CN113762116A - Motor crust breaking risk detection method and system and computer readable storage medium - Google Patents
Motor crust breaking risk detection method and system and computer readable storage medium Download PDFInfo
- Publication number
- CN113762116A CN113762116A CN202110995242.9A CN202110995242A CN113762116A CN 113762116 A CN113762116 A CN 113762116A CN 202110995242 A CN202110995242 A CN 202110995242A CN 113762116 A CN113762116 A CN 113762116A
- Authority
- CN
- China
- Prior art keywords
- motor
- detected
- crust breaking
- short signal
- signal
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
- 238000001514 detection method Methods 0.000 title claims abstract description 36
- 230000000737 periodic effect Effects 0.000 claims abstract description 53
- 238000012545 processing Methods 0.000 claims abstract description 9
- 230000005284 excitation Effects 0.000 claims abstract description 8
- 230000004044 response Effects 0.000 claims description 18
- 238000000034 method Methods 0.000 claims description 16
- 230000001133 acceleration Effects 0.000 claims description 13
- 238000005070 sampling Methods 0.000 claims description 11
- 230000002159 abnormal effect Effects 0.000 claims description 9
- 238000004891 communication Methods 0.000 claims description 8
- 238000005259 measurement Methods 0.000 claims description 5
- 238000004364 calculation method Methods 0.000 claims description 4
- 239000006260 foam Substances 0.000 claims description 2
- 206010039509 Scab Diseases 0.000 description 47
- 238000010586 diagram Methods 0.000 description 4
- 230000008569 process Effects 0.000 description 4
- 230000009471 action Effects 0.000 description 3
- 238000004590 computer program Methods 0.000 description 2
- 230000000694 effects Effects 0.000 description 2
- 238000004519 manufacturing process Methods 0.000 description 2
- 239000004065 semiconductor Substances 0.000 description 2
- 238000009825 accumulation Methods 0.000 description 1
- 230000002411 adverse Effects 0.000 description 1
- 238000004422 calculation algorithm Methods 0.000 description 1
- 238000013500 data storage Methods 0.000 description 1
- 238000013461 design Methods 0.000 description 1
- 239000000835 fiber Substances 0.000 description 1
- 230000006870 function Effects 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 230000003287 optical effect Effects 0.000 description 1
- 230000000750 progressive effect Effects 0.000 description 1
- 230000001737 promoting effect Effects 0.000 description 1
- 239000007787 solid Substances 0.000 description 1
- 230000001502 supplementing effect Effects 0.000 description 1
Images
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F2218/00—Aspects of pattern recognition specially adapted for signal processing
- G06F2218/08—Feature extraction
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
- G01R31/00—Arrangements for testing electric properties; Arrangements for locating electric faults; Arrangements for electrical testing characterised by what is being tested not provided for elsewhere
- G01R31/34—Testing dynamo-electric machines
- G01R31/343—Testing dynamo-electric machines in operation
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F2218/00—Aspects of pattern recognition specially adapted for signal processing
- G06F2218/12—Classification; Matching
- G06F2218/14—Classification; Matching by matching peak patterns
Landscapes
- Engineering & Computer Science (AREA)
- Computer Vision & Pattern Recognition (AREA)
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Theoretical Computer Science (AREA)
- Artificial Intelligence (AREA)
- Signal Processing (AREA)
- General Engineering & Computer Science (AREA)
- Control Of Electric Motors In General (AREA)
Abstract
The invention provides a motor crust breaking risk detection method, a motor crust breaking risk detection system and a computer readable storage medium. The motor crust breaking risk detection method comprises the following steps: acquiring working state data of a motor to be detected and acquiring a characteristic value of a reference short signal; processing the reference short signal according to the working state data and the characteristic value to obtain a target periodic short signal; inputting a target periodic short signal as an excitation signal to a motor to be detected, and carrying out crust breaking risk detection on the motor to be detected; when the motor to be detected does not have the crust breaking phenomenon, representing that the motor to be detected does not have the crust breaking risk caused by the periodic short signal. The invention can effectively reduce the crust breaking risk caused by the periodic short signals in the motor and bring better use experience for users.
Description
[ technical field ] A method for producing a semiconductor device
The invention relates to the technical field of tactile feedback, in particular to a motor crust breaking risk detection method and system and a computer readable storage medium.
[ background of the invention ]
In the related art, a linear motor as a good tactile feedback device is increasingly widely used in mobile terminals such as mobile phones, tablet computers, and smart wearable devices. One type of linear motor typically has a series of short signals, including a single short signal and/or a periodic short signal; the periodic short signal is generally used to represent periodic vibration in an actual scene, such as periodic vibration when shooting with a firearm. In practical applications, researchers found that a single short signal did not cause crust breaking in the linear motor, whereas a periodic short signal did cause crust breaking in the linear motor. Since the crust breaking phenomenon not only adversely affects the performance of the linear motor, but also seriously degrades the user experience, it is important to detect whether the motor has a crust breaking risk caused by a periodic short signal.
Therefore, it is necessary to design the method for detecting the existence of the crust breaking risk caused by the periodic short signal of the linear motor.
[ summary of the invention ]
The invention provides a motor crust breaking risk detection method, a motor crust breaking risk detection system and a computer readable storage medium, and aims to make up for the blank that a method for detecting whether a motor has a crust breaking risk caused by a periodic short signal is lacked in the related art.
In order to solve the above technical problem, a first aspect of an embodiment of the present invention provides a motor crust breaking risk detection method, including:
acquiring working state data of a motor to be detected and acquiring a characteristic value of a reference short signal;
processing the reference short signal according to the working state data and the characteristic value to obtain a target periodic short signal;
inputting the target periodic short signal as an excitation signal to the motor to be detected, and carrying out crust breaking risk detection on the motor to be detected; when the motor to be detected does not have a crust breaking phenomenon, representing that the motor to be detected does not have a crust breaking risk caused by a periodic short signal.
A second aspect of the embodiments of the present invention provides a motor crust breaking risk detection system, including: the system comprises a control terminal, a measuring platform in communication connection with the control terminal, and a motor to be detected in communication connection with the control terminal, wherein the measuring platform is used for measuring working state data of the motor to be detected;
the control terminal includes: a storage device for storing one or more programs, and one or more processors for executing one or more of the programs, which when executed by the one or more processors, cause the one or more processors to perform the method of motor crust breaking risk detection as described in the first aspect of an embodiment of the present invention.
A third aspect of embodiments of the present invention provides a computer-readable storage medium having stored thereon executable instructions that, when executed, perform a motor shelling risk detection method according to the first aspect of embodiments of the present invention.
As can be seen from the above description, the present invention has the following advantages compared with the related art:
and processing the reference short signal according to the working state data and the characteristic value of the reference short signal to obtain a target periodic short signal which is the periodic short signal most prone to causing a motor to be detected to generate a crust breaking phenomenon. When the target periodic short signal is used as an excitation signal of the motor to be detected, if the motor to be detected has no crust breaking phenomenon, the motor to be detected has no crust breaking risk caused by the periodic short signal, and at the moment, the target periodic short signal can be used for manufacturing a vibration effect; if the motor to be detected has a crust breaking phenomenon, it is indicated that the motor to be detected has a crust breaking risk caused by the target periodic short signal, at this time, the target periodic short signal can be avoided, and the periodic short signal of other motors similar to the motor to be detected can also be optimally designed by utilizing the target periodic short signal, so that the crust breaking risk caused by the periodic short signal in the motor can be effectively reduced, and better use experience can be brought to a user.
[ description of the drawings ]
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below. It is to be understood that the drawings in the following description are of some, but not all, embodiments of the invention. For a person skilled in the art, other figures can also be obtained from the provided figures without inventive effort.
Fig. 1 is a schematic flow chart of a motor crust breaking risk detection method according to an embodiment of the present invention;
FIG. 2 is a block diagram of a motor crust breaking risk detection system provided in an embodiment of the present invention;
fig. 3 is a block diagram of a computer-readable storage medium according to an embodiment of the present invention.
[ detailed description ] embodiments
For purposes of promoting a clear understanding of the objects, aspects and advantages of the invention, reference will now be made in detail to the present embodiments of the invention, examples of which are illustrated in the accompanying drawings, wherein like reference numerals refer to the same or similar elements throughout. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention. In addition, the technical features involved in the embodiments of the present invention described below may be combined with each other as long as they do not conflict with each other.
In the related art, there is a lack of a method of detecting whether the motor has a risk of crust breaking due to the presence of a periodic short signal. To this end, the embodiment of the present invention provides a motor crust breaking risk detection method, which can be applied to a single motor and also can be applied to a motor system including a plurality of motors.
Referring to fig. 1, fig. 1 is a schematic flow chart illustrating a motor crust breaking risk detection method according to an embodiment of the invention. As can be seen from fig. 1, the motor crust breaking risk detection method comprises the following steps 101 to 103.
In the embodiment of the invention, the working state data of the motor to be detected and the characteristic value of the reference short signal need to be acquired firstly. The working state data of the motor to be detected can include, but is not limited to, the abnormal frequency response data and the signal sampling data of the motor to be detected; at this time, the obtaining of the working state data of the motor to be detected may be: and acquiring the different-direction frequency response data and the signal sampling data of the motor to be detected. The characteristic value of the reference short signal may include, but is not limited to, a duration of the reference short signal; at this time, the characteristic value of the acquired reference short signal may be: the duration of the reference short signal is obtained. Here, it is necessary to explain that, since one type of motor generally has a series of short signals, one short signal may be selected from a series of short signals corresponding to the motor to be detected as a reference short signal.
As an embodiment, the abnormal frequency response data of the motor to be detected may include an abnormal frequency response peak; at this time, the obtaining of the abnormal frequency response data of the motor to be detected may be: acquiring a different-direction acceleration frequency response curve of a motor to be detected; and determining the peak value of the anisotropic frequency response according to the anisotropic acceleration frequency response curve.
As an embodiment, the signal sampling data of the motor to be detected may include a signal sampling frequency; at this time, the acquiring of the signal sampling data of the motor to be detected may be: and acquiring the signal sampling frequency of the motor to be detected.
It should be understood that the foregoing embodiment is only a preferred implementation of the embodiment of the present invention, and is not the only limitation on the type of the operating state data of the motor to be detected and the type of the characteristic value of the reference short signal in the embodiment of the present invention; in this regard, a person skilled in the art can flexibly set the setting according to the actual application scenario on the basis of the embodiment of the present invention.
102, processing the reference short signal according to the working state data and the characteristic value to obtain a target periodic short signal;
in the embodiment of the present invention, after the working state data of the motor to be detected and the characteristic value of the reference short signal are obtained, the reference short signal needs to be processed according to the working state data and the characteristic value, so as to obtain the target periodic short signal which most easily causes the motor to be detected to generate the crust breaking phenomenon.
As an embodiment, the reference short signal may be a binary signal. On this basis, processing the reference short signal according to the working state data and the characteristic value to obtain a target periodic short signal, which may include: calculating the number of targets according to the working state data and the characteristic value; according to the target number, compensating 0 for the reference short signal to obtain a target short signal; and (4) the target short signal is subjected to periodization to obtain a target periodic short signal.
Further, according to the working state data and the characteristic value, calculating the number of targets, which may be:
substituting the duration of the reference short signal into a formula I to calculate the corresponding frequency domain interval of the reference short signal, wherein the formula I can be expressed as df＝1/t，dfThe time interval is the corresponding frequency domain interval of the reference short signal, and t is the time length of the reference short signal;
the peak value of the different-direction frequency response of the motor to be detected is summed with the dfSubstituting the frequency domain interval into a formula II, and calculating the number of frequency domain intervals required by the accumulation of the reference short signal to the peak value of the abnormal frequency response, wherein the formula II can be expressed as num-ceil (m/d)f) Num is the number of frequency domain intervals required by accumulating the reference short signal to the peak value of the abnormal frequency response, m is the peak value of the abnormal frequency response, and ceil represents forward infinite integer taking operation;
substituting num into a third formula to calculate the frequency domain interval corresponding to num, wherein the third formula can be expressed as dF＝m/num，dFThe frequency domain interval corresponding to num;
will dFSubstituting into formula four to calculate dFCorresponding time length, wherein the formula four can be expressed as T1/dFT is dFThe corresponding time length;
substituting T and T into formula five, calculating the difference between T and T, wherein formula five can be expressed as dT＝T-t，dTIs the difference between T and T;
will dTSubstituting the signal sampling frequency of the motor to be detected into a formula six, and countingCalculating the target number, wherein the formula six can be expressed as dN＝round(dT×fs)，dNIs a target number, fsRound characterizes the rounding operation for the signal sampling frequency.
Combining the first formula to the sixth formula, a calculation formula for calculating the number of the targets can be obtained easily, and the calculation formula can be expressed as dn＝round[(ceil(m×t)/m-t)×fs]。
Further, according to the target number, performing 0 compensation on the reference short signal to obtain a target short signal, which may be: and (5) supplementing a plurality of target 0 s after the reference short signal to obtain a target short signal. Here, it is necessary to explain that the target number (i.e., d) is added after the short signal is referred ton) 0, corresponding to d being added after the reference short signalnBlank signals each having a duration dT(ii) a Then, when the motor is excited with the target short signal, the signal is at dnIn the blank signal section that individual blank signal constitutes, the motor can not take place to vibrate, and the length of time that the motor can not take place to vibrate is dn×dT。
It should be understood that the foregoing embodiment is only a preferred implementation of the embodiment of the present invention, and is not the only limitation of the embodiment of the present invention on the specific flow for processing the reference short signal according to the operating state data and the characteristic value; in this regard, a person skilled in the art can flexibly set the setting according to the actual application scenario on the basis of the embodiment of the present invention.
S103, inputting the target periodic short signal as an excitation signal to the motor to be detected, and carrying out crust breaking risk detection on the motor to be detected.
In the embodiment of the invention, after the target periodic short signal is obtained, the target periodic short signal can be directly input to the motor to be detected as the excitation signal, and the motor to be detected is subjected to crust breaking risk detection.
Further, when the motor to be detected is subjected to crust breaking risk detection, a result of whether the motor to be detected has a crust breaking phenomenon or not can be received, wherein if the result that the motor to be detected has no crust breaking phenomenon is received, the motor to be detected is characterized to have no crust breaking risk caused by periodic short signals; and if a result that the motor to be detected has a crust breaking phenomenon is received, representing that the motor to be detected has a crust breaking risk caused by target periodic short signals.
In summary, after the reference short signal is processed according to the working state data and the characteristic value of the reference short signal, the obtained target periodic short signal is a periodic short signal which is most likely to cause a crust breaking phenomenon to occur on the motor to be detected. Based on this, when the target periodic short signal is used as an excitation signal of the motor to be detected, if the motor to be detected has no crust breaking phenomenon, the motor to be detected has no risk of crust breaking caused by the periodic short signal, and at this time, the target periodic short signal or other periodic short signals except the target periodic short signal can be used for making a vibration effect; if the motor to be detected has the crust breaking phenomenon, the crust breaking phenomenon risk caused by the target periodic short signal of the motor to be detected is indicated, at the moment, the target periodic short signal can be avoided, the periodic short signal of other motors similar to the motor to be detected can also be optimally designed by utilizing the target periodic short signal, the crust breaking risk caused by the periodic short signal in the motor can be effectively reduced, and better use experience is brought to a user.
Referring to fig. 2, fig. 2 is a block diagram of a motor crust breaking risk detection system according to an embodiment of the present invention.
As shown in fig. 2, an embodiment of the present invention further provides a motor crust breaking risk detection system 200, which includes a control terminal 203, a motor 202 to be detected connected to the control terminal 203 in a communication manner, and a measurement platform 201 connected to the control terminal 203 in a communication manner and used for measuring working state data of the motor 202 to be detected; the control terminal 203 may include a storage device for storing one or more programs, and one or more processors for executing the one or more programs, and when the one or more programs are executed by the one or more processors, the one or more processors may be enabled to execute the motor shelling risk detection method provided by the embodiment of the present invention.
In practical application, the measurement platform 201 measures the working state data of the motor 202 to be detected, and transmits the measured working state data of the motor 202 to be detected to the control terminal 203, and after the control terminal 203 obtains the target periodic short signal through the motor crust breaking risk detection method provided by the embodiment of the invention, the target periodic short signal is input to the motor 202 to be detected as an excitation signal, and crust breaking risk detection is performed on the motor 202 to be detected.
As an embodiment, the measuring platform 201 may include a workbench, a fixture disposed on the workbench and accommodating the motor 202 to be detected, and an acceleration measuring device attached to a side wall of the fixture and in communication connection with the control terminal 203; the acceleration measurement device may include, but is not limited to, an accelerometer.
For this embodiment, the control terminal 203 may drive the motor 202 to be detected in the tool to vibrate, and measure the acceleration data of the motor 202 to be detected in different directions and in different directions by using the acceleration measuring device, at this time, the acceleration measuring device may transmit the acceleration data to the control terminal 203, and the control terminal 203 may obtain the working state data of the motor 202 to be detected after performing a series of processing on the acceleration data.
As an embodiment, in order to avoid the influence of the environment when the measuring platform 201 measures the motor 202 to be detected, the workbench may be a structure with a soft texture, for example, a whole piece of foam is used as the workbench.
It should be understood that the foregoing embodiments are merely preferred implementations of the embodiments of the present invention, and are not the only limitations on the specific configuration of the measurement platform 201 in the embodiments of the present invention; in this regard, a person skilled in the art can flexibly set the setting according to the actual application scenario on the basis of the embodiment of the present invention.
Referring further to fig. 3, fig. 3 is a block diagram of a computer-readable storage medium according to an embodiment of the present invention.
As shown in fig. 3, an embodiment of the present invention further provides a computer-readable storage medium 300, where the computer-readable storage medium 300 stores executable instructions 301, and when executed, the executable instructions 301 perform the motor shelling risk detection method provided by the embodiment of the present invention.
The steps of a method or algorithm described in connection with the embodiments disclosed herein may be embodied directly in hardware, in a software module executed by a processor, or in a combination of the two. A software module may reside in Random Access Memory (RAM), memory, Read Only Memory (ROM), electrically programmable ROM, electrically erasable programmable ROM, registers, hard disk, a removable disk, a CD-ROM, or any other form of storage medium known in the art.
In the above embodiments, the implementation may be wholly or partially realized by software, hardware, firmware, or any combination thereof. When implemented in software, may be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions. When loaded and executed on a computer, cause the processes or functions described in accordance with the invention to occur, in whole or in part. The computer may be a general purpose computer, a special purpose computer, a network of computers, or other programmable device. The computer instructions may be stored on a computer readable storage medium or transmitted from one computer readable storage medium to another, for example, the computer instructions may be transmitted from one website, computer, server, or data center to another website, computer, server, or data center by wire (e.g., coaxial cable, fiber optic, digital subscriber line) or wirelessly (e.g., infrared, wireless, microwave, etc.). The computer-readable storage medium can be any available medium that can be accessed by a computer or a data storage device, such as a server, a data center, etc., that incorporates one or more of the available media. The usable medium may be a magnetic medium (e.g., floppy Disk, hard Disk, magnetic tape), an optical medium (e.g., DVD), or a semiconductor medium (e.g., Solid State Disk), among others.
It should be noted that, in the summary of the present invention, each embodiment is described in a progressive manner, each embodiment focuses on differences from other embodiments, and the same and similar parts among the embodiments may be referred to each other. For the product class embodiment, since it is similar to the method class embodiment, the description is relatively simple, and for the relevant points, refer to the partial description of the method class embodiment.
It is further noted that, in the present disclosure, relational terms such as first and second, and the like may be used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Also, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of other identical elements in a process, method, article, or apparatus that comprises the element.
The previous description of the disclosed embodiments is provided to enable any person skilled in the art to make or use the present disclosure. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the generic principles defined in this disclosure may be applied to other embodiments without departing from the spirit or scope of the disclosure. Thus, the present disclosure is not intended to be limited to the embodiments shown herein but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.
Claims (8)
1. A motor crust breaking risk detection method is characterized by comprising the following steps:
acquiring working state data of a motor to be detected and acquiring a characteristic value of a reference short signal;
processing the reference short signal according to the working state data and the characteristic value to obtain a target periodic short signal;
inputting the target periodic short signal as an excitation signal to the motor to be detected, and carrying out crust breaking risk detection on the motor to be detected; when the motor to be detected does not have a crust breaking phenomenon, representing that the motor to be detected does not have a crust breaking risk caused by a periodic short signal.
2. A motor crust breaking risk detection method according to claim 1, wherein the reference short signal is a binary signal;
the processing the reference short signal according to the working state data and the characteristic value to obtain a target periodic short signal includes:
calculating the number of targets according to the working state data and the characteristic value;
according to the target number, performing 0 complementing on the reference short signal to obtain a target short signal;
and carrying out the cyclification on the target short signal to obtain a target cyclification short signal.
3. The motor crust breaking risk detection method of claim 2, wherein the operating condition data includes an alien frequency response peak;
the acquiring of the working state data of the motor to be detected comprises the following steps:
acquiring a different-direction acceleration frequency response curve of the motor to be detected;
and determining the peak value of the anisotropic frequency response according to the anisotropic acceleration frequency response curve.
4. A motor crust breaking risk detection method according to claim 3, wherein the characteristic value comprises a time duration, and the operating condition data further comprises a signal sampling frequency;
the calculating the number of targets according to the working state data and the characteristic value comprises:
respectively substituting the time length, the signal sampling frequency and the abnormal frequency response peak valueEntering a preset calculation formula, and calculating the number of targets; wherein the preset calculation formula is represented as dn＝round[(ceil(m×t)/m-t)×fs]，dnM is the peak value of the abnormal frequency response, t is the duration, fsFor the signal sampling frequency, round characterizes the rounding operation and ceil characterizes the integer-forward-infinity operation.
5. A motor crust breaking risk detection system, comprising: the system comprises a control terminal, a measuring platform in communication connection with the control terminal, and a motor to be detected in communication connection with the control terminal, wherein the measuring platform is used for measuring working state data of the motor to be detected;
the control terminal includes: a storage device for storing one or more programs, and one or more processors for executing one or more of the programs, which when executed by the one or more processors, cause the one or more processors to perform the method of any of claims 1-4.
6. A motor crust breaking risk detection system according to claim 5, wherein the measurement platform comprises: the workbench is arranged on the workbench and contains the tool for the motor to be detected and an acceleration measuring device which is attached to the side wall of the tool and is in communication connection with the control terminal.
7. A motor crust breaking risk detection system according to claim 6, wherein the bench is foam and the acceleration measuring device is an accelerometer.
8. A computer-readable storage medium having stored thereon executable instructions that, when executed, perform the method of any one of claims 1-4.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202110995242.9A CN113762116A (en) | 2021-08-27 | 2021-08-27 | Motor crust breaking risk detection method and system and computer readable storage medium |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202110995242.9A CN113762116A (en) | 2021-08-27 | 2021-08-27 | Motor crust breaking risk detection method and system and computer readable storage medium |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN113762116A true CN113762116A (en) | 2021-12-07 |
Family
ID=78791537
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202110995242.9A Pending CN113762116A (en) | 2021-08-27 | 2021-08-27 | Motor crust breaking risk detection method and system and computer readable storage medium |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN113762116A (en) |
Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106301137A (en) * | 2016-08-31 | 2017-01-04 | 歌尔股份有限公司 | Actively control the method for linear motor vibrations, device, system and electronic equipment |
| CN108347209A (en) * | 2018-02-02 | 2018-07-31 | 瑞声科技(新加坡)有限公司 | Overshoot response eliminates system and method |
| CN111076807A (en) * | 2019-12-18 | 2020-04-28 | 瑞声科技(新加坡)有限公司 | Method, device and equipment for processing vibration signal and readable medium |
| CN111797483A (en) * | 2020-06-30 | 2020-10-20 | 瑞声科技(新加坡)有限公司 | Method and apparatus for correcting motor balanced electrical signal, and computer readable storage medium |
| CN112667077A (en) * | 2020-12-24 | 2021-04-16 | 瑞声新能源发展(常州)有限公司科教城分公司 | Motor model parameter detection method and device, electronic equipment and medium |
| WO2021097890A1 (en) * | 2019-11-22 | 2021-05-27 | 瑞声声学科技(深圳)有限公司 | Method and apparatus for compensatig for low-frequency sense of vibration, and electronic device |
-
2021
- 2021-08-27 CN CN202110995242.9A patent/CN113762116A/en active Pending
Patent Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106301137A (en) * | 2016-08-31 | 2017-01-04 | 歌尔股份有限公司 | Actively control the method for linear motor vibrations, device, system and electronic equipment |
| CN108347209A (en) * | 2018-02-02 | 2018-07-31 | 瑞声科技(新加坡)有限公司 | Overshoot response eliminates system and method |
| WO2021097890A1 (en) * | 2019-11-22 | 2021-05-27 | 瑞声声学科技(深圳)有限公司 | Method and apparatus for compensatig for low-frequency sense of vibration, and electronic device |
| CN111076807A (en) * | 2019-12-18 | 2020-04-28 | 瑞声科技(新加坡)有限公司 | Method, device and equipment for processing vibration signal and readable medium |
| CN111797483A (en) * | 2020-06-30 | 2020-10-20 | 瑞声科技(新加坡)有限公司 | Method and apparatus for correcting motor balanced electrical signal, and computer readable storage medium |
| CN112667077A (en) * | 2020-12-24 | 2021-04-16 | 瑞声新能源发展(常州)有限公司科教城分公司 | Motor model parameter detection method and device, electronic equipment and medium |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN109462446B (en) | A radio frequency calibration method, device and electronic device and computer readable storage medium | |
| CN109085630B (en) | Signal capturing method and device | |
| WO2018103402A1 (en) | Wireless fidelity (wifi) scan method and related product | |
| CN110162976A (en) | Methods of risk assessment, device and terminal | |
| JP2021520161A (en) | Phase calibration method and equipment | |
| CN112213728A (en) | An ultrasonic ranging method, device, computer-readable storage medium and robot | |
| CN113588122A (en) | Method and device for measuring surface temperature of object | |
| EP3370074B1 (en) | Method for detecting background noise of sensor, and device thereof | |
| CN113762116A (en) | Motor crust breaking risk detection method and system and computer readable storage medium | |
| CN114356703A (en) | Root cause analysis method and device | |
| JP2024133672A (en) | Information processing device, method and program | |
| CN110263417B (en) | A method, device and electronic equipment for obtaining timing characteristics | |
| CN117906790A (en) | Temperature detection calibration method, device, equipment and medium | |
| CN108989105B (en) | Propagation path diagram generation method and device and server | |
| EP4220132A1 (en) | Rolling principal component analysis for dynamic process monitoring and end point detection | |
| RU2719310C2 (en) | System and method for measurement of path length using pocket electronic device | |
| CN114885153A (en) | Rapid focusing method and device, electronic equipment and medium | |
| CN110244251B (en) | Method and device for acquiring calibration coefficient and calibration chip | |
| CN109633236B (en) | Radio signal identification method and device | |
| CN113688351A (en) | Method, device, electronic equipment and readable medium for detecting weight of article | |
| CN108877875A (en) | physiological sensor device, method and system | |
| CN109242354B (en) | Machining effect evaluation method and machining effect evaluation device | |
| CN114125813A (en) | Signal coverage range determining method based on mobile phone signaling and related device | |
| CN112816940A (en) | Target distance estimation method and device based on sound pressure and particle vibration velocity | |
| CN110032488B (en) | Monitoring system, method and device for specific nodes in cluster and service server |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination |