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On-Line Fault Detection by using Filters Bank and Artificial Neural Networks

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6 Author(s)

The aim of this paper is to propose a method for the detection of faults in industrial systems, such as electrical machines and drives, through on-line monitoring system. Early fault detection, which reduces the possibility of catastrophic damage, is possible by comparing the measured signals with a database that contains characteristic signals for machines operating with and without faulty conditions. This approach is based on a Filters Bank that extracts frequency and energy characteristic features, and an artificial neural networks (ANN) that classifies these features. A link with the wavelet transform for features extraction is also presented. The faults that are concerned correspond to a change in frequency components of the signal

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Information and Communication Technologies, 2006. ICTTA '06. 2nd  (Volume:1 )

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