The Effect of Common-Mode Noise in Quadrature Radar Systems: Rotating Disc Imbalance Estimation

Abstract

Accurate and robust detection of imbalance in rotating machinery is critical for ensuring operational reliability in industrial environments. This study experimentally investigates the impact of common-mode noise (CN) on feature-based classification performance in quadrature radar systems, estimating the imbalance level in a rotating disk. The proposed methodology utilizes a homodyne radar architecture to acquire in-phase (I) and quadrature (Q) baseband signals, from which time-domain features are extracted. A Hilbert transform-based denoising approach is implemented to address the detrimental effects of CN caused by electromagnetic interference and hardware imperfections. The extracted features, both from raw and denoised signals, are evaluated using various machine learning classifiers, including Decision Trees, Support Vector Machines, k-nearest Neighbors, Artificial Neural Networks, and ensemble methods. Experimental results demonstrate that CN significantly degrades classification accuracy, particularly for features derived from the amplitude and phase of complex-valued signals. The application of the proposed denoising technique yields a substantial improvement in classification metrics, with k-nearest Neighbors and Support Vector Machines achieving over 97% accuracy on the denoised data. The findings highlight the importance of effective noise mitigation in radar-based condition monitoring pipelines and establish the practical viability of quadrature radar systems for non-contact, high-precision imbalance detection in rotating machinery.

Keywords

• Classification
• Fault diagnosis
• Feature extraction
• Noise removal
• Radar
• Vibration

Ethical Statement

Ethics committee approval was not required for this study because of there was no study on animals or humans.

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