Frugal Wavelet Transform for detecting faults in non-stationary signals (with Matlab codes)

According to the literature, one of the most notable weaknesses of the Wavelet Transform (WT) is its dependence on the selection of the wavelet function, resulting in different wavelet functions yielding varying results. To overcome this weakness, this paper introduces a new transform called the FrugWT (FrugWT). The Frugal Wavelet Transform, a type of discrete wavelet transform, emphasizes the preservation and approximation of detail signals, especially when the detection of local abrupt changes in the signal is apparently not possible. This study demonstrates that the FrugWT mitigates the influence of wavelet function selection on fault detection and exhibits resilience to acceptable noise levels. The FrugWT outperforms both the WT and signal derivative methods. Its effectiveness is demonstrated in detecting subtle faults within arrhythmic heartbeats (ECG) and abnormal brainwaves (EEG) signals. Matlab code is available in the appendix.

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Publication Details

Journal
HCMCOU Journal of Science – Advances in Computational Structures
Published
2025-08-28
DOI
https://doi.org/10.46223/hcmcoujs.acs.en.15.2.78.2025
Primary Topic
Image and Signal Denoising Methods
Type
article
Field-Weighted Citation Impact
0.00
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article

Frugal Wavelet Transform for detecting faults in non-stationary signals (with Matlab codes)

Morteza Saadatmorad, Bochra Khatir
HCMCOU Journal of Science – Advances in Computational Structures
Image and Signal Denoising Methods
article

Frugal Wavelet Transform for detecting faults in non-stationary signals (with Matlab codes)

Morteza Saadatmorad, Bochra Khatir
article en

Abstract

According to the literature, one of the most notable weaknesses of the Wavelet Transform (WT) is its dependence on the selection of the wavelet function, resulting in different wavelet functions yielding varying results. To overcome this weakness, this paper introduces a new transform called the FrugWT (FrugWT). The Frugal Wavelet Transform, a type of discrete wavelet transform, emphasizes the preservation and approximation of detail signals, especially when the detection of local abrupt changes in the signal is apparently not possible. This study demonstrates that the FrugWT mitigates the influence of wavelet function selection on fault detection and exhibits resilience to acceptable noise levels. The FrugWT outperforms both the WT and signal derivative methods. Its effectiveness is demonstrated in detecting subtle faults within arrhythmic heartbeats (ECG) and abnormal brainwaves (EEG) signals. Matlab code is available in the appendix.

HCMCOU Journal of Science – Advances in Computational Structures
Marche Polytechnic University (IT), University of Bologna (IT)
Openalex Percentile: Top 78%
Image and Signal Denoising Methods
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Frugal Wavelet Transform for detecting faults in non-stationary signals (with Matlab codes) — Morteza Saadatmorad, Bochra Khatir · HCMCOU Journal of Science – Advances in Computational Structures (2025) | TGRS Research Map | TGRS