JP2001335241A - Operation control method and control device for textile treatment - Google Patents

Abstract

PROBLEM TO BE SOLVED: To provide an operation control method and a control device for a textile treatment machine capable of showing abnormality data for easily discriminating if a cause for an abnormality generated is that in the machine itself or that in supplied yarn. SOLUTION: In this operation control method for the textile treatment machine for controlling an operation condition of the machine, tension of the yarn being processed at each spindle is measured, a fluctuation part where the tension is fluctuated by a specified value or more from a reference value is detected as a monitor event, generation of it is recorded by time series for each spindle, and the operation condition is monitored based on distribution of the generation by time series. The control device executes above method.

Description

DETAILED DESCRIPTION OF THE INVENTION

[0001]

BACKGROUND OF THE INVENTION 1. Field of the Invention The present invention relates to a method and apparatus for managing the operation of a fiber processing machine which measures the tension of a yarn during processing of the fiber processing machine such as a false twisting machine and monitors the operation state thereof. The present invention relates to an operation management method and a management apparatus for a fiber processing machine suitable for monitoring the operation state of a false twisting machine in which a processing process includes a large number of processing devices.

[0002]

2. Description of the Related Art As is well known, a false twisting machine used in a synthetic fiber manufacturing process, specifically, in a process of manufacturing a processed yarn of a long fiber, includes various guides and rollers in a section having a total length of 8 to 10 m. A large number of processing devices such as a heater, a false twisting disk, etc. are arranged, and are configured to run the yarn and perform continuous processing.

In the draw false twisting process of such a false twisting machine, for example, defects in the supplied yarn such as fluff, breakage, and poor processing are caused by untwisting tension, which is the tension of the yarn being processed. Appear as change. And Japanese Patent Application Laid-Open No. 7-138828
Japanese Patent Application Laid-Open Publication No. H11-163873 proposes performing quality control in a false twisting machine by monitoring the untwisting tension in a time series using this.

Japanese Patent Application Laid-Open No. 6-264318 proposes that the untwisting tension is detected and measured by a tension sensor, and the quality of the drawn false-twisted yarn package is rated based on the result. ing. Furthermore, there is a proposal in which a tension control means is additionally provided to adjust the yarn feeding force and the twisting force in the false twisting unit so that the untwisting tension falls within the target management range.

[0005] However, regarding the untwisting tension,
It has been confirmed that the untwisting tension level greatly changes due to the yarn physical properties of the supplied yarn and that the untwisting tension suddenly changes due to an abnormality in the false twisting machine itself. It is difficult to determine whether the cause is an abnormality in the false twisting machine itself or an abnormality in the supply yarn.

Nevertheless, the above-mentioned Japanese Patent Application Laid-Open No. 6-2
The method of uniformly controlling the untwisting tension level within the control range by the tension control means as disclosed in Japanese Patent No. 64318 discloses a possibility that these defective yarns may become apparently good, and in the worst case, There is a possibility that a processed yarn having any abnormality such as a false twisting machine abnormality or a supply yarn abnormality may be supplied to the market as a processed yarn package.

[0007]

On the other hand, the applicant of the present invention has previously disclosed in the specification of Japanese Patent Application No. 11-021695 a frequency analysis of untwisting tension (fast Fourier transform (FF)).
T))), a method for controlling the operation of the false twisting machine and controlling the supply yarn was proposed. By this method, it became possible to monitor the operation status of the specific device of the false twisting machine and the specific characteristics of the yarn, but from the aspect of improving the overall productivity of the false twisting machine, it was possible to analyze the abnormal factors, There is a growing demand for management that can reach urgent treatment.

[0008] Therefore, an object of the present invention is to solve the above-mentioned current situation.
It is an object of the present invention to provide an operation management method and a management apparatus for a textile processing machine that can present abnormal data so as to easily determine whether the supply yarn is abnormal.

[0009]

The above object is achieved by the present invention described below. That is, the present invention relates to an operation management method of a fiber processing machine for managing an operation state of the fiber processing machine, wherein a tension of the yarn being processed by each weight is measured, and the tension varies by a predetermined value or more from a reference value. The part is detected as a monitoring event and its occurrence is recorded in time series by weight,
An operation management method for a textile processing machine, wherein the operation state is monitored based on the time-series occurrence distribution; and a tension detection means for measuring a tension of a yarn being processed by a management device for performing the method, and the tension. Is provided with an event detecting means for detecting a fluctuation portion where a reference value abnormally fluctuates as a monitoring event and storing the occurrence in a time-series manner by weight, and a display means for displaying a time-series occurrence distribution of the monitoring event by weight. This is a management device for a fiber processing machine.

In the above-described present invention, the configuration in which the reference value is the latest predetermined number of moving average values of the measurement data obtained by A / D-converting the tension measurement signal at a predetermined sampling period is a time-dependent measure in the tension measurement. This is preferable because slow drift fluctuation can be removed and accurate event detection can be performed.

When a fluctuating portion is detected as a monitoring event, the tension measurement data for a certain period after the detection is stored, and the tension fluctuation is detected based on the stored measurement data for a predetermined section. A configuration in which threading and monitoring are required to be monitored (hereinafter, referred to as event fluctuations) is preferable in terms of accuracy in classification, facilitation of factor analysis, and the like.

Further, the configuration in which the monitoring event includes at least one of a characteristic value fluctuation obtained from a signal component of a specific frequency region of knot passage, generation of fluff, and tension, particularly a configuration including all of the above is preferable from the viewpoint of factor analysis. .

[0013] Further, the monitoring event is recorded in an operation management database including a weight file for recording the monitoring events for each weight and a yarn package file for recording each yarn package.
The configuration that displays the time series occurrence distribution of monitoring events by weight or / and yarn package by request not only can classify abnormalities into mechanical factors and yarn factors, but also provides information on the cause pursuit of each factor, It is preferable from the viewpoint of comprehensive operation management including the yarn.

The present invention described above is particularly effective in a false twisting machine which has a complicated structure and requires analysis of the causes of abnormal phenomena, but is widely applied to other fiber processing machines which process yarn under a predetermined tension. It is clear from the gist that it can be done.

As described above, the present invention provides information on the passage of a knot (splice portion of a yarn), which is the switching of the supply yarn, information on the generation of fuzz from the fuzz detecting means for detecting the fuzz of the supply yarn, and fiber processing. Based on the signal from the tension detecting means that measures the tension of the yarn processed by the machine online, information on the occurrence of a sudden change in the tension greater than or equal to a predetermined threshold is generated as thread breakage, threading, and event variation. Is to record, in a time-series manner, fluctuation information of a characteristic value obtained from a component of a predetermined frequency region by performing a fast Fourier transform (hereinafter abbreviated as FFT) of the tension signal at a predetermined cycle. By displaying the recorded data as a time-series occurrence distribution, each abnormal event can be easily classified into mechanical factors and yarn factors.

More specifically, as shown in the embodiment, the processing time for one supply yarn package can be easily understood from the knot passing time, and these data can be obtained in units of several days for each weight of the fiber processing machine. By recording, for example, if a large number of events occur only during the processing time of one specific supply yarn package, the abnormality of the supply yarn and the event continues at the weight regardless of the supply yarn. If many occur,
It can be determined that the weight of the fiber processing machine is abnormal.

If the time of thread breakage and the time of yarn threading are also recorded, the actual production time of each weight of the textile processing machine can be determined, which is effective for operation management and the factor of thread breakage when thread breakage occurs. It is also effective for analysis. For example, if the yarn is broken immediately after passing through the knot, the yarn is broken due to the knot factor, and if the doff time of the package of the processed yarn matches the yarn breakage time, the yarn breakage due to a paper tube switching error may occur. I can judge.

Further, in order to clarify the difference between the supply yarn and the processing machine, the tension is monitored by monitoring the component in a specific frequency band by FFT processing, and the presence or absence of the occurrence is recorded and displayed together with the above information. By doing so, the analysis reliability is improved.

[0019]

DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS The details of the present invention will be described below with reference to the drawings. FIG. 1 is an explanatory diagram of a weight configuration and a sensor mounting position of the false twisting machine of the embodiment. FIG. 2 is a block diagram of the configuration of the management device of the embodiment, FIGS. 3 and 4 are flowchart diagrams of the management unit of the machine management device of the embodiment, and FIG. 5 is a flowchart diagram of the central management unit of the central management device of the embodiment. 6, FIG. 6, and FIG. 7 are explanatory diagrams of display examples in the embodiment.

As is well known from the above-mentioned publications, the false twisting machine usually has a processing weight for processing one yarn of 200 to 200.
FIG. 1 shows the basic processing flow configuration of one weight of this false twisting machine.

In FIG. 1, reference numerals 1a and 1b denote original yarn packages each composed of a synthetic fiber such as polyester POY (partially oriented yarn) manufactured in a yarn-making process, which is set in a creel portion not shown. The yarn Y is pulled out of the yarn package 1a by 2 and supplied to the false twisting section. In this example, as shown in the figure, two yarn packages 1a and 1b are arranged per one spindle, and in this example one yarn package, the tail yarn derived from the innermost layer of 1a and the other yarn package are arranged. In this example, when the yarn Y of the yarn package 1a currently being supplied runs out in the drawing by tying the yarn Y derived from the outermost layer of the yarn package 1b, the yarn supply package is automatically placed in the standby yarn The yarn is switched to the yarn package 1b, and the yarn is continuously supplied.

The yarn Y supplied to the false twisting section is twisted by a false twisting unit 4 disposed on the upstream side of the feed roller 3, and the false twist goes back to the twisting guide 5. Then, the false twist that has been traced back to the twist stopping guide 5 is heat-set by the first heating device 6 to form a false twist shape. The cooling devices 8a and 8b cool the heated yarn Y. At this time, the second heating device 7 is applied as necessary to adjust the physical properties of the processed yarn.

Then, the yarn Y finally formed into the false twist shape, in other words, the stretched false twisted yarn Y is applied to the guide roller 9.
And 10 to a winder 11 and wound as a product package 12. The winding machine 11 is usually configured to automatically perform the doffing, and can perform continuous processing from yarn supply to winding.

The above-described units such as the false twist unit, the heating device, the cooling device, and the winder are the same as known devices.
The details are described in the above-mentioned publications and the like, and the detailed description is omitted.

In the false twisting machine configured as described above, the following management device according to the present invention is provided. This management device measures the untwisting tension of the yarn, the presence or absence of fuzz of the supplied yarn, and the switching timing of the yarn package online, and provides it as time-series information summarized for each weight. Abnormal untwisting tension, which is a composite of tension due to stretching, heat shrinkage due to heating, frictional force at the yarn guide, and tension fluctuations caused by the failure of the false twisting machine itself, which have a large correlation with the physical properties of the original yarn It is possible to investigate the factors of
The following configuration is shown in FIGS.

Each detection means which is the basis of the management apparatus is provided in the false twisting machine as shown in FIG. A tension detecting means 31 is provided downstream of the false twisting unit 4 and between the feed roller 3 to detect the untwisting tension online.
Also, the trailing yarn of the yarn package 1a and the yarn package 1b
The knot detecting means 32 for detecting the passage of the knot (knot) is provided at the yarn knot tied to the yarn, and the switching of the yarn supply package is detected by the passage of the knot. Further, a fluff detecting means 33 for detecting the fluff of the yarn Y is provided between the yarn feeding roller 2 and the twist stopping guide 5, and the presence or absence of the fluff of the original yarn is monitored. The tension detecting means 31, the knot detecting means 32, the fluff detecting means 33
Are known, and commercial products and the like can be applied as they are.

As shown in FIG. 2, the tension detecting means 31 of each weight is switched at a predetermined sampling cycle by a scanning circuit 35 through each filter circuit 34 in order to remove the noise, and a common analog / digital conversion circuit is used. (A / D conversion circuit) 36, is converted into a digital signal, and is input via an interface circuit 37 to a machine management device 38 provided for each machine and comprising a computer for managing the machine. . As is well known, the sampling period is selected based on the sampling theorem so that significant information is not lost from the tension signal. The output signals of the knot detecting means 32 and the fluff detecting means 33 of each weight are pulse signals indicating the passage of the knots and the presence or absence of fluff, in other words, digital signals, which are directly input to the machine management device 38 via the interface circuit 37. Is done.

In addition, a bar code reader 40 is connected to the machine management device 38, whereby the management information necessary for the later-described processing of the yarn manufacturing process of the yarn packages 1a and 1b set on the creels of the respective weights is performed. More specifically, yarn production management information such as a production machine stand number, its weight number, a doff number, or a production time is input. In the present example, this input is performed by reading the management card attached to each of the yarn packages 1a and 1b into the creel with the barcode reader 40. Alternatively, a scanner or the like may be used.

The machine management device 38 is connected to a higher-level central management device 39 common to the plurality of machine management devices 38, and as described later, it takes a relatively long time for processing, and the need for immediate processing is low. The processing is performed by the central management device 39. With such a hierarchical structure, high-speed processing for data recording, which is necessary for online processing, is realized.

The machine management device 38 is constituted by a microcomputer,
The machine management means consisting of the flowcharts shown in FIGS. 3 and 4 is stored, and the following processing is performed. Note that the machine management means is configured to perform simultaneous processing of two tasks in the background and the foreground as shown in FIGS.

In the background, the data collection task shown in FIG. 3 is performed. In the data collection task, an interrupt command is input every 10 milliseconds in this example at a fixed period, and when the interrupt command is input, data is collected as follows. First, a knot signal / fuzz signal scanning step is entered, and the knot passage signal of the knot passage detection means 32 of all weights of the machine, the fuzz detection means 3
The fuzz generation signal of No. 3 is scanned, the knot passage of each weight during this period, and the presence or absence of fuzz generation are read. If yes, the date and time of occurrence and the weight number are stored.

Next, the tension data is collected as follows.
First, in order to collect tension data from the tension detecting means 31 of each weight, the first weight is set to the weight number of the scanning circuit 35 in the A / D conversion step, and the A / D conversion circuit 36 is set.
To start the A / D conversion of the tension signal. Thereby, A / D conversion of the tension signal from the tension detecting means 31 of the first weight is performed. When the A / D conversion is completed, the process proceeds to a tension data storage step, and the obtained tension data is stored in the tension data storage area together with the collection date and time and the weight number.

Next, in this embodiment, a moving average is obtained in order to obtain a reference value for fluctuation determination based on the tension data. The moving average is a time-series average of a predetermined number of data as is well known. In this example, the moving average is obtained by taking a moving average of 120 pieces of data. Therefore, it is necessary to collect 120 pieces of data until a steady state in which a normal moving average value is obtained, and it takes 1 time in time. It takes 2 seconds.

Therefore, an average data number discriminating step is provided as shown, and it is discriminated whether or not the number of data collected for the moving average has reached 120 required for the moving average.
In the case of "No" of less than 20 pieces, the process proceeds to a knot / fluff discrimination step to be described later during this time, that is, until the steady state.

When the number reaches 120, the result is "Yes". In this example, the moving average calculation step of the moving average means is entered, and the moving average is obtained to obtain a reference value for fluctuation determination. As described above, in this example, the moving average is obtained by taking a moving average of 120 data, and therefore, the latest 120 data is stored in the tension data storage area for each weight. Then, in this step, the average value of the latest 120 data stored in the weight is calculated, the result is stored as the reference value of the weight, and the process proceeds to the next tension fluctuation detection processing.

In the tension fluctuation detecting process of the event detecting means of the present embodiment, the detection of the tension fluctuation is carried out for a predetermined period, specifically, based on a predetermined number of tension data. First, in a variation flag ON determination step, it is determined whether the variation flag of the weight is ON.
If the answer is "Yes", which is ON, the latest data stored in the weight is stored in the variable data storage area and the number of detected data is increased by one, and the next data number determination step is performed. If it is not “ON”, the process proceeds to a variation candidate determination step.

In this variation candidate determination step, a candidate event of a tension variation is detected as follows. First, the moving average value obtained for the tension, that is, the reference value, and A /
The current tension values collected in the D conversion step are compared, and the difference is equal to or greater than a preset candidate detection value for detecting a candidate event of a tension variation, and in this example, a difference of 5 g or more is defined as a tension variation described later. It is determined that there is a candidate event.

In the case of "Yes" with a variation candidate, the process enters a variation flag ON step, turns on the variation flag of the weight, saves the latest data in the variation data storage area, and sets the number of detected data to one. Then, the process proceeds to the next data number determining step. In the case of "No" with no change candidate,
The process proceeds to a knot / fluff determination step described later.

In the next detection data number discriminating step, the fluctuation flag is turned ON, that is, the number of stored data after the detection of the fluctuation candidate is a predetermined number necessary to obtain the whole image of the fluctuation (500 in this example, which corresponds to 5 seconds). ) Has been determined. And "No" in which the number of data is less than 500
In the case of, as in the case where there is no change candidate, the process proceeds to a knot / fluff determination step described later.

In the case of "Yes" in which the number of data has reached 500, the event flag ON step is entered as the completion of the collection of the detection data of the variation candidate, the event flag is turned on, and the tension data and the generation of tension data during the detection time are generated. The date, occurrence time, and occurrence weight are stored and saved in the event candidate storage area, and the process proceeds to the next knot / fluff determination step.

In the knot / fuzz discriminating step, the previously collected data of knot passing and fluff occurrence are scanned to check whether the weight has passed the knot and fluff occurrence.
In the case of "s", the process proceeds to an event flag ON step indicating the occurrence of an event to be a management event. In this step, the event flag is turned ON, and the contents of the event, that is, the occurrence of fluff, the passage of the knot, the date and time of occurrence, the time of occurrence, and the weight number are stored and saved in the event candidate storage area, and the process proceeds to the next all weight end determination step. In the case of "No" in the knot / fluff discrimination step, the flow immediately proceeds to the all-spindle end determination step as shown.

In the step of determining the end of all weights, it is determined whether or not the entire weight has been completed based on whether or not the weight number has reached the final number. The number is incremented by 1 and the process proceeds to the next spindle. If the weight number is the last number and “Yes” indicating the end of all weights, the process proceeds to the FFT sampling step for collecting the next frequency conversion data.

By doing so, the event detecting means of the present embodiment can accurately detect a tension fluctuation equal to or more than a predetermined value which is a management event from 10 milliseconds to a fluctuation detecting time of 5 seconds. The classifying means can classify the management events into each of events such as occurrence of thread breakage, execution of thread hooking, and occurrence of a required change exceeding a predetermined value.

As described above, if any management event is detected, including the case where the knot is passed and the fluff is generated in addition to the fluctuation of the tension, the event flag is set as the data of the occurrence of the noticeable event in the event flag ON step shown in the figure. When turned on, necessary data, specifically, the weight number and the content of the event, that is, tension fluctuation, knot presence, fluff presence, and the like are stored in the event candidate storage area.

When all the weights have been completed, the process enters the FFT sampling step for collecting the next data for frequency conversion. In this frequency conversion data collection routine, data collection of tension signals of all weights required for fast Fourier transform (FFT) is performed. First, in the FFT sampling step, the latest data stored in the tension data storage area is sequentially scanned for all weights and stored in the FFT storage area of each weight. In this example, the frequency range and the frequency resolution of the fast Fourier transform can be changed, and the data can be collected by setting the number of sampling data determined from the frequency range and the frequency resolution according to the purpose.

Therefore, in the next FFT sampling completion determination step, completion is determined based on whether or not the number of data collected for each spindle has reached the number of sampling data required for the set fast Fourier transform. The weight that reaches the number of sampling data required for the fast Fourier transform is "Ye
s ", and enters a sampling completion flag ON step, in which the sampling completion flag of the spindle is turned on as completion of sampling of data necessary for fast Fourier transform (FFT). Then, when all the weights become “Yes” indicating completion in the all weight end step, the background interrupt processing ends. In addition, the weight for which the number of data has not been reached is "N
o ", and the completion flag is not turned ON only by data collection.

As described above, in the background, 10
The above processing is repeated every millisecond to collect fluff generation, knot passage, tension fluctuation, and FFT data.

On the other hand, in the foreground, the following management event collection task is constantly repeated while the machine is operating. First, in the in-operation determining step, whether or not the machine is operating is confirmed by a flag or the like linked with the operation switch. If it is not running, no processing is performed.

During operation, the following processing is constantly repeated. First, an event flag ON determination step checks whether the event flag is ON. If “Yes” is ON, the process proceeds to the event determination process, and if “No” is not ON, the process proceeds to a fast Fourier transform process described later.

In the event discrimination processing, first, a knot / fuzz discrimination step is entered, the relational data in the event candidate storage area is read, and it is checked whether the event content is a knot passing or fluff occurrence. And if either is "Yes",
Proceed to the data storage step, and the event details, specifically knot passing or fluff occurrence, date and time of occurrence,
The generated weight is extracted and stored in the event file set in the storage device.

In the case of "No" in which no knot has passed and no fluff has occurred, the event is regarded as a tension fluctuation, and the event content is determined to be thread breakage, thread hooking, and a predetermined time based on 500 pieces of tension data collected in the background by the event classification means. The following tension fluctuation classification processing of classifying as any of the tension fluctuations greater than or equal to the value is performed. The tension variation classification processing of the event classification means of this example uses the moving average value of 120 pieces of tension data in the same manner as the above-mentioned moving average, and first, in the thread breakage determination step, the moving average value is determined from a predetermined thread breakage determination value by a predetermined value. It is determined that the thread breakage has occurred when the value falls below the time. Then, in the case of "Yes" indicating that thread breakage has occurred, the process proceeds to the above-described data storage step with the content of the event being broken, and the above-described related data is similarly stored.

On the other hand, when the moving average value exceeds the yarn breakage determination value and the yarn breakage does not occur (No), the flow proceeds to the yarn hooking determination step. Judgment is performed. Then, in the case of "Yes" of the thread hooking execution, the content of the event is the thread hooking execution, and the process proceeds to the data saving step as in the case of the thread breakage, and the related data is saved.

If the state does not continue for a predetermined time or more, "N
o ", the event content is regarded as a change in tension required for monitoring.
Proceeding to the next data storage step, the relevant data is stored and stored in the event file in the same manner as in the above-described events. Therefore, the event file contains the content of the event (whether knots pass, fluff occurs, thread breaks, thread
The date of occurrence, the time of occurrence, and the weight generated are extracted together with whether or not there is a need for monitoring exceeding a predetermined value. In the operation example described later, good results were obtained with the yarn breakage determination value set to 20 g and the predetermined time set to 3 seconds.

In the event determination step described above, if the event is the occurrence of a yarn breakage, a conventional yarn breaker that cuts and holds the yarn Y with a yarn cutter provided upstream of the existing yarn feed roller 2. (Not shown), a yarn break signal is output to perform the yarn processing. By the way, regarding the detection of the yarn breakage, a commercially available yarn breakage detector may be used in addition to the method based on the tension data, and the processing may be performed in the same manner as with the fluff.

After the above processing is completed, or when the event flag is OFF (No), the next fast Fourier transform (FFT) processing is performed. In this FFT processing unit, first,
In the sampling completion determination step, it is confirmed whether or not the sampling of data necessary for the fast Fourier transform (FFT) has been completed based on the sampling completion flag. Sampling incomplete and sampling complete flag is OF
In the case of “No” of F, the process returns to the top of the process.

In the case of "Yes" of ON, the process proceeds to the FFT execution step, in which the fast Fourier transform (FFT) is executed for all the sampling-completed weights at this timing. The fast Fourier transform (FFT) used a well-known fast Fourier transform method. A commercially available program or the like can be used for this.

When the FFT is completed, the process proceeds to a characteristic value extracting step, in which characteristic values are extracted by the characteristic value extracting means from the frequency distribution data obtained by the fast Fourier transform, and the characteristic values set in the storage device including the related data. Save to file sequentially. The characteristic value extracting means of the present embodiment integrates a frequency component of a specific frequency region set in advance and stores the integrated value as a characteristic value.

The characteristic values of this example are the first specific frequency range of 0.01 Hz to 0.3 Hz, which has been confirmed to correlate with U% relating to the unevenness of the yarn thickness of the original yarn package, and the oil agent adhesion. U% characteristic value and OPU characteristic value obtained by integrating the components in each of the second specific frequency regions 0.6 Hz to 1.4 Hz, which have been correlated with the OPU which is an index of the amount,
And the traverse frequency (0.04H in this example) of the yarn running thereon related to the supply roller abnormality of the false twisting machine.
z) third specific frequency region around 0.38 Hz
This is a roller abnormality obtained by integrating the component of 0.42 Hz. The obtained characteristic values are stored in a characteristic value file including the weight number, the date and time at which the characteristic value was extracted.

The specific numerical example of this example is that the processing speed of the false twisting machine is about 1000 m / min, the stretching ratio is about 1.8 times,
The yarn Y of the yarn package is 12 m at approximately 3000 m / min.
It is a study result under the condition of spun partially oriented yarn (POY) of 5 de. Although depending on the desired characteristics, the characteristic value related to the original yarn package may slightly vary in this frequency range depending on the spinning conditions, and is preferably determined by actual measurement. Regarding the above-mentioned U% and OPU, good results were obtained irrespective of slight changes in the processing conditions in the above-mentioned frequency region due to the characteristic values using the above-mentioned integrated values. Further, as a frequency range of a characteristic value related to a roller abnormality such as a supply roller, a frequency range having a center frequency of a traverse cycle of a yarn thereon or a cycle of a periodic motion such as a rotation speed is selected.

When the characteristic value extraction is completed, the process returns to the beginning, and the above process is repeated. Thus, the machine management device 3
8 is a collection of management events such as the time of occurrence of fluff of the original yarn package, the time of passing through the knot of the false twisting machine, the time of occurrence of thread breakage, the time of thread hooking, the time of occurrence of tension fluctuations exceeding a predetermined value, and the characteristics by high speed Fourier transform. Value extraction is performed, and these are stored in an event file and a characteristic value file.

On the other hand, the central management device 39 takes out data from each machine management device 38 at predetermined time intervals, and performs data recording processing of the weight that has detected the passage of the knot. When a distribution display request command is received from the operator console, the time series distribution status of the management events for each weight is displayed on the display device. The details will be described below based on the flowchart of FIG.

First, in a stop request determination step, the presence or absence of a stop request is checked at all times, and if "Yes" in response to a stop request input from an operator or the like, the necessary stop processing is performed in the stop processing step and the processing is terminated. . On the other hand, in the case of "No" without a stop request, the process proceeds to the next yarn information processing step. That is, the following processing is repeated unless there is a stop request.

Then, in the raw yarn information processing step,
It checks whether there is an input from the barcode reader 40, and if there is, based on the inputted yarn production management information, a raw yarn package file including a necessary management item field of the raw yarn package in a storage area for managing the raw yarn package. To create
The machine number and weight number of the set false twisting machine are stored in the column along with each item of the yarn production management information.

Next, a display discriminating step of the display means is entered, and the presence or absence of a distribution display command from the operator console is checked. In the case of “Yes” with the distribution display command, the process proceeds to the distribution display processing. This processing will be described later.

In the case of "No" with no distribution display command,
Proceed to the next time determination step. This is for determining the time because the data of each machine management device 38 is read every predetermined time, that is, at a predetermined cycle. In this example, the predetermined time is 2 minutes. If "No" has not been reached for the predetermined time, the process returns to the first stop request determination step.

On the other hand, in the case of “Yes” that has reached the predetermined time, the process proceeds to the data fetching step, and each machine management device 3
At 8, all the data stored in the processing of FIGS. 3 and 4 is taken out and stored in the central management device 39. At this time, the corresponding machine number is also stored according to the number of the machine management device 38.

Upon completion of the data extraction, the process proceeds to the next yarn event detection step. In the yarn event detection step, the yarn characteristic fluctuation event is detected as a management event by the yarn event detection means as a management event based on the characteristic value obtained in the characteristic value extraction step of the machine management means described above. . That is, in the yarn event detecting means of this example, the average value of the past characteristic values during normal operation is compared with the characteristic value obtained in the characteristic value extraction step as a control value, and the difference is determined as the control limit value. The case where the value is more than twice the control value is detected as a yarn characteristic change event,
As the management event, the time of occurrence is stored in the file of the original yarn package of the spindle together with the characteristic value.

Next, it is checked whether or not there is a knot passage corresponding to the switching of the original yarn package in the data of the management event extracted from the machine management device 38 in the above-described data extraction step. In the case of “No” with no knot passing, the processing returns to the top. In the case of "Yes" with knot passing, the following data recording processing is performed.

First, the data recording processing section stores the current knot passing time as the processing end time in the storage file of the current yarn package being processed by the spindle in the data recording step, and terminates the processing of this yarn package. At the same time, the current knot passage time is entered as the processing start time in this file as the storage file of the new yarn package that started feeding after passing the knot through the storage file of the weight.

As described above, the data recording process is performed at the time of detecting the passage of the knot. In other words, the data recording processing unit enters every time the yarn package is replaced, and starts processing, processing end time, each control event, the number of the spinning machine in the yarn manufacturing process, the number of the spindle, Management information such as a manufacturing lot number is extracted from the stored data for the weight.

These data are stored in the storage device of the central management device 39 in the machine unit, and the original yarn package file of the original yarn package of the weight is fetched, and the respective items of the management information are stored in the respective management information item columns. I do. Therefore, the central management device 39 stores management information necessary for managing the yarn packages in one file for each yarn package.

Incidentally, the recorded processing start time and processing completion time are the knot passing time by the knot detecting means 32 as described above, and the actual processing start time, that is, the passing time of the twist stopping guide 5, the actual processing end time. The point in time is different from the point in time when the sheet passes through the feed roller 3 or the guide roller 9.
Therefore, in the next data correction step, the processing start time and the processing end time are corrected as follows. That is, the time at which the correction time obtained by dividing the distance to the twisting guide 5 and the feed roller 3 by the processing speed is added to the current recorded data, that is, the time at which the knot passes, is the processing start time,
Rewrite as the end of processing.

At the same time, a management event whose occurrence time is before the processing start time of the old yarn package processed immediately before the original yarn package is extracted from the stored file of the original yarn package, and this management event is extracted from the original yarn package. The file of the yarn package is moved to the file of the old yarn package. Whether the control event at the time of switching should be assigned to any of the new and old yarn packages should be accurately determined in consideration of the processing end time for each control event. There is no practical problem with the judgment.

Next, based on the file of the old original yarn package determined by this correction in the data alignment step, each data is processed for each control event for all control events from the processing start time to the processing end time. Using the start time as a reference time, each occurrence time is sorted in chronological order according to the elapsed time from that time, and stored in the file again. As a result, for each management event, each event is stored in each yarn package file in the order of occurrence with the processing start time as a reference time, specifically, as a zero point, and the distribution display processing described later is simplified. Become.

Next, proceed to the weight file recording step.
A weight file is created from the above-mentioned yarn package file as follows. That is, the central management device 39 is provided with a weight file that records monitoring events of a predetermined period for each machine weight in advance, and extracts necessary data from the yarn package file obtained above and processes the weight data. Are sequentially recorded in time series in the weight file. As a result, in the weight file, the event contents of all the monitoring events generated for each weight and the time of occurrence are recorded in chronological order.

Thus, the data recording process ends. By this processing, the central management device 39 stores the necessary management information relating to the latest yarn package up to the processed yarn package in a predetermined format for each yarn package and a predetermined information for each weight. An operation management database consisting of a weight file containing all monitoring events during the period is sequentially constructed.

By the way, when the display request command is input from the keyboard of the operator console or the like, that is, when the display determination step is "Yes" in FIG. 5, the processing by the display means is as follows.

First, in the request determination step, it is determined whether the content of the request is display by weight or by yarn. In the case of a request for display by weight, the process proceeds to a range specification step, and a range specification table of a format in which a range such as a machine number and a weight number can be specified is displayed on a display device such as a liquid crystal display device of the central management device 39. Therefore, the range and the range are designated by inputting the range of the machine number and weight number and the period to be displayed.

Then, the process proceeds to the next range extracting step, in which the data of the monitoring event for the designated period of the designated number of the designated number is read out from the weight file, and the data is applied to the liquid crystal display device or the like in the next distribution display step. The time series occurrence distribution of the weight is displayed. In this example, in addition to the essential tension fluctuation events, the displayed events include thread breaks, knot passages, fluff occurrences, and characteristic value abnormalities, and are displayed in a time-series line with different codes so that these can be distinguished. I am trying to do it. FIG. 6 shows an example of this display. The data in FIG. 6 shows that the false twisting machine of this example is used to process a raw yarn package made of a polyester partially oriented yarn (POY) spun at a spinning speed of approximately 3000 m / min at a processing speed of approximately 1000 m / min and a draw ratio of approximately 1.8 when false twisting.

The figure shows a parallel display example in which the time-series occurrence distribution of a typical example from the corresponding weight in which an event has occurred during the designated period of the designated weight is displayed side by side in the vertical direction. The time is represented by the vertical axis, and the weight is indicated by a sequential number to distinguish the weight. The mark 50 in the figure indicates the time of thread breakage, the mark 51 indicates the time of threading,
A diamond 52 indicates the point at which a tension variation exceeding a predetermined value occurs, and a Δ 53
Is the point at which fluff is generated, the filled square 54 is the point at which the knot is passed,
An asterisk 55 indicates the time point at which the characteristic value fluctuation of U% occurs.

From the display of the time-series occurrence distribution, useful information in operation management is obtained as follows. First, in the case of the weight of No. 1, as shown in the figure, the time point 50 at which thread break occurs and the time point 51 at which thread re-threading is performed after the thread-cutting process are clarified. It is effective for process control. In addition, it is possible to determine the operation status of each spindle as follows based on the generation status of each piece of information.

In the weight of No. 2, the required tension fluctuation exceeding the predetermined value frequently occurs. However, the period of occurrence thereof is separated by the knot passage, and therefore, the length of the single yarn package supplied during this period is changed. It is presumed to be based on the yarn abnormality, and can be determined to be due to the yarn factor. Further, since this tension variation causes abnormal dyeing of the processed yarn, it can be understood that the processed yarn package produced during this time must be treated as defective.

In the case of the weight of No. 3, fuzzing occurs three times,
From the detection position, it is clear that the possibility of the yarn factor is large. Then, useful information for quality control, such as identification of a product package containing these fluffs from the point of occurrence, can be obtained. In addition,
If a lot of fluff occurs, it is determined that the yarn is abnormal,
At some point, the thread is forcibly cut off, the yarn is replaced, and the processed yarn package up to that point is treated as a defective product.
Productivity can be increased.

In the weight of No. 4, the characteristic value fluctuation of U% occurs frequently, and thus the occurrence period is separated by the knot passage similarly to the weight of No. 2. Accordingly, this can be recognized as a factor of the yarn, and it occurs only in the specific yarn package. By examining the history of the yarn package, it is possible to pursue an abnormality in the manufacturing process of the yarn. Note that this U
Since the% abnormality also causes dyed abnormality in the processed yarn, the processed yarn package produced during this period needs to be treated as a quality abnormality.

With the weight of No. 5, it can be seen that the thread required to be monitored has fluctuated more than a predetermined value for a predetermined period immediately after the time of threading, and then the thread has been broken, and then the thread has been re-threaded. And
From this, it is estimated that the tension fluctuation during this period is due to a yarn guiding abnormality due to a yarn hooking error at the time of yarn hooking, and can be determined as a work factor.

In the weight of No. 6, the thread is cut after suddenly a required change of the monitored tension exceeding a predetermined value frequently occurs during the processing. It is estimated that there is an abnormality due to mechanical factors. Actually, in this example, the yarn was detached from the false twisting unit 4. This number 5,6
For such abnormalities, it is necessary to forcibly cut the yarn with the yarn cutting device when it is detected, to prevent damage to the device, to prevent the spread of abnormalities of adjacent weights, etc. It is more preferable from the viewpoint.

In the weight of No. 7, the fluctuation of the required monitoring tension exceeding a predetermined value frequently occurs at intervals over time without passing through the knot, that is, regardless of the switching of the yarn package, without causing the yarn breakage over a long period. This can be determined not to be due to the original yarn but to an abnormality in the false twisting machine itself. Then, it is presumed that the cause is an abnormality of the processing equipment such as yarn waste entangled in the false twisting unit 4 or contamination of the guide of the heating device 6.
In this example, the thread guide was dirty. In addition, in this weight, a thread break occurs almost simultaneously with the passage of the knot, and it can be seen that the thread break is a thread break during the passage of the knot.

As described above, the cause of the abnormality can be classified into the yarn side or the machine side based on the time-series occurrence distribution of the fluctuation of the tension equal to or more than the predetermined value, so that the cause can be easily investigated and combined with other management events. It can be seen that information useful for operation management can be obtained.

By displaying the doff timing of the stretch-twisted yarn, that is, the processed yarn package, in addition to the display of FIG. It is determined that the thread is broken due to this, and this is also effective for analyzing the factor of the thread break. In addition, if there is a weight with a large number of thread breaks whose factors are known, measures can be determined for each of the factors, leading to a reduction in the thread break rate.

Further, by synchronizing the time of the displayed weights, specifically, by displaying a plurality of weights of the same machine in parallel with the time axis being the same time, an abnormality common to all the weights of the machine can be detected. It is effective for investigating the cause of the abnormality.

On the other hand, if the request is for each yarn, the display means first proceeds to the range designation step, and similarly, the range designation means displays the range designation table of a predetermined format for the yarn management on the display device of the central management unit 39. Is displayed. Then, input the lot designation items such as the range of the original yarn package to be displayed, for example, the number of the spinning machine, the range of the spinning spindle, the manufacturing doff number or the time which can specify the same lot, and the completion of the input. To instruct.

Then, the process proceeds to a package extracting step,
A yarn package belonging to the specified range is extracted from the yarn package file of the operation management database formed in the data recording process. Specifically, the extraction process of this example searches the data of the yarn package file for each yarn package in the operation management database obtained by the data recording process, and finds that the yarn management information is within the specified range, for example, for the same lot. In the case of the designation, the original yarn package is extracted based on whether or not the designated items of the spinning machine number, its weight range, and the forming time are within the specified range.

When the extraction is completed, the process proceeds to the next distribution calculation step, in which the original yarn characteristics related to the physical properties of the original yarn In this example, the management events including the above-mentioned U% and OPU fluctuation events are extracted from all extracted original yarns. The package can be displayed as a time-series occurrence distribution arranged by generation point along a time axis with the processing start point as a reference point, in other words, the origin. In addition, the number of all management events targeted for all the yarn packages extracted as necessary is added up. It is also displayed as the time series occurrence frequency distribution shown.
The latter display has the advantage that, for example, quality abnormalities of the same lot can be detected with high sensitivity.

Further, in this example, all the control events of all the yarn packages are extracted in the same manner as the above-mentioned fluctuation phenomenon of the yarn properties for all the control events, specifically, in addition to the above-mentioned fluctuation events of the yarn properties. Then, the occurrence of thread breakage and fluff is integrated to obtain the time-series occurrence frequency distribution of all management events. Then, the distribution calculation means causes the obtained time series occurrence distribution to be generated in the lot file and stores it as management data.

Then, in the next display step, the information is displayed on the display device of the central management device 39. FIG. 7 shows a typical example of this display. The example of FIG. 7 is an example in which the time series occurrence distribution of the obtained management events is displayed with the time axis as the horizontal axis and each yarn package is displayed in parallel with the vertical axis. The time-series occurrence distribution is displayed in parallel on the time axis with the origin set at the same position on the screen.

FIG. 7 shows a false twisting machine of this example, in which a raw yarn package made of partially oriented polyester yarn (POY) spun at a spinning speed of about 3000 m / min was processed at a processing speed of about 1000 m / min and a draw ratio of about 1. 8 is a display example of a time-series occurrence distribution of all management events of the yarn package in the designated range when false twisting is performed, and is a typical example in which the management event has occurred.

In the figure, the vertical axis 20 is a typical example of the original yarn package in the applicable period range of the applicable weight of the designated spinning machine. The number of the indicated feed yarn number is 1 for mere distinction.
Although numbers 9 to 9 are used, a lot number or the like of the yarn package is actually used. The horizontal axis 21 represents the time from the start of processing of each yarn package. The processing start time is 00:00 of the origin 22 on the horizontal axis, and the knot passing time indicated by the filled square mark 23 represents the processing end time. Therefore, a period between the processing start time 22 and the processing end time 23 represents the processing time of the original yarn package. In addition, at the time of thread breakage, since the execution time of re-threading is known, time during which no processing is performed can be omitted.

[0098] In Fig. 24, rhombic marks indicate the point in time at which a tension variation exceeding a predetermined value occurs, crosses 26a to 26e indicate the point in time when thread breakage occurs, and triangular marks 25 indicate the point in time when characteristic value variation occurs. . The 27 circle indicates the point at which fluff occurs. Displaying each type of management event in this manner is effective for factor analysis. In FIG. 7, the horizontal axis represents time, but it may be represented by the warp or winding weight of the yarn package. These are also equivalent to time, and the time-series distribution of the present invention includes such a display.

In the drawing, the period from time 22 to time 23 corresponds to the complete winding of the original yarn package and the start of winding. If it is displayed, it will be displayed corresponding to the start and end of winding of the yarn package in the spinning machine, and it is easy to grasp the correspondence between the occurrence of management events and the spinning history, but it is disadvantageous in terms of processing. Become.

As shown in the figure, by displaying the management events of each yarn package as a time-series occurrence distribution on the same time basis, the production history of the yarn package can be effectively compared with that of the yarn package. It is possible to find out which weight has a problem with what was produced at which time, and it is possible to easily narrow down a portion to be investigated for the cause of the defect. Then, investigation and countermeasures can be immediately performed on the weight.

For example, in the yarn package of the supply yarn number 3, the characteristic value fluctuates frequently over almost the entire period 28, and it is estimated that the U% of the yarn package or the OPU abnormality has occurred. . In this example, the inspection result indicates that the abnormality is U%. However, if the U% abnormality and the OPU abnormality are distinguished and displayed, this point can be more easily understood. Then, since the U% abnormality occurs during the entire period of the package, the U condition may vary depending on the production conditions, equipment conditions, and the like when the yarn package is produced.
The direction to pursue the cause of the abnormality, such as investigating the part related to the% abnormality, is also determined.

Further, in the yarn package of the supply yarn number 8, as shown in FIG. This abnormal tension causes abnormal dyeing of the processed yarn and can be eliminated as a defective product before shipment. On the other hand, by checking the production history of the yarn package in each period, checking the production conditions on the spinning machine side that causes the tension abnormality, the state of occurrence of the abnormality, etc., it is possible to pursue the cause and further take measures against the abnormality. it can.

Further, in FIG. 6, the fluff occurs only twice in the yarn package of supply yarn number 5, which is presumed to be sudden. It is possible to estimate which part is located, and useful information can be obtained in terms of quality control. Further, depending on the occurrence situation, the cause can be pursued. For example, when this occurs continuously, it is presumed that a problem has occurred in a device having a nozzle of the spindle of the spinning machine, particularly a device such as an oil application device and an interlace application device.

By the above-described management of each yarn package, it is possible to classify the cause of the product abnormality relating to the inner yarn package, obtain information on the cause of the abnormality on the spinning machine side, and to quickly take countermeasures. It has a great effect on improving the performance and reducing the production cost.

Further, pursuit of the cause of the yarn breakage is facilitated as follows. For example, when looking at the occurrences of thread breakage 26a to 26e, the cause of the thread breakage occurrence can be known from the time of occurrence in the present method as follows. That is, from the timing of the occurrence, the yarn breaks 26a and 26d are the yarn breaks (transfer breakage) when passing through the knot of the false twisting machine, 26e is the yarn breakage at the end of the original yarn package supply (no knot portion), and Thread breakage occurring at the time of the doff of twisted package (switching mistake)
It is estimated to be.

Further, by omitting these specific thread breaks from FIG. 7 and displaying them, the distribution of other breaks occurring becomes even clearer. As a result, for example, the specific yarn break position in the winding shape of the original yarn package is known, and in the case where the specific yarn breakage occurs frequently, it is possible to estimate a cause such as a problem in control at the time of winding the original yarn package during spinning, Furthermore, we can pursue the measures. As described above, according to the present invention, even the problem of winding of the supply yarn is found out, and the present invention is also effective in reducing costs by reducing the yarn breakage rate of the false twisting machine.

As described above, in the present embodiment, the processing is performed by the management device including the detection means and the microcomputer, but the processing of the central management device can be performed offline. Further, it is also possible to display the waveform of the tension fluctuation and the waveform of the result of the fast Fourier transform on a graph for further detailed analysis. Further, the present invention can be widely applied not only to the false twisting machine having the configuration shown in FIG. 1 but also to a false twisting machine having another configuration that performs false twisting.

[0108]

As described above, the present invention detects a time-dependent fluctuation of a yarn at a predetermined value or more during processing and displays the occurrence as a time-series occurrence distribution for each spindle, so that the abnormality is regarded as a factor of the original yarn. It can be classified into processing machine factors and provides data desired for management of the processing machine and the yarn to be processed by the processing machine, and has a great effect on its stable operation and improvement of productivity. is there. In addition, by displaying the time-series occurrence distribution of specific control events for each yarn package in accordance with this, useful information for pursuing the cause of abnormality of the yarn to be processed can be obtained, and comprehensive information including the yarn production process can be obtained. It has a great effect on the overall productivity improvement. As described above, the present invention greatly contributes to the production of processed yarn, and further to the stabilization of the process in the production of the original yarn and the improvement of productivity.

[Brief description of the drawings]

FIG. 1 is an explanatory diagram of a weight configuration and a sensor mounting position of a false twisting machine according to an embodiment.

FIG. 2 is a block diagram illustrating a configuration of a management device according to an embodiment;

FIG. 3 is a flowchart of a background data collection task of the management unit of the machine management device of the embodiment.

FIG. 4 is a flowchart of a foreground management event collection task of the management unit.

FIG. 5 is a flowchart of a central management unit of the central management device of the embodiment.

FIG. 6 is an explanatory diagram of a display example by weight according to the embodiment.

FIG. 7 is an explanatory diagram of a display example for each yarn package of the embodiment.

[Explanation of symbols]

 1a, 1b Original yarn package 2 Supply roller 3 Feed roller 4 False twist unit 5 Twist stop guide 6, 7 Heating device 8a, 8b Cooling device 9, 10 Guide roller 11 Winding machine 12 Stretched false twisted yarn package 31 Tension detection means 32 Knot passage detection means 33 Fuzz detection means 34 Filter circuit 35 Scanning circuit 36 Analog / digital conversion circuit 37 Interface circuit 38 Machine management device 39 Central management device 40 Barcode reader

 ──────────────────────────────────────────────────続 き Continuing on the front page (72) Inventor Bunji Hamasu 77 Kitayoshida-cho, Matsuyama-shi, Ehime F-term in Teijin Limited Matsuyama Office (reference) 3F115 CA21 CA22 CA53 CB08 CB20 CB21 CD05 CD08 CF40 CF41

Claims (8)

[Claims] An operation management method for a textile processing machine for managing an operation state of a textile processing machine, wherein a tension of a yarn being processed is measured, and a fluctuation portion where the tension fluctuates by a predetermined value or more from a reference value is monitored. And recording the occurrence in time series for each weight, and monitoring the operating condition based on the time series occurrence distribution. 2. A textile processing machine management device for managing an operation state of a textile processing machine, comprising: a tension detecting means for measuring a tension of a yarn being processed; And a display means for displaying a time-series occurrence distribution of monitoring events by weight, and a display device for displaying a time-series occurrence distribution of monitoring events by weight. 3. The operation management method for a textile processing machine according to claim 1, wherein the reference value is a latest predetermined number of moving average values of the measurement data obtained by A / D converting the tension measurement signal at a predetermined sampling period. The management device for a textile processing machine according to claim 2. 4. When a fluctuating portion is detected as a monitoring event, tension measurement data for a certain period after the detection is stored, and the tension fluctuation is cut off based on the stored measurement data for a predetermined section. The operation management method or management apparatus for a fiber processing machine according to any one of claims 1 to 3, wherein the operation management method or the management apparatus is classified into thread hooking and fluctuation required for monitoring. 5. The operation of a textile processing machine according to claim 1, wherein the monitoring event includes at least one of knot passage, generation of fluff, and fluctuation of a characteristic value obtained from a signal component of a specific frequency region of tension. Management method or management device. 6. The monitoring event is recorded in an operation management database comprising a weight file for recording the monitoring events by weight and a yarn package file for each yarn package. The operation management method or management apparatus for a textile processing machine according to any one of claims 1 to 5, wherein a time-series occurrence distribution is displayed. 7. The operation management method or management apparatus for a fiber processing machine according to claim 1, wherein the fiber processing machine is a false twisting machine. 8. The operation management method or management apparatus for a textile processing machine according to claim 7, wherein the characteristic value is a characteristic value of U%, an OPU, or a guide roller.