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Radial basis and LVQ neural network algorithm for real time fault diagnosis of bottle filling plant

Mustafa Demetgul ; Marmara University, Technology Faculty, Goztepe, Istanbul, Turkey
Osman Yazicioglu ; ITICU, Department of Industrial Engineering, Uskudar, Istanbul, Turkey
Aykut Kentli   ORCID icon orcid.org/0000-0002-4098-7220 ; Marmara University, Engineering Faculty, Goztepe, Istanbul, Turkey

Puni tekst: engleski, pdf (2 MB) str. 689-695 preuzimanja: 675* citiraj
APA 6th Edition
Demetgul, M., Yazicioglu, O. i Kentli, A. (2014). Radial basis and LVQ neural network algorithm for real time fault diagnosis of bottle filling plant. Tehnički vjesnik, 21 (4), 689-695. Preuzeto s https://hrcak.srce.hr/126062
MLA 8th Edition
Demetgul, Mustafa, et al. "Radial basis and LVQ neural network algorithm for real time fault diagnosis of bottle filling plant." Tehnički vjesnik, vol. 21, br. 4, 2014, str. 689-695. https://hrcak.srce.hr/126062. Citirano 01.03.2021.
Chicago 17th Edition
Demetgul, Mustafa, Osman Yazicioglu i Aykut Kentli. "Radial basis and LVQ neural network algorithm for real time fault diagnosis of bottle filling plant." Tehnički vjesnik 21, br. 4 (2014): 689-695. https://hrcak.srce.hr/126062
Harvard
Demetgul, M., Yazicioglu, O., i Kentli, A. (2014). 'Radial basis and LVQ neural network algorithm for real time fault diagnosis of bottle filling plant', Tehnički vjesnik, 21(4), str. 689-695. Preuzeto s: https://hrcak.srce.hr/126062 (Datum pristupa: 01.03.2021.)
Vancouver
Demetgul M, Yazicioglu O, Kentli A. Radial basis and LVQ neural network algorithm for real time fault diagnosis of bottle filling plant. Tehnički vjesnik [Internet]. 2014 [pristupljeno 01.03.2021.];21(4):689-695. Dostupno na: https://hrcak.srce.hr/126062
IEEE
M. Demetgul, O. Yazicioglu i A. Kentli, "Radial basis and LVQ neural network algorithm for real time fault diagnosis of bottle filling plant", Tehnički vjesnik, vol.21, br. 4, str. 689-695, 2014. [Online]. Dostupno na: https://hrcak.srce.hr/126062. [Citirano: 01.03.2021.]
Puni tekst: hrvatski, pdf (2 MB) str. 689-695 preuzimanja: 262* citiraj
APA 6th Edition
Demetgul, M., Yazicioglu, O. i Kentli, A. (2014). Algoritam radijalne osnove i LVQ algoritam neuronske mreže za pravovremenu dijagnozu greške pogona za punjenje boca. Tehnički vjesnik, 21 (4), 689-695. Preuzeto s https://hrcak.srce.hr/126062
MLA 8th Edition
Demetgul, Mustafa, et al. "Algoritam radijalne osnove i LVQ algoritam neuronske mreže za pravovremenu dijagnozu greške pogona za punjenje boca." Tehnički vjesnik, vol. 21, br. 4, 2014, str. 689-695. https://hrcak.srce.hr/126062. Citirano 01.03.2021.
Chicago 17th Edition
Demetgul, Mustafa, Osman Yazicioglu i Aykut Kentli. "Algoritam radijalne osnove i LVQ algoritam neuronske mreže za pravovremenu dijagnozu greške pogona za punjenje boca." Tehnički vjesnik 21, br. 4 (2014): 689-695. https://hrcak.srce.hr/126062
Harvard
Demetgul, M., Yazicioglu, O., i Kentli, A. (2014). 'Algoritam radijalne osnove i LVQ algoritam neuronske mreže za pravovremenu dijagnozu greške pogona za punjenje boca', Tehnički vjesnik, 21(4), str. 689-695. Preuzeto s: https://hrcak.srce.hr/126062 (Datum pristupa: 01.03.2021.)
Vancouver
Demetgul M, Yazicioglu O, Kentli A. Algoritam radijalne osnove i LVQ algoritam neuronske mreže za pravovremenu dijagnozu greške pogona za punjenje boca. Tehnički vjesnik [Internet]. 2014 [pristupljeno 01.03.2021.];21(4):689-695. Dostupno na: https://hrcak.srce.hr/126062
IEEE
M. Demetgul, O. Yazicioglu i A. Kentli, "Algoritam radijalne osnove i LVQ algoritam neuronske mreže za pravovremenu dijagnozu greške pogona za punjenje boca", Tehnički vjesnik, vol.21, br. 4, str. 689-695, 2014. [Online]. Dostupno na: https://hrcak.srce.hr/126062. [Citirano: 01.03.2021.]

Sažetak
In this study, an Artificial Neural Network (ANN) is developed to find faults rapidly on a pneumatic system. The data were saved and evaluated considering system is working perfectly and faults are predetermined. These faults include having no bottle, a nonworking cap closing cylinder B, a nonworking bottle cap closing cylinder C, insufficient air pressure, water not filling and low air pressure faults. The signals of six sensors were collected during the entire sequence and the 18 most descriptive features of the data were encoded to present to the ANNs. Two different ANNs were applied for interpretation of the encoded signals. The ANNs tested in the study were learning vector quantization (LVQ) and radial basis network (RBN). The performance of LVQ and RBN was found to be fine with the presented procedures for a system having very repetitive sequential data.

Ključne riječi
artificial neural network; bottle filling plant; fault diagnosis; pneumatic

Hrčak ID: 126062

URI
https://hrcak.srce.hr/126062

[hrvatski]

Posjeta: 1.344 *