Please use this identifier to cite or link to this item:
|Title:||Industrial fault detection and isolation using Dominant Feature Identification||Authors:||Pang, C.K.
|Keywords:||Least Square Error (LSE)
Neural Network (NN)
Singular Value Decomposition (SVD)
|Issue Date:||2011||Citation:||Pang, C.K.,Zhou, J.-H.,Zhong, Z.-W.,Lewis, F.L. (2011). Industrial fault detection and isolation using Dominant Feature Identification. ASCC 2011 - 8th Asian Control Conference - Final Program and Proceedings : 1018-1023. ScholarBank@NUS Repository.||Abstract:||In this paper, we show how to find a reduced feature subset which is optimal in both estimation and clustering least square errors using two new Dominant Feature Identification (DFI) methods. We apply DFI to to identify the important features in a given set of faults, and a Neural Network (NN) is used for online fault classification based on the determined reduced feature set in the proposed two-stage framework. Our experimental results on an industrial machine fault simulator show the effectiveness in fault diagnosis and classification. Accuracy of 99.4% for fault identification is observed when using proposed new DFI followed by NN classification, reducing the number of required features from 120 to 13 and the number of sensors from 8 to 4. © 2011 Asian Control Association.||Source Title:||ASCC 2011 - 8th Asian Control Conference - Final Program and Proceedings||URI:||http://scholarbank.nus.edu.sg/handle/10635/70589||ISBN:||9788995605646|
|Appears in Collections:||Staff Publications|
Show full item record
Files in This Item:
There are no files associated with this item.
checked on Jan 26, 2023
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.