Fault diagnosis on material handling system using feature selection and data mining techniques
Küçük Resim Yok
Tarih
2014
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
Elsevier B.V.
Erişim Hakkı
info:eu-repo/semantics/closedAccess
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. © 2014 Elsevier Ltd. All rights reserved.
Açıklama
Anahtar Kelimeler
Data mining, Dimension reduction, Fault diagnosis, Feature selection, Gustafson-Kessel, k-Medoids, Material handling system, Servo-pneumatic
Kaynak
Measurement: Journal of the International Measurement Confederation
WoS Q Değeri
Q2
Scopus Q Değeri
Q1
Cilt
55
