Paper Title:
Sensor Drifting Fault Diagnosis Based RS and ANN
  Abstract

A strategy based on rough set (RS) and artificial neural network (ANN) is developed to detect and diagnose sensor drifting faults. The reduced information is used to develop classification rules and train the neural network to infer appropriate parameters. The differences between measured thermodynamic states and predicted states obtained from models for normal performance (residuals) are used as performance indices for sensor fault detection and diagnosis. Simultaneous temperature sensor drifting faults of the supply chilled water (SCW) and return chilled water (RCW) can be diagnosis successfully.

  Info
Periodical
Advanced Materials Research (Volumes 204-210)
Edited by
Helen Zhang, Gang Shen and David Jin
Pages
1848-1851
DOI
10.4028/www.scientific.net/AMR.204-210.1848
Citation
Z. J. Hou, H. X. Chen, "Sensor Drifting Fault Diagnosis Based RS and ANN", Advanced Materials Research, Vols. 204-210, pp. 1848-1851, 2011
Online since
February 2011
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Price
$32.00
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