CN111190104A - Motor online fault monitoring system and analysis method thereof - Google Patents
Motor online fault monitoring system and analysis method thereof Download PDFInfo
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Abstract
The invention relates to a motor on-line fault monitoring system and an analysis method thereof, wherein the system comprises a plurality of voltage sensors, a plurality of current sensors, a data acquisition unit, a data transmission unit, a power supply unit, an operation analysis unit, a data analysis result parameter display unit and a remote on-line monitoring unit; the plurality of voltage sensors and the plurality of current sensors are connected with the data acquisition unit, the power supply unit is connected with the data acquisition unit, the data acquisition unit is connected with the data processing unit through the data transmission unit, and the data processing unit is connected with the data analysis result parameter display unit and the remote online monitoring unit. The invention provides a system and a method for automatically acquiring data, autonomously analyzing the data, storing the data and displaying an analysis result in real time, which solve the problems of high requirement on the professional level of an inspection worker, long inspection time, consumption of labor and material cost, low detection efficiency and the like.
Description
Technical Field
The invention relates to the field of electrical equipment detection and diagnosis, in particular to a motor online fault monitoring system and an analysis method thereof.
Background
With the development of modern industrial technology and the improvement of the manufacturing level of equipment, the number of motors as the most important transmission and execution mechanism in the production system is continuously increasing. Whether the motor can operate normally, safely, efficiently and with low consumption or not in the industrial production manufacturing process has very important significance on the operation and development of industry, the motor fault can not only lead the load operated by the motor to work normally, but also can influence the operation of the whole production system sometimes, even endanger the safety of personnel, cause great economic loss and generate severe social influence.
The current fault detection equipment of the motor is mainly relay protection, which can only perform simple judgment and power-off protection on the voltage, current phase loss and amplitude of the motor and cannot judge the detailed running state of the motor. Whether motor body has the trouble at the operation in-process, still need professional patrolling and examining personnel to carry out periodic inspection, have certain requirement to patrolling and examining personnel's professional level like this, detection efficiency is lower, and the cost is higher, and detection effect is not good. The fault monitoring system capable of realizing the on-line continuous automatic motor becomes an urgent requirement of the application occasions of the motors such as factories and the like
Disclosure of Invention
The invention provides a motor online fault monitoring system and an analysis method thereof, aiming at the problems that the current motor fault detection in industrial production basically needs a routing inspection mode which is passed by a professional, the requirement on the professional level of the routing inspection personnel is high, the routing inspection time is long, the cost of manpower and material resources is consumed, the detection efficiency is low and the like, and the detection system can automatically acquire data, autonomously analyze the data, store the data and display the analysis result in real time.
In order to achieve the purpose, the technical scheme of the invention is as follows:
the motor online fault monitoring system comprises a plurality of voltage sensors, a plurality of current sensors, a data acquisition unit, a data transmission unit, a power supply unit, an operation analysis unit, a data analysis result parameter display unit and a remote online monitoring unit; the system comprises a plurality of voltage sensors, a plurality of current sensors, a data acquisition unit, a power supply unit, a data processing unit and a data analysis result parameter display unit, wherein the voltage sensors and the current sensors are connected with the data acquisition unit, the power supply unit is connected with the data acquisition unit, the data acquisition unit is connected with the data processing unit through a data transmission unit, and the data processing unit is connected with the data analysis result parameter display unit and the remote online monitoring unit.
Further, the data transmission unit is an internet access.
Further, the data transmission unit is a GPRS wireless transceiver unit.
Further, the plurality of voltage sensors are hall voltage sensors HCS1, HCS2 and HCS3, respectively, and the plurality of current sensors are hall current sensors HVS1, HVS2 and HCS3, respectively.
Furthermore, the operation analysis unit comprises a fault representation data extraction module, a motor fault factor and historical data database module and a fault analysis prejudgment module.
Furthermore, the data analysis result parameter display unit is a computer screen or a television.
Furthermore, the remote online monitoring unit is a computer screen or a television, and is connected with the operation analysis unit through a wired network or a wireless network.
The analysis method of the motor online fault monitoring system specifically comprises the following steps:
s1: extracting data capable of representing fault information by using original digital quantity data acquired by a Hall current sensor and a Hall voltage sensor, converted by a data acquisition unit and transmitted by a data transmission unit, and analyzing a time domain waveform aiming at three-phase current data;
s2: for the time domain waveform of the A-phase current, the acquired N data are recorded as YanLet Y'anFundamental wave data of the fitted N real-time data; approximating the signal to be extracted with minimum mean square error, calculating Y'anEven if E (E)2 Y)＝(Yan-Y’an)2＝min，Y’an＝A(ω1t+θ1)；
S3: aiming at the A-phase current time domain waveform, the peak value time sequence X of the time domain periodic waveform is obtained through first differentiation and second differentiationa(k) T is the length of time of sampling in seconds, fsIs the sampling rate of the acquisition unit;
s4: for the time domain waveform of the phase B current, the acquired N data are recorded as YbnLet Y'bnFundamental wave data of the fitted N real-time data; approximating the signal to be extracted with minimum mean square error, calculating Y'bnEven if E (E)2 Y)＝(Ybn-Y’bn)2＝min，Y’bn＝B(ω1t+θ1)；
S5: aiming at the time domain waveform of the phase B current, the peak value time sequence X of the time domain periodic waveform is obtained through first differentiation and second differentiationb(k) T is the length of time of sampling in seconds, fsIs the sampling rate of the acquisition unit;
s6: for the C-phase current time domain waveform, the acquired N data are recorded as YcnLet Y'cnFundamental wave data of the fitted N real-time data; approximating the signal to be extracted with minimum mean square error, calculating Y'cnEven if E (E)2 Y)＝(Ycn-Y’cn)2＝min，Y’cn＝C(ω1t+θ1)。
S7: aiming at the C-phase current time domain waveform, the peak value time sequence X of the time domain periodic waveform is obtained through first differentiation and second differentiationc(k)；
S8: by obtaining a time series Xa(k) Data size value k of and
by comparison, when
Time, T is the length of time sampled in seconds, fsIs the sampling rate of the acquisition unit; and determine Xa(k) Maximum and minimum difference △ xaAcquiring the time difference △ t (m) between the maximum value and the minimum value, wherein m is 1 and 2 …, analyzing the numerical value between the maximum value and the minimum value, intercepting data, defining the value corresponding to the maximum value as x (1) and the time corresponding to the minimum value as x (n), and calculating
Comparison
And
when in use
Calculating in a loop, wherein m is 1,2 … n;
s9: when in use
Computing
The process of judging the result as acquisition is a starting process; if it is not
Judging the process as an operation process; if x (1)<1, judging the process as a stopping process;
s10: the later analysis process only judges the running state; in the time domain, the maximum and minimum values of the three-phase current amplitudes A, B and C are obtained, (maximum-minimum)/intermediate value is-15%>0, this value is denoted g1As a time domain current fault judgment value;
s11: aiming at the time domain waveform of the phase current A, fast Fourier change is carried out, the phase current A is segmented according to frequency, 5Hz is taken as the variable quantity, the highest value is obtained, the highest inflection point is obtained through one-time differentiation in the range of 50Hz plus or minus 5Hzd, the highest amplitude is deleted, the phase current A and the phase current A are arranged according to the frequency sequence, the value is different from the value in normal operation, the difference is averaged and is recorded as g2；
S12: respectively carrying out fast Fourier change on time domain waveforms of phase B and phase C currents, segmenting according to frequency, taking 5Hz as a variable quantity to obtain the highest value, in the range of 50Hz plus or minus 5Hzd, obtaining the highest inflection point through one-time differentiation, deleting the highest amplitude, arranging the highest inflection point and the highest amplitude according to the frequency sequence, and comparing the highest inflection point with the highest inflection point and the highest amplitudeCalculating the difference value and the average value of the difference value in normal operation and recording as g3And g4；
S13: g is prepared from1、g2、g3、g4The module and the fault analysis pre-judging module read the data of the motor fault factor and historical data database and the fault characterization data g obtained in the process1、g2、g3、g4Analyzing the variable quantity parameters, and solving a weighted average to obtain a fault operation predicted value; and storing the value in a motor fault factor and historical data database, and displaying the value on a display unit.
Compared with the prior art, the invention has the following beneficial effects:
the invention provides an online fault detection system and method capable of automatically acquiring data, autonomously analyzing the data, storing the data and displaying the analysis result in real time, and solves the problems of high requirement on the professional level of inspection personnel, long inspection time, consumption of manpower and material resource cost, low detection efficiency and the like; the device has 6 analog data sources, performs analog quantity and digital quantity conversion through the data acquisition unit, and has high-speed transmission capability of digital signals.
Drawings
In order to more clearly illustrate the technical solution of the present invention, the drawings that should be used will be briefly described below.
Fig. 1 is a schematic diagram of a hardware configuration of embodiment 1 of the present invention;
FIG. 2 is a schematic diagram of the hardware configuration of embodiment 2 of the present invention;
FIG. 3 is a schematic flow diagram of the method of the present invention.
Detailed Description
To facilitate an understanding of the invention, the invention will now be described more fully with reference to the accompanying drawings. Preferred embodiments of the present invention are shown in the drawings. This invention may, however, be embodied in many different forms and should not be construed as limited to the embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete.
Example 1:
referring to fig. 1, the motor online fault monitoring system includes a plurality of voltage sensors, a plurality of current sensors, a data acquisition unit, a data transmission unit, a power supply unit, an operation analysis unit, a data analysis result parameter display unit, and a remote online monitoring unit; the system comprises a plurality of voltage sensors, a plurality of current sensors, a data acquisition unit, a power supply unit, a data processing unit and a data analysis result parameter display unit, wherein the voltage sensors and the current sensors are connected with the data acquisition unit, the power supply unit is connected with the data acquisition unit, the data acquisition unit is connected with the data processing unit through a data transmission unit, and the data processing unit is connected with the data analysis result parameter display unit and the remote online monitoring unit.
The voltage sensor is a Hall voltage sensor which has high-frequency response capability and can capture signals with frequency variation of 50KHz or above; the 3-way Hall voltage sensors HCS1, HCS2 and HCS3 are selected to acquire voltage source information of the 3 ways of the motor, and real-time change information of the voltage can be provided during later calculation and analysis.
The current sensor is a Hall current sensor, the installation mode is a perforated mode, the installation is easy, the Hall current mode is selected, the high-frequency response capability is realized, and signals with frequency change of 50KHz and above can be captured; the 3-path current sensors HVS1, HVS2 and HCS3 are selected to acquire the load information of the 3 paths in the motor, and real-time change information of equivalent load in the motor can be provided in later calculation and analysis.
The 3 hall voltage sensors and 3 hall current sensors HVS serve as the underlying sources of data for the device, the accuracy and performance of which affect the later data processing, analysis and results of the entire device.
The data acquisition unit selects a high-frequency acquisition module capable of realizing multi-path acquisition, and has high-speed serial port or network port transmission capability; the high-frequency acquisition capability and the high-speed communication capability of the unit provide relatively complete original real-time data for the mapping process of the fault characterization data at the rear end of the unit.
The power supply unit is used as a unit for connecting the outside and the whole device, has power conditioning capacity and a power isolation function, and can effectively convert an external alternating current power supply into a low-voltage direct current power supply required by electronic components.
The operation analysis unit is used as the brain of the device, has operation and analysis capabilities, and mainly comprises a fault representation data extraction module, a motor fault factor and historical data base module and a fault analysis prejudgment module.
The data analysis result parameter display unit displays the judgment result and the content such as the data parameter trend graph and the like serving as the judgment basis on a visual window after the operation analysis unit, and the device can be a computer screen or other visual terminals such as a television and the like.
The remote online monitoring unit is mainly a visual window for displaying the judgment result and the contents such as a data parameter trend graph and the like serving as a judgment basis, the device can be a computer screen or other visual terminals such as a television and the like, and the remote online monitoring unit is connected with the data processing unit in a network mode to exchange data. The network mode may be a wired mode or a wireless mode.
In this embodiment, the data transmission unit selects a network port with a high transmission speed.
The digital quantity data output by the data acquisition unit can be connected with a core part data operation and analysis unit of the device through a data transmission unit, and the digital quantity data can be composed of a common limited network exchange unit or a wireless GPRS (general packet radio service) transceiver module transmitted through 5G.
And the data operation and analysis unit extracts fault characterization data, corrects a motor fault factor and historical data database and finally performs fault analysis and prejudgment by using the data of the data acquisition unit transmitted by the data transmission unit. The motor fault real-time monitoring system is a core part of a system for realizing real-time monitoring of motor faults on line and provides a data source for a data analysis result parameter display unit.
And the data analysis result parameter display unit displays the parameters and the judgment results which are processed by the data operation and analysis unit and have certain basis. The contents of the data parameter trend graph and the like which are the judgment results and the judgment basis are presented in the visual window.
The data analysis result parameter display unit not only comprises the visualization of data on site, but also can realize the remote presentation of online monitoring data, namely a remote online monitoring unit; the unit is also a visual window for presenting the judgment result and contents such as a data parameter trend graph and the like serving as a judgment basis, and the device can be a computer screen television, a mobile phone and other visual terminals. The remote online monitoring unit is connected with the data processing unit in a network mode to exchange data; the network connection may be a wired or wireless connection.
Example 2:
referring to fig. 2, a difference between this embodiment and embodiment 1 is that in this embodiment, a data transmission unit selects a 5G network to establish a GPRS radio transceiver.
Referring to fig. 3, when the online motor fault monitoring system is used, a specific analysis method thereof includes the following steps:
s1: extracting data capable of representing fault information by using original digital quantity data acquired by a Hall current sensor and a Hall voltage sensor, converted by a data acquisition unit and transmitted by a data transmission unit, and analyzing a time domain waveform aiming at three-phase current data;
s2: for the time domain waveform of the A-phase current, the acquired N data are recorded as YanLet Y'anFundamental wave data of the fitted N real-time data; approximating the signal to be extracted with minimum mean square error, calculating Y'anEven if E (E)2 Y)＝(Yan-Y’an)2＝min，Y’an＝A(ω1t+θ1)；
S3: aiming at the A-phase current time domain waveform, the peak value time sequence X of the time domain periodic waveform is obtained through first differentiation and second differentiationa(k) T is the length of time of sampling in seconds, fsIs the sampling rate of the acquisition unit;
s4: for the time domain waveform of the phase B current, the acquired N data are recorded as YbnLet Y'bnTo be fitted withFundamental wave data of the N real-time data; approximating the signal to be extracted with minimum mean square error, calculating Y'bnEven if E (E)2 Y)＝(Ybn-Y’bn)2＝min，Y’bn＝B(ω1t+θ1)；
S5: aiming at the time domain waveform of the phase B current, the peak value time sequence X of the time domain periodic waveform is obtained through first differentiation and second differentiationb(k) T is the length of time of sampling in seconds, fsIs the sampling rate of the acquisition unit;
s6: for the C-phase current time domain waveform, the acquired N data are recorded as YcnLet Y'cnFundamental wave data of the fitted N real-time data; approximating the signal to be extracted with minimum mean square error, calculating Y'cnEven if E (E)2 Y)＝(Ycn-Y’cn)2＝min，Y’cn＝C(ω1t+θ1)。
S7: aiming at the C-phase current time domain waveform, the peak value time sequence X of the time domain periodic waveform is obtained through first differentiation and second differentiationc(k)；
S8: by obtaining a time series Xa(k) Data size value k of and
by comparison, when
Time, T is the length of time sampled in seconds, fsIs the sampling rate of the acquisition unit; and determine Xa(k) Maximum and minimum difference △ xaAcquiring the time difference △ t (m) between the maximum value and the minimum value, wherein m is 1 and 2 …, analyzing the numerical value between the maximum value and the minimum value, intercepting data, defining the value corresponding to the maximum value as x (1) and the time corresponding to the minimum value as x (n), and calculating
Comparison
And
when in use
Calculating in a loop, wherein m is 1,2 … n;
s9: when in use
Computing
The process of judging the result as acquisition is a starting process; if it is not
Judging the process as an operation process; if x (1)<1, judging the process as a stopping process;
s10: the later analysis process only judges the running state; in the time domain, the maximum and minimum values of the three-phase current amplitudes A, B and C are obtained, (maximum-minimum)/intermediate value is-15%>0, this value is denoted g1As a time domain current fault judgment value;
s11: aiming at the time domain waveform of the phase current A, fast Fourier change is carried out, the phase current A is segmented according to frequency, 5Hz is taken as the variable quantity, the highest value is obtained, the highest inflection point is obtained through one-time differentiation in the range of 50Hz plus or minus 5Hzd, the highest amplitude is deleted, the phase current A and the phase current A are arranged according to the frequency sequence, the value is different from the value in normal operation, the difference is averaged and is recorded as g2；
S12: respectively carrying out fast Fourier change on time domain waveforms of phase B and phase C currents, segmenting according to frequency, obtaining the highest value by taking 5Hz as a variable quantity, obtaining the highest inflection point of the time domain waveforms by one-time differentiation in the range of 50Hz plus or minus 5Hzd, deleting the highest amplitude, arranging the highest inflection point and the highest amplitude according to the frequency sequence, obtaining a difference value between the value and a value in normal operation, obtaining an average value of the difference value, and recording the average value as g3And g4；
S13: g is prepared from1、g2、g3、g4The module and the fault analysis pre-judging module read the data of the motor fault factor and historical data database and the fault characterization data g obtained in the process1、g2、g3、g4Analyzing the variable quantity parameters, and solving a weighted average to obtain a fault operation predicted value; and storing the value in a motor fault factor and historical data database, and displaying the value on a display unit.
Claims (8)
1. Motor on-line fault monitoring system, its characterized in that: the device comprises a plurality of voltage sensors, a plurality of current sensors, a data acquisition unit, a data transmission unit, a power supply unit, an operation analysis unit, a data analysis result parameter display unit and a remote online monitoring unit; the system comprises a plurality of voltage sensors, a plurality of current sensors, a data acquisition unit, a power supply unit, a data processing unit and a data analysis result parameter display unit, wherein the voltage sensors and the current sensors are connected with the data acquisition unit, the power supply unit is connected with the data acquisition unit, the data acquisition unit is connected with the data processing unit through a data transmission unit, and the data processing unit is connected with the data analysis result parameter display unit and the remote online monitoring unit.
2. The motor online fault monitoring system of claim 1, wherein: the data transmission unit is a network port.
3. The motor online fault monitoring system of claim 1, wherein: the data transmission unit is a GPRS wireless receiving and transmitting unit.
4. The motor online fault monitoring system according to claim 2 or 3, characterized in that: the plurality of voltage sensors are respectively hall voltage sensors HCS1, HCS2 and HCS3, and the plurality of current sensors are respectively hall current sensors HVS1, HVS2 and HCS 3.
5. The motor online fault monitoring system of claim 4, wherein: the operation analysis unit comprises a fault representation data extraction module, a motor fault factor and historical data database module and a fault analysis prejudgment module.
6. The motor online fault monitoring system of claim 5, wherein: the data analysis result parameter display unit is a computer screen or a television.
7. The motor online fault monitoring system of claim 6, wherein: the remote online monitoring unit is a computer screen or a television, and is connected with the operation analysis unit through a wired network or a wireless network.
8. The analysis method of the motor online fault monitoring system is characterized by comprising the following steps:
s1: extracting data capable of representing fault information by using original digital quantity data acquired by a Hall current sensor and a Hall voltage sensor, converted by a data acquisition unit and transmitted by a data transmission unit, and analyzing a time domain waveform aiming at three-phase current data;
s2: for the time domain waveform of the A-phase current, the acquired N data are recorded as YanLet Y'anFundamental wave data of the fitted N real-time data; approximating the signal to be extracted with minimum mean square error, calculating Y'anEven if E (E)2 Y)＝(Yan-Y’an)2＝min，Y’an＝A(ω1t+θ1)；
S3: aiming at the A-phase current time domain waveform, the peak value time sequence X of the time domain periodic waveform is obtained through first differentiation and second differentiationa(k) T is the length of time of sampling in seconds, fsIs the sampling rate of the acquisition unit;
s4: for the time domain waveform of the phase B current, the acquired N data are recorded as YbnLet Y'bnFundamental wave data of the fitted N real-time data; approximating the signal to be extracted by means of a minimum mean square errorCalculating Y'bnEven if E (E)2 Y)＝(Ybn-Y’bn)2＝min，Y’bn＝B(ω1t+θ1)；
S5: aiming at the time domain waveform of the phase B current, the peak value time sequence X of the time domain periodic waveform is obtained through first differentiation and second differentiationb(k) T is the length of time of sampling in seconds, fsIs the sampling rate of the acquisition unit;
s6: for the C-phase current time domain waveform, the acquired N data are recorded as YcnLet Y'cnFundamental wave data of the fitted N real-time data; approximating the signal to be extracted with minimum mean square error, calculating Y'cnEven if E (E)2 Y)＝(Ycn-Y’cn)2＝min，Y’cn＝C(ω1t+θ1)。
S7: aiming at the C-phase current time domain waveform, the peak value time sequence X of the time domain periodic waveform is obtained through first differentiation and second differentiationc(k)；
S8: by obtaining a time series Xa(k) Data size value k of and
by comparison, when
Time, T is the length of time sampled in seconds, fsIs the sampling rate of the acquisition unit; and determine Xa(k) Maximum and minimum difference △ xaAcquiring the time difference △ t (m) between the maximum value and the minimum value, wherein m is 1 and 2 …, analyzing the numerical value between the maximum value and the minimum value, intercepting data, defining the value corresponding to the maximum value as x (1) and the time corresponding to the minimum value as x (n), and calculating
Comparison
And
when in use
Calculating in a loop, wherein m is 1,2 … n;
s9: when in use
Computing
The process of judging the result as acquisition is a starting process; if it is not
Judging the process as an operation process; if x (1)<1, judging the process as a stopping process;
s10: the later analysis process only judges the running state; in the time domain, the maximum and minimum values of the three-phase current amplitudes A, B and C are obtained, (maximum-minimum)/intermediate value is-15%>0, this value is denoted g1As a time domain current fault judgment value;
s11: aiming at the time domain waveform of the phase current A, fast Fourier change is carried out, the phase current A is segmented according to frequency, 5Hz is taken as the variable quantity, the highest value is obtained, the highest inflection point is obtained through one-time differentiation in the range of 50Hz plus or minus 5Hzd, the highest amplitude is deleted, the phase current A and the phase current A are arranged according to the frequency sequence, the value is different from the value in normal operation, the difference is averaged and is recorded as g2；
S12: respectively carrying out fast Fourier change on time domain waveforms of phase B and phase C currents, segmenting according to frequency, taking 5Hz as a variable quantity, obtaining the highest value, in the range of 50Hz plus or minus 5Hzd, obtaining the highest inflection point through one-time differentiation, and deleting the highest inflection pointThe amplitudes are arranged according to the frequency sequence, the difference value between the amplitude and the value in normal operation is calculated, the average value of the difference value is calculated and recorded as g3And g4；
S13: g is prepared from1、g2、g3、g4The module and the fault analysis pre-judging module read the data of the motor fault factor and historical data database and the fault characterization data g obtained in the process1、g2、g3、g4Analyzing the variable quantity parameters, and solving a weighted average to obtain a fault operation predicted value; and storing the value in a motor fault factor and historical data database, and displaying the value on a display unit.
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Cited By (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111650514A (en) * | 2020-06-15 | 2020-09-11 | 珠海万力达电气自动化有限公司 | Multi-parameter joint diagnosis method for typical faults of asynchronous motor |
| CN111781496A (en) * | 2020-06-04 | 2020-10-16 | 扬州工业职业技术学院 | Motor control device and operation data acquisition and storage method |
| CN113203949A (en) * | 2021-04-27 | 2021-08-03 | 西安工业大学 | Coordinate parameter correction method in motor diagnosis process |
| CN113848476A (en) * | 2021-10-11 | 2021-12-28 | 海南核电有限公司 | Motor on-line monitoring system |
| CN114460466A (en) * | 2022-04-12 | 2022-05-10 | 杭州杰牌传动科技有限公司 | Virtual sensor equipment for transmission monitoring and monitoring method thereof |
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2020
- 2020-02-27 CN CN202010124478.0A patent/CN111190104A/en active Pending
Cited By (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111781496A (en) * | 2020-06-04 | 2020-10-16 | 扬州工业职业技术学院 | Motor control device and operation data acquisition and storage method |
| CN111650514A (en) * | 2020-06-15 | 2020-09-11 | 珠海万力达电气自动化有限公司 | Multi-parameter joint diagnosis method for typical faults of asynchronous motor |
| CN111650514B (en) * | 2020-06-15 | 2022-09-09 | 珠海万力达电气自动化有限公司 | Multi-parameter joint diagnosis method for typical faults of asynchronous motor |
| CN113203949A (en) * | 2021-04-27 | 2021-08-03 | 西安工业大学 | Coordinate parameter correction method in motor diagnosis process |
| CN113203949B (en) * | 2021-04-27 | 2024-03-08 | 西安工业大学 | Coordinate parameter correction method in motor diagnosis process |
| CN113848476A (en) * | 2021-10-11 | 2021-12-28 | 海南核电有限公司 | Motor on-line monitoring system |
| CN114460466A (en) * | 2022-04-12 | 2022-05-10 | 杭州杰牌传动科技有限公司 | Virtual sensor equipment for transmission monitoring and monitoring method thereof |
| CN114460466B (en) * | 2022-04-12 | 2022-08-05 | 杭州杰牌传动科技有限公司 | Virtual sensor equipment for transmission monitoring and monitoring method thereof |
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