Juan-José González de-la-Rosa

Wavelets and wavelet packets applied to termite detection

In this paper we present an study which shows the possibility of using wavelets and wavelet packets to detect transients produced by termites. Identification has been developed by means of analyzing the impulse response of three sensors undergoing natural excitations. De-noising exhibits good performance up to SNR=-30 dB, in the presence of white Gaussian noise. The test can be extended to similar vibratory or acoustic signals resulting from impulse responses.

Recognition of insect emissions applying the discrete wavelet transform

The time-domain fingerprint of termite alarm signals is enhanced by wavelets and wavelet packets, using multi-resolution analysis. We take advantage of these emission patterns, characterized by four-impulse bursts. Identification has been developed by means of analyzing the impulse response of three sensors undergoing natural excitations. De-noising exhibits good performance up to SNR=-30 dB, in the presence of white Gaussian noise. The test can be extended to similar vibratory or acoustic signals resulting from impulse responses.

Categorization of power quality transients using higher-order statistics and competitive layers-based neural networks

This paper deals with power-quality (PQ) event detection, classification and characterization using higher-order sliding cumulants (which are calculated over high-pass filtered signals to avoid the low-frequency 50-Hz sinusoid), whose maxima and minima are the coordinates of two-dimensional feature vectors. The classification strategy is based in competitive layers. We focus on the problem of differentiating two types of transients: short-duration (impulsive transients) and long-duration (oscillatory transients). The results show that the measured vectors are classified into clearly differentiated clusters in the feature space. The experience aims to set the foundations of an automatic procedure for PQ event detection.

Comparison of two ICA algorithms in BSS applied to non-destructive vibratory tests

Two independent component analysis (ICA) algorithms are applied for blind source separation (BSS) in a synthetic, multi-sensor situation, within a non-destructive pipeline test. CumICA is based in the computation of the cross-cumulants of the mixtures and needs the aid of a digital high-pass filter to achieve the same SNR (up to –40 dB) as Fast-ICA. Acoustic Emission (AE) sequences were acquired by a wide frequency range transducer (100-800 kHz) and digitalized by a 2.5 MHz, 8-bit ADC. Four common sources in AE testing are linearly mixed, involving real AE sequences, impulses and parasitic signals modelling human activity.

On the performance of a HOS-Based ICA algorithm in BSS of acoustic emission signals

A cumulant-based independent component analysis (Cum-ICA) is applied for blind source separation (BSS) in a synthetic, multi-sensor scenario, within a non-destructive pipeline test. Acoustic Emission (AE) sequences were acquired by a wide frequency range transducer (100-800 kHz) and digitalized by a 2.5 MHz, 8-bit ADC. Four common sources in AE testing are linearly mixed, involving real AE sequences, impulses and parasitic signals from human activity. A digital high-pass filter achieves a SNR up to –40 dB.

Third-Order spectral characterization of termite’s emission track

A higher-order frequency-domain characterization of termite activity (feeding and excavating) has been performed by means of analyzing diagonal slices of the bi-spectrum. Five sets of signals of different qualities were acquired using a high sensitivity probe-accelerometer. We conclude that it is possible to establish a third-order pattern (spectral track) associated to the termite emissions, and resulting from the impulsive response of the sensor and the body or substratum through which the emitted waves propagate.

Two ICA algorithms applied to BSS in non-destructive vibratory tests

Two independent component analysis (ICA) algorithms have been applied for blind source separation (BSS) in a synthetic, multi-sensor scenario, within a non-destructive pipeline test. The first one, CumICA, is based in the computation of the cross-cumulants of the mixed observed signals, and needs the aid of a digital high-pass filter to achieve the same SNR (up to -40 dB) as the second algorithm, Fast-ICA. Vibratory signals were acquired by a wide frequency range transducer (100-800 kHz) and digitalized by a 2.5 MHz, 8-bit ADC. Different types of commonly observed source signals are linearly mixed, involving acoustic emission (AE) sequences, impulses and other parasitic signals modelling human activity. Both ICA algorithms achieve to separate the impulse-like and the AE events, which often are associated to cracks or sudden non-stationary vibrations.