CN116288881B - Air jet loom monitoring system and method - Google Patents
Air jet loom monitoring system and method Download PDFInfo
- Publication number
- CN116288881B CN116288881B CN202310449597.7A CN202310449597A CN116288881B CN 116288881 B CN116288881 B CN 116288881B CN 202310449597 A CN202310449597 A CN 202310449597A CN 116288881 B CN116288881 B CN 116288881B
- Authority
- CN
- China
- Prior art keywords
- air
- preset
- pressure
- jet loom
- processor
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
- 238000012544 monitoring process Methods 0.000 title claims abstract description 84
- 238000000034 method Methods 0.000 title claims abstract description 34
- 238000001816 cooling Methods 0.000 claims abstract description 77
- 238000002347 injection Methods 0.000 claims abstract description 31
- 239000007924 injection Substances 0.000 claims abstract description 31
- 238000003780 insertion Methods 0.000 claims abstract description 24
- 230000037431 insertion Effects 0.000 claims abstract description 24
- 238000012806 monitoring device Methods 0.000 claims abstract description 14
- 238000001914 filtration Methods 0.000 claims abstract description 7
- 230000008859 change Effects 0.000 claims description 55
- 230000007613 environmental effect Effects 0.000 claims description 48
- 238000005057 refrigeration Methods 0.000 claims description 34
- 239000004744 fabric Substances 0.000 claims description 32
- XLYOFNOQVPJJNP-UHFFFAOYSA-N water Substances O XLYOFNOQVPJJNP-UHFFFAOYSA-N 0.000 claims description 15
- 230000007246 mechanism Effects 0.000 claims description 10
- 230000004044 response Effects 0.000 claims description 10
- 229920001296 polysiloxane Polymers 0.000 claims description 3
- 239000013598 vector Substances 0.000 description 22
- 238000004519 manufacturing process Methods 0.000 description 18
- 230000008569 process Effects 0.000 description 13
- 238000012545 processing Methods 0.000 description 13
- 238000012549 training Methods 0.000 description 13
- 230000000875 corresponding effect Effects 0.000 description 8
- 238000012986 modification Methods 0.000 description 8
- 230000004048 modification Effects 0.000 description 8
- 238000010586 diagram Methods 0.000 description 6
- 238000003062 neural network model Methods 0.000 description 6
- 230000001276 controlling effect Effects 0.000 description 4
- 230000006870 function Effects 0.000 description 4
- 238000010801 machine learning Methods 0.000 description 4
- 230000008439 repair process Effects 0.000 description 4
- 238000013528 artificial neural network Methods 0.000 description 3
- 238000005265 energy consumption Methods 0.000 description 3
- 230000006872 improvement Effects 0.000 description 3
- 238000004140 cleaning Methods 0.000 description 2
- 230000007423 decrease Effects 0.000 description 2
- 230000007547 defect Effects 0.000 description 2
- 238000011478 gradient descent method Methods 0.000 description 2
- 239000012535 impurity Substances 0.000 description 2
- 239000000463 material Substances 0.000 description 2
- 230000000306 recurrent effect Effects 0.000 description 2
- 239000004753 textile Substances 0.000 description 2
- 239000002699 waste material Substances 0.000 description 2
- 241000282414 Homo sapiens Species 0.000 description 1
- VYPSYNLAJGMNEJ-UHFFFAOYSA-N Silicium dioxide Chemical compound O=[Si]=O VYPSYNLAJGMNEJ-UHFFFAOYSA-N 0.000 description 1
- 238000010521 absorption reaction Methods 0.000 description 1
- 230000006978 adaptation Effects 0.000 description 1
- 230000032683 aging Effects 0.000 description 1
- 230000000712 assembly Effects 0.000 description 1
- 238000000429 assembly Methods 0.000 description 1
- 230000033228 biological regulation Effects 0.000 description 1
- 230000005540 biological transmission Effects 0.000 description 1
- 230000006835 compression Effects 0.000 description 1
- 238000007906 compression Methods 0.000 description 1
- 230000002596 correlated effect Effects 0.000 description 1
- 238000003745 diagnosis Methods 0.000 description 1
- 238000009826 distribution Methods 0.000 description 1
- 230000000694 effects Effects 0.000 description 1
- 230000014509 gene expression Effects 0.000 description 1
- 238000012966 insertion method Methods 0.000 description 1
- 238000009434 installation Methods 0.000 description 1
- 239000003607 modifier Substances 0.000 description 1
- 230000002441 reversible effect Effects 0.000 description 1
- 230000006403 short-term memory Effects 0.000 description 1
- 239000000741 silica gel Substances 0.000 description 1
- 229910002027 silica gel Inorganic materials 0.000 description 1
- 239000000243 solution Substances 0.000 description 1
- 239000007921 spray Substances 0.000 description 1
- 230000036962 time dependent Effects 0.000 description 1
Classifications
-
- D—TEXTILES; PAPER
- D03—WEAVING
- D03D—WOVEN FABRICS; METHODS OF WEAVING; LOOMS
- D03D47/00—Looms in which bulk supply of weft does not pass through shed, e.g. shuttleless looms, gripper shuttle looms, dummy shuttle looms
- D03D47/28—Looms in which bulk supply of weft does not pass through shed, e.g. shuttleless looms, gripper shuttle looms, dummy shuttle looms wherein the weft itself is projected into the shed
- D03D47/30—Looms in which bulk supply of weft does not pass through shed, e.g. shuttleless looms, gripper shuttle looms, dummy shuttle looms wherein the weft itself is projected into the shed by gas jet
- D03D47/3026—Air supply systems
- D03D47/3033—Controlling the air supply
Landscapes
- Engineering & Computer Science (AREA)
- Textile Engineering (AREA)
- Looms (AREA)
Abstract
Embodiments of the present disclosure provide a monitoring system and method for an air jet loom, the system comprising: the device comprises an air jet loom, an air compressor, an air supply pipe, a monitoring device and a processor; the air jet loom comprises a weft insertion device for performing air jet weft insertion; the air compressor comprises a cooling device and an air filtering device, and the cooling device is used for cooling the compressed air; the air supply pipe is connected with the air jet loom and the air compressor and is used for conveying compressed air to the air jet loom; the monitoring device is used for acquiring environment monitoring data; the processor is used for determining air injection parameters based on the environment monitoring data and controlling the operation of the air-jet loom and the air compressor based on the air injection parameters.
Description
Technical Field
The specification relates to the technical field of air jet looms, in particular to an air jet loom monitoring system and an air jet loom monitoring method.
Background
The jet weft insertion method uses air as weft insertion medium, uses jet compressed air flow to produce friction traction force to pull weft yarn, makes weft yarn pass through shed, and utilizes jet produced by jet to attain the goal of weft insertion. Air jet weft insertion is a passive weft insertion mode, and the tension of weft yarns is small when the weft yarns fly through a shed, and the weft yarns are uncontrolled, so that the weft yarns with high linear density or fancy yarns lack sufficient traction. Meanwhile, the open state of the warp yarn has great influence on weft insertion quality, and fabric defects such as weft shrinkage, weft foldback and the like are easily generated. Because of more factors which easily cause defects or faults, the fault diagnosis speed of the textile production line is low, the efficiency is low, the fault removal difficulty is high, the judgment and analysis difficulty of the textile quality accidents is high, and the production efficiency is low.
In view of the above problems, CN104499164B discloses an energy-saving weft insertion system of an air jet loom, which can effectively ensure the stability of air pressure supplied to main jet and auxiliary jet by providing a main air bag and an auxiliary air bag for storing air; in addition, the main air bag and each auxiliary air bag are provided with electromagnetic valves, and the air injection time of the main air bag and the auxiliary air bag is controlled through the electromagnetic valves, so that compressed air is fully utilized, and the energy consumption is reduced. CN104499164B does not take into account the dynamic regulation of the compressed air supply pressure and temperature, and the correlation between the air compressor operating parameters and the environmental monitoring data and fabric data, there may be insufficient accuracy of the pressure or temperature of the output compressed air, causing production failures or quality problems.
Therefore, it is desirable to provide a monitoring system and method for an air jet loom, which can reduce the consumption of compressed air and improve the production quality and efficiency of the loom by dynamically monitoring the air supply pressure and temperature.
Disclosure of Invention
One of the embodiments of the present specification provides an air jet loom monitoring system, comprising: the device comprises an air jet loom, an air compressor, an air supply pipe, a monitoring device and a processor; the air jet loom comprises a weft insertion device for performing air jet weft insertion; the air compressor comprises a cooling device and an air filtering device, wherein the cooling device is used for cooling compressed air; the air supply pipe is connected with the air jet loom and the air compressor and is used for conveying compressed air to the air jet loom; the monitoring device is used for acquiring environment monitoring data; the processor is used for determining air injection parameters based on the environment monitoring data and controlling the operation of the air jet loom and the air compressor based on the air injection parameters.
One of the embodiments of the present disclosure provides a method for monitoring an air jet loom, the method being executed by a processor of the air jet loom monitoring system, the method comprising: acquiring environmental monitoring data; determining a jet parameter based on the environmental monitoring data; and controlling the operation of the air jet loom and the air compressor based on the air jet parameters.
Drawings
The present specification will be further elucidated by way of example embodiments, which will be described in detail by means of the accompanying drawings. The embodiments are not limiting, in which like numerals represent like structures, wherein:
FIG. 1 is a schematic diagram of an exemplary architecture of an air jet loom monitoring system according to some embodiments of the present disclosure;
FIG. 2 is an exemplary flow chart for adjusting a preset cooling power according to some embodiments of the present description;
FIG. 3 is an exemplary schematic diagram of determining a predicted temperature of compressed air based on a temperature prediction model, according to some embodiments of the present disclosure;
FIG. 4 is an exemplary schematic diagram of determining a predicted pressure based on a pressure prediction model, according to some embodiments of the present description.
Detailed Description
In order to more clearly illustrate the technical solutions of the embodiments of the present specification, the drawings that are required to be used in the description of the embodiments will be briefly described below. It is apparent that the drawings in the following description are only some examples or embodiments of the present specification, and it is possible for those of ordinary skill in the art to apply the present specification to other similar situations according to the drawings without inventive effort. Unless otherwise apparent from the context of the language or otherwise specified, like reference numerals in the figures refer to like structures or operations.
It will be appreciated that "system," "apparatus," "unit" and/or "module" as used herein is one method for distinguishing between different components, elements, parts, portions or assemblies at different levels. However, if other words can achieve the same purpose, the words can be replaced by other expressions.
As used in this specification and the claims, the terms "a," "an," "the," and/or "the" are not specific to a singular, but may include a plurality, unless the context clearly dictates otherwise. In general, the terms "comprises" and "comprising" merely indicate that the steps and elements are explicitly identified, and they do not constitute an exclusive list, as other steps or elements may be included in a method or apparatus.
A flowchart is used in this specification to describe the operations performed by the system according to embodiments of the present specification. It should be appreciated that the preceding or following operations are not necessarily performed in order precisely. Rather, the steps may be processed in reverse order or simultaneously. Also, other operations may be added to or removed from these processes.
In the working process of the air jet loom, various parameter control is involved, wherein more important is the air pressure and temperature of compressed air, and insufficient air pressure can cause insufficient traction force of air jet weft insertion; too high a temperature may cause shrinkage or wrinkling of the fabric. In addition, the air jet loom has different environment data, different fabric types and different number of the air jet loom, and different requirements on the pressure and temperature of compressed air. Therefore, if the production is performed by only adopting fixed operation parameters, the quality and efficiency of the production cannot be ensured, and the waste of the compressed air may be caused. CN104499164B discloses an energy-saving weft insertion system of an air jet loom, which can effectively ensure the stability of air pressure supplied to main jet and auxiliary jet by arranging a main air bag and an auxiliary air bag for storing air; in addition, the main spray and each auxiliary air bag are provided with electromagnetic valves, the air discharge time is controlled through the electromagnetic valves, stable air flow is provided, meanwhile, the air quantity is ensured to be sufficient, and the waste of compressed air is avoided. But it does not take into account the differences in environmental monitoring data and fabric data, which may have an impact on the quality and efficiency of the air jet weft insertion.
In view of this, in some embodiments of the present disclosure, environmental monitoring data and fabric data during the operation of the air jet loom are obtained through dynamic monitoring, and air jet parameters are determined based on the changes of the environmental monitoring data and the fabric data, so that the pressure and the temperature of the compressed air can be dynamically adjusted, the energy consumption is reduced, the failure rate is reduced, and the production quality and the production efficiency are improved.
Fig. 1 is a schematic diagram of an exemplary architecture of an air jet loom monitoring system according to some embodiments of the present disclosure. In some embodiments, the air jet loom monitoring system 100 may include an air jet loom 110, an air supply tube 120, an air compressor 130, a monitoring device 140, and a processor 150.
The air jet loom 110 may refer to a shuttleless loom that employs a jet of air to draw a weft yarn across a shed. In some embodiments, the air jet loom 110 may include a weft insertion device 110-1.
The weft insertion device 110-1 may refer to an apparatus for performing air jet weft insertion.
The air supply pipe 120 may refer to a pipe that delivers compressed air to an air jet loom. In some embodiments, the air supply pipe 120 mechanically connects the air jet loom 110 and the air compressor 130.
In some embodiments, the gas supply pipe 120 may include main and branch pipes, as well as a three-way solenoid valve. The three-way electromagnetic valve is characterized in that an air inlet channel is connected with an air supply pipe, and the other two air outlet channels are respectively connected with a main pipeline and a branch pipeline. Three-way solenoid valves may be used to control the flow of compressed air from the main or branch conduits, as described in more detail below.
In some embodiments, the air supply pipe may comprise a plurality of main pipes, each of which may supply air to one air jet loom.
In some embodiments, the gas supply pipe 120 may further include a reserve pipe 120-1.
The spare pipe 120-1 may refer to a pipe for further cooling the compressed air. In some embodiments, the backup tube 120-1 may be externally wrapped with a water tube and/or heat absorbing silicone tubing to cool the compressed air. For more details on the backup tube 120-1, see the associated description in FIG. 2.
The air compressor 130 may refer to an apparatus for generating compressed air. For example, the air compressor may be any of a screw air compressor, a centrifugal air compressor, and the like.
In some embodiments, the air compressor may include a cooling device 130-1 and an air filtering device 130-2.
The cooling device 130-1 may be used to cool the compressed air. For example, the cooling device may cool the compressed air by any of air cooling, water cooling, and the like.
The air filtering device 130-2 may refer to a device for filtering compressed air. For example, the air filtration device may be a filter. In some embodiments, the compressed air enters the weft insertion device 110-1 after passing through a filter to remove impurities before entering the air jet loom.
The monitoring device 140 may refer to a device for acquiring environmental monitoring data. For example, the monitoring device may include any one or combination of a leak detector, an ultrasonic scanning gun, a thermal infrared imager, a temperature sensor, a humidity sensor, a pressure sensor, a barometer, a flow meter, and the like.
In some embodiments, the processor 150 may be communicatively coupled to the monitoring device 140, the air jet loom 110, and the air compressor 130. The processor 150 may be configured to determine air jet parameters based on the environmental monitoring data and to control operation of the air jet loom, air compressor, based on the air jet parameters. For the content of the processor in determining the jet parameters based on the environmental monitoring data, reference may be made to the description elsewhere in this specification.
The temporary brake mechanism 160 may be used to temporarily suspend the supply of compressed air from the supply tube to the airjet loom.
In some embodiments, after the temporary brake mechanism is activated, the air supply tube may be suspended from supplying air to the airjet loom and compressed air may be expelled through other means. For more details on temporary brake mechanism 160, see FIG. 2 and its associated description.
It should be noted that the above description of the air jet loom monitoring system and its respective units is for convenience of description only, and is not intended to limit the present description to the scope of the illustrated embodiments. It will be appreciated by those skilled in the art that, given the principles of the system, various modules may be combined arbitrarily or a subsystem may be constructed in connection with other modules without departing from such principles.
In some embodiments, the air jet loom monitoring method may be performed by a processor in an air jet loom monitoring system. In some embodiments, the air jet loom monitoring method may be implemented by:
step one, acquiring environment monitoring data.
The environmental monitoring data may refer to monitoring data related to the operation of the air jet loom monitoring system. For example, the environmental monitoring data may include temperature data, pressure data, etc. related to the operation of the air jet loom monitoring system. For more content on environmental monitoring data, see fig. 2 and its associated description.
In some embodiments, the processor may obtain environmental monitoring data through the monitoring device 140.
And step two, determining jet parameters based on the environment monitoring data.
The air jet parameters may refer to the operating parameters of the air compressor to supply air to the air jet loom. For example, the air injection parameters may include, but are not limited to, air supply pressure, cooling efficiency of the compressed air, operating power of the air compressor, and the like. For more details on the jet parameters, see the description elsewhere in this specification.
In some embodiments, the processor may determine the jet parameters in a variety of ways based on the environmental monitoring data. For example, the processor may establish a preset table based on the correspondence between the environmental monitoring data and the jet parameters in the historical data, and determine the jet parameters by looking up a table based on the environmental monitoring data.
In some embodiments, to determine the jet parameters, the processor may be further configured to: before the air jet loom and the air compressor are started, determining whether to adjust preset air jet parameters based on environmental monitoring data and fabric data; and adjusting the preset air injection parameters based on the environmental monitoring data change value and the fabric data change value, and determining the adjusted air injection parameters.
The fabric data may include, among other things, the type of fabric, the number of air jet looms activated, etc. In some embodiments, the processor may obtain the fabric data based on a production schedule.
The preset air injection parameter may refer to an air injection parameter determined in advance for the air compressor. The preset air injection parameters may also refer to initial air injection parameters of the air compressor.
In some embodiments, the processor may determine whether at least one of the environmental monitoring data and the fabric data has changed based on the environmental monitoring data and the fabric data; if the air jet parameters change, the preset air jet parameters are adjusted. Specifically, the processor may compare the real-time environmental monitoring data with the initial environmental monitoring data, and compare the real-time fabric data with the preset fabric data to determine whether a change has occurred. The processor can also compare the real-time environmental monitoring data with the environmental monitoring data of the previous round of production, and compare the real-time fabric data with the fabric data of the previous round of production to determine whether the change occurs.
In some embodiments, the processor may adjust the preset air injection parameters based on the environmental monitoring data change values and the fabric data change values in a variety of ways to determine adjusted air injection parameters. For example, the processor may establish a preset table based on the relationship between the environmental monitoring data change value and the fabric data change value and the air injection parameter change value in the history data, and determine the air injection parameter change value by looking up a table based on the environmental monitoring data change value and the fabric data change value to adjust the preset air injection parameter.
In some embodiments, the preset jet parameters may include a preset cooling power. The preset cooling power may be adjusted based on a temperature difference between an intake temperature and a predicted temperature of the compressed air, as described in detail with reference to fig. 2 and the related description.
In some embodiments, the preset air injection parameter may include a preset air supply pressure. The preset supply air pressure may be adjusted based on the total loss of pressure, as described in detail with reference to fig. 3 and its associated description.
And thirdly, controlling the operation of the air jet loom and the air compressor based on the air jet parameters.
In some embodiments, the processor may send air jet parameters to the air compressor and the air jet loom to control the air compressor to supply air to the air jet loom and to control the air jet loom to perform air jet weft insertion.
According to the embodiment of the specification, the air jet parameters are adjusted based on the change of the environmental monitoring data, compressed air with proper temperature and pressure can be provided for the air jet loom, the condition that weft shrinkage or cloth wrinkling is caused by overhigh temperature or the air jet loom cannot normally run due to insufficient pressure of the compressed air is avoided, and the production quality is improved.
Fig. 2 is an exemplary flow chart for adjusting a preset cooling power according to some embodiments of the present description. In some embodiments, the process 200 may be performed by a processor. As shown in fig. 2, the process 200 may include the steps of:
in some embodiments, the preset air injection parameters may include a preset cooling power, and the adjustment of the preset air injection parameters may be implemented through the process 200.
The preset cooling power may refer to a cooling power of a preset cooling device. The preset cooling power may be based on a system default setting.
At step 210, a predicted temperature of the compressed air is determined based on the environmental monitoring data.
The predicted temperature may refer to a predicted temperature of the cooled compressed air.
In some embodiments, the processor may determine the predicted temperature of the compressed air in a variety of ways based on the environmental monitoring data. For example, the processor may acquire historical environmental monitoring data identical to the environmental monitoring data in the historical data, and determine the temperature of the compressed air after the actual cooling of the history corresponding to the historical environmental monitoring data as the predicted temperature.
In some embodiments, determining the predicted temperature of the compressed air based on the environmental monitoring data may include: processing the environmental monitoring data based on a temperature prediction model to determine a predicted temperature; the environmental monitoring data may include an intake air temperature, an intake water temperature of the cooling device, an outlet water temperature, a compressed air pressure, an outlet air quantity, and a preset cooling power.
In some embodiments, the temperature prediction model may be a machine learning model. For example, the temperature prediction model may be a neural network model such as a deep neural network (Deep Neural Network, DNN).
In some embodiments, the temperature prediction model may be obtained through training, and the relevant description of fig. 3 may be referred to for the content of the training temperature prediction model.
In some embodiments, the intake air temperature may refer to the temperature of the air as it enters the air compressor, i.e., the outside ambient temperature. The inlet water temperature of the cooling device is the initial temperature of water flowing into the cooling device, and the outlet water temperature is the water temperature flowing out of the cooling device. The compressed air pressure may refer to the pressure of compressed air output by the air compressor, and the air output may refer to the volume of air output by the air compressor each time the air compressor is operated.
According to some embodiments of the present disclosure, the temperature prediction model is used to process the environmental monitoring data to determine the predicted temperature of the compressed air, so that the prediction efficiency and accuracy can be improved.
FIG. 3 is an exemplary schematic diagram of determining a predicted temperature of compressed air based on a temperature prediction model, according to some embodiments of the present description.
In some embodiments, as shown in fig. 3, the temperature prediction model may include an efficiency determination layer 320 and an air temperature prediction layer 360; the efficiency determination layer 320 may be used to process the inlet water temperature 310-1, the outlet water temperature 310-2, the outlet air amount 310-3, and the preset cooling power 310-4 to determine the cooling characteristics 330; the air temperature prediction layer 360 may be used to process the cooling characteristics 330, the compressed air pressure 340, the intake air temperature 350, and determine a predicted temperature 370.
Wherein the cooling feature 330 may reflect the level of cooling efficiency of the cooling device. In some embodiments, the cooling characteristic may be a vector of refrigeration efficiencies at a plurality of points in time over the actual refrigeration time, with the elements in the vector being numbers of [0,1 ]. The cooling efficiency may be expressed as a ratio of a temperature difference of the compressed air to an actual cooling time, for example, the cooling efficiency at each time point is a ratio of a temperature difference of the compressed air at a current time point to a previous time point to a time difference from the previous time point to the current time point. The refrigeration efficiency units are as follows: degrees celsius/second.
In some embodiments, the efficiency determination layer 320 may be a neural network model (NN), and the air temperature prediction layer 360 may be a recurrent neural network model (Recurrent Neural Network, RNN).
In some embodiments, the efficiency decision layer 320 and the air temperature prediction layer 360 of the temperature prediction model may be obtained by joint training.
In some embodiments, the first training sample of the training temperature prediction model may include a sample inlet water temperature, a sample outlet air volume, a sample preset cooling power, a sample inlet air temperature, a sample compressed air pressure, and may be obtained from historical data. The first label is the temperature of the historical actual compressed air output by the air compressor corresponding to the first training sample, and can be marked and determined by the processor.
In some embodiments, the processor may input the sample inlet water temperature, the sample outlet air volume, the sample preset cooling power to the initial efficiency judgment layer to obtain an initial cooling characteristic; and inputting the initial cooling characteristic, the sample inlet air temperature and the sample compressed air pressure into an initial air temperature prediction layer to obtain an initial prediction temperature. And constructing a loss function based on the initial predicted temperature and the first label, and synchronously updating parameters of the initial efficiency judgment layer and the initial air temperature prediction layer by using the loss function through a gradient descent method and the like. And acquiring a trained efficiency judgment layer and an air temperature prediction layer through parameter updating.
According to some embodiments of the present disclosure, by setting the temperature prediction model as the efficiency judgment layer and the air temperature prediction layer, and respectively processing corresponding data through different layers, the data processing efficiency can be further improved, and the prediction accuracy can be improved. Through joint training, the problem that labels are not good to acquire when the single training efficiency judges the fault can be solved, and the training effect is improved.
Step 220, determining a temperature difference based on the intake air temperature and the predicted temperature.
In some embodiments, the processor may determine the temperature difference based on a difference between the intake air temperature and the predicted temperature.
In step 230, the preset cooling power is adjusted in response to the temperature difference being greater than the preset temperature difference threshold.
The preset temperature difference threshold may be set by system defaults.
In some embodiments, the processor may determine an overrun based on the temperature difference and a preset temperature difference threshold, and adjust the preset cooling power based on the overrun. For example, the processor may determine and store the corresponding cooling power for different overages in advance, and call the corresponding cooling power based on the actual overages in practice.
In some embodiments, the processor may also determine an accumulated number of times the temperature difference exceeds a preset temperature difference threshold, and each time the number of times is increased, the cooling power is increased by one gear on the basis of the current gear until the highest gear is reached.
According to some embodiments of the present disclosure, the temperature of the air discharged from the air compressor is predicted, the temperature difference with the air inlet temperature is determined, the preset cooling power of the cooling device is adjusted based on the temperature difference, and the cooling power of the cooling device can be adjusted in advance, so that the temperature of the compressed air output by the air compressor is ensured to be within a reasonable range, and the quality problem of yarn distribution caused by overlarge temperature difference is avoided.
It should be noted that the above description of the process 200 is for illustration and description only, and is not intended to limit the scope of applicability of the present disclosure. Various modifications and changes to flow 200 will be apparent to those skilled in the art in light of the present description. However, such modifications and variations are still within the scope of the present description.
In some embodiments, the air jet loom monitoring system may further comprise a temporary braking mechanism, and the processor may be further configured to: determining predicted refrigeration efficiency based on the temperature difference and a preset refrigeration time; comparing the predicted refrigeration efficiency with the maximum refrigeration efficiency of the cooling refrigeration device; and controlling the temporary braking mechanism to work so as to stop air supply to the air-jet loom in response to the predicted refrigeration efficiency being greater than the maximum refrigeration efficiency.
In some embodiments, the processor may divide the temperature difference by a preset cooling time to determine the predicted cooling efficiency.
The preset cooling time may refer to a preset cooling time. The refrigerating time of the compressed air can be controlled within a certain range by determining the preset refrigerating time, so that the production efficiency is improved. In some embodiments, the preset cool down time may be determined based on system default settings, or based on production experience.
The predicted cooling efficiency may refer to the efficiency of the predicted cooling device when cooling the compressed air to the predicted temperature for a preset cooling time, expressed as degrees celsius/second, i.e. how much degrees celsius the compressed air can be cooled per second.
The maximum refrigeration efficiency may refer to the maximum value that the cooling device can cool down the compressed air per second.
In some embodiments, the processor may compare the predicted refrigeration efficiency to a maximum refrigeration efficiency of the cooling device and determine whether the predicted refrigeration efficiency is greater than the maximum refrigeration efficiency.
In some embodiments, in response to the predicted cooling efficiency being greater than the maximum cooling efficiency, the processor may control the temporary braking mechanism to operate, closing the air supply duct, to stop its air supply to the air jet loom.
In some embodiments, after the temporary braking mechanism is turned on, the processor may control the air outlet of the three-way solenoid valve connected to the main pipe to be closed and open the air outlet of the three-way solenoid valve and the branch pipe to discharge the compressed air from the branch pipe to the atmosphere.
Some embodiments of the present disclosure provide that, by comparing the predicted refrigeration efficiency with the maximum refrigeration efficiency, when the predicted refrigeration efficiency is greater than the maximum refrigeration efficiency, it is indicated that the cooling device cannot cool the compressed air to a suitable temperature for a preset refrigeration time, which may affect the quality of the fabric. At this time, by the temporary braking mechanism, the compressed air with unsuitable temperature can be prevented from being delivered to the air-jet loom, and the quality of the fabric can be prevented from being influenced.
In some embodiments, the gas supply tube may further comprise a reserve tube, the reserve tube being externally wrapped with a water tube and heat absorbing silicone sheets; the processor may be further configured to: in response to the predicted refrigeration efficiency being greater than the maximum refrigeration efficiency, supplying air to the air jet loom through the backup tube; the backup pipe is used for further compressing the compressed air.
When the predicted refrigeration efficiency is greater than the maximum refrigeration efficiency, it is indicated that the temperature of the compressed air is not reduced to a reasonable range after the compressed air is cooled by the cooling device, and the fabric quality may be affected. At this time, the processor can close the air supply pipe, open the reserve pipe and supply air to the air jet loom to utilize water pipe and the heat absorption silica gel piece that the reserve pipe outside twined to further cool down compressed air.
In some embodiments, the air pressure may drop as the compressed air passes through a length of backup tubing. The processor may further compress the compressed air through the backup line to achieve the desired air pressure.
In some embodiments, the length and pressure of the reserve tube is adjustable; the reserve tube may include a pressurizing means for pressurizing the compressed air flowing through the reserve tube; the length of the reserve tube is related to the difference between the temperature of the compressed air cooled based on the maximum cooling efficiency and the intake air temperature.
In some embodiments, the pressurizing device may be disposed at the end of the backup pipe, and the processor may monitor the pressure of the compressed air output from the air compressor by the monitoring device, monitor the pressure of the compressed air flowing through the backup pipe based on the pressurizing device, and determine the pressurizing amount of the pressurizing device based on the difference between the two pressures.
In some embodiments, the length of the backup tube is directly related to the temperature of the compressed air after cooling based on maximum refrigeration efficiency, the difference from the intake air temperature. The greater the difference, the greater the strength required to be cooled and the longer the corresponding reserve tube length. In some embodiments, the processor may determine the reserve tube length based on the difference.
According to the embodiment of the specification, by arranging the pressurizing device and determining the length of the standby pipe based on the temperature difference, compression temperatures of different temperatures or pressures can be further pressurized or refrigerated to obtain compressed air with proper pressure and temperature, so that the problem of insufficient injection weft insertion traction caused by insufficient pressure or the problem of fabric quality caused by too high temperature is avoided, and the operation is flexible and simple.
According to some embodiments of the present disclosure, by providing the backup pipe, when the temperature of the compressed air output by the air compressor is higher than a reasonable range, the compressed air may be further processed (such as cooling, pressurizing, etc.), so as to avoid the influence of the excessive temperature on the fabric quality.
In some embodiments, the preset gas injection parameter may include a preset gas supply pressure, the preset gas supply pressure being related to a total pressure loss of the gas supply pipe. The processor may determine a total pressure loss based on the environmental monitoring data; the preset air supply pressure is adjusted based on the total pressure loss.
The preset supply air pressure may refer to the pressure of compressed air that the preset air compressor needs to supply. For example, the preset air supply pressure may include a minimum air supply pressure that satisfies the air jet weft insertion requirement.
In some embodiments, the processor may determine the preset air supply pressure by default settings of the system or based on production experience, depending on the air jet loom specifications and the type of fabric.
The total loss of pressure may refer to the total loss of air pressure of the compressed air during transportation of the gas supply pipe. The impurities in the air supply pipe and the leakage of the air supply pipe can cause the pressure loss of the compressed air to a certain extent.
In some embodiments, the preset gas supply pressure is positively correlated with the total loss of pressure from the gas supply pipe. The larger the total pressure loss of the air supply pipe, the larger the preset air supply pressure is in order to ensure that the compressed air has sufficient pressure to perform air jet weft insertion.
In some embodiments, the processor may determine the total pressure loss in a variety of ways. For example, the processor may construct a feature vector based on the outlet pressure variation data, the pipe length, etc. in the environmental monitoring data; based on the feature vector retrieval in the database, a historical feature vector is determined for which the vector distance satisfies a distance threshold. And determining the total pressure loss stored in association with the historical feature vector as the total pressure loss currently required. The database is used for storing historical characteristic vectors constructed based on historical air outlet pressure change data and pipeline length and historical pressure total loss corresponding to each historical characteristic vector. For more information on the change in the outlet pressure, see below.
In some embodiments, the processor may adjust the preset air supply pressure based on a correspondence between the total pressure loss and the air supply pressure. The correspondence between the total loss of pressure and the supply pressure may be preset in advance. For example, when the total pressure loss increases (or decreases) K1 pa, the preset air supply pressure increases (or decreases) K2 pa, or the like, accordingly.
According to the embodiment of the specification, the preset air supply pressure is adjusted according to different total pressure losses, so that the problem that the air supply pressure of compressed air is insufficient to provide normal air jet weft insertion traction force due to overlarge total pressure loss can be effectively avoided while energy consumption is saved, production faults are reduced, and the production efficiency and quality of an air jet loom are improved.
In some embodiments, the processor may process the outlet pressure change data based on the data processing layer to determine a pressure change vector; the preliminary total pressure loss is determined based on the pressure change vector.
The outlet air pressure change data may refer to data obtained by monitoring the time-dependent change of the outlet air pressure discharged from the air compressor by the monitoring device. For example, the air outlet pressure change data may include compressed air outlet pressure change data due to changes in climate conditions, or operating conditions disturbances (e.g., fluctuations in air load), and compressed air outlet pressure change data under normal conditions.
For example, a set of outlet pressure change data is (0,3.1,3.1,3.08,3.07,2.99,0,8,8,8,7.98,7.85), where the first 0 represents machine start-up; 3.1,3.1,3.08,3.07,2.99 is the change data of the air outlet pressure of the compressed air under normal conditions; the second 0 represents compressed air outlet pressure change data caused by working condition change (machine midway shutdown); 8,8,8,7.98,7.85 shows the variation data of the air outlet pressure of the compressed air caused by the variation of the working condition (such as increasing the number of the looms).
In some embodiments, the processor may determine the outlet pressure change data in a variety of ways. For example, the processor may obtain the outlet pressure change data by acquiring the outlet pressure at a plurality of time points in the preset time through the monitoring device based on the preset time (for example, 30 days, 60 days).
The pressure change vector can be used for reflecting the vector of the air outlet pressure change data after the interference of the working condition is eliminated. Each element in the pressure change vector is air outlet pressure data which are arranged in time sequence and are used for eliminating interference of working conditions. For example, the pressure change vectors obtained based on the above-described outlet pressure change data (0,3.1,3.1,3.08,3.07,2.99,0,8,8,8,7.98,7.85) are (0,3.1,3.1,3.08,3.07,2.99) and (8,8,8,7.98,7.85).
In some embodiments, the data processing layer may be a machine learning model. For example, the data processing layer may be a Long Short-Term Memory network (LSTM), or the like.
In some embodiments, the data processing layer may be obtained by co-training with the pressure prediction layer, see in particular fig. 4 and its associated description.
The preliminary total pressure loss may refer to a rough total pressure loss determined based on the pressure change vector. For example, the preliminary total pressure loss may include any one or more of a transmission loss, a leakage loss, and the like of the gas supply pipe.
In some embodiments, the processor may determine the magnitude of the pressure change based on differences in adjacent outlet gas pressure change data in the pressure change vector. The magnitude of the pressure change is determined as the preliminary total pressure loss. By differencing sets of adjacent outlet gas pressure variation data, a plurality of preliminary total pressure losses may be determined.
According to some embodiments of the present disclosure, the pressure change vector is determined based on the outlet air pressure change data, so that the cause of the pressure loss can be comprehensively determined, and the power consumption of the air compressor can be reduced. Meanwhile, analysis is carried out based on a large number of pressure change data at time points/sections so as to determine the initial total pressure loss, so that the interference of the air pressure change data caused by normal working condition change can be removed, and a reliable data base is provided for the subsequent adjustment of the preset air supply pressure based on the total pressure loss.
In some embodiments, the preliminary total pressure loss includes leakage loss, and the processor may determine the predicted pressure based on a pressure loss model; based on the comparison of the monitored pressure and the predicted pressure, a leak loss and a leak level are determined.
Leakage loss may refer to the loss of compressed air pressure caused by compressed air leakage. The leakage of compressed air is caused by the aging and damage of the pipeline of the air supply pipe, and the leakage loss is caused. The degree of leakage can be used to measure the amount of leakage loss of the pressure of the compressed air and can be expressed slightly or severely.
The pressure loss model may be a machine learning model of the custom structure hereinafter. The pressure loss model may also be a machine learning model of other structure, such as a neural network model or the like.
FIG. 4 is an exemplary schematic diagram of determining a predicted pressure based on a pressure prediction model, according to some embodiments of the present description.
In some embodiments, as shown in FIG. 4, the pressure loss model may include a data processing layer 420 and a pressure prediction layer 460.
The data processing layer 420 may be configured to process the outlet pressure change data 410 to determine a pressure change vector 430.
The pressure prediction layer 460 may be used to process the pressure change vector 430, the gas supply pipe length 440, the gas supply pipe usage time 450 to determine a predicted pressure 470.
The predicted pressure may refer to a predicted pressure of the compressed air output from the air supply pipe. The air supply pipe use time may refer to the time period from installation of the air supply pipe to the current operation.
In some embodiments, the pressure prediction layer 460 may be a neural network model, such as DNN, or the like.
In some embodiments, the data processing layer and the pressure prediction layer may be obtained through joint training.
In some embodiments, the second training sample of the pressure loss model may include sample pressure data, sample gas supply tube length, sample gas supply tube time of use, and may be obtained from historical data. The second label is the actual historical pressure output by the air compressor corresponding to the second training sample, and can be marked and determined by the processor.
In some embodiments, the processor may input sample pressure data into an initial data processing layer to obtain an initial pressure change vector. And inputting the initial pressure change vector, the length of the sample gas supply pipe and the service time of the sample gas supply pipe into an initial pressure prediction layer to obtain initial predicted pressure. And constructing a loss function based on the initial predicted pressure and the second label, and synchronously updating parameters of the initial data processing layer and the initial pressure prediction layer by using the loss function through a gradient descent method. And acquiring a trained data processing layer and a trained pressure prediction layer through parameter updating.
According to the embodiments of the specification, the pressure loss model of the multi-layer structure is used for determining the predicted pressure based on the pressure change data, the length of the air supply pipe and the service time of the air supply pipe, so that the output result of the model is more accurate, the accuracy of the pressure loss prediction is further improved, and the production efficiency is improved.
In some embodiments, the processor may compare the monitored pressure to the predicted pressure and determine that there is a leak loss in the compressed air pressure when the monitored pressure is less than the predicted pressure.
In some embodiments, the processor may determine the pressure difference based on the monitored pressure and the predicted pressure. When the pressure difference is greater than a first preset pressure threshold, it is determined that the extent of the gas supply pipe leakage is severe. The first preset pressure threshold may be set by the system or by human beings.
Some embodiments of the present disclosure, by comparing the monitored pressure to the predicted pressure, can determine if there is a leak loss in the gas supply pipe and quantitatively analyze the extent of the leak to provide a data reference for cleaning or repair of the gas supply pipe, to facilitate repair of the gas supply pipe leak or replacement of components.
In some embodiments, the processor may determine whether to clean the gas supply pipe based on the preliminary total pressure loss; whether to repair the gas supply pipe is determined based on the leakage loss and the leakage degree.
In some embodiments, the processor may determine whether the preliminary total pressure loss is greater than a second preset pressure threshold, and if so, clean the gas supply pipe. The second preset pressure threshold may be obtained based on empirical or historical data. When there are a plurality of preliminary pressure losses, the gas supply pipe is cleaned as long as one of the preliminary pressure losses exceeds a second preset pressure threshold.
In some embodiments, the processor determines to repair the gas supply pipe when it is determined that there are multiple leak losses or the extent of the leak is severe.
According to some embodiments of the specification, the air supply pipe is cleaned or repaired based on the pressure loss of the compressed air, so that the flexibility and pertinence of cleaning and repairing the air supply pipe can be improved, leakage is effectively reduced or even eliminated, and the loss of the compressed air is reduced.
While the basic concepts have been described above, it will be apparent to those skilled in the art that the foregoing detailed disclosure is by way of example only and is not intended to be limiting. Although not explicitly described herein, various modifications, improvements, and adaptations to the present disclosure may occur to one skilled in the art. Such modifications, improvements, and modifications are intended to be suggested within this specification, and therefore, such modifications, improvements, and modifications are intended to be included within the spirit and scope of the exemplary embodiments of the present invention.
Meanwhile, the specification uses specific words to describe the embodiments of the specification. Reference to "one embodiment," "an embodiment," and/or "some embodiments" means that a particular feature, structure, or characteristic is associated with at least one embodiment of the present description. Thus, it should be emphasized and should be appreciated that two or more references to "an embodiment" or "one embodiment" or "an alternative embodiment" in various positions in this specification are not necessarily referring to the same embodiment. Furthermore, certain features, structures, or characteristics of one or more embodiments of the present description may be combined as suitable.
Furthermore, the order in which the elements and sequences are processed, the use of numerical letters, or other designations in the description are not intended to limit the order in which the processes and methods of the description are performed unless explicitly recited in the claims. While certain presently useful inventive embodiments have been discussed in the foregoing disclosure, by way of various examples, it is to be understood that such details are merely illustrative and that the appended claims are not limited to the disclosed embodiments, but, on the contrary, are intended to cover all modifications and equivalent arrangements included within the spirit and scope of the embodiments of the present disclosure. For example, while the system components described above may be implemented by hardware devices, they may also be implemented solely by software solutions, such as installing the described system on an existing server or mobile device.
Likewise, it should be noted that in order to simplify the presentation disclosed in this specification and thereby aid in understanding one or more inventive embodiments, various features are sometimes grouped together in a single embodiment, figure, or description thereof. This method of disclosure, however, is not intended to imply that more features than are presented in the claims are required for the present description. Indeed, less than all of the features of a single embodiment disclosed above.
In some embodiments, numbers describing the components, number of attributes are used, it being understood that such numbers being used in the description of embodiments are modified in some examples by the modifier "about," approximately, "or" substantially. Unless otherwise indicated, "about," "approximately," or "substantially" indicate that the number allows for a 20% variation. Accordingly, in some embodiments, numerical parameters set forth in the specification and claims are approximations that may vary depending upon the desired properties sought to be obtained by the individual embodiments. In some embodiments, the numerical parameters should take into account the specified significant digits and employ a method for preserving the general number of digits. Although the numerical ranges and parameters set forth herein are approximations that may be employed in some embodiments to confirm the breadth of the range, in particular embodiments, the setting of such numerical values is as precise as possible.
Each patent, patent application publication, and other material, such as articles, books, specifications, publications, documents, etc., referred to in this specification is incorporated herein by reference in its entirety. Except for application history documents that are inconsistent or conflicting with the content of this specification, documents that are currently or later attached to this specification in which the broadest scope of the claims to this specification is limited are also. It is noted that, if the description, definition, and/or use of a term in an attached material in this specification does not conform to or conflict with what is described in this specification, the description, definition, and/or use of the term in this specification controls.
Finally, it should be understood that the embodiments described in this specification are merely illustrative of the principles of the embodiments of this specification. Other variations are possible within the scope of this description. Thus, by way of example, and not limitation, alternative configurations of embodiments of the present specification may be considered as consistent with the teachings of the present specification. Accordingly, the embodiments of the present specification are not limited to only the embodiments explicitly described and depicted in the present specification.
Claims (6)
1. An air jet loom monitoring system comprising: the device comprises an air jet loom, an air compressor, an air supply pipe, a monitoring device and a processor;
The air jet loom comprises a weft insertion device for performing air jet weft insertion;
the air compressor comprises a cooling device and an air filtering device, wherein the cooling device is used for cooling compressed air;
the air supply pipe is connected with the air jet loom and the air compressor and is used for conveying compressed air to the air jet loom;
the monitoring device is used for acquiring environment monitoring data;
the processor is used for determining air injection parameters based on the environment monitoring data and controlling the operation of the air jet loom and the air compressor based on the air injection parameters; wherein, to determine the jet parameters, the processor is further to:
before the air jet loom and the air compressor are started, determining whether to adjust preset air jet parameters based on the environment monitoring data and the fabric data;
in response, adjusting the preset air injection parameters based on the environmental monitoring data change value and the fabric data change value, and determining the adjusted air injection parameters; wherein the preset jet parameters include a preset cooling power, the processor is further configured to:
determining a predicted temperature of the compressed air based on the environmental monitoring data;
Determining a temperature difference based on the intake air temperature and the predicted temperature;
and adjusting the preset cooling power in response to the temperature difference being greater than a preset temperature difference threshold.
2. The system of claim 1, further comprising a temporary braking mechanism, the processor further configured to:
determining predicted refrigeration efficiency based on the temperature difference and a preset refrigeration time;
comparing the predicted refrigeration efficiency with a maximum refrigeration efficiency of the cooling refrigeration device;
and controlling the temporary braking mechanism to work so as to stop air supply to the air-jet loom in response to the predicted refrigeration efficiency being greater than the maximum refrigeration efficiency.
3. The system of claim 2, wherein the gas supply tube further comprises a backup tube externally wrapped with a water tube and heat absorbing silicone sheets; the processor is further configured to:
supplying air to the air jet loom through the backup pipe in response to the predicted refrigeration efficiency being greater than the maximum refrigeration efficiency; the standby pipe is used for further refrigerating the compressed air.
4. The system of claim 1, wherein the preset air injection parameter comprises a preset air supply pressure, the preset air supply pressure being related to a total loss of pressure from the air supply pipe; the processor is further configured to:
Determining the total pressure loss based on the environmental monitoring data;
and adjusting the preset air supply pressure based on the total pressure loss.
5. A method of monitoring an air jet loom, the method being performed based on a processor of an air jet loom monitoring system according to claim 1, the method comprising:
acquiring environmental monitoring data;
determining a jet parameter based on the environmental monitoring data; wherein the determining jet parameters based on the environmental monitoring data comprises:
determining whether to adjust preset air injection parameters based on the environment monitoring data and the fabric data;
in response, adjusting the preset air injection parameters based on the environmental monitoring data change value and the fabric data change value, and determining the adjusted air injection parameters; wherein the preset jet parameters include a preset cooling power, the method further comprising:
determining a predicted temperature of the compressed air based on the environmental monitoring data;
determining a temperature difference based on the intake air temperature and the predicted temperature;
adjusting the preset cooling power in response to the temperature difference being greater than a preset temperature difference threshold;
and controlling the operation of the air jet loom and the air compressor based on the air jet parameters.
6. The method of claim 5, wherein the preset gas injection parameter comprises a preset gas supply pressure, the preset gas supply pressure being related to a total loss of pressure from the gas supply pipe; the method further comprises:
determining the total pressure loss based on the environmental monitoring data;
and adjusting the preset air supply pressure based on the total pressure loss.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202310449597.7A CN116288881B (en) | 2023-04-24 | 2023-04-24 | Air jet loom monitoring system and method |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202310449597.7A CN116288881B (en) | 2023-04-24 | 2023-04-24 | Air jet loom monitoring system and method |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN116288881A CN116288881A (en) | 2023-06-23 |
| CN116288881B true CN116288881B (en) | 2023-10-20 |
Family
ID=86827149
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202310449597.7A Active CN116288881B (en) | 2023-04-24 | 2023-04-24 | Air jet loom monitoring system and method |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN116288881B (en) |
Families Citing this family (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116736817B (en) * | 2023-08-11 | 2023-11-03 | 西安启银致远企业管理有限公司 | Intelligent control method for hydrophilic silver fiber dressing production and processing |
| CN117538088B (en) * | 2024-01-09 | 2024-03-22 | 山东日发纺织机械有限公司 | Air consumption measuring and calculating method, device and equipment of air jet loom and storage medium |
Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JPH05140844A (en) * | 1991-11-22 | 1993-06-08 | Nissan Motor Co Ltd | Prediction system and control unit for malweaving in loom |
| CN102808273A (en) * | 2011-05-31 | 2012-12-05 | 株式会社丰田自动织机 | Air supply system in air jet looms |
| CN204401240U (en) * | 2014-12-11 | 2015-06-17 | 青岛天一集团红旗纺织机械有限公司 | A kind of energy-saving air-jet loom weft insertion system |
| CN104894731A (en) * | 2015-03-12 | 2015-09-09 | 天津工业大学 | Air jet loom weft insertion air flow pressure flow measurement system |
| CN115182083A (en) * | 2022-07-11 | 2022-10-14 | 浙江理工大学 | Method and device for constructing intelligent prediction model of loom |
-
2023
- 2023-04-24 CN CN202310449597.7A patent/CN116288881B/en active Active
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JPH05140844A (en) * | 1991-11-22 | 1993-06-08 | Nissan Motor Co Ltd | Prediction system and control unit for malweaving in loom |
| CN102808273A (en) * | 2011-05-31 | 2012-12-05 | 株式会社丰田自动织机 | Air supply system in air jet looms |
| CN204401240U (en) * | 2014-12-11 | 2015-06-17 | 青岛天一集团红旗纺织机械有限公司 | A kind of energy-saving air-jet loom weft insertion system |
| CN104894731A (en) * | 2015-03-12 | 2015-09-09 | 天津工业大学 | Air jet loom weft insertion air flow pressure flow measurement system |
| CN115182083A (en) * | 2022-07-11 | 2022-10-14 | 浙江理工大学 | Method and device for constructing intelligent prediction model of loom |
Also Published As
| Publication number | Publication date |
|---|---|
| CN116288881A (en) | 2023-06-23 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN116288881B (en) | Air jet loom monitoring system and method | |
| US7222491B2 (en) | Air conditioner and method for performing oil equalizing operation in the air conditioner | |
| CN101184877B (en) | Method for introducing a weft thread in a weaving machine | |
| US7819142B2 (en) | Method for introducing a weft thread in an air weaving machine and air weaving machine | |
| CN105928123B (en) | Chamber pressure control system and chamber pressure control method | |
| CN102203334B (en) | Method and device for monitoring an insertion system for a weaving machine | |
| KR101004323B1 (en) | Method of operating air-compressure system for improving energy efficiency and subsidiary equipment for the method | |
| KR102270342B1 (en) | air compressor control system for power saving using big data analysis | |
| CN107131616A (en) | Variable frequency air conditioner and anti-freezing method, device and system thereof | |
| CN103048099B (en) | Air leakage detection method in air-jet loom | |
| CN102808273B (en) | Air supply system in air jet looms | |
| CH700991A1 (en) | Method for operating a gas turbine plant with a compressor station for gaseous fuel. | |
| CN209782246U (en) | Automatic pressure-regulating compressed air centralized air supply system | |
| CN104514072B (en) | Detect the method for the air leakage in air-jet loom | |
| KR102393531B1 (en) | High pressure compressor able to maintain performance and perform self diagnosis | |
| CN108291338B (en) | Integrated monitoring system for textile weaving machine and method for identifying fault or deterioration | |
| CN111979615B (en) | Method for monitoring the air flow required for processing yarns and/or fibre slivers, and spinning machine unit | |
| JP4976704B2 (en) | Compressed air pressure switching system | |
| CN113586395A (en) | Intelligent flow adjusting method and system for air compressor set | |
| CN118361885B (en) | Refrigerating equipment management system adopting AI model algorithm | |
| JP2002047948A (en) | Gas single fuel firing gas turbine power generation device | |
| IT201600114834A1 (en) | Control method of a compressed air production and distribution plant | |
| CN218580150U (en) | Spinning cross air blowing circulation energy-saving system for producing elastic weft yarns | |
| KR100972594B1 (en) | Method of operating boiler system for improving energy efficiency and subsidiary equipment for the method | |
| CN113106607A (en) | Multilayer sheet yarn tension pneumatic control system and method |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |