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Online Monitoring-Based Prediction Model of Knitting Machine Productivity

Sherien Elkateb

Sherien Elkateb

Textile Engineering Department, Faculty of Engineering, Alexandria UniversityEgypt.
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Elkateb, Sherien
, 
Ahmed Métwalli

Ahmed Métwalli

Electrical Engineering Department, Arab Academy for Science, Technology and Maritime TransportAlexandria, Egypt.
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Métwalli, Ahmed
, 
Abdelrahman Shendy

Abdelrahman Shendy

Computer Engineering Department, Arab Academy for Science, Technology and Maritime TransportAlexandria, Egypt.
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Shendy, Abdelrahman
, 
Karim Moussa

Karim Moussa

Electrical (Electronics and Communications) Engineering Department, Horus UniversityDamietta, Egypt.
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Moussa, Karim
 und 
Ahmed E. B. Abu-Elanien

Ahmed E. B. Abu-Elanien

Electrical (Power) Engineering Department, Faculty of Engineering, Alexandria UniversityEgypt.
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Abu-Elanien, Ahmed E. B.
  
18. Okt. 2023
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Artikel-Kategorie: Research Article
Online veröffentlicht: 18. Okt. 2023
Seitenbereich: 46 - 52
DOI: https://doi.org/10.2478/ftee-2023-0035
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© 2023 Łukasiewicz Research Network-Łódź Institute of Technology, published by Sciendo
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Fig. 1.

ESP32 MQTT Client Connection
ESP32 MQTT Client Connection

Fig. 2.

Online monitoring device deployment on the circular knitting machine Where: (1) The proximity sensor, (2) The yarn feeder sensor, and (3) IoT components
Online monitoring device deployment on the circular knitting machine Where: (1) The proximity sensor, (2) The yarn feeder sensor, and (3) IoT components

Fig. 3.

MQTT Broker interaction with the machine
MQTT Broker interaction with the machine

Fig. 4.

Pearson’s correlation matrix
Pearson’s correlation matrix

Fig. 5.

Relation between the production rate and measured parameters
Relation between the production rate and measured parameters

Fig. 6.

Regression hyper-plane for first prediction model
Regression hyper-plane for first prediction model

Fig. 7.

Regression hyper-plane for second prediction model
Regression hyper-plane for second prediction model

Statistical data of measured parameters and the production rate

  n1(rpm) n2(rpm) L(mm) P(kg/h)
Mean 18.00 1174.63 2.11 6.26
Std 2.82 182.78 0.05 0.97
Min 1.00 21.00 0.68 0.11
25% 19.00 1237.00 2.10 6.59
50% 19.00 1238.00 2.10 6.59
75% 19.00 1238.00 2.13 6.59
Max 21.00 1362.00 2.68 7.26

Statistical measure of degrees of multicollinearity

Independent Parameter n1&n2 L&n1 L&n2
Ri2 0.980 0.005 0.000
Tolerance 0.020 0.995 1.000
VIF 50.251 1.005 1.000

Results of prediction precision using (n1 and L)

MAE Standard deviation of MAE MAPE Accuracy
0.029 0.002 0.65% 99.73%

Results of prediction precision using (n2 and L)

MAE Standard deviation of MAE MAPE Accuracy
0 0 0 100%
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