CN113866635A - Method for determining motor fault occurrence time in chemical equipment - Google Patents
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Abstract
The invention relates to the technical field of chemical equipment, in particular to a method for determining the occurrence time of motor faults in the chemical equipment, which comprises the following steps: acquiring the average ambient temperature of the environment where the motor is located in a preset historical time period; determining an operational control parameter of the motor based on the average ambient temperature; acquiring an operation log of the motor in a historical time period for each acquisition moment; the running log comprises the current and power of the motor; determining the target power and the target acquisition time of the motor based on the ratio of the power of the motor; the target acquisition moment is the acquisition moment corresponding to the target power; inputting the target acquisition time and the current as input into a time determination model for training, and outputting the incidence relation between the current and the time of the motor; and determining the fault occurrence time of the motor in response to the current of the motor reaching a preset alarm value based on the incidence relation. The scheme can accurately determine the time when the motor breaks down, so that the production efficiency of chemical equipment is ensured.
Description
Technical Field
The embodiment of the invention relates to the technical field of chemical equipment, in particular to a method for determining the occurrence time of motor faults in the chemical equipment.
Background
Petroleum is the primary raw material for many chemical products, such as solvents, fertilizers, pesticides, and plastics. With the development of society, the demand of people on chemical products is gradually increased, and the efficient processing and utilization of chemical equipment on petroleum are required to be ensured.
In the prior art, no matter the transport of chemical raw materials, still the transport of chemical products all need to set up power unit. Generally, the power mechanism is usually selected as a motor, and chemical materials or chemical products are conveyed to target equipment by using the motor.
However, as the motor of the chemical equipment is operated for a long time, the motor may malfunction. If the motor is maintained or replaced after the motor breaks down, the production efficiency of the chemical equipment can not be ensured.
Therefore, a method for determining the occurrence time of the motor failure in the chemical equipment is needed to solve the above technical problems.
Disclosure of Invention
In order to accurately obtain the time of the motor failure of the chemical equipment and guide a worker to reserve the time for maintaining or replacing the motor in advance, so that the production efficiency of the chemical equipment can be ensured, the embodiment of the invention provides a method for determining the time of the motor failure in the chemical equipment.
The embodiment of the invention provides a method for determining the occurrence time of motor faults in chemical equipment, which comprises the following steps:
acquiring the average ambient temperature of the environment where the motor is located in a preset historical time period;
determining an operation control parameter of the motor based on the average ambient temperature so that the motor operates according to the operation control parameter;
acquiring an operation log of the motor at each acquisition moment in the historical time period; wherein the log includes current and power of the motor;
determining target power and target acquisition time of the motor based on the ratio of the power of the motor; the target acquisition moment is the acquisition moment corresponding to the target power;
inputting the target acquisition time and the current corresponding to the target acquisition time as input into a preset time determination model for training, and outputting the incidence relation between the current and the time of the motor; wherein the incidence relation comprises a plurality of parameters, and the parameters are obtained by training the time determination model;
and determining the fault occurrence time of the motor in response to the current of the motor reaching a preset alarm value based on the incidence relation.
In one possible design, the determining an operational control parameter of the electric machine based on the average ambient temperature includes:
determining a target environment temperature interval corresponding to the average environment temperature in a pre-stored environment temperature interval based on the average environment temperature;
determining operation control parameters of the motor based on the target environment temperature interval and a preset mapping relation; and the mapping relation is the corresponding relation between the environment temperature interval and the operation control parameter.
In one possible design, after the determining the target power and the target collection time of the motor and before the taking the target collection time and the current corresponding to the target collection time as the input, the method further includes:
determining a current statistic value of the current in a preset time length aiming at a preset dimension in the target acquisition moments arranged according to the time sequence; wherein the preset dimension comprises a mean, an extremum and/or a variance;
determining whether the current corresponding to the current target acquisition moment is abnormal or not based on the current statistic value in the preset time and the current corresponding to each target acquisition moment;
and if so, deleting the current corresponding to the current target acquisition moment.
In one possible design, the predetermined dimensions include a mean and an extremum;
the determining whether the current corresponding to the current target acquisition time is abnormal or not based on the current statistic value in the preset time and the current corresponding to each target acquisition time comprises:
and if the absolute value of the difference between the current corresponding to each target acquisition moment in the preset time length and the current statistic value aiming at the mean value is larger than a first preset value, and the absolute value of the difference between the current statistic value aiming at the extreme value is smaller than a second preset value, determining that the current corresponding to the current target acquisition moment is abnormal.
In one possible design, after the determining the target power and the target collection time of the motor and before the taking the target collection time and the current corresponding to the target collection time as the input, the method further includes:
when the following preset conditions are met, the target acquisition time and the current corresponding to the target acquisition time are taken as input:
the number of the target acquisition moments is larger than a preset number;
the interval between two adjacent target acquisition moments is smaller than a preset time interval.
In one possible design, after the determining the target power and the target collection time of the motor, the method further includes:
and when the number of the target acquisition moments is not more than a preset number or the interval between two adjacent target acquisition moments is not less than a preset time interval, generating a motor replacement prompt.
In one possible design, the relationship between the current and the time of the motor is:
in the formula (I), the compound is shown in the specification,
for characterizing the current, x for characterizing the time,
for characterizing said parameter;
wherein n is a predetermined number,
is obtained by training the time determination model.
In one possible design, the operation control parameter includes any one of a motor speed and a duty ratio.
In one possible design, the determining the target power of the motor based on the duty ratio of the power of the motor includes:
and taking the power with the power ratio of the motor higher than a certain threshold value as the target power.
The embodiment of the invention provides a method for determining the occurrence time of motor faults in chemical equipment, which comprises the steps of obtaining an operation log of a motor in a historical time period aiming at each acquisition moment, and then determining target power by using power included in the operation log so as to determine the target acquisition moment; then, the target acquisition time and the current corresponding to the target acquisition time are input into a preset time determination model for training to obtain the incidence relation between the current and the time of the motor; and finally, determining the fault occurrence time of the motor based on the incidence relation. Therefore, the technical scheme can detect the motor failure in advance to determine the time of the motor failure, and guide the working personnel to reserve the time for maintaining or replacing the motor in advance, so that the production efficiency of chemical equipment can be ensured.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below, and it is obvious that the drawings in the following description are some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to these drawings without creative efforts.
Fig. 1 is a flowchart of a method for determining a failure occurrence time of a motor in a chemical device according to an embodiment of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present invention clearer and more complete, the technical solutions in the embodiments of the present invention will be described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are some, but not all, embodiments of the present invention, and based on the embodiments of the present invention, all other embodiments obtained by a person of ordinary skill in the art without creative efforts belong to the scope of the present invention.
As described above, as the motor of the chemical equipment operates for a long time, the motor may malfunction. If the motor is maintained or replaced after the motor breaks down, the production efficiency of the chemical equipment can not be ensured.
In order to solve the technical problem, the motor can be detected to break down in advance so as to determine the time of the motor when breaking down, thereby facilitating the timely maintenance or replacement of the motor and further ensuring the production efficiency of chemical equipment.
In the embodiment of the invention, whether the motor fails or not is determined by collecting the current of the motor in the working state, and the technical concept of the invention is explained below.
Referring to fig. 1, an embodiment of the present invention provides a method for determining a time when a motor fault occurs, including:
step 100: acquiring the average ambient temperature of the environment where the motor is located in a preset historical time period;
step 102: determining an operation control parameter of the motor based on the average ambient temperature so that the motor operates according to the operation control parameter;
step 104: acquiring an operation log of the motor in a historical time period for each acquisition moment; wherein the operation log comprises the current and power of the motor;
step 108, inputting the target acquisition time and the current corresponding to the target acquisition time as input into a preset time determination model for training, and outputting the incidence relation between the current and the time of the motor; wherein the incidence relation comprises a plurality of parameters, and the parameters are obtained by training the time determination model.
And step 110, responding to the current of the motor reaching a preset alarm value based on the incidence relation, and determining the fault occurrence time of the motor.
In the embodiment of the invention, the target acquisition time is determined by acquiring the running log of the motor in the historical time period aiming at each acquisition time and then determining the target power by using the power included in the running log; then, the target acquisition time and the current corresponding to the target acquisition time are input into a preset time determination model for training to obtain the incidence relation between the current and the time of the motor; and finally, determining the fault occurrence time of the motor based on the incidence relation. Therefore, the technical scheme can detect the motor failure in advance to determine the time of the motor failure, and guide the working personnel to reserve the time for maintaining or replacing the motor in advance, so that the production efficiency of chemical equipment can be ensured.
The manner in which the various steps shown in fig. 1 are performed is described below.
For step 100, a temperature acquisition device such as a thermometer or a temperature sensor may be disposed in an environment where the electric machine (i.e., the electric motor or the motor) is located to acquire an environment temperature, where each acquisition time (e.g., one day) corresponds to one environment temperature in a preset historical time period (e.g., one month), and after acquiring the environment temperatures, an average environment temperature of the environment where the electric machine is located in the preset historical time period is calculated.
With reference to step 102, in some embodiments, to control the operation of the motor, corresponding operation control parameters for controlling the operation of the motor are preset and stored in different environmental temperature intervals in advance, that is, a corresponding relationship between the environmental temperature interval and the operation control parameters is preset. For example, when the ambient temperature is 20-22 degrees celsius, the corresponding operation control parameter is M; when the ambient temperature is 28-30 ℃, the corresponding operation control parameter is N, and the like; the operation control parameter includes any one of a motor rotation speed and a duty ratio. Accordingly, the operational control parameters of the electric machine may be determined based on the above correspondence, and in some embodiments, step 102 includes:
determining a target environment temperature interval corresponding to the average environment temperature in a pre-stored environment temperature interval based on the average environment temperature;
determining operation control parameters of the motor based on the target environment temperature interval and a preset mapping relation; wherein, the mapping relation is the corresponding relation between the environment temperature interval and the operation control parameter.
In this embodiment, the target ambient temperature interval is determined by the average ambient temperature, and further the operation control parameters of the motor are determined by the target ambient temperature interval and the preset mapping relationship. So set up, can conveniently confirm the current operation control parameter of motor fast.
The manner in which the current and power are collected for step 104 is not specifically described herein and is well known to those skilled in the art.
In step 106, during the daily operation of the motor, the motor is usually in a relatively stable operating state, that is, the power of the motor is usually a constant value, that is, the motor can be operated in a constant power manner.
The longer the preset historical time period is, the more the operation logs are acquired, so that the more accurate the association relation obtained by training the time determination model is. However, the training speed of the association is also reduced due to the excessive number of the operation logs. Therefore, it may be considered to select a preset historical time period with a large time span (for example, two months), and in order to simultaneously improve the training speed of the association relationship, the noise of the running log may be reduced. For example, the power with the power ratio of the motor higher than a certain threshold may be used as the target power, and the acquisition time corresponding to the target power, that is, the target acquisition time may be further determined.
With respect to step 108, it can be appreciated that common fault diagnostic parameters for electric machines include, but are not limited to, current, voltage, torque, flux, temperature, vibration, and the like. However, in the embodiment of the present invention, the inventor finds that the motor fault caused by the current parameter abnormality accounts for a large proportion in the research, so the embodiment of the present invention mainly determines the fault occurrence time based on the current parameter abnormality.
Between step 106 and step 108, the method further comprises:
step A1, determining a current statistic value of current in a preset time length aiming at a preset dimension in target acquisition moments arranged according to a time sequence; the preset dimensionality comprises a mean value, an extreme value and/or a variance;
in step a1, the target collection times are arranged in time sequence, which is beneficial to ensure that the target collection times corresponding to the collected currents are continuous, and is beneficial to accurately calculating the current statistic value so as to accurately determine whether the current parameter is abnormal.
For example, the number of the target acquisition times determined in step 106 is 1000, and the step length selected by the preset time period may be a number including at least 50 target acquisition times, so as to ensure that the statistics of the current within the preset time period has a certain reference meaning.
It should be noted that step a1 selects the current collected within the preset time period, rather than performing subsequent anomaly detection based on each current. The reason is that: when the motor runs at a certain power, if the current changes due to normal reasons (such as the delivery flow of the water-absorbing resin is increased), the current change is a slow continuous change process; whereas if the current changes due to an abnormal cause, such as a data acquisition error or a transmission error, the current changes may be an abrupt process. Therefore, step a1 statistically determines the current statistic, which can increase the reliability of the subsequent anomaly detection (i.e. eliminate the current variation caused by abnormal reasons).
Step A2, determining whether the current corresponding to the current target acquisition time is abnormal or not based on the current statistic value in the preset time and the current corresponding to each target acquisition time; and if so, deleting the current corresponding to the current target acquisition moment.
In step a2, for example, in step a1, the average value of the acquired currents corresponding to 50 target acquisition times is counted (that is, a current statistical value for the average value is obtained), then the current corresponding to each of the 50 target acquisition times is compared with the average value, and if the absolute value of the difference between the current and the average value is greater than a threshold, it indicates that the current corresponding to the current target acquisition time deviates from the average value more, and at this time, the current at the time may be considered to be abnormal, and is not suitable for being applied to subsequent training, that is, to be deleted.
For another example, in step a1, the extreme values of the acquired currents corresponding to 50 target acquisition times are counted (that is, a current statistical value for the extreme values is obtained), then the current corresponding to each of the 50 target acquisition times is compared with the extreme value, if the absolute value of the difference between the current and the extreme value is smaller than a threshold, it is indicated that the current corresponding to the current target acquisition time is close to the minimum value, and at this time, it may be considered that the current at the time is abnormal, and is not suitable for being applied to subsequent training, that is, needs to be deleted.
For another example, in step a1, the variance of the collected currents corresponding to 50 target collection times is counted (i.e., a current statistic value for the variance is obtained), and if the variance is greater than a threshold, it indicates that the greater the dispersion degree of the currents corresponding to the 50 target collection times is, and at this time, the current at the time may be considered to be abnormal, and is not suitable for being applied to subsequent training, that is, to be deleted.
Of course, the above three dimensions of mean, extremum and variance may be used alone or at least two of them may be combined to perform anomaly detection.
In some embodiments, step a2 may specifically include:
and if the absolute value of the difference between the current corresponding to each target acquisition moment in the preset time and the current statistic value aiming at the mean value is greater than a first preset value and the absolute value of the difference between the current statistic value aiming at the extreme value is less than a second preset value, determining that the current corresponding to the current target acquisition moment is abnormal.
In this embodiment, the anomaly detection is performed by combining the mean value and the extreme value, so that the anomaly detection process can be simplified, and the accuracy of anomaly detection can be improved.
In some embodiments, between step 106 and step 108, the method further comprises:
when the following preset conditions are met, the target acquisition time and the current corresponding to the target acquisition time are taken as input:
the number of target acquisition moments is greater than the preset number;
the interval between two adjacent target acquisition moments is smaller than the preset time interval.
In this embodiment, the number of the target acquisition times is greater than the preset number, the data indicating the current is sufficient, the interval between two adjacent target acquisition times is smaller than the preset time interval, and the interval between two adjacent target acquisition times is smaller, so that the current data deleted is not excessive. That is to say, by ensuring that the current data is sufficient and the deleted current data is not excessive, the samples for training the time determination model can be ensured to be sufficient, so as to further ensure the accuracy of the obtained association relationship.
In some embodiments, after step 106, the method further comprises:
in some embodiments, the above method further comprises: and when the number of the target acquisition moments is not more than the preset number or the interval between two adjacent target acquisition moments is not less than the preset time interval, generating a motor replacement prompt.
In this embodiment, the number of the target acquisition times is not greater than the preset number, which indicates that the current data is insufficient, the interval between two adjacent target acquisition times is not less than the preset time interval, which indicates that the interval between two adjacent target acquisition times is relatively large, and thus the current data deleted is excessive. That is, this makes insufficient samples for training the time determination model, which may reduce the accuracy of the resulting correlation. Meanwhile, on the other hand, the abnormal current data is more, which indicates that the motor needs to be replaced, so that a motor replacement prompt can be generated at this time.
For step 110, the correlation between the current of the motor and the time is as follows:
in the formula (I), the compound is shown in the specification,
for characterizing the current, x for characterizing the time,
for characterizing the parameters;
wherein n is a predetermined number,
is obtained by training a time determination model.
In this embodiment, the correlation between the current of the motor and the time is obtained by training the time determination model, wherein parameters included in the correlation are known quantities. Therefore, if the current of the motor reaches a preset alarm value, the time corresponding to the alarm value is the motor fault occurrence time. Therefore, the time of the motor when the motor breaks down can be obtained by means of the incidence relation, so that the motor can be conveniently and timely replaced, and the production efficiency of chemical equipment can be ensured.
It is noted that, herein, relational terms such as first and second, and the like may be used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Also, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising an …" does not exclude the presence of other similar elements in a process, method, article, or apparatus that comprises the element.
Finally, it should be noted that: the above examples are only intended to illustrate the technical solution of the present invention, but not to limit it; although the present invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some technical features may be equivalently replaced; and such modifications or substitutions do not depart from the spirit and scope of the corresponding technical solutions of the embodiments of the present invention.
Claims (9)
1. A method for determining the occurrence time of motor faults in chemical equipment is characterized by comprising the following steps:
acquiring the average ambient temperature of the environment where the motor is located in a preset historical time period;
determining an operation control parameter of the motor based on the average ambient temperature so that the motor operates according to the operation control parameter;
acquiring an operation log of the motor at each acquisition moment in the historical time period; wherein the log includes current and power of the motor;
determining target power and target acquisition time of the motor based on the ratio of the power of the motor; the target acquisition moment is the acquisition moment corresponding to the target power;
inputting the target acquisition time and the current corresponding to the target acquisition time as input into a preset time determination model for training, and outputting the incidence relation between the current and the time of the motor; wherein the incidence relation comprises a plurality of parameters, and the parameters are obtained by training the time determination model;
and determining the fault occurrence time of the motor in response to the current of the motor reaching a preset alarm value based on the incidence relation.
2. The method of claim 1, wherein determining the operational control parameter of the electric machine based on the average ambient temperature comprises:
determining a target environment temperature interval corresponding to the average environment temperature in a pre-stored environment temperature interval based on the average environment temperature;
determining operation control parameters of the motor based on the target environment temperature interval and a preset mapping relation; and the mapping relation is the corresponding relation between the environment temperature interval and the operation control parameter.
3. The method of claim 1, after determining a target power and a target collection time of the motor and before taking as input a current corresponding to the target collection time and the target collection time, further comprising:
determining a current statistic value of the current in a preset time length aiming at a preset dimension in the target acquisition moments arranged according to the time sequence; wherein the preset dimension comprises a mean, an extremum and/or a variance;
determining whether the current corresponding to the current target acquisition moment is abnormal or not based on the current statistic value in the preset time and the current corresponding to each target acquisition moment;
and if so, deleting the current corresponding to the current target acquisition moment.
4. The method of claim 3, wherein the predetermined dimensions include a mean and an extremum;
the determining whether the current corresponding to the current target acquisition time is abnormal or not based on the current statistic value in the preset time and the current corresponding to each target acquisition time comprises:
determining that an abnormality exists when the current corresponding to each target acquisition time within the preset time meets the following conditions: the absolute value of the difference from the current statistic for the mean value is greater than a first preset value, and the absolute value of the difference from the current statistic for the extreme value is less than a second preset value.
5. The method of claim 1, after determining a target power and a target collection time of the motor and before taking as input a current corresponding to the target collection time and the target collection time, further comprising:
when the following preset conditions are met, the target acquisition time and the current corresponding to the target acquisition time are taken as input:
the number of the target acquisition moments is larger than a preset number;
the interval between two adjacent target acquisition moments is smaller than a preset time interval.
6. The method of claim 1, further comprising, after determining the target power and target acquisition time for the motor:
and when the number of the target acquisition moments is not more than a preset number or the interval between two adjacent target acquisition moments is not less than a preset time interval, generating a motor replacement prompt.
7. The method according to any one of claims 1-6, wherein the current of the motor is related to time by:
in the formula (I), the compound is shown in the specification,
for characterizing the current, x for characterizing the time,
for characterizing said parameter;
wherein n is a predetermined number,
is obtained by training the time determination model.
8. The method of claim 1, wherein the operation control parameter includes any one of a motor speed, a duty cycle.
9. The method of claim 1, wherein determining the target power of the motor based on the duty cycle of the power of the motor comprises:
and taking the power with the power ratio of the motor higher than a certain threshold value as the target power.
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Citations (13)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN202676882U (en) * | 2012-04-25 | 2013-01-16 | 北车风电有限公司 | Power monitoring system for wind-turbine master control system |
| US20160241171A1 (en) * | 2015-02-16 | 2016-08-18 | I-Shou University | Device for Monitoring Synchronous Signals of a Motor |
| CN107330160A (en) * | 2017-06-09 | 2017-11-07 | 华中科技大学 | A kind of short circuit current zero predicting method and system |
| CN107479367A (en) * | 2017-04-27 | 2017-12-15 | 宝沃汽车（中国）有限公司 | Control method, system and the vehicle of motor |
| CN107609574A (en) * | 2017-08-18 | 2018-01-19 | 上海电力学院 | Wind turbines fault early warning method based on data mining |
| CN108375732A (en) * | 2018-03-01 | 2018-08-07 | 北京迪利科技有限公司 | Motor monitoring and pre-alarming method and system |
| CN108471271A (en) * | 2018-04-16 | 2018-08-31 | 成都市极米科技有限公司 | A kind of projecting apparatus slide motor control method and control device |
| CN111858526A (en) * | 2020-06-19 | 2020-10-30 | 国网福建省电力有限公司信息通信分公司 | Failure time space prediction method and system based on information system log |
| CN112213640A (en) * | 2020-11-17 | 2021-01-12 | 润电能源科学技术有限公司 | Motor fault diagnosis method and related equipment thereof |
| CN112798951A (en) * | 2020-12-25 | 2021-05-14 | 上海法雷奥汽车电器系统有限公司 | Fault processing method and device, electronic equipment and storage medium |
| CN113075547A (en) * | 2021-03-24 | 2021-07-06 | 河南中烟工业有限责任公司 | Motor data acquisition method and system |
| CN113219330A (en) * | 2021-05-26 | 2021-08-06 | 广西电网有限责任公司电力科学研究院 | Method and system for detecting state of isolating switch |
| CN113311331A (en) * | 2021-05-12 | 2021-08-27 | 上海和夏新能源科技有限公司 | Motor testing device and system |
-
2021
- 2021-12-01 CN CN202111446762.0A patent/CN113866635B/en active Active
Patent Citations (13)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN202676882U (en) * | 2012-04-25 | 2013-01-16 | 北车风电有限公司 | Power monitoring system for wind-turbine master control system |
| US20160241171A1 (en) * | 2015-02-16 | 2016-08-18 | I-Shou University | Device for Monitoring Synchronous Signals of a Motor |
| CN107479367A (en) * | 2017-04-27 | 2017-12-15 | 宝沃汽车（中国）有限公司 | Control method, system and the vehicle of motor |
| CN107330160A (en) * | 2017-06-09 | 2017-11-07 | 华中科技大学 | A kind of short circuit current zero predicting method and system |
| CN107609574A (en) * | 2017-08-18 | 2018-01-19 | 上海电力学院 | Wind turbines fault early warning method based on data mining |
| CN108375732A (en) * | 2018-03-01 | 2018-08-07 | 北京迪利科技有限公司 | Motor monitoring and pre-alarming method and system |
| CN108471271A (en) * | 2018-04-16 | 2018-08-31 | 成都市极米科技有限公司 | A kind of projecting apparatus slide motor control method and control device |
| CN111858526A (en) * | 2020-06-19 | 2020-10-30 | 国网福建省电力有限公司信息通信分公司 | Failure time space prediction method and system based on information system log |
| CN112213640A (en) * | 2020-11-17 | 2021-01-12 | 润电能源科学技术有限公司 | Motor fault diagnosis method and related equipment thereof |
| CN112798951A (en) * | 2020-12-25 | 2021-05-14 | 上海法雷奥汽车电器系统有限公司 | Fault processing method and device, electronic equipment and storage medium |
| CN113075547A (en) * | 2021-03-24 | 2021-07-06 | 河南中烟工业有限责任公司 | Motor data acquisition method and system |
| CN113311331A (en) * | 2021-05-12 | 2021-08-27 | 上海和夏新能源科技有限公司 | Motor testing device and system |
| CN113219330A (en) * | 2021-05-26 | 2021-08-06 | 广西电网有限责任公司电力科学研究院 | Method and system for detecting state of isolating switch |
Non-Patent Citations (1)
| Title |
|---|
| 陈坤杰，张伟林编著: "《大学生科研训练教程》", 30 September 2009 * |
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