CN112903001A - Operation method of fabric setting machine - Google Patents
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- 239000004744 fabric Substances 0.000 title claims abstract description 62
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- 238000010606 normalization Methods 0.000 claims description 7
- 238000006073 displacement reaction Methods 0.000 claims description 5
- 238000002360 preparation method Methods 0.000 abstract description 5
- 238000012360 testing method Methods 0.000 description 8
- 238000004804 winding Methods 0.000 description 5
- 230000000875 corresponding effect Effects 0.000 description 4
- 238000009413 insulation Methods 0.000 description 3
- 238000012423 maintenance Methods 0.000 description 3
- 230000003068 static effect Effects 0.000 description 3
- 238000004364 calculation method Methods 0.000 description 2
- 238000004519 manufacturing process Methods 0.000 description 2
- 239000000463 material Substances 0.000 description 2
- 230000005526 G1 to G0 transition Effects 0.000 description 1
- 238000005452 bending Methods 0.000 description 1
- 230000002596 correlated effect Effects 0.000 description 1
- 238000010219 correlation analysis Methods 0.000 description 1
- 230000001419 dependent effect Effects 0.000 description 1
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01D—MEASURING NOT SPECIALLY ADAPTED FOR A SPECIFIC VARIABLE; ARRANGEMENTS FOR MEASURING TWO OR MORE VARIABLES NOT COVERED IN A SINGLE OTHER SUBCLASS; TARIFF METERING APPARATUS; MEASURING OR TESTING NOT OTHERWISE PROVIDED FOR
- G01D21/00—Measuring or testing not otherwise provided for
- G01D21/02—Measuring two or more variables by means not covered by a single other subclass
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- D—TEXTILES; PAPER
- D06—TREATMENT OF TEXTILES OR THE LIKE; LAUNDERING; FLEXIBLE MATERIALS NOT OTHERWISE PROVIDED FOR
- D06C—FINISHING, DRESSING, TENTERING OR STRETCHING TEXTILE FABRICS
- D06C7/00—Heating or cooling textile fabrics
- D06C7/02—Setting
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Abstract
一种织物定型机的操作方法包括以下步骤。采集马达的电流数据、振动数据、局部放电数据以及故障类型。建立电流数据、振动数据以及局部放电数据对故障类型的关联性。重复执行上述步骤，以建立数据库。依据数据库内的多笔电流数据、多笔振动数据以及多笔局部放电数据，建立回归方程式。在定型机的工作阶段采集马达的工作电流数据、工作振动数据以及工作局部放电数据，并透过关联性及回归方程式分别获得预计故障类型以及预计故障时间。织物定型机的控制系统可分别依据所建立的关联性以及回归方程式预测马达的故障类型以及故障时间，从而提前作出相对应的准备。此外，控制系统亦可逐步修正已建立好的关联性以及回归方程式，从而提升其准确性及可靠性。
An operation method of a fabric setting machine includes the following steps. Collect motor current data, vibration data, partial discharge data, and fault type. Correlate current data, vibration data, and partial discharge data to fault types. Repeat the above steps to build the database. According to the multiple current data, multiple vibration data and multiple partial discharge data in the database, a regression equation is established. In the working stage of the setting machine, the working current data, working vibration data and working partial discharge data of the motor are collected, and the predicted failure type and predicted failure time are obtained respectively through correlation and regression equations. The control system of the fabric setting machine can predict the failure type and failure time of the motor according to the established correlation and regression equation, so as to make corresponding preparations in advance. In addition, the control system can gradually correct established correlations and regression equations, thereby improving their accuracy and reliability.
Description
Technical Field
The present disclosure relates to a method for operating a fabric setting machine, and more particularly, to a method for predicting a failure of a motor of a fabric setting machine.
Background
With the improvement of the standard of living, consumers have new requirements for the functions of fabrics, and therefore, the demands of fabrics are increasing. In the mass production process of the fabric, the cloth material as the fabric material is subjected to a setting treatment to make the surface of the fabric flat. Because general fabric design equipment lacks trouble detection device, therefore operating personnel often can't predict the emergence of equipment trouble in advance and make corresponding preparation, and when equipment broke down, often must wait that maintenance person further inspects can find out the reason of trouble and repair to cause the production line to stop the pendulum and cause serious loss.
Disclosure of Invention
The present disclosure provides a method of operating a fabric setter having a motor.
According to one embodiment of the present disclosure, a method of operating a fabric setter includes the following steps. Current data, vibration data, partial discharge data, and a fault type of the motor are collected. And establishing the relevance of the current data, the vibration data and the partial discharge data to the fault type. Repeatedly executing the steps to establish a database; and establishing a regression equation according to the multiple current data, the multiple vibration data and the multiple partial discharge data in the database. And acquiring working current data, working vibration data and working partial discharge data of the motor at the working stage of the setting machine, and respectively obtaining the predicted fault type and the predicted fault time through the correlation and the regression equation.
In one embodiment of the present disclosure, the step of establishing the database includes: normalizing the plurality of current data, the plurality of vibration data and the plurality of partial discharge data to respectively obtain a plurality of normalized current data, a plurality of normalized vibration data and a plurality of normalized partial discharge data; and establishing a regression equation according to the normalized current data, the normalized vibration data and the normalized partial discharge data.
In one embodiment of the present disclosure, the regression equation is [ expected failure time (a × (normalized current data) — B × (normalized vibration data) — (normalized partial discharge data) + D × (normalized current data) × (normalized vibration number)According) + E x (normalized current data)1/2X (normalized partial discharge data)]Wherein A, B, C, D and E are constants and 7.2 ≦ A ≦ 8.8, 18.3 ≦ B ≦ 22.3, 1.4 ≦ C ≦ 1.8, 1.1 ≦ D ≦ 1.3, and 2.2 ≦ E ≦ 2.7.
In one embodiment of the present disclosure, the method of operating a fabric setting machine further includes: and modifying the correlation, the database and the regression equation according to the working current data, the working vibration data and the working partial discharge data.
In one embodiment of the present disclosure, the current data, the vibration data, and the partial discharge data are respectively presented through a three-phase current trace diagram, a vibration trace diagram, and a partial discharge frequency spectrum diagram.
In one embodiment of the present disclosure, the three-phase current trajectory diagram presents a trajectory shape of a three-dimensional ellipse, and the current data is a length of a major axis of the three-dimensional ellipse to a length of a minor axis of the three-dimensional ellipse.
In one embodiment of the present disclosure, the vibration data is a displacement of a bearing of the motor in the vibration trace diagram.
In one embodiment of the present disclosure, the fault types include stator faults, rotor faults, bearing faults, and centering faults.
In one embodiment of the present disclosure, the current data, the vibration data, and the partial discharge data are measured by a first sensor, a second sensor, and a third sensor, respectively, and the first sensor, the second sensor, and the third sensor are disposed on a bearing of the motor.
In one embodiment of the present disclosure, the method of operating a fabric setting machine further includes: and transmitting the predicted fault type and the predicted fault time to a scheduling system so as to reschedule the working stage of the fabric setting machine.
According to the above embodiments of the present disclosure, in the operating method of the fabric setting machine, the current data, the vibration data, the partial discharge data and the fault type of the motor are collected, so as to establish the correlation and the regression equation. The control system of the fabric setting machine can predict the failure type and failure time of the motor according to the established correlation and the regression equation respectively, so as to make corresponding preparation in advance. In addition, by comparing the difference between each predicted result and the actual fault condition, the control system can gradually correct the established correlation and regression equation, thereby improving the accuracy and reliability of the obtained predicted fault type and predicted fault time.
Drawings
Figure 1 is a flow chart illustrating a method of operating a fabric setter according to one embodiment of the present disclosure.
[ notation ] to show
S10, S20, S30, S40, S50, S60, S70: step (ii) of
Detailed Description
In the following description, for purposes of explanation, numerous implementation details are set forth in order to provide a thorough understanding of the various embodiments of the present disclosure. It should be understood, however, that these implementation details are not to be interpreted as limiting the disclosure. That is, in some embodiments of the disclosure, such implementation details are not necessary. In addition, for the sake of simplicity, some conventional structures and elements are shown in the drawings in a simple schematic manner.
The present disclosure provides an operation method of a fabric setting machine, which can predict the failure type and failure time of a motor of the fabric setting machine, so as to predict the occurrence of failure and make corresponding preparations in advance. For example, the operation stages of the fabric setting machine are rescheduled, and the maintenance operation of the fabric setting machine is scheduled.
Figure 1 is a flow chart illustrating a method of operating a fabric setter according to one embodiment of the present disclosure. The method of operating the fabric setter includes steps S10, S20, S30, S40, S50, S60, and S70. In step S10, current data, vibration data, partial discharge data, and a fault type of the motor are collected. In step S20, the correlation of the current data, the vibration data, and the partial discharge data to the type of the fault is established. In step S30, steps S10 and S20 are repeatedly performed to build the database. In step S40, a regression equation is established according to the plurality of current data, the plurality of vibration data and the plurality of partial discharge data in the database. In step S50, the working current data, the working vibration data and the working partial discharge data of the motor are collected during the working phase of the fabric setting machine. In step S60, the predicted failure type and the predicted failure time are obtained through the correlation and regression equations, respectively. In step S70, the correlation, the database and the regression equation are modified according to the working current data, the working vibration data and the working partial discharge data. In the following description, the above steps will be further explained.
In some embodiments, a sensor may be mounted on a bearing of a motor of the fabric setter. The sensors may include a first sensor configured to measure a current value of the motor, a second sensor configured to measure a vibration value of the motor, and a third sensor configured to measure a partial discharge value of the motor. In detail, the first sensor, the second sensor and the third sensor can respectively measure a current value, a vibration value and a partial discharge value of the motor at the working stage of the fabric setting machine, and transmit the values to a control system of the fabric setting machine to further convert the values into current data, vibration data and partial discharge data.
In step S10, the fault type of the motor is collected, and the current value, the vibration value and the partial discharge value of the motor are measured by the first sensor, the second sensor and the third sensor, respectively. The collection of the fault type and the measurement of the current value, the vibration value and the partial discharge value are carried out in the working stage of the fabric setting machine. Then, the measured current value, vibration value and partial discharge value are further converted into current data, vibration data and partial discharge data respectively through a control system of the fabric setting machine.
In some embodiments, the types of faults of the motor include stator faults, rotor faults, bearing faults, and centering faults. In detail, the stator fault includes a stator winding fault, an internal discharge, a slot discharge, a winding end discharge, or a conductive particle fault; rotor faults include rotor bar breakage; bearing faults include bearing strip breakage, loose bearing parts or bearing bending; the centering faults comprise dynamic and static eccentricity, non-centering of a rotor, unbalance of the rotor or friction of dynamic and static parts. The 13 fault types are common fault types of the motor, and the operation method of the fabric setting machine disclosed by the invention can be used for collecting the fault characteristics of the 13 fault types.
In some embodiments, the current values may be three-phase current values of a stator of the motor, and the three-phase current values may be presented through a three-phase current trajectory diagram. Specifically, the three-phase current trajectory diagram is obtained by converting three amplitudes of three-phase current values into three-dimensional current trajectories, respectively. It should be understood that in a normal three-phase current trajectory diagram of a motor, three-dimensional current trajectory shapes represented by three-phase current values are three-dimensional circles respectively; on the contrary, in the three-phase current trajectory diagram of the motor with the fault, the three-dimensional current trajectory shapes presented by the three-phase current values are three-dimensional ellipses respectively.
In the three-phase current trajectory diagram of the failed motor, the failed phase of the motor coincides with the major axis direction of the three-dimensional ellipse, that is, the failed phase of the motor can be determined according to the major axis direction of the three-dimensional ellipse. Further, in the three-phase current trace plot of a failed motor, the length of the major axis of the three-dimensional ellipse is positively correlated to the severity of the motor at the particular failed phase. For example, the more turns of the coil that the motor is short-circuited at the same load current, the longer the length of the major axis of the three-dimensional ellipse. In some embodiments, the control system of the fabric setter further converts the measured current value into current data (also referred to as a severity factor) and determines the severity of the motor failure from the current data, wherein the current data is the length of the major axis of the three-dimensional ellipse to the length of the minor axis of the three-dimensional ellipse. The greater the absolute value of the current data, the greater the degree of failure of the motor in the particular failed phase. In some embodiments, the current data may range from-4 units to 4 units, i.e., the difference between the major axis minus the minor axis of the three-dimensional ellipse may range from-4 units to 4 units.
In some embodiments, the vibration value may be of a motorThe amount of displacement of the bearing in a particular direction (e.g., horizontal x-direction or vertical y-direction), for example, the vibration values may include horizontal vibration values (x) and vertical vibration values (y). The vibration data can be the displacement of the bearing of the motor on a two-dimensional plane in the working stage relative to the static stage. For example, in the vibration trajectory diagram, when the coordinate position of the bearing of the motor in the stationary phase is (0, 0) and the coordinate position of the bearing of the motor in the working phase is (x, y), the vibration data may be represented as [ (x, y)2+y2)1/2]. The displacement direction of the bearing of the motor in the vibration trace diagram and the magnitude of the vibration data can be used for judging the deviation direction and the fault degree of the motor in the working stage. In some embodiments, the vibration data may range between 0 microns to 300 microns.
In some embodiments, the partial discharge value may be a partial discharge value of a three-phase current inside the motor. The partial discharge values may be presented in the form of partial discharge data through a partial discharge spectrogram. In some embodiments, the partial discharge data may reflect an insulation condition of the stator winding of the motor, that is, whether the stator winding of the motor achieves its intended insulation effect may be determined according to the magnitude of the partial discharge data. Specifically, the relationship between the magnitude of the partial discharge data and the insulation condition of the stator winding of the motor is shown in table one. In some embodiments, the partial discharge data has a range of three segments, respectively less than 10000pC, between 10000pC to 30000pC, and greater than 30000 pC.
Watch 1
In step S20, a summary and correlation analysis is performed on the current data, the vibration data, the partial discharge data and the fault type collected in step S10 to establish the correlation between the current data, the vibration data and the partial discharge data to the fault type. Through the establishment of the correlation, when the fabric setting machine generates similar (or same) working current data, working vibration data and working partial discharge data in the next working stage, the control system can predict the type of possible fault of the motor through the correlation.
After the above-mentioned stages are completed, the collection and the establishment of the correlation of the current data, the vibration data, the partial discharge data and the fault type can be regarded as being carried out completely once. For single data and type acquisition and correlation establishment, some parameters can be output as experimental data for establishing a database. Such parameters include current data, vibration data, partial discharge data, and fault type. In other words, after the data and type are collected once and the correlation is established, a piece of test data can be obtained, wherein the test data comprises a piece of current data, a piece of vibration data, a piece of partial discharge data and a piece of fault type. After the data and type collection and the establishment of the relevance are repeatedly carried out for a plurality of times, a plurality of test data can be obtained. Next, a plurality of test data are collected, so that a database is built up through the plurality of test data in step S30.
In step S40, a motor failure time prediction model is built through a plurality of test data in the database. The failure time prediction model of the motor may be expressed in a "regression equation" of the predicted failure time with respect to the current data, the vibration data, and the partial discharge data, which is expressed as a "regression equation" herein. In the regression equation, the expected failure time is a dependent variable, and the current data, the vibration data, and the partial discharge data are independent variables. Through the establishment of the regression equation, the control system can predict the time when the motor is expected to have a fault by collecting the working current data, the working vibration data and the working partial discharge data of the motor in the working stage.
In some embodiments, to reduce the variation in operation of different fabric formers, a normalization (or normalization) step may be performed before the motor failure time prediction model is built. Specifically, before each test datum is stored in the database, a normalization process may be performed on each current data, each vibration data and each partial discharge data in each test datum. In some embodiments, the current data, the vibration data and the partial discharge data are normalized by different normalization algorithms, and the current data, the vibration data and the partial discharge data have different normalization ranges. For example, the current data is normalized by: normalized current data is current data +4, and the normalized current data ranges from 0 to 8; the normalized calculation for the vibration data is: normalized vibration data is vibration data/1000, and the range of normalized vibration data is between 0 and 0.3; the normalized calculation formula of the partial discharge data is: normalized partial discharge data is partial discharge data/10000, and the range of the normalized partial discharge data is between 0 and 3.
After the normalization step, a plurality of normalized current data, a plurality of normalized vibration data and a plurality of normalized partial discharge data can be obtained. In some embodiments, the regression equation is established based on a plurality of normalized data. For example, the regression equation may be expressed as: [ expected failure time (a × (normalized current data) × (normalized vibration data) × (C × (normalized partial discharge data) + D × (normalized current data) × (normalized vibration data) + E × (normalized current data))1/2X (normalized partial discharge data)]Wherein A, B, C, D and E are constants and 7.2 ≦ A ≦ 8.8, 18.3 ≦ B ≦ 22.3, 1.4 ≦ C ≦ 1.8, 1.1 ≦ D ≦ 1.3, and 2.2 ≦ E ≦ 2.7.
In step S50, the operating current data, the operating vibration data, and the operating partial discharge data of the motor are collected. The collection of the working current data, the working vibration data and the working partial discharge data is carried out at the working stage of the fabric setting machine. In some embodiments, the working current data, the working vibration data and the working partial discharge data are collected by measuring a working current value, a working vibration value and a working partial discharge value with a first sensor, a second sensor and a third sensor, respectively, and converting the values by a control system.
In step S60, through the correlation between the operating current data, the operating vibration data and the operating partial discharge data collected in step S50 and the current data, the vibration data and the partial discharge data established in step S20 to the failure type, the type of the next failure of the motor of the fabric setting machine (i.e., the expected failure type) and the probability of the expected failure type can be predicted, and the expected failure type can be any one of the 13 failure types collected in step S10. In addition, through the working current data, the working vibration data and the working partial discharge data collected in step S50 and the failure time prediction model (i.e., regression equation) established in step S40, the time when the motor of the fabric setting machine fails next (i.e., the predicted failure time) can be predicted. Table two shows the types of predicted failures and the predicted failure times obtained through the correlation and failure time prediction models in embodiments 1 to 15.
Watch two
In some embodiments, the type of failure and the time of failure predicted in step S60 may be sent to a scheduling system of the fabric setter, so as to reschedule the working phase of the fabric setter. Specifically, the scheduling system may reschedule subsequent processing sequences (e.g., schedule earlier-in-hand orders to be executed before a fabric setter fails) and process yields based on the type of predicted failure and the predicted failure time. In addition, the scheduling system can also schedule maintenance operation in advance according to the predicted failure type and the predicted failure time so as to reduce the time that the fabric setting machine cannot operate due to failure.
In step S70, after the fabric setting machine fails, the control system can correct each data in the database and the established correlation and regression equation by comparing the difference between the predicted failure type and time and the actual failure type and time, so as to improve the accuracy and reliability of the obtained predicted failure type and predicted failure time.
The present disclosure provides a method for operating a fabric setting machine, which can predict the type and time of failure of a motor of the fabric setting machine. In the operating method of the fabric setting machine, current data, vibration data, partial discharge data and fault types of a motor are collected, and therefore correlation and a regression equation of the data are established. The control system of the fabric setting machine can predict the failure type and failure time of the motor according to the established correlation and the regression equation respectively, so as to make corresponding preparation in advance. In addition, by comparing the difference between each predicted result and the actual fault condition, the control system can gradually correct the established correlation and regression equation, thereby improving the accuracy and reliability of the obtained predicted fault type and predicted fault time.
Although the present disclosure has been described with reference to particular embodiments, it will be understood by those skilled in the art that various changes in form and details may be made therein without departing from the spirit and scope of the present disclosure, and therefore the scope of the present disclosure should be limited only by the terms of the appended claims.
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| CN201911222921.1A CN112903001A (en) | 2019-12-03 | 2019-12-03 | Operation method of fabric setting machine |
| TW109100502A TWI754879B (en) | 2019-12-03 | 2020-01-07 | Method for operating fabric setting machine |
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