Genetic Algorithm-Wavelet Transform Feature Extraction for Data-Driven Acoustic Resonance Spectroscopy

John Greenhall

doi:10.1109/TUFFC.2023.3278494

2023-07-01

John Greenhall *

Dipen N. Sinha

Cristian Pantea

DOI：10.1109/TUFFC.2023.3278494

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摘要

En 中文

Acoustic resonance spectroscopy (ARS) enables highly accurate measurement of the properties (geometry/material) of a structure based on the structure's natural vibrational resonances. In general, measuring a specific property in multibody structures presents a significant challenge due to the complex overlapping peaks within the resonance spectrum. We present a technique for extracting useful features from a complex spectrum by isolating resonance peaks that are sensitive to the measured property and insensitive to other properties (noise peaks). We isolate specific peaks by selecting frequency regions of interest and performing wavelet transformation, where the frequency regions and wavelet scales are tuned via a genetic algorithm. This contrasts greatly from the traditional wavelet transformation/decomposition techniques, which use a large number of wavelets at different scales to represent the signal, including the noise peaks, and results in a large feature size, thus decreasing machine learning (ML) generalizability. We provide a detailed description of the technique and demonstrate the feature extraction technique, for example, regression and classification problems. We observe reductions of 95% and 40% in regression and classification errors, respectively, when using the genetic algorithm/wavelet transform feature extraction, compared to using no feature extraction, or using wavelet decomposition, which is common in optical spectroscopy. The feature extraction has potential to significantly increase the accuracy of spectroscopy measurements based on a wide range of ML techniques. This would have significant implications for ARS, as well as other data-driven methods for other types of spectroscopy, e.g., optical.

Keyword:

Acoustic resonance spectroscopy (ARS)

data-driven spectroscopy

machine learning (ML)

spectral feature extraction