RADIAL BASIS AND LVQ NEURAL NETWORK ALGORITHM FOR REAL TIME FAULT DIAGNOSIS OF BOTTLE FILLING PLANT
Küçük Resim Yok
Tarih
2014
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
Univ Osijek, Tech Fac
Erişim Hakkı
info:eu-repo/semantics/closedAccess
Özet
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.
Açıklama
Anahtar Kelimeler
artificial neural network, bottle filling plant, fault diagnosis, pneumatic
Kaynak
Tehnicki Vjesnik-Technical Gazette
WoS Q Değeri
Q3
Scopus Q Değeri
Cilt
21
Sayı
4
