Fault diagnosis on bottle filling plant using genetic-based neural network

Demetgul M., Unal M., Tansel I. N., Yazicioglu O.

ADVANCES IN ENGINEERING SOFTWARE, cilt.42, sa.12, ss.1051-1058, 2011 (SCI-Expanded) identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 42 Sayı: 12
  • Basım Tarihi: 2011
  • Doi Numarası: 10.1016/j.advengsoft.2011.07.004
  • Dergi Adı: ADVANCES IN ENGINEERING SOFTWARE
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus
  • Sayfa Sayıları: ss.1051-1058
  • Anahtar Kelimeler: Neural network, Genetic algorithm, Bottle filling plant, Pneumatic, Fault diagnosis, Back-propagation algorithm, SUPPORT VECTOR MACHINES, ALGORITHMS, VALVE, SELECTION, MODELS
  • Marmara Üniversitesi Adresli: Evet

Özet

Timely detection of the pneumatic system problems is important in industry. Many techniques have been employed to solve this problem. In this paper, Genetic Algorithm (GA) based optimal configuration of neural networks is proposed for fault diagnostic of bottle filling systems. Back-propagation is used for neural networks algorithm. The back-propagation algorithm had six inputs and one output. A fitness function was designed to the minimize execution time of ANN model by keeping the number of hidden layer(s) and nodes as low as possible while the mean square error of estimated output error is minimized. The designed GA-ANN combination and the graphical user interface (GUI) eliminate the trial and error process for selection of the fastest and most accurate configuration. The performance of the proposed system was evaluated by using experimental data collected at a pneumatic work cell which attach caps to the bottles. The sensory data was collected at normal operating conditions and a series of faults were imposed to the system such as missing bottle, attaching nonworking bottle caps at two different cylinders, two air pressure problems (insufficient and low air), and not filling water. The study demonstrated the convenience, accuracy and speed of the proposed GA-NN environment. It may also be used for training for selection of ANN configurations at various applications. (C) 2011 Elsevier Ltd. All rights reserved.