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

Öz

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.

Anahtar Kelimeler

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

Etik Beyan

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

Kaynakça

1. Acar YE. 2024. Radar-enabled non-contact speed estimation for rotating electrical machinery. Meas, 235: 114989.
2. Acar YE. 2025. Radar data for rotational mass imbalance detection. URL: https://www.kaggle.com/datasets/yunusemreacar1/radar-data-for-machine-fault-detection (accessed date: August 11, 2025).
3. Cho S, Gao Z, Moan T. 2018. Model-based fault detection, fault isolation and fault-tolerant control of a blade pitch system in floating wind turbines. Renew Energy, 120: 306-321.
4. Du M, Zhong P, Cai X, Bi D. 2022. DNCNet: Deep radar signal denoising and recognition. IEEE Trans Aerosp Electron Syst, 58(4): 3549-3562.
5. Ge Y, Wang R, Zeng X. 2025. Robust and accurate eye-blink detection using a 24-GHz CW radar. IEEE Trans Instrum Meas, 74: 1-10.
6. Goyal D, Dhami SS, Pabla BS. 2020. Non-contact fault diagnosis of bearings in machine learning environment. IEEE Sens J, 20(9): 4816-4823.
7. Hansen S, Bredendiek C, Briese G, Froehly A, Herschel R, Pohl N. 2022. A SiGe-chip-based D-band FMCW-radar sensor with 53-GHz tuning range for high resolution measurements in industrial applications. IEEE Trans Microw Theory Tech, 70(1): 719-731.
8. Jiao H, Sun W, Wang H, Wan X. 2025. Comprehensive exploitation of time- and frequency-domain information for bearing fault diagnosis on imbalanced datasets via adaptive wavelet-like transform general adversarial network and ensemble learning. Sensors, 25(7): 2328.

9. Kilic ME, Acar YE. 2024. Performance evaluation of the time-frequency transformation methods on electrical machinery fault detection. Bitlis Eren Univ J Sci, 13: 1147-1157.
10. Li H, Wu X, Liu T, Li S. 2023. Rotating machinery fault diagnosis based on typical resonance demodulation methods: A review. IEEE Sens J, 23(7): 6439-6459.
11. Liu H, Ji D, Lin J, Liu Z, Li H. 2025. Residual angular speed analysis based on laser Doppler vibrometer and its application in planetary gearbox diagnosis. Meas, 250: 116987.
12. Nayana BR, Geethanjali P. 2017. Analysis of statistical time-domain features effectiveness in identification of bearing faults from vibration signal. IEEE Sens J, 17(17): 5618-5625.
13. Park JE, Lee GH, Lee IS, Yang JR. 2025. Heart rate extraction technique with mitigation of respiration harmonic for bio-radar sensors. IEEE Sens J, 25(1): 929-939.
14. Wang H, Sun W, He L, Zhou J. 2022. Rolling bearing fault diagnosis using multi-sensor data fusion based on 1D-CNN model. Entropy, 24(5): 573.
15. Xu W, He J, Li G, Wu C, Lv J, Qian C. 2025. Progressive orthogonal matching pursuit and adaptive modal screening for fault diagnosis of rotating machinery components using acoustic signals. Meas, 252: 117345.
16. Zhao N, Zhang J, Mao Z, Jiang Z, Li H. 2023. Time-frequency feature extraction method of the multi-source shock signal based on improved VMD and bilateral adaptive Laplace wavelet. Chin J Mech Eng, 36(1): 36.
17. Zhao Y, Yang X, Huang J, Gao J, Zhou X, Zhang T. 2025. Ensemble targeted stacked denoising autoencoders with mutual information constraint for rotating machinery fault diagnosis. IEEE Trans Ind Inform, 21(2): 1329-1338.