CN117074942A - Motor fault self-diagnosis method, device and storage medium - Google Patents
Motor fault self-diagnosis method, device and storage medium Download PDFInfo
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- CN117074942A CN117074942A CN202311331451.9A CN202311331451A CN117074942A CN 117074942 A CN117074942 A CN 117074942A CN 202311331451 A CN202311331451 A CN 202311331451A CN 117074942 A CN117074942 A CN 117074942A
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- 238000000034 method Methods 0.000 title claims abstract description 38
- 238000004092 self-diagnosis Methods 0.000 title claims abstract description 34
- 230000004907 flux Effects 0.000 claims abstract description 65
- 238000004364 calculation method Methods 0.000 claims description 18
- 238000001514 detection method Methods 0.000 claims description 5
- 230000006870 function Effects 0.000 claims description 4
- 238000004590 computer program Methods 0.000 claims description 3
- 230000002159 abnormal effect Effects 0.000 abstract 1
- 238000004519 manufacturing process Methods 0.000 description 6
- RYGMFSIKBFXOCR-UHFFFAOYSA-N Copper Chemical compound [Cu] RYGMFSIKBFXOCR-UHFFFAOYSA-N 0.000 description 5
- 229910052802 copper Inorganic materials 0.000 description 5
- 239000010949 copper Substances 0.000 description 5
- 238000004458 analytical method Methods 0.000 description 2
- 230000009286 beneficial effect Effects 0.000 description 2
- 238000005516 engineering process Methods 0.000 description 2
- 239000013307 optical fiber Substances 0.000 description 2
- 230000008569 process Effects 0.000 description 2
- 238000003745 diagnosis Methods 0.000 description 1
- 230000006872 improvement 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
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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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Abstract
The invention discloses a motor fault self-diagnosis method, a device and a storage medium, wherein the method relies on the motor fault self-diagnosis device to collect model parameters of a motor, and comprises the following steps: stator resistance observationsR s Rotor resistance observationsR r Mutual inductance observation value of motorL m Leakage inductance observation value of motorL σ The method comprises the steps of carrying out a first treatment on the surface of the By calculating the theoretical value of the stator resistanceR s ' Theoretical value of rotor resistanceR r ' Theoretical value of first motor flux linkageψ 1 Flux linkage observation value of rated motorψ 2 Theoretical value of second motor flux linkageψ 3 Judging whether the motor is in a normal error range or not according to the motor model parameter observation value, and indicating that the motor is abnormal if the motor is out of the error range, and reporting the motor fault. The method can realize that whether the motor has faults or not can be judged by detecting and analyzing the state parameters of the motor under the condition of carrying out load operation or not without dismantling other hardware equipmentThe cost is saved, the precision is high, and the online self-diagnosis of motor faults can be realized.
Description
Technical Field
The invention relates to the field of motor fault self-diagnosis, in particular to a motor fault self-diagnosis method, a motor fault self-diagnosis device and a storage medium.
Background
With the progress of modern science, the development of production systems and the improvement of equipment manufacturing level are improved, the number of motors adopted by the production systems is continuously increased, the single-machine capacity is continuously improved, the normal operation of the motors is very important for guaranteeing the safety, high efficiency, agility, high quality and low consumption operation of the production and manufacturing process, motor faults not only damage the motors themselves, but also influence the normal operation of the whole system, even endanger life safety, cause huge economic loss, and through the self-diagnosis of motor faults, the motor faults and the further occurrence of preventing faults can be discovered early, the production stopping loss caused by sudden accidents is reduced, threatens to equipment and personnel are generated, and conditions are created for overhaul. With the development and application of high and new technologies such as sensors, optical fibers and computers, the motor online diagnosis technology is rapidly developed, and the motor fault self-diagnosis method and device and the storage medium provided by the invention rely on a frequency converter system to collect various information generated in the motor operation process, and judge whether the motor operates normally or not according to rated parameters of the motor, so that whether faults, fault positions and reasons exist or not can be judged through detection and analysis of state parameters of the motor when the motor operates under load or not.
Disclosure of Invention
In order to solve the problems, the invention provides a motor fault self-diagnosis method, a device and a storage medium.
In order to achieve the above purpose, the technical scheme provided by the invention is as follows:
a motor fault self-diagnosis method comprises the following steps:
s1, rated voltage of a motor nameplateRated power->Rated current->Rated frequency->Efficiency rating ofRated power factor->Inputting into a motor diagnosis program;
s2, parameter self-learning is carried out on the motor by a motor diagnosis program to obtain the stator resistance observation valueRotor resistance observation->Motor mutual inductance observation value->Leakage inductance observation value of motor>；
S3, calculating a theoretical value of the stator resistance according to the motor lossStator resistance observation +.>Compared with the theoretical value of the stator resistance->S4 is carried out when the error is not more than 15%, and a motor fault signal is reported when the error is more than 15%;
s4, calculating a rotor resistance theoretical value according to the motor lossRotor resistance observation +.>Compared with the theoretical value of the rotor resistance->S5 is carried out when the error is not more than 15%, and a motor fault signal is reported when the error is more than 15%;
s5, calculating a first motor flux linkage theoretical value according to a mutual inductance empirical formulaCalculating rated motor flux linkage observation value according to rated flux linkage formula>Calculating a second motor flux theoretical value +.>First motor flux theory value +.>Second motor flux linkage theory value->Compared to the nominal motor flux linkage observation +.>S6 is carried out when the error is not more than 15%, and a motor fault signal is reported when the error is more than 15%;
s6, observing leakage inductance value of motorCompared with the observation value of 1/10 motor mutual inductance +.>Step S7 is carried out when the error is not more than 15%, and a motor fault signal is reported when the error is more than 15%;
s7, exiting the motor diagnosis program, and reporting the motor to be normal.
Further, in S3, a theoretical value of stator resistanceThe calculation formula is as follows:
，
wherein,for the theoretical value of the stator resistance, < > f>For rated power +.>For the purpose of the rated efficiency,Mfor the number of motor phases>Is rated current.
Further, in S4, a theoretical value of rotor resistanceThe calculation formula is as follows:
，
wherein,for the theoretical value of the rotor resistance, < >>For rated power +.>For the purpose of the rated efficiency,Mfor the number of motor phases>Is rated current.
Further, in the step S5, a first motor flux linkage theoretical valueThe calculation formula is as follows:
，
wherein,for the first motor flux linkage theory, +.>For rated current +.>The motor mutual inductance observation value;
rated motor flux linkage observationsThe calculation formula is as follows:
，
wherein,for the nominal motor flux linkage observation, +.>Rated frequency, < >>Rated voltage, pi is the circumference ratio;
second motor flux linkage theory valueThe calculation formula is as follows:
，
wherein,for the second motor flux linkage theory, +.>For rated current +.>For the motor mutual inductance observation value, < >>Is the rated motor power factor.
The invention also provides a motor fault self-diagnosis device, which is a frequency converter or an integrated electronic device with the function of the frequency converter, and the device comprises:
network reading component: the method is used for reading rated voltage, rated power, rated current, rated frequency, rated voltage, rated efficiency and rated power factor of the motor;
a network learning component: the method is used for learning the current stator resistance observation value, the rotor resistance observation value, the motor mutual inductance observation value and the motor leakage inductance observation value of the motor;
network computing component: the method is used for calculating a motor stator resistance theoretical value, a rotor resistance theoretical value, a first motor flux linkage theoretical value, a rated motor flux linkage observation value and a second motor flux linkage theoretical value on line;
a network comparison component: the method is used for comparing stator resistance errors, rotor resistance errors, errors between the first motor flux linkage, the second motor flux linkage and the rated motor flux linkage, and errors of motor leakage inductance observation values and 1/10 motor mutual inductance observation on line;
a network decision component: the motor failure detection method is used for judging whether the motor fails or not, and reporting the motor failure type or the motor is normal.
The present invention also provides a motor fault self-diagnosis storage medium storing a computer program programmed or configured to perform a motor fault self-diagnosis method as described above.
Compared with the prior art, the invention has the beneficial effects that: the motor can be in a loaded running state or not, whether normal running or faults exist can be judged through detection and analysis of state parameters of the motor, other hardware equipment is not needed, cost is saved, and accuracy is high.
Drawings
The invention is described in further detail below with reference to the attached drawings and detailed description:
fig. 1 is a flow chart of steps of a motor fault self-diagnosis method provided by the invention.
Detailed Description
The following is only a preferred embodiment of the present invention. The present invention will be described in further detail with reference to the drawings and detailed description below to facilitate understanding of the present invention by those skilled in the art, and it should be noted that all the inventions which make use of the inventive concept are protected by the present invention insofar as the various changes are within the spirit and scope of the present invention as defined and defined by the appended claims without departing from the principle of the present invention.
Fig. 1 is a step flow chart of a motor fault self-diagnosis method provided by the invention, as shown in fig. 1, the motor fault self-diagnosis method comprises the following steps:
step one: rated voltage of motor nameplateRated power->Rated current->Rated frequency->Rated efficiency->Rated power factor->Inputting into a motor diagnosis program;
step two: the motor diagnosis program carries out parameter self-learning on the motor to obtain the stator resistance observation valueRotor resistance observations/>Motor mutual inductance observation value->Leakage inductance observation value of motor>；
Step three: calculating a theoretical value of stator resistance according to motor lossStator resistance observation +.>Compared with the theoretical value of the stator resistance->Step four is carried out when the error is not more than 15%, and a motor fault signal is reported when the error is more than 15%; the calculation formula of the total loss value of the motor is as follows:
，
wherein,for the total loss value of the motor, < >>For rated power +.>Is rated efficiency;
is obtained according to the total loss value of the motorMThe single-phase loss value of the phase motor is calculated as follows:
，
wherein,for motor single-phase loss value, < >>Is the total loss value of the motor,Mis the number of motor phases.
The stator copper loss value represents the power consumed by the stator resistor, is about 43% of the single-phase loss value of the motor, and has the following calculation formula:
，
wherein,for the copper loss value of the stator, < > is->For motor single-phase loss value, < >>For rated current +.>Is the theoretical value of the stator resistance;
the theoretical value of stator resistance obtained according to the above formula is as follows:
，
wherein,for the theoretical value of the stator resistance, < > f>For rated power +.>For the purpose of the rated efficiency,Mfor the number of motor phases>Is rated current.
Determining stator resistance observationsIf the motor fault signal belongs to the following range, entering a step four, and if the motor fault signal does not belong to the following range:
，
wherein,for the theoretical value of the stator resistance, < > f>Is the stator resistance observation.
Step four: calculating a theoretical value of rotor resistance according to motor lossRotor resistance observation +.>Compared with the theoretical value of the rotor resistance->Step five is carried out when the error is not more than 15%, and a motor fault signal is reported when the error is more than 15%; according to the motor single-phase loss value in the third step +.>The copper loss value of the rotor represents the power consumed by the rotor resistor, which is about 20% of the single-phase loss value of the motor, and the copper loss value of the rotor has the following calculation formula:
，
wherein,for the copper loss value of the rotor,/->For motor single-phase loss value, < >>For rated current +.>The theoretical value of the resistance of the rotor;
according to the above, the theoretical value of the rotor resistance can be obtainedThe calculation formula is as follows:
，
wherein,for the theoretical value of the rotor resistance, < >>For rated power +.>For the purpose of the rated efficiency,Mfor the number of motor phases>Is rated current.
Determining rotor resistance observationsIf the motor fault signal belongs to the following range, entering a step five, and if the motor fault signal does not belong to the following range:
，
wherein,for the theoretical value of the rotor resistance, < >>Is the rotor resistance observation.
Step five: calculating the theoretical value of the magnetic linkage of the first motor according to a mutual inductance empirical formulaCalculating rated motor flux linkage observation value according to rated flux linkage formula>Calculating a second motor flux theoretical value +.>First motor flux theory value +.>Second motor flux linkage theory value->Compared to the nominal motor flux linkage observation +.>Step six, if the error is not more than 15%, reporting a motor fault signal if the error is more than 15%;
wherein, the first motor flux linkage theoretical valueThe calculation formula is as follows:
，
wherein,for the first motor flux linkage theory, +.>For rated current +.>For observing mutual inductance of motorA value;
rated motor flux linkage observationsThe calculation formula is as follows:
，
wherein,for the nominal motor flux linkage observation, +.>Rated frequency, < >>Rated voltage, pi is the circumference ratio;
rated motor power factor isConsidering that the reactive power of the motor is exciting power, under the rated condition, the exciting current of the motor is +.>About:
，
wherein,exciting current for motor, ">For rated current +.>Is the rated motor power factor;
then the motor mutual inductance observation valueThe method comprises the following steps:
，
wherein,for the motor mutual inductance observation value, < >>For the second motor flux linkage theory, +.>For rated current +.>For rated motor power factor, < >>Exciting current for the motor;
second motor flux linkage theory valueThe calculation formula is as follows:
，
wherein,for the second motor flux linkage theory, +.>For rated current +.>For the motor mutual inductance observation value, < >>Is the rated motor power factor.
Determining a first motor flux linkage theoretical valueSecond motor flux linkage theory value->Meanwhile, if the following conditions are met, entering a step six, and if the following conditions are not met, reporting a motor fault signal:
，
，
wherein,for the first motor flux linkage theory, +.>For the nominal motor flux linkage observation, +.>Is the second motor flux linkage theoretical value.
Step six: leakage inductance observation value of motorCompared with the observation value of 1/10 motor mutual inductance +.>Step seven is carried out when the error is not more than 15%, and a motor fault signal is reported when the error is more than 15%;
judging leakage inductance observation value of motorIf the motor fault signal belongs to the following range, entering a step seven, and if the motor fault signal does not belong to the following range:
，
wherein,for the motor mutual inductance observation value, < >>Is an observation value of leakage inductance of the motor.
Step seven: and exiting the motor diagnosis program to report the motor to be normal.
The invention also provides a motor fault self-diagnosis device, which is a frequency converter or an integrated electronic device with the function of the frequency converter, and comprises:
network reading component: the method is used for reading rated voltage, rated power, rated current, rated frequency, rated voltage, rated efficiency and rated power factor of the motor;
a network learning component: the method is used for learning the current stator resistance observation value, the rotor resistance observation value, the motor mutual inductance observation value and the motor leakage inductance observation value of the motor;
network computing component: the method is used for calculating a motor stator resistance theoretical value, a rotor resistance theoretical value, a first motor flux linkage theoretical value, a rated motor flux linkage observation value and a second motor flux linkage theoretical value on line;
a network comparison component: the method is used for comparing stator resistance errors, rotor resistance errors, errors between the first motor flux linkage, the second motor flux linkage and the rated motor flux linkage, and errors of motor leakage inductance observation values and 1/10 motor mutual inductance observation on line;
a network decision component: the motor failure detection method is used for judging whether the motor fails or not, and reporting the motor failure type or the motor is normal.
The present invention also provides a motor fault self-diagnosis storage medium storing a computer program programmed or configured to perform a motor fault self-diagnosis method as described above. The content of the method embodiment is applicable to the storage medium embodiment, and the realized functions and beneficial effects are the same as those of the method embodiment.
The technical solution of the present invention or a part of the technical solution of the present invention is embodied substantially in the form of a software product, and the motor fault self-diagnosis method of the present invention is stored in a computer readable storage medium, where the storage medium is any device that contains, stores, communicates, transmits all or part of the technical points of the present program for use by or in connection with a control system, an instruction device or an apparatus. Including but not limited to: an electrical connection having one or more wires, a portable computer disk cartridge, RAM, ROM, CDROM, EPROM, flash memory, optical fiber device, USB flash disk, optical disk, removable hard disk, computer, server, network terminal, chip, and the like, and various media capable of storing program code.
The motor fault self-diagnosis method, the motor fault self-diagnosis device and the storage medium provided by the invention are used for collecting various information generated in the motor operation process by depending on the motor fault self-diagnosis device, judging whether the motor is normal or not according to the rated parameters of the motor, and can judge whether the motor is normal or has faults by detecting and analyzing the state parameters of the motor under the condition of on-load operation or not, so that other hardware equipment is not needed, the cost is saved, the precision is high, and the online self-diagnosis of the motor faults is realized.
Although specific embodiments of the invention have been described in detail with reference to the accompanying drawings, it should not be construed as limiting the scope of protection of the present patent. Various modifications and variations which may be made by those skilled in the art without the creative effort are within the scope of the patent described in the claims.
Claims (6)
1. The motor fault self-diagnosis method is characterized by comprising the following steps of:
s1, rated voltage of a motor nameplateRated power->Rated current->Rated frequency->Rated efficiency->Rated power factor->Inputting into a motor diagnosis program;
s2, parameter self-learning is carried out on the motor by a motor diagnosis program to obtain the stator resistance observation valueRotor resistance observation->Motor mutual inductance observation value->Leakage inductance observation value of motor>；
S3, calculating a theoretical value of the stator resistance according to the motor lossStator resistance observation +.>Compared with the theoretical value of the stator resistance->S4 is carried out when the error is not more than 15%, and a motor fault signal is reported when the error is more than 15%;
s4, calculating a rotor resistance theoretical value according to the motor lossRotor resistance observationValue->Compared with the theoretical value of the rotor resistance->S5 is carried out when the error is not more than 15%, and a motor fault signal is reported when the error is more than 15%;
s5, calculating a first motor flux linkage theoretical value according to a mutual inductance empirical formulaCalculating rated motor flux linkage observation value according to rated flux linkage formula>Calculating a second motor flux theoretical value +.>First motor flux theory value +.>Second motor flux linkage theory value->Compared to the nominal motor flux linkage observation +.>S6 is carried out when the error is not more than 15%, and a motor fault signal is reported when the error is more than 15%;
s6, observing leakage inductance value of motorCompared with the observation value of 1/10 motor mutual inductance +.>Step S7 is carried out when the error is not more than 15%, and a motor fault signal is reported when the error is more than 15%;
s7, exiting the motor diagnosis program, and reporting the motor to be normal.
2. The motor fault self-diagnosis method according to claim 1, wherein in S3, a theoretical value of stator resistanceThe calculation formula is as follows:
，
wherein,for the theoretical value of the stator resistance, < > f>For rated power +.>For the purpose of the rated efficiency,Mfor the number of motor phases>Is rated current.
3. The motor fault self-diagnosis method according to claim 1, wherein in S4, a theoretical value of rotor resistanceThe calculation formula is as follows:
，
wherein,for the theoretical value of the rotor resistance, < >>For rated power +.>For the purpose of the rated efficiency,Mfor the number of motor phases>Is rated current.
4. The motor fault self-diagnosis method according to claim 1, wherein in S5, the first motor flux linkage theoretical valueThe calculation formula is as follows:
，
wherein,for the first motor flux linkage theory, +.>For rated current +.>The motor mutual inductance observation value;
rated motor flux linkage observationsThe calculation formula is as follows:
，
wherein,for the nominal motor flux linkage observation, +.>Rated frequency, < >>Rated voltage, pi is the circumference ratio;
second motor flux linkage theory valueThe calculation formula is as follows:
，
wherein,for the second motor flux linkage theory, +.>For rated current +.>For the motor mutual inductance observation value, < >>Is the rated motor power factor.
5. The utility model provides a motor fault self-diagnosis device which characterized in that, this device is converter or has the integrated electronic device of converter function, includes in its device:
network reading component: the method is used for reading rated voltage, rated power, rated current, rated frequency, rated efficiency and rated power factor of the motor;
a network learning component: the method is used for learning the current stator resistance observation value, the rotor resistance observation value, the motor mutual inductance observation value and the motor leakage inductance observation value of the motor;
network computing component: for on-line calculation of motor stator resistance theoretical value, rotor resistance theoretical value, first motor flux linkage theoretical valueRated motor flux linkage observation value +.>Second motor flux linkage theory value->；
A network comparison component: the method is used for comparing stator resistance errors, rotor resistance errors, errors between the first motor flux linkage, the second motor flux linkage and the rated motor flux linkage, and errors of motor leakage inductance observation values and 1/10 motor mutual inductance observation on line;
a network decision component: the motor failure detection method is used for judging whether the motor fails or not, and reporting the motor failure type or the motor is normal.
6. A motor failure self-diagnosis storage medium, characterized in that the motor failure self-diagnosis storage medium stores a computer program programmed or configured to implement a motor failure self-diagnosis method as claimed in any one of claims 1 to 4.
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| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117879441A (en) * | 2024-03-11 | 2024-04-12 | 成都希望森兰传动控制科技发展有限公司 | Frequency converter self-checking and fault-tolerant method |
| CN117879441B (en) * | 2024-03-11 | 2024-05-14 | 成都希望森兰传动控制科技发展有限公司 | Frequency converter self-checking and fault-tolerant method |
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