CN117054872A - Motor fault prediction detection system based on data model - Google Patents
Motor fault prediction detection system based on data model Download PDFInfo
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- CN117054872A CN117054872A CN202311189771.5A CN202311189771A CN117054872A CN 117054872 A CN117054872 A CN 117054872A CN 202311189771 A CN202311189771 A CN 202311189771A CN 117054872 A CN117054872 A CN 117054872A
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- 238000013499 data model Methods 0.000 title claims abstract description 80
- 238000001514 detection method Methods 0.000 title claims abstract description 50
- 238000001228 spectrum Methods 0.000 claims abstract description 53
- 238000012549 training Methods 0.000 claims abstract description 21
- 230000009467 reduction Effects 0.000 claims abstract description 13
- 210000000988 bone and bone Anatomy 0.000 claims abstract description 9
- 238000006243 chemical reaction Methods 0.000 claims abstract description 7
- 210000003454 tympanic membrane Anatomy 0.000 claims description 23
- 238000004891 communication Methods 0.000 claims description 5
- 238000012545 processing Methods 0.000 claims description 4
- 238000004590 computer program Methods 0.000 claims description 3
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Classifications
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- 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
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01H—MEASUREMENT OF MECHANICAL VIBRATIONS OR ULTRASONIC, SONIC OR INFRASONIC WAVES
- G01H1/00—Measuring characteristics of vibrations in solids by using direct conduction to the detector
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01M—TESTING STATIC OR DYNAMIC BALANCE OF MACHINES OR STRUCTURES; TESTING OF STRUCTURES OR APPARATUS, NOT OTHERWISE PROVIDED FOR
- G01M13/00—Testing of machine parts
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- G—PHYSICS
- G10—MUSICAL INSTRUMENTS; ACOUSTICS
- G10L—SPEECH ANALYSIS OR SYNTHESIS; SPEECH RECOGNITION; SPEECH OR VOICE PROCESSING; SPEECH OR AUDIO CODING OR DECODING
- G10L25/00—Speech or voice analysis techniques not restricted to a single one of groups G10L15/00 - G10L21/00
- G10L25/03—Speech or voice analysis techniques not restricted to a single one of groups G10L15/00 - G10L21/00 characterised by the type of extracted parameters
- G10L25/18—Speech or voice analysis techniques not restricted to a single one of groups G10L15/00 - G10L21/00 characterised by the type of extracted parameters the extracted parameters being spectral information of each sub-band
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- G—PHYSICS
- G10—MUSICAL INSTRUMENTS; ACOUSTICS
- G10L—SPEECH ANALYSIS OR SYNTHESIS; SPEECH RECOGNITION; SPEECH OR VOICE PROCESSING; SPEECH OR AUDIO CODING OR DECODING
- G10L25/00—Speech or voice analysis techniques not restricted to a single one of groups G10L15/00 - G10L21/00
- G10L25/27—Speech or voice analysis techniques not restricted to a single one of groups G10L15/00 - G10L21/00 characterised by the analysis technique
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- G—PHYSICS
- G10—MUSICAL INSTRUMENTS; ACOUSTICS
- G10L—SPEECH ANALYSIS OR SYNTHESIS; SPEECH RECOGNITION; SPEECH OR VOICE PROCESSING; SPEECH OR AUDIO CODING OR DECODING
- G10L25/00—Speech or voice analysis techniques not restricted to a single one of groups G10L15/00 - G10L21/00
- G10L25/48—Speech or voice analysis techniques not restricted to a single one of groups G10L15/00 - G10L21/00 specially adapted for particular use
- G10L25/51—Speech or voice analysis techniques not restricted to a single one of groups G10L15/00 - G10L21/00 specially adapted for particular use for comparison or discrimination
Abstract
The application discloses a motor fault prediction detection system based on a data model, which comprises the following components: the noise acquisition module is used for acquiring noise signals generated when the motor operates through bone conduction; the signal conversion module is used for converting the noise signal into a charge signal and uploading the charge signal; and the data model building module is used for generating a noise spectrum through the charge signals and extracting features. According to the application, the noise spectrum of the motor is collected, the characteristics are extracted after noise reduction, the optimal characteristic value and the weight value of the noise spectrum are determined, the optimal model is repeatedly extracted for a plurality of times, then the noise spectrum of a new motor is collected, the extracted characteristic value is imported into the original data model for training, whether the characteristic value is matched with the optimal characteristic value of the original data model is checked by comparing and analyzing the characteristic value of the new noise spectrum, and whether the motor fails or not can be obtained through rapid training, and the optimal model is combined with the existing manual detection, so that the motor failure detection efficiency is improved.
Description
Technical Field
The application relates to the technical field of motor fault detection, in particular to a motor fault prediction detection system based on a data model.
Background
The motor is widely applied to the fields of transportation, household appliances and the like. And is an essential component thereof. So its quality directly affects our daily lives. Along with the improvement of the living standard of people, the requirements of consumers on intelligent products are higher and higher, so that manufacturers such as intelligent home, household appliances, transportation means and the like have very high requirements on the quality of motors, and the intelligent home, household appliances, transportation means and the like can not only reliably run, but also cannot have any abnormal sound during running. However, the whole motor industry is basically to detect the noise quality of the motor by using the human ears, and the development of the motor industry is severely restricted by the mode, because the detection by using the human ears is low in efficiency and poor in consistency, and the detection result is greatly dependent on the state of a person, and particularly, the quality of the motor cannot be accurately and efficiently detected for a long time in a complex and noisy environment.
Disclosure of Invention
The application aims to provide a motor fault prediction detection system, a motor fault prediction detection device, motor fault prediction detection equipment and motor fault prediction detection media based on a data model, so as to solve the problems in the background art.
In order to achieve the above purpose, the present application provides the following technical solutions: a data model-based motor fault prediction detection system, comprising:
the noise acquisition module is used for acquiring noise signals generated when the motor operates through bone conduction;
the signal conversion module is used for converting the noise signal into a charge signal and uploading the charge signal;
the data model building module is used for generating a noise frequency spectrum through the charge signals, extracting characteristics, determining an optimal characteristic value and a weight value, generating an original data model of the motor according to the characteristics, and determining recognition logic;
the data model analysis module is used for collecting characteristic values of the new noise frequency spectrum, importing the characteristic values into the original data model for training, and analyzing data comparison results.
Preferably, the noise collection module includes:
the bone conduction acquisition probe unit is in direct contact with the motor, acquires a vibration signal of the motor, and converts the vibration signal of the motor into a vibration signal of the tympanic membrane;
and the pickup unit is used for picking up vibration signals of the tympanic membrane and collecting noise of the tympanic membrane.
Preferably, the signal conversion module amplifies and transmits the signal while converting the noise signal of the tympanic membrane into the charge signal.
Preferably, the data model building module specifically includes:
generating a noise spectrum from the charge signal of the tympanic membrane, which is the initial noise spectrum of the motor;
noise is reduced on the initial noise spectrum, characteristics are extracted after noise reduction, and the optimal characteristic value and the weight value of the noise spectrum are determined;
and generating an original data model of the motor according to the noise frequency spectrum, and determining the recognition logic of the noise of the subsequent motor by the optimal characteristic value and the weight value.
Preferably, the noise reduction of the initial noise spectrum includes:
decomposing the initial noise spectrum or the subsequent noise spectrum to enable the noise to be distributed in the high-frequency coefficient;
then, the high-frequency coefficient of wavelet analysis is subjected to threshold processing, the high-frequency part is restrained, and the effective signals are reserved;
and finally, feeding back the signal reconstruction to the original signal for noise reduction.
Preferably, when the data model building module generates an original data model of the motor, the data model building module repeatedly collects noise for a plurality of times, generates the original data model, imports a training set, and then trains and selects an optimal model in the training set through the model to serve as a follow-up fault comparison analysis model.
Preferably, the data model analysis module specifically includes:
collecting characteristic values of a new noise spectrum, and importing an original data model for training;
comparing the characteristic value of the new noise spectrum with the optimal characteristic value and the weight value of the original data model, if the optimal characteristic value is matched, the new motor is proved to have no fault, otherwise, the motor is proved to have faults.
The application also provides an electronic device, which is entity equipment, comprising:
the device comprises a processor and a memory, wherein the memory is in communication connection with the processor;
the memory is configured to store executable instructions that are executed by at least one of the processors, the processor configured to execute the executable instructions to implement a data model-based motor fault prediction detection system as described above.
The present application also provides a computer readable storage medium having stored therein a computer program which when executed by a processor implements a data model based motor fault prediction detection system as described above.
Compared with the prior art, the application has the beneficial effects that:
the method comprises the steps of collecting a noise spectrum of a motor, extracting features after noise reduction, determining an optimal feature value and a weight value of the noise spectrum, repeatedly extracting an optimal model for a plurality of times, collecting a new noise spectrum of the motor, extracting feature values, importing the feature values into an original data model for training, comparing and analyzing the feature values of the new noise spectrum with the optimal feature values of the original data model, checking whether the feature values are matched, and quickly training to obtain whether the motor fails or not, and optimizing and combining the motor failure detection with the existing manual detection to improve the motor failure detection efficiency.
Drawings
FIG. 1 is a block diagram of a motor fault prediction detection system based on a data model according to an embodiment of the present application;
FIG. 2 is a flowchart of steps of a motor failure prediction detection system based on a data model according to an embodiment of the present application;
FIG. 3 is an initial noise spectrum diagram of a motor fault prediction detection system based on a data model according to an embodiment of the present application;
FIG. 4 is an initial noise spectrum diagram of a motor fault prediction detection system based on a data model after noise reduction according to an embodiment of the present application;
fig. 5 is a diagram of an original data model of a motor fault prediction detection system based on a data model according to an embodiment of the present application;
fig. 6 is a schematic structural diagram of a motor fault prediction detection device based on a data model according to an embodiment of the present application.
Detailed Description
The following description of the embodiments of the present application will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present application, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the application without making any inventive effort, are intended to be within the scope of the application.
Referring to fig. 1 to 5, the present application provides a motor fault prediction detection system based on a data model, including:
the noise acquisition module is used for acquiring noise signals generated when the motor operates through bone conduction;
the signal conversion module is used for converting the noise signal into a charge signal and uploading the charge signal;
the data model building module is used for generating a noise frequency spectrum through the charge signals, extracting characteristics, determining an optimal characteristic value and a weight value, generating an original data model of the motor according to the characteristics, and determining recognition logic;
the data model analysis module is used for collecting characteristic values of the new noise frequency spectrum, importing the characteristic values into the original data model for training, and analyzing data comparison results.
Further, the noise collection module includes:
the bone conduction acquisition probe unit is in direct contact with the motor, acquires a vibration signal of the motor, and converts the vibration signal of the motor into a vibration signal of the tympanic membrane;
and the pickup unit is used for picking up vibration signals of the tympanic membrane and collecting noise of the tympanic membrane.
Further, the signal conversion module is used for amplifying and transmitting the signal when converting the noise signal of the tympanic membrane into the charge signal.
Further, as shown in fig. 5, the data model building module specifically includes:
generating a noise spectrum from the charge signal of the tympanic membrane, which is the initial noise spectrum of the motor;
noise is reduced on the initial noise spectrum, characteristics are extracted after noise reduction, and the optimal characteristic value and the weight value of the noise spectrum are determined;
and generating an original data model of the motor according to the noise frequency spectrum, and determining the recognition logic of the noise of the subsequent motor by the optimal characteristic value and the weight value.
Further, as shown in fig. 3 and 4, the noise reduction of the initial noise spectrum includes:
decomposing the initial noise spectrum or the subsequent noise spectrum to enable the noise to be distributed in the high-frequency coefficient;
then, the high-frequency coefficient of wavelet analysis is subjected to threshold processing, the high-frequency part is restrained, and the effective signals are reserved;
and finally, feeding back the signal reconstruction to the original signal for noise reduction.
Furthermore, when the data model building module generates an original data model of the motor, noise is repeatedly collected for a plurality of times, the original data model is generated, a training set is imported, and then an optimal model is selected in the training set through model training to serve as a follow-up fault comparison analysis model.
Further, the data model analysis module specifically includes:
collecting characteristic values of a new noise spectrum, and importing an original data model for training;
comparing the characteristic value of the new noise spectrum with the optimal characteristic value and the weight value of the original data model, if the optimal characteristic value is matched, the new motor is proved to have no fault, otherwise, the motor is proved to have faults.
In this embodiment, by collecting the noise spectrum of the motor and extracting the features after noise reduction, determining the optimal feature value and the weight value of the noise spectrum, repeatedly extracting the optimal model for a plurality of times, then collecting the noise spectrum of the new motor, extracting the feature value, importing the feature value into the original data model for training, comparing and analyzing the feature value of the new noise spectrum with the optimal feature value of the original data model, checking whether the feature value is matched, and then, quickly training to obtain whether the motor fails, and optimizing and combining with the existing manual detection to improve the motor failure detection efficiency.
For better understanding of the foregoing embodiments, as shown in fig. 2, the present application further provides a step flow chart of a motor fault prediction detection system based on a data model, and the method at least includes:
step S1, collecting noise signals generated when a motor runs through bone conduction;
s2, converting the noise signal into a charge signal and uploading the charge signal;
step S3, generating a noise spectrum through the charge signal, extracting characteristics, determining an optimal characteristic value and a weight value, generating an original data model of the motor according to the optimal characteristic, and determining recognition logic;
and S4, collecting a new noise signal when the motor runs through bone conduction, converting the noise signal into a charge signal again, uploading the charge signal to generate a new noise frequency spectrum, collecting a new characteristic value, importing an original data model for training, and analyzing a data comparison result.
In order to better understand the foregoing embodiments, as shown in fig. 6, the present application further provides a data model-based motor failure prediction detection device, configured to support the data model-based motor failure prediction detection system of the foregoing embodiments, where the data model-based motor failure prediction detection device includes:
the high-precision sound conduction device comprises a threaded spring thimble 1 and a high-precision sound conduction rod 6, wherein one end of the high-precision sound conduction rod 6 is connected with the threaded spring thimble 1 through a copper embedded round nut 2, the high-precision sound conduction rod 6 is connected with a spherical tympanic membrane 3, a sound pickup unit 4 is installed inside the spherical tympanic membrane 3 and below the high-precision sound conduction rod 6, and a circuit assembly 5 is installed inside the spherical tympanic membrane 3 and on one side of the sound pickup unit 4.
In this embodiment, motor trouble prediction detection device based on data model includes screw spring thimble 1 and high accuracy sound conduction stick 6, screw spring thimble 1 is installed at the top of high accuracy sound conduction stick 6, spherical tympanic membrane 3 is overlapped in the below outside of high accuracy sound conduction stick 6, the inside of spherical tympanic membrane 3 and the below that is located high accuracy sound conduction stick 6 are installed pickup unit 4, circuit assembly 5 is installed to the inside of spherical tympanic membrane 3 and the one side that is located pickup unit 4, and in actual use, screw spring thimble 1 and motor housing contact, the noise when beginning to gather motor operation, transmit spherical tympanic membrane 3 through high accuracy sound conduction stick 6, pick up the sound signal of spherical tympanic membrane 3 by pickup unit 4 and convert the sound signal into charge signal transmission to the detection case through circuit assembly 5, the noise's when whole gathering motor operation link and the data transmission who gathers to the detection case have eliminated the environmental disturbance completely compared with traditional detection mode, and any air-borne noise can not cause the interference, and the sound attenuation device is used to the sound attenuation that the sound attenuation device is widely applied to the sound attenuation device is improved to the sound attenuation device, the sound attenuation device is widely used to the improvement of the sound attenuation device, the system is convenient, the sound attenuation device is used to the whole.
Further, the PLC controller is arranged on the outer part of the motor fault prediction detection device based on the data model, and the motor fault prediction detection device based on the data model, the pickup unit 4, the circuit assembly 5, the sound amplifying mechanism, the detection circuit assembly 5, the pc platform and the display are electrically connected with the PLC controller, so that the overall normal control and use are realized.
On the basis of the embodiment, the application further provides electronic equipment, which comprises:
the device comprises a processor and a memory, wherein the processor is in communication connection with the memory;
in this embodiment, the memory may be implemented in any suitable manner, for example: the memory can be read-only memory, mechanical hard disk, solid state disk, USB flash disk or the like; the memory is used for storing executable instructions executed by at least one of the processors;
in this embodiment, the processor may be implemented in any suitable manner, e.g., the processor may take the form of, for example, a microprocessor or processor and a computer-readable medium storing computer-readable program code (e.g., software or firmware) executable by the (micro) processor, logic gates, switches, an application specific integrated circuit (ApplicationSpecificIntegratedCircuit, ASIC), a programmable logic controller, and an embedded microcontroller, etc.; the processor is configured to execute the executable instructions to implement a data model-based motor fault prediction detection system as described above.
On the basis of the above embodiment, the present application further provides a computer readable storage medium, in which a computer program is stored, which when executed by a processor, implements a motor failure prediction detection system based on a data model as described above.
Those of ordinary skill in the art will appreciate that the modules and method steps of the examples described in connection with the embodiments disclosed herein can be implemented as electronic hardware, or as a combination of computer software and electronic hardware. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the solution. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present application.
It will be clearly understood by those skilled in the art that, for convenience and brevity of description, specific working procedures of the apparatus, device and module described above may refer to corresponding procedures in the foregoing method embodiments, which are not repeated herein.
In the several embodiments provided by the present application, it should be understood that the disclosed apparatus, device and method may be implemented in other manners. For example, the apparatus embodiments described above are merely illustrative, and for example, the division of the modules is merely a logical function division, and there may be additional divisions when actually implemented, for example, multiple modules or units may be combined or integrated into another apparatus, or some features may be omitted or not performed. Alternatively, the coupling or direct coupling or communication connection shown or discussed with each other may be an indirect coupling or communication connection via some interfaces, devices or apparatuses, which may be in electrical, mechanical or other form.
The modules described as separate components may or may not be physically separate, and components shown as modules may or may not be physical units, may be located in one place, or may be distributed over multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.
In addition, each functional module in each embodiment of the present application may be integrated into one processing module, or each module may exist alone physically, or two or more modules may be integrated into one module.
The functions, if implemented in the form of software functional modules and sold or used as a stand-alone product, may be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application may be embodied essentially or in a part contributing to the prior art or in a part of the technical solution in the form of a software product stored in a storage medium, comprising several instructions for causing a computer device (which may be a personal computer, a server, a network device, or the like) to perform all or part of the steps of the method according to the embodiments of the present application. And the aforementioned storage medium includes: a usb disk, a removable hard disk, a read-only memory server, a random access memory server, a magnetic disk or an optical disk, or other various media capable of storing program instructions.
In addition, it should be noted that the combination of the technical features described in the present application is not limited to the combination described in the claims or the combination described in the specific embodiments, and all the technical features described in the present application may be freely combined or combined in any manner unless contradiction occurs between them.
It should be noted that the above-mentioned embodiments are merely examples of the present application, and it is obvious that the present application is not limited to the above-mentioned embodiments, and many similar variations are possible. All modifications attainable or obvious from the present disclosure set forth herein should be deemed to be within the scope of the present disclosure.
The foregoing is merely illustrative of the preferred embodiments of the present application, and is not intended to limit the scope of the present application. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application should be included in the protection scope of the present application.
Claims (9)
1. A data model-based motor fault prediction detection system, comprising:
the noise acquisition module is used for acquiring noise signals generated when the motor operates through bone conduction;
the signal conversion module is used for converting the noise signal into a charge signal and uploading the charge signal;
the data model building module is used for generating a noise frequency spectrum through the charge signals, extracting characteristics, determining an optimal characteristic value and a weight value, generating an original data model of the motor according to the characteristics, and determining recognition logic;
the data model analysis module is used for collecting characteristic values of the new noise frequency spectrum, importing the characteristic values into the original data model for training, and analyzing data comparison results.
2. The data model-based motor fault prediction detection system of claim 1, wherein the noise acquisition module comprises:
the bone conduction acquisition probe unit is in direct contact with the motor, acquires a vibration signal of the motor, and converts the vibration signal of the motor into a vibration signal of the tympanic membrane;
and the pickup unit is used for picking up vibration signals of the tympanic membrane and collecting noise of the tympanic membrane.
3. The data model based motor fault prediction detection system of claim 1, wherein the signal conversion module simultaneously amplifies and transmits the signal while converting the noise signal of the tympanic membrane into the charge signal.
4. The motor fault prediction detection system based on a data model according to claim 1, wherein the data model building module specifically comprises:
generating a noise spectrum from the charge signal of the tympanic membrane, which is the initial noise spectrum of the motor;
noise is reduced on the initial noise spectrum, characteristics are extracted after noise reduction, and the optimal characteristic value and the weight value of the noise spectrum are determined;
and generating an original data model of the motor according to the noise frequency spectrum, and determining the recognition logic of the noise of the subsequent motor by the optimal characteristic value and the weight value.
5. The data model based motor fault prediction detection system of claim 4, wherein the noise reduction of the initial noise spectrum comprises:
decomposing the initial noise spectrum or the subsequent noise spectrum to enable the noise to be distributed in the high-frequency coefficient;
then, the high-frequency coefficient of wavelet analysis is subjected to threshold processing, the high-frequency part is restrained, and the effective signals are reserved;
and finally, feeding back the signal reconstruction to the original signal for noise reduction.
6. The motor fault prediction detection system based on the data model according to claim 4, wherein the data model building module repeatedly collects noise and generates the original data model a plurality of times when generating the original data model of the motor, and imports a training set, and then selects an optimal model in the training set through model training as a subsequent fault comparison analysis model.
7. The motor fault prediction detection system based on a data model of claim 1, wherein the data model analysis module specifically comprises:
collecting characteristic values of a new noise spectrum, and importing an original data model for training;
comparing the characteristic value of the new noise spectrum with the optimal characteristic value and the weight value of the original data model, if the optimal characteristic value is matched, the new motor is proved to have no fault, otherwise, the motor is proved to have faults.
8. An electronic device, the electronic device comprising:
the device comprises a processor and a memory, wherein the memory is in communication connection with the processor;
the memory is configured to store executable instructions that are executed by at least one of the processors, the processor configured to execute the executable instructions to implement the data model-based motor fault prediction detection system of any one of claims 1 to 6.
9. A computer readable storage medium, characterized in that the computer readable storage medium has stored therein a computer program which, when executed by a processor, implements the data model based motor fault prediction detection system of any one of claims 1 to 7.
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