WO2022112261A1 - Verfahren zur überwachung einer maschinenanlage und maschinenanlage zur herstellung synthetischer stapelfasern - Google Patents
Verfahren zur überwachung einer maschinenanlage und maschinenanlage zur herstellung synthetischer stapelfasern Download PDFInfo
- Publication number
- WO2022112261A1 WO2022112261A1 PCT/EP2021/082708 EP2021082708W WO2022112261A1 WO 2022112261 A1 WO2022112261 A1 WO 2022112261A1 EP 2021082708 W EP2021082708 W EP 2021082708W WO 2022112261 A1 WO2022112261 A1 WO 2022112261A1
- Authority
- WO
- WIPO (PCT)
- Prior art keywords
- machine
- sensor data
- system messages
- monitoring
- operator
- Prior art date
Links
- 238000000034 method Methods 0.000 title claims abstract description 102
- 238000012544 monitoring process Methods 0.000 title claims abstract description 30
- 238000009434 installation Methods 0.000 title abstract 4
- 238000004519 manufacturing process Methods 0.000 claims abstract description 36
- 238000007405 data analysis Methods 0.000 claims abstract description 25
- 238000004886 process control Methods 0.000 claims abstract description 23
- 238000010801 machine learning Methods 0.000 claims abstract description 9
- 238000007619 statistical method Methods 0.000 claims abstract description 5
- 239000000835 fiber Substances 0.000 claims description 53
- 238000004458 analytical method Methods 0.000 claims description 12
- 238000012545 processing Methods 0.000 claims description 7
- 230000005856 abnormality Effects 0.000 claims description 6
- 238000004393 prognosis Methods 0.000 claims description 4
- 230000004720 fertilization Effects 0.000 claims 1
- 238000009987 spinning Methods 0.000 description 11
- 230000003750 conditioning effect Effects 0.000 description 5
- 238000002788 crimping Methods 0.000 description 5
- 238000001035 drying Methods 0.000 description 5
- 238000010924 continuous production Methods 0.000 description 3
- 238000005520 cutting process Methods 0.000 description 3
- 238000000605 extraction Methods 0.000 description 3
- 229920000642 polymer Polymers 0.000 description 3
- 230000015572 biosynthetic process Effects 0.000 description 2
- 238000000151 deposition Methods 0.000 description 2
- 238000011161 development Methods 0.000 description 2
- 230000018109 developmental process Effects 0.000 description 2
- 238000005755 formation reaction Methods 0.000 description 2
- 238000012423 maintenance Methods 0.000 description 2
- 108090000623 proteins and genes Proteins 0.000 description 2
- 238000012300 Sequence Analysis Methods 0.000 description 1
- 238000013459 approach Methods 0.000 description 1
- 238000004140 cleaning Methods 0.000 description 1
- 230000006835 compression Effects 0.000 description 1
- 238000007906 compression Methods 0.000 description 1
- 230000001143 conditioned effect Effects 0.000 description 1
- 238000001816 cooling Methods 0.000 description 1
- 230000001419 dependent effect Effects 0.000 description 1
- 238000001514 detection method Methods 0.000 description 1
- 238000001125 extrusion Methods 0.000 description 1
- 239000012530 fluid Substances 0.000 description 1
- 239000000463 material Substances 0.000 description 1
- 238000012067 mathematical method Methods 0.000 description 1
- 238000013021 overheating Methods 0.000 description 1
- 230000000704 physical effect Effects 0.000 description 1
- 239000012209 synthetic fiber Substances 0.000 description 1
- 229920002994 synthetic fiber Polymers 0.000 description 1
- 238000012360 testing method Methods 0.000 description 1
- 238000007669 thermal treatment Methods 0.000 description 1
- 238000011144 upstream manufacturing Methods 0.000 description 1
- XLYOFNOQVPJJNP-UHFFFAOYSA-N water Substances O XLYOFNOQVPJJNP-UHFFFAOYSA-N 0.000 description 1
- 238000009736 wetting Methods 0.000 description 1
Classifications
-
- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
- G05B23/00—Testing or monitoring of control systems or parts thereof
- G05B23/02—Electric testing or monitoring
- G05B23/0205—Electric testing or monitoring by means of a monitoring system capable of detecting and responding to faults
- G05B23/0259—Electric testing or monitoring by means of a monitoring system capable of detecting and responding to faults characterized by the response to fault detection
- G05B23/0283—Predictive maintenance, e.g. involving the monitoring of a system and, based on the monitoring results, taking decisions on the maintenance schedule of the monitored system; Estimating remaining useful life [RUL]
-
- D—TEXTILES; PAPER
- D01—NATURAL OR MAN-MADE THREADS OR FIBRES; SPINNING
- D01D—MECHANICAL METHODS OR APPARATUS IN THE MANUFACTURE OF ARTIFICIAL FILAMENTS, THREADS, FIBRES, BRISTLES OR RIBBONS
- D01D13/00—Complete machines for producing artificial threads
-
- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
- G05B23/00—Testing or monitoring of control systems or parts thereof
- G05B23/02—Electric testing or monitoring
- G05B23/0205—Electric testing or monitoring by means of a monitoring system capable of detecting and responding to faults
- G05B23/0218—Electric testing or monitoring by means of a monitoring system capable of detecting and responding to faults characterised by the fault detection method dealing with either existing or incipient faults
- G05B23/0224—Process history based detection method, e.g. whereby history implies the availability of large amounts of data
- G05B23/024—Quantitative history assessment, e.g. mathematical relationships between available data; Functions therefor; Principal component analysis [PCA]; Partial least square [PLS]; Statistical classifiers, e.g. Bayesian networks, linear regression or correlation analysis; Neural networks
Definitions
- the invention relates to a method for monitoring a machine system for the production of synthetic staple fibers with a plurality of machine devices interacting in the production process and a machine system for the production of synthetic staple fibers according to the preamble of claim 10.
- a generic method for monitoring a machine for the production of synthetic staple fibers and a generic machine is known for example from DE 102005062826 A1.
- staple fibers of this type can be carried out continuously from the spinning device through to bale formation, or discontinuously by storing the fiber strands.
- DE 102005062826 A1 discloses a machine system for the continuous production of synthetic staple fibers.
- a camera system is used in the known method to monitor the material web that is fed to the individual machine devices in terms of its condition and uniformity.
- this can only be used to monitor one possible cause of a process fault within the machine system.
- Such Maschinenanla gene for the production of synthetic staple fibers are very complex and contain a number of sources of interference that can bring the manufacturing process to a standstill. Due to the relatively high production output of such systems, considerable losses are generated by process disruptions even with short downtimes.
- this object is achieved by a method having the features of claim 1 and by a machine system having the features of claim 10 .
- system messages from several control components of the machine equipment are also recorded.
- system messages contain direct status descriptions of individual machine components and are usually available as text formations.
- processing, combining and analyzing the system messages and the sensor data using statistical methods and machine learning processes the status of the manufacturing process can be recorded and process events and process disruptions can be recognized before they occur.
- the method according to the invention is therefore particularly suitable for obtaining a production process that is as stable as possible without significant process disturbances. Using machine learning methods, even large amounts of data from such complex machine systems can be analyzed in a targeted manner.
- the machine system according to the invention has at least one network for this purpose
- a data analysis unit which is coupled to the process control station, is provided for processing the data.
- the data analysis unit has the means to prepare, combine and analyze the data processing using statistical methods and machine learning processes.
- Claim 2 an essential link to increase the effectiveness of monitoring. In this way, all feedback from at least one operator is recorded and used to continuously improve the identification of process events and process disruptions.
- the data analysis unit is connected to a touch screen, an operating station.
- the result, in particular the predicted result, of the analysis can be displayed to an operator, so that the operator can compare this with the real state of the manufacturing process.
- the operator can enter previously unrecognized process events and process disturbances directly into the analysis system in order to combine them with the result and/or historical and/or current process data.
- a process event is a state of the manufacturing process understand- hen, which could include an intended process interruption due to a can change, for example.
- a process disruption in the manufacturing process represents an unwanted and, in particular, unplanned interruption that should be avoided as far as possible.
- system messages When combining the system messages with the sensor data, these are combined with one another and/or linked according to the time of the coincidence.
- the system messages can be compressed and anomalies identified.
- the analyzes can be improved by preparing the system messages beforehand in order to highlight any abnormalities in the system messages.
- the system messages are identified continuously according to sequence.
- the system messages can be continuously analyzed and processed accordingly.
- the sensor data by continuously transferring them to one or more prognosis-relevant process characteristics (feature extraction).
- feature extraction includes, for example, removing statistical outliers from the sensor data and using mathematical methods to convert the raw sensor data into a form (features) suitable for the respective algorithm in order to predict process disruptions and identify process events such as a can change to enable.
- features suitable for the respective algorithm
- the associated sensor data could represent relevant process criteria that do not lead to a process disruption and are identifiable as a process event.
- the variant of the method is particularly advantageous in which forecasts for and/or the identification of process events and/or the forecast of process faults by using system messages and/or or sensor data and/or abnormalities in the system message and/or process characteristics and/or sensor orders are created.
- predictive maintenance can be carried out on individual machine equipment by the operator, such as cleaning roller surfaces.
- the provision of new cans in the can creel can also be initiated in advance.
- sequences of the system messages and/or the patterns in the sensor data are used by combining and analyzing them in order to find the cause of a specific process disruption.
- the analysis result is fed to the operator and/or a control device in order to enable a targeted process change or process intervention.
- these are preferably generated as text information.
- the sensor data are generated as status values by the monitoring sensors assigned to the machine devices.
- Fig. 1 shows a machine system for the continuous production of synthetic staple fibers
- Fig. 2 shows a schematic flowchart for monitoring the machine system according to the embodiment of Fig. 1
- Fig. 3 shows a schematic of an embodiment of the machine system according to the invention for the discontinuous production of synthetic staple fibers
- Fig. 4 shows a schematic of another flowchart for monitoring the machine system according to the embodiment of Fig. 1 or 3
- the exemplary embodiment has a spinning device 1, which usually carries a plurality of spinning nozzles on a spinning beam in order to produce a large number of filament strands from a polymer melt. After cooling, the filament strands are brought together to form a fiber strand 2, preferably by means of a blow candle and wetting.
- the fiber strands 2 produced by the spinning device 1 are then drawn off via a take-off mechanism 3 and stretched verses within a drafting mechanism 4.1 and 4.2.
- a conditioning device 5 is provided in the drafting system 4.1 and 4.2 in order to carry out a thermal treatment on the fiber strands 2.
- the fiber strands can be conditioned by a water bath or steam.
- the fiber strands 2 are brought together by a laying device 6 and fed to a crimping device 7 .
- the fiber strands are crimped by what is known as compression crimping by the crimping device 7 and then dried in the drying device 9 .
- the drying device 9 is provided with an optional tension setting device 8 in order to feed the fiber strands 2 to a cutting device 10 .
- the fiber strands are cut into staple fibers 22 in the cutting device 10 .
- the staple fibers 22 are fed pneumatically to a baling device 11 and pressed there into bales 12 .
- the spinning device 1, the draw-off unit 3, the drafting units 4.1 and 4.2, the conditioning device 5, the laying device 6, the crimping device 7, the pulling device 8, the drying device 9, the cutting device 10 and the bale press device 11 thus form the machine devices of the machine system, which are jointly controlled and monitored for the manufacturing process of the synthetic staple fibers.
- each of the machine devices 1 and 3 to 11 is assigned at least one control component 13 and at least one monitoring sensor 14 .
- the control components 13 and the monitoring sensors 14 are assigned symbolically to the respective machine devices 1 and 3 to 11 .
- the control components 13 of the machine devices 1 and 3 to 11 are connected to a process control system 16 via a first network 15.1.
- the monitoring sensors 14 of the machine devices 1 and 3 to 11 are connected to the process control system 16 via a further network 15.2.
- the process control system 16 is coupled to an operator station 17, which has a touch screen 17.1 at least for input.
- the process control system is connected to a data analysis unit 18 .
- the system reports generated by the control components 13 are continuously fed to the process control system 16 .
- the sensor signals generated by the monitoring sensors 14 are also fed to the process control system 16 .
- the process control system 16 transmits the system messages and the sensor data to the data analysis unit 18.
- Microprocessors and analysis programs are provided within the data analysis unit 18 in order to combine the system messages and the sensor data with one another, process them and/or use statistical methods and methods of machine learning.
- the data analysis is shown schematically in FIG. 2 in a flowchart.
- the data analysis unit 18 is shown schematically with a symbol for machine learning. All system messages from the control components 13 are continuously supplied to the data analysis unit 18 .
- the system messages are marked with the letters SM.
- the system messages SM are essentially text information or pure text information that contain alarm messages, for example.
- the control component 13 of the conditioning device 5 could issue the warning "steam pressure too high” or "steam temperature too low”. rig".
- Each of the control components 13 of the machine devices 1 and 3 to 11 can generate corresponding text messages, which are fed to the data analysis unit 18.
- the sensor signals and/or sensor data that occur at the same time and/or are continuously generated are monitored by the monitoring sensors 14 in parallel with the data analysis unit 18.
- the system messages can be supplemented by further information such as a time stamp.
- the sensor data are marked with the capital letter SD.
- These sensor data contain pure status values, so that in the example of the conditioning device 5 the monitoring sensors 13, for example, have a Steam pressure with the value "0.5 bar” or a steam temperature with, for example, "105°C”.
- the system messages SM and the sensor data SD are supplied to the data analysis unit 18. Processing, combining and analysis are carried out by programs and algorithms the system message ments SM and the sensor data SD. In particular, with the help of machine learning programs, process events are identified and/or process disruptions are predicted and output as a result. These results are denoted by the capital letter R in Figure 2 and are then immediately displayed to an operator.
- a planned process interruption due to a doctoring cycle in the area of the spinning device could be displayed as a process event.
- a prognosis of a process disruption could also be displayed as a result, since, for example, too much fluid is fed to the fiber strands during conditioning, which would no longer ensure sufficient drying in the drying device 9 over time and would lead to a process interruption.
- univariate or multivariate data may have to be taken into account.
- a process event or a process disruption could also be displayed to the operator that does not correspond to the real state of the manufacturing process, for example a false positive prognosis.
- the operator makes entries in order to overrule the result of the data analysis and thus to improve.
- the operator also has the option of giving the system additional data from his experience with regard to process events and process disruptions.
- the data analysis unit 18 offers the possibility of providing the operator with an overview of the most important results for the production process. process of the staple fibers. In this respect, the operator is able to get a quick overview of the production process despite the multitude of data.
- FIG. 3 another exemplary embodiment of the machine system according to the invention for the discontinuous production of synthetic staple fibers is shown.
- the fiber strands are temporarily stored before they are stretched, crimped and cut after extrusion.
- the machine devices for the production of the synthetic staple fibers are essentially identical to the aforementioned exemplary embodiment according to FIG. 1, so that only the differences will be explained at this point.
- a fiber laying device 19 is assigned to the spinning device.
- the fiber strand 2 produced by the spinning device 1 is deposited in a can 20 by the fiber depositing device 19 .
- the can 20 serves to temporarily store the fiber strands. Thus, several cans 20 are filled one after the other by the fiber depositing devices 19 .
- the cans 20 are arranged in a so-called can creel 21 for further processing.
- the fiber strands deposited in the cans 20 are combined together to form a so-called fiber tow 23 and drawn off by a number of machine devices, stretched, crimped, dried and cut and pressed into a bale 12 .
- the machine devices 3 to 11, which are also referred to as the fiber line, are functionally identical to the aforementioned exemplary embodiment, so that no further explanations are given at this point.
- each control components 13 and monitoring sensors 14 are assigned.
- the control components 13 and the monitoring sensors 14 are connected to the process control station 16 by a plurality of networks 15.1 to 15.4.
- the process control station 16 is coupled to the data analysis unit 18 and the operator station 17 .
- system messages are continuously generated by the control components 13 and fed to the process control station 16 .
- the sensor data are transmitted by the monitoring sensors 14 in particular continuously to the process control station 16 .
- the raw data of the system messages and the sensor data are used together in order to carry out a data analysis.
- the system messages and also the sensor data are first processed by upstream programs in order to then carry out a final analysis.
- FIG. 4 A further flow chart of a possible data analysis is shown in FIG. 4 .
- the system messages SM are continuously analyzed with regard to sequences in order to identify special features, in particular abnormalities and anomalies in the system messages. Such abnormalities are marked in FIG. 4 with the capital letter Ab and can then be used for further analysis.
- Process characteristics of this type are denoted by the capital letter C and are combined and analyzed with the abnormalities A of the system messages SM for further analysis.
- the creation of forecasts and process events and process disruptions could be significantly improved.
- the predictions of process disturbances can be displayed to the operator with the highest possible probability. This allows unwanted process interruptions to be reduced to a minimum.
- the raw data of the system messages and/or the sensor data can be used in different ways, in particular to identify process to forecast events, to condense system messages and/or to identify process events.
- System messages and sensor data can be combined or analyzed separately.
- methods for sequence analysis can be applied to the system messages and methods for anomaly detection to system messages and sensor data.
- the results can be processed accordingly and fed to a user interface, but also optionally to further data analyses.
- sensor data in particular can be transformed and combined (feature extraction) before being fed to a machine learning algorithm for analysis or inference. In this way, it is advantageously possible in particular to significantly improve the creation of forecasts of process disturbances. What is essential here, however, is that the predictions of process disturbances can be displayed to the operator with the greatest possible probability. This allows unwanted process interruptions to be reduced to a minimum.
- the data analysis unit preferably offers the possibility of using various previously developed methods for feature extraction and/or algorithms for identifying and forecasting process events and/or process disruptions productively and/or to combine them with one another and/or to test them against one another.
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- Automation & Control Theory (AREA)
- Artificial Intelligence (AREA)
- Evolutionary Computation (AREA)
- Mathematical Physics (AREA)
- Mechanical Engineering (AREA)
- Textile Engineering (AREA)
- General Factory Administration (AREA)
Abstract
Description
Claims
Priority Applications (1)
Application Number | Priority Date | Filing Date | Title |
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CN202180078358.4A CN116615584A (zh) | 2020-11-25 | 2021-11-23 | 用于监视机器设备的方法和生产合成短纤维的机器设备 |
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DE102020007195.3 | 2020-11-25 | ||
DE102020007195 | 2020-11-25 |
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WO2022112261A1 true WO2022112261A1 (de) | 2022-06-02 |
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PCT/EP2021/082708 WO2022112261A1 (de) | 2020-11-25 | 2021-11-23 | Verfahren zur überwachung einer maschinenanlage und maschinenanlage zur herstellung synthetischer stapelfasern |
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WO (1) | WO2022112261A1 (de) |
Citations (6)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
EP0358503A2 (de) * | 1988-09-08 | 1990-03-14 | E.I. Du Pont De Nemours And Company | Zeitoptimale Inferenzsteuerung einer Fasernherstellende Spinnmaschine durch ein auf Computerkenntnis gestütztes System |
EP0712949A2 (de) * | 1991-11-08 | 1996-05-22 | Maschinenfabrik Rieter Ag | Prozess-Steuerung im Textilbetrieb |
DE102005062826A1 (de) | 2005-12-27 | 2007-06-28 | Zimmer Ag | Verfahren und Vorrichtung zur Produktionsüberwachung von Endlosformkörpern wie Filamenten und Filamentbündeln |
WO2018055508A1 (en) * | 2016-09-26 | 2018-03-29 | Maschinenfabrik Rieter Ag | Method and system of predictive maintenance of a textile machine |
DE102017010473A1 (de) * | 2017-11-10 | 2019-05-16 | Oerlikon Textile Gmbh & Co. Kg | Maschinenanlage zur Herstellung oder Behandlung synthetischer Fäden |
WO2020113773A1 (zh) * | 2018-12-04 | 2020-06-11 | 深圳码隆科技有限公司 | 一种基于图像识别技术的纺丝机故障监测系统及方法 |
-
2021
- 2021-11-23 WO PCT/EP2021/082708 patent/WO2022112261A1/de active Application Filing
- 2021-11-23 CN CN202180078358.4A patent/CN116615584A/zh active Pending
Patent Citations (6)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
EP0358503A2 (de) * | 1988-09-08 | 1990-03-14 | E.I. Du Pont De Nemours And Company | Zeitoptimale Inferenzsteuerung einer Fasernherstellende Spinnmaschine durch ein auf Computerkenntnis gestütztes System |
EP0712949A2 (de) * | 1991-11-08 | 1996-05-22 | Maschinenfabrik Rieter Ag | Prozess-Steuerung im Textilbetrieb |
DE102005062826A1 (de) | 2005-12-27 | 2007-06-28 | Zimmer Ag | Verfahren und Vorrichtung zur Produktionsüberwachung von Endlosformkörpern wie Filamenten und Filamentbündeln |
WO2018055508A1 (en) * | 2016-09-26 | 2018-03-29 | Maschinenfabrik Rieter Ag | Method and system of predictive maintenance of a textile machine |
DE102017010473A1 (de) * | 2017-11-10 | 2019-05-16 | Oerlikon Textile Gmbh & Co. Kg | Maschinenanlage zur Herstellung oder Behandlung synthetischer Fäden |
WO2020113773A1 (zh) * | 2018-12-04 | 2020-06-11 | 深圳码隆科技有限公司 | 一种基于图像识别技术的纺丝机故障监测系统及方法 |
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