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Feature-Based Resampling for Classification Using Discrete Wavelet Transform for Diagnostic Purposes of Industrial Processes with Periodic Data
Feature-Based Resampling for Classification Using Discrete Wavelet Transform for Diagnostic Purposes of Industrial Processes with Periodic Data [1930]
Автор(ы): M.-S. Saadawia, D. Soffker
Количество страниц: 8
Год: 2012
Аннотация[240 КБ] 
Код: 10959
Описание
6th European Workshop on Structural Health Monitoring. Dresden. Germany. 2012. Report. In this contribution a feature-based resampling approach for industrial processes with periodic data is proposed. This approach is used for fault classification and diagnostic purposes and based on the Discrete Wavelet Transform (DWT). The approach is used to define a set of reliable features which is used as signal dividers specifying the segments to be resampled. A real industrial example of process cycles with a periodic nature of signals is presented to demonstrate the efficiency of the approach compared to other usual approaches.
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