Fault diagnosis on material handling system using feature selection and data mining techniques

Demetgul M., Yildiz K., Taskin S., Tansel I. N., Yazicioglu O.

MEASUREMENT, cilt.55, ss.15-24, 2014 (SCI-Expanded) identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 55
  • Basım Tarihi: 2014
  • Doi Numarası: 10.1016/j.measurement.2014.04.037
  • Dergi Adı: MEASUREMENT
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus
  • Sayfa Sayıları: ss.15-24
  • Anahtar Kelimeler: Servo-pneumatic, Material handling system, Fault diagnosis, Feature selection, Data mining, Dimension reduction, Gustafson-Kessel, k-Medoids, DIFFUSION MAPS, DIMENSIONALITY REDUCTION, NEURAL-NETWORK, ALGORITHM, ACTUATOR, MODELS, PLANT
  • Marmara Üniversitesi Adresli: Evet

Özet

The material handling systems are one of the key components of the most modern manufacturing systems. The sensory signals of material handling systems are nonlinear and have unique characteristics. It is very difficult to encode and classify these signals by using multipurpose methods. In this study, performances of multiple generic methods were studied for the diagnostic of the pneumatic systems of the material handling systems. Diffusion Map (DM), Local Linear Embedding (LLE) and AutoEncoder (AE) algorithms were used for future extraction. Encoded signals were classified by using the Gustafson-Kessel (GK) and k-medoids algorithms. The accuracy of the estimations was better than 90% when the LLE was used with GK algorithm. (C) 2014 Elsevier Ltd. All rights reserved.