Contactless Detection of Electrical Machine Bearing Faults: A Radar-Based Solution

Abstract

Bearing failures represent the most prevalent fault type in electrical machines, potentially leading to catastrophic consequences if not detected early. Conventional detection methods primarily rely on thermal, acoustic, and vibration sensors. Traditional vibration-based techniques have gained widespread adoption due to their stable and straightforward signal-processing capabilities. However, these approaches require direct motor mounting, introducing economic, temporal, and safety inefficiencies. This study presents the first investigation of contactless radar-based detection of bearing faults according to the authors' knowledge. The research employs the absolute value of complex signals derived from quadrature signals recorded by a 24 GHz radar transceiver as the vibration signal. Various defects like corrosion, improper oil levels, and scratches were deliberately introduced to the inner race, outer race, and balls of bearings, establishing 16 distinct fault classes. Classification performance was evaluated using both time-domain statistical features and frequency-domain PSD features. Multiple machine learning algorithms were applied to both approaches, consistently achieving accuracy rates exceeding 98%. This study validates the potential of radar-based systems for bearing fault diagnosis and introduces a novel paradigm for contactless bearing fault detection comprising radar signal data from 880 experiments. The results demonstrate that radar technology offers a promising alternative to traditional contact-requiring methods, enabling efficient and reliable bearing fault classification through non-invasive vibration detection.

Keywords

• Bearing
• Fault detection
• Power spectral density
• Radar
• Time-domain features

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