Wavelet packets and de-noising based on higher-order-statistics for transient detection
Résumé
In this paper, we present a detector of transient acoustic signals that combines two powerful detection tools: a local wavelet analysis and higher-order statistical properties of the signals. The use of both techniques makes detection possible in low signal-to-noise ratio conditions, when other means of detection are no longer su1cient. The proposed algorithm uses the adapted wavelet packet transform. It leads to a partition of the signal which is 'optimal' according to a criterion that tests the Gaussian nature of the frequency bands. To get a time dependent detection curve, we perform a de-noising procedure on the wavelet coe1cients: The Gaussian coe1cients are set to zero. We then apply a classical method of detection on the time reconstructed de-noised signal. We study the performance of the detector in terms of experimental ROC curves. We show that the detector performs better than decompositions using other classical splitting criteria. In the last part, we present an application of the algorithm on real 8ow recordings of nuclear plant pipings. The detector indicates the presence of a missing body in the piping at some instants not seen with a classical energy detector.
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