Real-Time Prediction of the Yarn Break Position Using Vibration Measurement
Warping is a crucial process that connects two main stages of production: yarn manufacturing and fabric creation. Two interrelated parameters affect the efficiency of this technological process: warping speed and the ability to swiftly detect the yarn breaks caused by various defects. The faster a b...
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MDPI AG
2025-01-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/25/2/299 |
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| author | Marcin Idzik Tomasz Rybicki |
| author_facet | Marcin Idzik Tomasz Rybicki |
| author_sort | Marcin Idzik |
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| description | Warping is a crucial process that connects two main stages of production: yarn manufacturing and fabric creation. Two interrelated parameters affect the efficiency of this technological process: warping speed and the ability to swiftly detect the yarn breaks caused by various defects. The faster a break is detected and the warping machine stopped, the higher the machine’s working speed can be. Since the beginning of such devices, various types of yarn break detectors have been proposed, primarily based on different mechanical solutions. To enhance the break detection process, a solution involving the use of an accelerometer to measure yarn vibrations and thereby detect whether the moving yarn has broken is proposed. Based on the detection of a threshold value of 22 m/s<sup>2</sup>, the warping machine could be stopped within 2.752 to 2.808 ms, which is 50 times faster than in the traditional mechanical detectors under investigation. Furthermore, through a precise analysis of yarn vibration patterns, it became possible to determine the distance from the sensor at which the break occurred. This analysis was conducted using the proprietary MRSCEK coefficient, which aggregates data obtained from six standard coefficients: mean, root mean square, standard deviation, crest factor, energy, and kurtosis. This information could potentially lead to the development of automated systems for removing breaks without human intervention in the future. Research efforts focused on analyzing the vibration signals received from yarns made with different linear densities. The results showed that such a system could effectively replace commonly used mechanical yarn break detectors and operate much faster. |
| format | Article |
| id | doaj-art-da6110309d02443f8dca905a081c2078 |
| institution | Kabale University |
| issn | 1424-8220 |
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| publishDate | 2025-01-01 |
| publisher | MDPI AG |
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| spelling | doaj-art-da6110309d02443f8dca905a081c20782025-01-24T13:48:25ZengMDPI AGSensors1424-82202025-01-0125229910.3390/s25020299Real-Time Prediction of the Yarn Break Position Using Vibration MeasurementMarcin Idzik0Tomasz Rybicki1Institute of Automatic Control, Lodz University of Technology, 90-537 Lodz, PolandInstitute of Automatic Control, Lodz University of Technology, 90-537 Lodz, PolandWarping is a crucial process that connects two main stages of production: yarn manufacturing and fabric creation. Two interrelated parameters affect the efficiency of this technological process: warping speed and the ability to swiftly detect the yarn breaks caused by various defects. The faster a break is detected and the warping machine stopped, the higher the machine’s working speed can be. Since the beginning of such devices, various types of yarn break detectors have been proposed, primarily based on different mechanical solutions. To enhance the break detection process, a solution involving the use of an accelerometer to measure yarn vibrations and thereby detect whether the moving yarn has broken is proposed. Based on the detection of a threshold value of 22 m/s<sup>2</sup>, the warping machine could be stopped within 2.752 to 2.808 ms, which is 50 times faster than in the traditional mechanical detectors under investigation. Furthermore, through a precise analysis of yarn vibration patterns, it became possible to determine the distance from the sensor at which the break occurred. This analysis was conducted using the proprietary MRSCEK coefficient, which aggregates data obtained from six standard coefficients: mean, root mean square, standard deviation, crest factor, energy, and kurtosis. This information could potentially lead to the development of automated systems for removing breaks without human intervention in the future. Research efforts focused on analyzing the vibration signals received from yarns made with different linear densities. The results showed that such a system could effectively replace commonly used mechanical yarn break detectors and operate much faster.https://www.mdpi.com/1424-8220/25/2/299warping machineyarndetectionMEMS |
| spellingShingle | Marcin Idzik Tomasz Rybicki Real-Time Prediction of the Yarn Break Position Using Vibration Measurement Sensors warping machine yarn detection MEMS |
| title | Real-Time Prediction of the Yarn Break Position Using Vibration Measurement |
| title_full | Real-Time Prediction of the Yarn Break Position Using Vibration Measurement |
| title_fullStr | Real-Time Prediction of the Yarn Break Position Using Vibration Measurement |
| title_full_unstemmed | Real-Time Prediction of the Yarn Break Position Using Vibration Measurement |
| title_short | Real-Time Prediction of the Yarn Break Position Using Vibration Measurement |
| title_sort | real time prediction of the yarn break position using vibration measurement |
| topic | warping machine yarn detection MEMS |
| url | https://www.mdpi.com/1424-8220/25/2/299 |
| work_keys_str_mv | AT marcinidzik realtimepredictionoftheyarnbreakpositionusingvibrationmeasurement AT tomaszrybicki realtimepredictionoftheyarnbreakpositionusingvibrationmeasurement |