Time-frequency signatures based on a fuzzy-cluster representation as a means for automatic classification of targets buried underground

1998 ◽  
Author(s):  
Hans C. Strifors ◽  
Steffan Abrahamson ◽  
Anders Gustafsson ◽  
Guillermo C. Gaunaurd
Keyword(s):  
Automatic Classification ◽  
Fuzzy Cluster ◽  
Time Frequency ◽  
Cluster Representation

Comparison of fuzzy-cluster-based time-frequency signatures for automatic classification of underground minelike targets

1999 ◽  
Author(s):  
Steffan Abrahamson ◽  
Anders Ericsson ◽  
Anders Gustafsson ◽  
Hans C. Strifors ◽  
Guillermo C. Gaunaurd
Keyword(s):  
Automatic Classification ◽  
Fuzzy Cluster ◽  
Time Frequency

Application of Time-Frequency Analysis & Blind Source Separation to Diagnosis of Faults with Generator Rotor System

2014 ◽  
Vol 556-562 ◽  
pp. 2748-2751
Author(s):  
Hong Li Wang ◽  
Bing Xu ◽  
Xue Dong Xue ◽  
Kan Cheng
Keyword(s):  
Blind Source Separation ◽  
Source Separation ◽  
Automatic Classification ◽  
Wave Method ◽  
Nerve Network ◽  
Time Frequency ◽  
Generator Rotor ◽  
Independent Components ◽  
Local Wave

One method for diagnosis of faults with generator rotor is contrived by combining local wave method and blind source separation. Time-frequency image varies with local wave of different fault signals, and this feature is applied to identify different faults. In order to realize automatic classification of faults, blind source separation is employed for separation of independent components in time-frequency image of local wave of different fault signals, so as to derive projection coefficients for a set of source images. On the basis of this, automatic classification of faults is realized with probability nerve network. Taking fault signal of rotor as an example, this method is investigated, and the validity is proved by experimental results.

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