A Comparative Study on Wavelet and EMD Methods for Remote detection of Rotational Imbalance
DOI:
https://doi.org/10.64470/elene.2025.1007Keywords:
Empirical Mode Decomposition (EMD), Machine Learning (ML), Multi-Resolution Analysis (MRA), Radar-Based Monitoring, Rotational Imbalance Detection, Wavelet Decomposition (WD)Abstract
This paper presents a radar-driven method for detecting rotational imbalance in machinery using multi-resolution signal processing and machine learning. Two feature extraction strategies—Empirical Mode Decomposition (EMD) and Wavelet Decomposition (WD)—are compared with WD evaluated using Haar, Coiflet (coif2), and Symlet (sym4) mother wavelets. Four classifiers—Support Vector Machine (SVM), k-Nearest Neighbors (KNN), Random Forest (RF), and Decision Tree (DT)—are used to assess performance. Radar signals undergo multi-resolution analysis to derive features that capture imbalance signatures. The performances of the methods are evaluated through classification accuracy, computational cost, and real-time feasibility. Wavelet-based features consistently outperform EMD across all classifiers and wavelets; Haar delivers the best accuracy of 95.28% with RF. A notable overall trade-off is achieved by Haar features with SVM, providing high accuracy with low latency (~219 ms training, ~13.5 ms prediction), while DT provides the fastest inference (0.6 ms) for strict real-time needs. These results provide a practical framework and clear guidelines for algorithm selection in industrial radar-based condition monitoring.
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Data Availability Statement
The datasets analyzed during the current study are publicly available in the KAGGLE (Acar, 2025)
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Copyright (c) 2025 Yunus Emre Acar
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