schematic structural block diagram of an embodiment of a computer device of the present application.

The best mode of the invention

The motor state monitoring method provided by the present application is applied to a smart device, where multiple motors and microphone arrays are installed, and the relative position of each motor and the microphone array is fixed, and the motor status Monitoring methods include:

S1. Acquire current speed information of each motor of the smart device respectively;

S2. Obtain the target noise signal generated by each motor through the microphone array;

S3. Analyze each target noise signal separately to obtain a corresponding target noise parameter value;

S4. Compare the target noise parameter value corresponding to each motor with the preset noise parameter value at the current rotation speed corresponding to each motor, and determine whether the current state of each motor is It is a fault state.

The above-mentioned smart devices include smart sweeping robots. The smart sweeping robots include multiple motors. The motors include fans, roller brush motors, side brush motors, turbines, etc. Each motor is used to drive different components to run to achieve corresponding functions. The motors are distributed inside the intelligent sweeping robot, and the relative positions of the motors are fixed and known. The above-mentioned microphone array is fixedly installed in an intelligent sweeping robot, the microphone array includes a plurality of microphones, and the relative position of each microphone is fixed and known. Therefore, the relative position (including distance and angle) of each motor and the microphone array is fixed and known.

In the above step S1, taking the smart device as an intelligent sweeping robot as an example, when the smart sweeping robot is running, the rotation speed of each motor will be adjusted according to the specific use situation, and the noise level of each motor will be different at different rotation speeds. When the motor is in normal use (as opposed to the fault state), the speed of the motor is positively correlated with the noise emitted by the motor. If the speed of the motor is determined, the noise emitted by the motor is also determined. In different operating states, the rotation speeds of multiple motors in the intelligent sweeping robot may also be different. Therefore, it is necessary to obtain the current rotation speed information of each motor separately. After the intelligent sweeping robot is turned on, the system can directly read the speed information of each motor.

In the above step S2, a fixed beamforming method is used, and a microphone array can be used to obtain sound signals in a specified direction, so as to temporarily shield sound signals in other directions. Since the relative position of each motor and the microphone array is fixed and known, it is possible to sequentially form a pickup beam in the direction of the relative position of each motor and the microphone to collect the noise signal of each motor as the target noise signal.

In the above step S3, spectrum analysis is performed on the target noise signal, and Fourier transform is performed on the target noise signal to obtain target noise parameter values, including amplitude, sound pressure, phase, frequency, etc.

In the above step S4, the preset noise parameter value is a preset value of a normal noise parameter at the current rotation speed corresponding to the target noise parameter value. Taking the target noise parameter as sound pressure as an example, the preset noise parameter value is the preset sound pressure value at the current speed; if the target sound pressure value of the motor exceeds the preset sound pressure value at the current speed, it means The current noise of the motor is too loud. At this time, the possibility of the motor failure is very high, and the current state of the motor is judged as a failure state. In the same intelligent sweeping robot, the preset sound pressure values corresponding to different motors can be different, and can be set according to the sound pressure values of the motors in normal use. Specifically, the noise sound pressure ranges corresponding to the different rotation speeds of each motor in the normal state are respectively recorded, and the upper limit value of the noise sound pressure range is set as the first preset sound pressure threshold value. The above-mentioned preset noise parameter value may also be other parameter values such as a preset frequency value. The specific setting method is the same as the setting method of the preset sound pressure value, and will not be repeated here.

The motor status monitoring method of this embodiment first obtains the current speed information of each motor of the smart device separately; then collects the target noise signal generated by each motor through the microphone array; then analyzes the target noise signal to obtain the corresponding The target noise parameter value of each motor; then compare the target noise parameter value corresponding to each motor with the preset noise parameter value at the current speed corresponding to each motor, and determine whether the current state of each motor is a fault state according to the comparison results ; Therefore, the status of each motor can be monitored separately, and the faulty motor can be detected in time, which is convenient for maintenance and repair of intelligent equipment.

In one embodiment, a motor whose current state is in a fault state is used as a faulty motor, and the target noise signal corresponding to each motor is compared with a preset noise signal at the current rotation speed corresponding to each motor. After the step S4 of separately judging whether the current state of each of the motors is a fault state according to the comparison result, the method includes: S5, storing the target noise parameter value and the current speed of the faulty motor in a preset abnormal noise database The fault type of the faulty motor is obtained by comparing and matching in, wherein the preset abnormal noise database stores a list of mapping relationships of motor number, motor speed, noise parameter range, and fault type.

After the faulty motor is detected, the fault type of the faulty motor is further determined through the above step S5. By comparing the target noise parameter value of the faulty motor with the current speed in the preset abnormal noise database, the fault type of the faulty motor is obtained. The aforementioned target noise parameter values include, for example, target noise frequency, target sound pressure value, and so on. In the intelligent sweeping robot, a preset abnormal noise database is pre-stored. The abnormal noise includes mechanical noise, electromagnetic noise, and aerodynamic noise. The above-mentioned motor number is used to distinguish multiple motors in the sweeping robot, which can be numbered by motor model or Arabic numerals. The specific method for establishing the above-mentioned preset abnormal noise database is as follows: acquiring abnormal noise information corresponding to the first specified speed of the first motor under the specified fault type, wherein the first motor is selected from a plurality of the motors; The abnormal noise information to obtain the noise parameter range of the first motor under the specified fault type and the specified speed. The noise parameter range includes the noise frequency range and the noise sound pressure range; and the motor number of the first motor , The first designated speed, the noise parameter range, and the designated fault type are stored in the preset abnormal noise database in association.

In an embodiment, the step S2 of separately acquiring the target noise signal generated by each of the motors through the microphone array includes:

S201: Generate a pickup beam by using a beamforming algorithm, the pickup beam is directed to a single designated motor in turn, and the designated motor is selected from a plurality of the motors; S202. Obtain the direction of the pickup beam through the microphone array. Designated noise signals in each direction; S203. Filter out corresponding non-correlated noise signals from each of the designated noise signals to obtain target noise signals of each of the designated motors, wherein the non-correlated noise signals are State the ambient noise signal in the direction of the pickup beam.

In the above step S201, beam forming is a method in which the output signals of each microphone of a microphone array arranged in a certain geometric structure are processed (for example, weighting, time delay, summation, etc.) to form a spatial directivity method to form a pickup beam. The sound in the area corresponding to the sound beam is picked up, and the sound interference outside the sound beam area is suppressed. Beam forming can be divided into conventional beam forming CBF (Conventional Beam Forming), CBF + Adaptive Filter and ABF (Adaptive Beam forming). Since the relative position of each motor and the microphone array is fixed and known, the microphone array can be controlled to generate a pickup beam in the direction of a single motor.

In the above step S202, the designated noise signal includes a noise signal generated by a designated motor in the direction of the sound pickup beam and an environmental noise signal in the direction of the sound pickup beam. Noise signals generated by motors other than the direction of the pickup beam are suppressed.

In the above step S203, since the noise signals generated by the motors respectively received by the multiple microphones in the microphone array are related signals, and the environmental noise signals are non-correlated signals, the specified noise signal is filtered to filter out non-related noises. Signal, the target noise signal of the specified motor is obtained.

In an embodiment, the microphone array includes a plurality of microphones, and the step S202 of respectively acquiring designated noise signals in various directions to which the sound pickup beam points through the microphone array includes:

S2021. Acquire a first noise signal through each of the microphones respectively; wherein the first noise signal is a noise signal obtained by a single microphone, and the pickup beam is directed upward; S2022, according to each microphone The phase delay time between each of the first noise signals is subjected to phase delay processing to obtain a second noise signal; S2023, adding the second noise signals of each microphone to obtain the specified noise signal.

In this embodiment, in the foregoing step S2021, the microphone array includes a plurality of microphones arranged in a preset structure, and the distance between each microphone and the designated motor is fixed and known. The first noise signal is a noise signal obtained by a single microphone, and the pickup beam is directed upward; specifically, the first noise signal is obtained by a single microphone and is generated by a designated motor in the direction in which the pickup beam is directed The sum of the noise signal and the environmental noise signal in the direction in which the sound pickup beam is pointed. Since the distance between each microphone and the designated motor is not equal, the time when the first noise signal is acquired by different microphones is sequential. For ease of understanding, take 4 microphones in the microphone array as an example. The 4 microphones are numbered 1#, 2#, 3# and 4#, and the 1#, 2#, 3# and 4# microphones are the same as the above The distances of the designated motors in the direction of the pickup beam are: r ₁ , r ₂ , r ₃ and r ₄ , and r ₁ <r ₂ <r ₃ <r ₄ , then the noise of the designated motor reaches 1# microphone earliest , The latest time to reach the 4# microphone.

In the above step S2022, the above phase delay time can be calculated according to formula (1-1):

t _i =r _i /c, i=1,2,3,4……; (1-1)

Where t _i is the phase delay time for the noise of the specified motor to reach the microphone numbered i#, r _i is the distance between the specified motor and the microphone numbered i#, and c is the speed of sound.

The time when the specified motor emits noise is taken as the origin of the time axis t ₀ , t ₀ =0, then the starting time of the first noise signal obtained by the microphone number i# is t _i , taking 1# microphone as an example, the specified motor emits The time point of the noise is t ₀ , t ₀ =0, and the 1# microphone starts to acquire the first noise signal after the duration of t ₁ . Perform phase delay processing on the first noise signal acquired by the microphone numbered i#, and use t _i as the new time axis origin to obtain the corresponding second noise signal. The above-mentioned phase delay processing is performed on the first noise signal obtained by each microphone to eliminate the signal phase delay caused by different microphone positions, and the obtained second noise signal of each microphone has the same signal starting time.

In the above step S2023, since the second noise signals of the microphones obtained in step S2022 have the same signal starting time, the second noise signals of all microphones are added to obtain the specified noise signal.

In an embodiment, before step S1 of separately acquiring the current rotation speed information of each of the motors of the smart device, the method includes:

S011: Determine whether the number of motors in the running state is zero;

S012. If yes, obtain the environmental noise sound pressure value.

In this embodiment, in the above steps S011 to S012, when the number of motors in the running state is zero, and the motors are not running at this time, then the noise signal collected by the microphone array at this time is entirely from environmental noise. The above-mentioned environmental noise sound pressure value is the total environmental noise sound pressure value, and there is no need to distinguish the noise of each motor direction. Preferably, when it is detected that the smart device is turned on and the number of motors in the running state is zero, the ambient noise sound pressure value recorded in the smart sweeper is acquired and updated. The method for obtaining the above-mentioned environmental noise sound pressure value is as follows: collect the first environmental noise signal through each microphone in the microphone array; according to the phase delay time between each microphone, the time delay of each first environmental noise signal corresponds to Phase delay time, and use the delayed first environmental noise signal as the second environmental noise signal; add the second environmental noise signals of each microphone to obtain the total environmental noise signal; analyze the total environmental noise signal to obtain the environment Noise sound pressure value. As another embodiment of the present application, an existing sound pressure measuring device can also be used to directly measure the environmental noise sound pressure value.

In one embodiment, before step S2 of separately acquiring the target noise signal generated by each of the motors through the microphone array, it includes:

S021. Obtain a total noise sound pressure value, where the total noise sound pressure value is all noise signals generated when each motor of the smart device is at each of the current rotation speeds;

S022. Calculate the difference between the total noise sound pressure value and the environmental noise sound pressure value, where the difference is the noise sound pressure value currently generated by the smart device;

S023. Determine whether the difference value is greater than a first preset sound pressure threshold value, where the first preset sound pressure threshold value is the second value of each motor at each corresponding current rotation speed. The sum of preset sound pressure thresholds;

S024. If yes, execute the step of separately acquiring the target noise signal generated by each motor through the microphone array.

In this embodiment, in the above step S021, after the intelligent cleaning robot starts to operate, each motor is operated at each current speed, and the above total noise sound pressure value is obtained at this time. The above-mentioned total noise sound pressure value does not need to distinguish the noise of each motor direction. The method for obtaining the above-mentioned total noise sound pressure value is as follows: collect the first total noise signal through each microphone in the microphone array; according to the phase delay time between each microphone, the time delay of each first total noise signal is correspondingly Phase delay time, and use the delayed first total noise signal as the second total noise signal; add the second total noise signals of each microphone to obtain the current total noise signal; analyze the total noise signal to obtain the total noise signal Noise sound pressure value. As another embodiment of the present application, the existing sound pressure measuring equipment can also be used to directly measure the total noise sound pressure value.

In the above steps S022 to S024, the difference between the total noise sound pressure value and the environmental noise sound pressure value is the noise sound pressure value generated by all motors of the smart device. The second preset sound pressure threshold is related to the rotation speed of the motor, and is the upper limit of the sound pressure of the noise generated by each motor at the current rotation speed. The second preset sound pressure threshold value of each motor at each corresponding current rotation speed is added to form the first preset sound pressure threshold value. When the above difference exceeds the first preset sound pressure threshold, it indicates that the noise generated by the intelligent sweeping robot is abnormal. It may be that one of the motors has failed, and then the target noise signal generated by each motor is obtained through the microphone array. Steps, one by one to detect which motor is malfunctioning.

In an embodiment, the target noise parameter value corresponding to each of the motors is compared with the preset noise parameter value at the current rotation speed corresponding to each of the motors, and each of the noise parameters is determined separately according to the comparison result. After step S4 of whether the current state of the motor is a fault state, it includes:

S6. Send the motor number information of the motor whose current state is the fault state to a designated terminal.

In the above step S6, the above-mentioned motor number information is used to distinguish multiple motors in the cleaning robot, which can be numbered by motor model or Arabic numerals. The above-mentioned designated terminal may be an intelligent mobile terminal associated with the intelligent sweeping robot, so that the user can directly learn on the mobile terminal that the intelligent sweeping robot is faulty and which motor is the faulty motor.

In another embodiment, the target noise parameter value of the faulty motor and the current speed may be compared in a preset abnormal noise database to obtain the fault type of the faulty motor, and the preset abnormality After step S5 of the mapping relationship between the motor number, motor speed, noise parameter range, and fault type is stored in the noise database, the step of sending the motor number information of the motor whose current state is the fault state to the designated terminal is executed, so that the user can On the mobile terminal, you can directly learn about the fault of the intelligent sweeping robot, which motor has the fault, and the cause of the motor fault.

With reference to Figure 2, the present application also provides a motor state monitoring device, which is set in a smart device, where multiple motors and microphone arrays are installed, and the relative position of each motor and the microphone array Fixedly, the motor state monitoring device includes: a first obtaining unit 10, configured to obtain the current rotational speed information of each motor of the smart device; a second obtaining unit 20, configured to obtain respectively through the microphone array The target noise signal generated by each motor; the signal analysis unit 30 is used to analyze each target noise signal to obtain the corresponding target noise parameter value; the fault judgment unit 40 is used to correspond each motor The target noise parameter value is compared with the preset noise parameter value at the current rotation speed corresponding to each motor, and the current state of each motor is determined according to the comparison result as a fault state.

In the above-mentioned first acquisition unit 10, taking the smart device as an intelligent sweeping robot as an example, when the smart sweeping robot is running, it adjusts the speed of each motor according to the specific use situation. At different speeds, the noise level of each motor is different. When the motor is in normal use (as opposed to the fault state), the speed of the motor is positively correlated with the noise emitted by the motor. If the speed of the motor is determined, the noise emitted by the motor is also determined. In different operating states, the rotation speeds of multiple motors in the intelligent sweeping robot may also be different. Therefore, it is necessary to obtain the current rotation speed information of each motor separately. After the smart sweeping robot is turned on, it can directly read the rotational speed information of each motor through the first acquiring unit 10.

In the above-mentioned second acquisition unit 20, a fixed beamforming method is used and a microphone array can be used to acquire sound signals in a specified direction, thereby temporarily shielding sound signals in other directions. Since the relative position of each motor and the microphone array is fixed and known, the second acquisition unit 20 can be used to sequentially form a pickup beam in the relative position direction of each motor and the microphone, so as to collect the noise signal of each motor separately as Target noise signal.

In the signal analysis unit 30, the signal analysis unit 30 performs spectrum analysis on the target noise signal, and performs Fourier transform on the target noise signal to obtain target noise parameter values, including amplitude, sound pressure, phase, frequency, etc.

In the failure judgment unit 40, the preset noise parameter value is a preset value of the noise parameter at the current rotation speed corresponding to the target noise parameter value. Taking the target noise parameter as sound pressure as an example, the preset noise parameter value is the preset sound pressure value at the current speed; if the target sound pressure value of the motor exceeds the preset sound pressure value at the current speed, it indicates The current noise of the motor is too large. At this time, it is very likely that the motor will malfunction. The fault determination unit 40 determines the current state of the motor as a fault state. In the same intelligent sweeping robot, the preset sound pressure values corresponding to different motors can be different, and can be set according to the sound pressure values of the motors in normal use. Specifically, the noise sound pressure ranges corresponding to the different rotation speeds of each motor in the normal state are respectively recorded, and the upper limit value of the noise sound pressure range is set as the first preset sound pressure threshold value. The above-mentioned preset noise parameter value may also be other parameter values such as a preset frequency value. The specific setting method is the same as the setting method of the preset sound pressure value, and will not be repeated here.

The motor state monitoring device of this embodiment first obtains the current speed information of each motor of the smart device separately; then collects the target noise signal generated by each motor through the microphone array; then analyzes the target noise signal to obtain the corresponding The target noise parameter value of each motor; then compare the target noise parameter value corresponding to each motor with the preset noise parameter value at the current speed corresponding to each motor, and determine whether the current state of each motor is a fault state according to the comparison results ; Therefore, the status of each motor can be monitored separately, and the faulty motor can be detected in time, which is convenient for maintenance and repair of intelligent equipment.

3, in one embodiment, a motor whose current state is a fault state is used as a faulty motor. The above-mentioned motor state monitoring device includes: a fault type identification unit 50 for calculating the target noise parameter value of the faulty motor Compare and match the current rotation speed in a preset abnormal noise database to obtain the fault type of the faulty motor; wherein the preset abnormal noise database stores a motor number, motor rotation speed, noise parameter range, and fault type The list of mapping relationships.

In this embodiment, after the faulty motor is detected, the fault type identification unit 50 further determines the fault type of the faulty motor. The fault type of the faulty motor is obtained by comparing the target noise parameter value and the current speed of the faulty motor in the preset abnormal noise database. The aforementioned target noise parameter values include, for example, target noise frequency, target sound pressure value, and so on. In the smart sweeping robot, the aforementioned preset abnormal noise database is pre-stored, and the aforementioned abnormal noise includes mechanical noise, electromagnetic noise, and aerodynamic noise. The above-mentioned motor number is used to distinguish multiple motors in the sweeping robot, which can be numbered by motor model or Arabic numerals. The specific method for establishing the above-mentioned preset abnormal noise database is as follows: acquiring abnormal noise information corresponding to the first specified speed of the first motor under the specified fault type, wherein the first motor is selected from a plurality of the motors; The abnormal noise information to obtain the noise parameter range of the first motor under the specified fault type and the specified speed. The noise parameter range includes the noise frequency range and the noise sound pressure range; and the motor number of the first motor , The first designated speed, the noise parameter range, and the designated fault type are stored in the preset abnormal noise database in association.

Referring to FIG. 4, in one embodiment, the second acquisition unit 20 includes: a beam generating subunit 201, configured to generate a pickup beam through a beam forming algorithm, the pickup beam is directed to a single designated motor in turn, and the designated The motor is selected from a plurality of the motors; the signal acquisition sub-unit 202 is used to obtain the designated noise signals in each direction of the pickup beam through the microphone array; the signal processing sub-unit 203 is used to obtain the designated noise Corresponding non-correlated noise signals are filtered out of the designated noise signals to obtain target noise signals of each designated motor respectively, wherein the non-correlated noise signals are environmental noise signals in the direction of the sound pickup beam.

In this embodiment, in the above-mentioned beam forming subunit 201, beam forming is a method in which the output signals of each microphone of a microphone array arranged in a certain geometric structure are processed (for example, weighting, time delay, summation, etc.) to form spatial directivity. The pickup beam is formed, and only the sound in the area corresponding to the pickup beam will be picked up, and the sound interference outside the pickup beam area will be suppressed. Beam forming can be divided into conventional beam forming CBF (Conventional Beam Forming), CBF + Adaptive Filter and ABF (Adaptive Beam forming). Since the relative position of each motor and the microphone array is fixed and known, the beam generating subunit 201 can control the microphone array to generate a pickup beam in the direction of a single motor.

In the signal acquisition subunit 202, the designated noise signal includes a noise signal generated by a designated motor in the direction of the pickup beam and an environmental noise signal in the direction of the pickup beam. Noise signals generated by motors other than the direction of the pickup beam are suppressed by the signal acquisition subunit 202.

In the above-mentioned signal processing sub-unit 203, since the noise signals generated by the motors respectively received by the multiple microphones in the microphone array are related signals, and the environmental noise signals are non-correlated signals, therefore, the signal processing sub-unit 203 removes the specified noise signals. The signal is filtered, and non-correlated noise signals are filtered, and then the target noise signal of the specified motor is obtained.

Referring to FIG. 5, in one embodiment, the microphone array includes a plurality of microphones, and the signal acquisition subunit 202 includes: a signal acquisition module 2021 for acquiring the first noise signal through each of the microphones; wherein, The first noise signal is a noise signal obtained by a single microphone, and the pickup beam is directed upward; a delay processing module 2022 is configured to perform an analysis on each microphone according to the phase delay time between each microphone. The first noise signal undergoes phase delay processing to obtain a second noise signal; a signal superimposing module 2023 is configured to add the second noise signals of the microphones to obtain the designated noise signal.

In the above-mentioned signal acquisition module 2021, the microphone array includes a plurality of microphones arranged in a preset structure, and the distance between each microphone and the designated motor is fixed and known. The above-mentioned first noise signal is a noise signal obtained by a single microphone, and the pickup beam is directed upward; specifically, the first noise signal is obtained by a single microphone, and noise generated by a designated motor in the direction in which the pickup beam is directed The sum of the signal and the ambient noise signal in the direction in which the pickup beam is pointing. Since the distance between each microphone and the designated motor is not equal, the time when different microphones obtain the above-mentioned first noise signal through the signal obtaining module 2021 is sequential. For ease of understanding, take 4 microphones in the microphone array as an example. The 4 microphones are numbered 1#, 2#, 3# and 4#, and the 1#, 2#, 3# and 4# microphones are the same as the above The distances of the designated motors in the direction of the pickup beam are: r ₁ , r ₂ , r ₃ and r ₄ , and r ₁ <r ₂ <r ₃ <r ₄ , then the noise of the designated motor reaches 1# microphone earliest , The latest time to reach the 4# microphone.

In the above delay processing module 2022, the above phase delay time can be calculated according to formula (1-1):

t _i =r _i /c, i=1,2,3,4……; (1-1)

Where ti is the phase delay time for the noise of the specified motor to reach the microphone numbered i#, r _i is the distance between the specified motor and the microphone numbered i#, and c is the speed of sound.

The time when the specified motor emits noise is taken as the time axis origin t ₀ , t0=0, then the starting time of the first noise signal obtained by the microphone with the number i# is t _i , taking 1# microphone as an example, the specified motor emits noise The time point of is t ₀ , t ₀ =0, and 1# microphone starts to acquire the first noise signal after the duration of t ₁ . The delay processing module 2022 performs phase delay processing on the first noise signal acquired by the microphone numbered i#, and uses t _i as the new time axis origin to obtain the corresponding second noise signal. The above-mentioned phase delay processing is performed on the first noise signal obtained by each microphone to eliminate the signal phase delay caused by different microphone positions, and the obtained second noise signal of each microphone has the same signal starting time.

In the above-mentioned signal superimposing module 2023, since the second noise signals of each microphone obtained by the delay processing module 2022 have the same signal starting time, the signal superimposing module 2023 adds the second noise signals of all microphones to obtain the above-mentioned specified noise signal.

Referring to FIG. 6, in one embodiment, the above-mentioned motor state monitoring device further includes: a first judging unit 011 for judging whether the number of the motors in the running state is zero; an environmental noise acquiring unit 012 for If the number of the motors in the running state is zero, then the ambient noise sound pressure value is obtained.

In this embodiment, in the above-mentioned first judging unit 011 and the environmental noise acquiring unit 012, when the number of motors in the running state is zero, and the motors are not running at this time, then the noise signal collected by the microphone array at this time is completely derived from Environmental noise. The above-mentioned environmental noise sound pressure value is the total environmental noise sound pressure value, and there is no need to distinguish the noise of each motor direction. Preferably, when it is detected that the smart device is turned on and the number of motors in the running state is zero, the ambient noise acquiring unit 012 acquires and updates the ambient noise sound pressure value recorded in the smart sweeper. The method for obtaining the above-mentioned environmental noise sound pressure value is as follows: collect the first environmental noise signal through each microphone in the microphone array; according to the phase delay time between each microphone, the time delay of each first environmental noise signal corresponds to Phase delay time, and use the delayed first environmental noise signal as the second environmental noise signal; add the second environmental noise signals of each microphone to obtain the total environmental noise signal; analyze the total environmental noise signal to obtain the environment Noise sound pressure value. As another embodiment of the present application, an existing sound pressure measuring device can also be used to directly measure the environmental noise sound pressure value.

Referring to FIG. 7, in an embodiment, the above-mentioned motor state monitoring device further includes:

The total noise obtaining unit 021 is configured to obtain a total noise sound pressure value, where the total noise sound pressure value is all the noise signals generated when each motor of the smart device is at each of the current rotation speeds; calculation unit 022, for calculating the difference between the total noise sound pressure value and the environmental noise sound pressure value, where the difference is the noise sound pressure value currently generated by the smart device; the second judgment unit 023 is used for Determine whether the difference value is greater than a first preset sound pressure threshold value, where the first preset sound pressure threshold value is a second preset value of each motor at each of the current rotation speeds corresponding to it The sum of the sound pressure threshold; the determining unit 024, if the difference is greater than the first preset sound pressure threshold, execute the step of separately acquiring the target noise signal generated by each motor through the microphone array.

In this embodiment, in the above-mentioned total noise obtaining unit 021, after the smart sweeping robot starts to operate, each motor is operated at each current speed. At this time, the total noise obtaining unit 021 obtains the above-mentioned total noise sound pressure value. The above-mentioned total noise sound pressure value does not need to distinguish the noise of each motor direction. The method for obtaining the above-mentioned total noise sound pressure value is as follows: collect the first total noise signal through each microphone in the microphone array; according to the phase delay time between each microphone, the time delay of each first total noise signal is correspondingly Phase delay time, and use the delayed first total noise signal as the second total noise signal; add the second total noise signals of each microphone to obtain the current total noise signal; analyze the total noise signal to obtain the total noise signal Noise sound pressure value. As another embodiment of the present application, the existing sound pressure measuring equipment can also be used to directly measure the total noise sound pressure value.

In the calculation unit 022, the second judgment unit 023, and the judgment unit 024, the difference between the total noise sound pressure value and the environmental noise sound pressure value is the noise sound pressure value generated by all motors of the smart device. The second preset sound pressure threshold is related to the rotation speed of the motor, and is the upper limit of the sound pressure of the noise generated by each motor at the current rotation speed. The second preset sound pressure threshold value of each motor at each corresponding current rotation speed is added to form the first preset sound pressure threshold value. When the above difference exceeds the above first preset sound pressure threshold, it indicates that the noise generated by the smart sweeping robot is abnormal. It may be that one of the motors has failed, and then the microphone array is used to separately obtain the generation of each motor. The steps of the target noise signal are to detect which motor is malfunctioning one by one.

Referring to FIG. 8, in one embodiment, the above-mentioned motor status monitoring device further includes: a fault information sending unit 60 configured to send the motor number information of the motor whose current status is a fault status to a designated terminal.

In the above-mentioned fault information sending unit 60, the motor number information is used to distinguish multiple motors in the sweeping robot, which can be numbered by motor model or Arabic numerals. The designated terminal may be an intelligent mobile terminal associated with the intelligent sweeping robot, so that the user can directly learn on the mobile terminal that the intelligent sweeping robot is faulty and which motor is the faulty motor.

In another embodiment, the above-mentioned fault information sending unit 60 can also be used to send the fault type and motor number information of the faulty motor to the designated terminal, so that the user can directly learn that the smart sweeping robot has failed or has failed on the mobile terminal. Which motor is the specific motor and what is the cause of the motor failure.

Referring to FIG. 9, the present application also provides a computer device 3, which includes a processor 4, a memory 1, and a computer program 2 stored on the memory 1 and running on the processor 4, and the processor 4 executes The computer program 2 realizes the above-mentioned motor state monitoring method.

Claims

A method for monitoring the state of a motor, characterized in that it is applied to a smart device. A plurality of motors and microphone arrays are installed in the smart device, and the relative position of each motor and the microphone array is fixed. Monitoring methods include:

Acquiring current speed information of each of the motors of the smart device respectively;

Separately acquiring the target noise signal generated by each of the motors through the microphone array;

Analyze each target noise signal separately to obtain a corresponding target noise parameter value;

Compare the target noise parameter value corresponding to each motor with the preset noise parameter value at the current speed corresponding to each motor, and determine whether the current state of each motor is a fault according to the comparison result status.
The motor state monitoring method according to claim 1, wherein the motor whose current state is a fault state is used as a faulty motor, and the target noise signal corresponding to each of the motors corresponds to each of the motors. After the steps of comparing the preset noise signals at the current speed of the motor, and determining whether the current state of each motor is a fault state according to the comparison result, the steps include:

The target noise parameter value of the faulty motor and the current speed are compared and matched in a preset abnormal noise database to obtain the fault type of the faulty motor; wherein, the preset abnormal noise database stores The mapping relationship list of motor number, motor speed, noise parameter range and fault type.
The motor state monitoring method according to claim 1, wherein the step of separately acquiring the target noise signal generated by each motor through the microphone array comprises:

Generating a pickup beam through a beamforming algorithm, the pickup beam is directed to a single designated motor in turn, and the designated motor is selected from a plurality of the motors;

Respectively acquiring designated noise signals in various directions in which the sound pickup beam points through the microphone array;

Filter out the corresponding non-correlated noise signals from each of the designated noise signals to obtain the target noise signal of each of the designated motors, wherein the non-correlated noise signal is the environmental noise signal in the direction of the pickup beam .
The motor status monitoring method according to claim 3, wherein the microphone array includes a plurality of microphones, and the microphone array is used to obtain the specified noise signals in each direction of the pickup beam. The steps include:

Obtaining a first noise signal through each of the microphones respectively; wherein the first noise signal is obtained by a single microphone, and the sound pickup beam is directed upward;

Performing phase delay processing on each of the first noise signals respectively according to the phase delay time between the microphones to obtain a second noise signal;

The second noise signal of each microphone is added to obtain the designated noise signal.
The motor state monitoring method according to claim 1, wherein before the step of separately acquiring the current rotation speed information of each of the motors of the smart device, the method comprises:

Determine whether the number of the motors in the running state is zero;

If yes, obtain the ambient noise sound pressure value.
5. The motor status monitoring method according to claim 5, wherein before the step of separately acquiring the target noise signal generated by each motor through the microphone array, the method comprises:

Obtaining a total noise sound pressure value, where the total noise sound pressure value is all noise signals generated when each motor of the smart device is at each of the current rotation speeds;

Calculating the difference between the total noise sound pressure value and the environmental noise sound pressure value, where the difference is the noise sound pressure value currently generated by the smart device;

Determine whether the difference value is greater than a first preset sound pressure threshold value, where the first preset sound pressure threshold value is a second preset value of each motor at each of the current rotation speeds corresponding to it Sum of sound pressure threshold;

If yes, execute the step of separately acquiring the target noise signal generated by each motor through the microphone array.
The motor state monitoring method according to claim 1, wherein the target noise parameter value corresponding to each motor is compared with the preset noise parameter value at the current rotation speed corresponding to each motor. After comparison, after the step of separately judging whether the current state of each motor is a fault state according to the comparison result, the steps include:

Send the motor number information of the motor whose current state is the fault state to a designated terminal.
A motor state monitoring device, which is characterized in that it is set in a smart device, and a plurality of motors and microphone arrays are installed in the smart device, and the relative position of each motor and the microphone array is fixed, and the motor Condition monitoring devices include:

The first obtaining unit is configured to obtain the current rotation speed information of each of the motors of the smart device respectively;

A second acquiring unit, configured to separately acquire the target noise signal generated by each of the motors through the microphone array;

The signal analysis unit is used to analyze each target noise signal separately to obtain the corresponding target noise parameter value;

The fault judgment unit is configured to compare the target noise parameter value corresponding to each motor with the preset noise parameter value at the current rotation speed corresponding to each motor, and judge each motor separately according to the comparison result Whether the current status of is a fault status.
8. The motor state monitoring device according to claim 8, wherein the motor whose current state is a fault state is used as a faulty motor, and the motor state monitoring device comprises:

The fault type identification unit is configured to compare and match the target noise parameter value of the faulty motor and the current speed in a preset abnormal noise database to obtain the fault type of the faulty motor; wherein, the prediction It is assumed that the abnormal noise database stores a list of mapping relations of motor number, motor speed, noise parameter range and fault type.
The motor state monitoring device according to claim 8, wherein the second acquiring unit comprises:

The beam generation subunit is configured to generate a pickup beam through a beamforming algorithm, the pickup beam is directed to a single designated motor in turn, and the designated motor is selected from a plurality of the motors;

A signal acquisition subunit, configured to respectively acquire designated noise signals in various directions in which the sound pickup beam points through the microphone array;

The signal processing subunit is used to filter out the corresponding non-correlated noise signals from each of the designated noise signals to obtain the target noise signals of each of the designated motors respectively, wherein the non-correlated noise signals are the pickup waves Ambient noise signal in the beam direction.
The motor state monitoring device according to claim 10, wherein the microphone array includes a plurality of microphones, and the signal acquisition subunit includes

A signal acquisition module, configured to acquire a first noise signal through each of the microphones respectively; wherein the first noise signal is acquired by a single microphone, and the pickup beam is directed upward;

A delay processing module, configured to perform phase delay processing on each of the first noise signals according to the phase delay time between each of the microphones to obtain a second noise signal;

The signal superposition module is used to add the second noise signals of the microphones to obtain the designated noise signal.
8. The motor state monitoring device according to claim 8, wherein the motor state monitoring device further comprises:

The first judging unit is used to judge whether the number of the motors in the running state is zero;

The environmental noise obtaining unit is configured to obtain the environmental noise sound pressure value if the number of the motors in the running state is zero.
The motor state monitoring device according to claim 12, wherein the motor state monitoring device further comprises:

A total noise obtaining unit, configured to obtain a total noise sound pressure value, where the total noise sound pressure value is all noise signals generated when each motor of the smart device is at each of the current rotation speeds;

A calculation unit for calculating the difference between the total noise sound pressure value and the environmental noise sound pressure value, where the difference is the noise sound pressure value currently generated by the smart device;

The second judging unit is configured to judge whether the difference is greater than a first preset sound pressure threshold value, wherein the first preset sound pressure threshold value is the current value of each motor corresponding to it The sum of the second preset sound pressure threshold value at the rotation speed;

The determining unit, if the difference is greater than the first preset sound pressure threshold, executes the step of separately acquiring the target noise signal generated by each of the motors through the microphone array.
8. The motor state monitoring device according to claim 8, wherein the motor state monitoring device further comprises:

The fault information sending unit is used to send the motor number information of the motor whose current state is the fault state to a designated terminal.
A computer device, characterized in that it includes a processor, a memory, and a computer program stored on the memory and capable of running on the processor. The processor executes the computer program as claimed in claim 1. The motor state monitoring method described in any one of ~7.