Electrical & Computer Engineering: Faculty Publications

CNN-Based Detection of Bearing Race Defects from Variable Reluctance Sensor Signals

Document Type

Article

Publication Date

4-20-2026

Publication Title

SoutheastCon 2026 Proceedings

DOI

10.1109/SoutheastCon63549.2026.11476701

Abstract

This paper presents a non-intrusive framework for detecting and classifying bearing outer-race defects using the existing inductive magnetic reluctance (MR) gear-tooth sensor signal available in equipment such as positive-displacement flowmeters operating in hazardous locations. Rather than installing accelerometers or other added instrumentation, often impractical in HazLoc due to certification and retrofit constraints, we exploit fault-induced amplitude and phase disturbances in the near-sinusoidal tooth-passage waveform. We transform 20 sec recordings into time-frequency spectrogram images and classify bearing conditions using transfer-learned CNNs (GoogLeNet and ResNet-50). The novelty is the repurposing of an already-installed MR speed/flow sensor for bearing diagnostics in HazLoc settings, coupled with a spectrogram-to-CNN transfer-learning pipeline tailored to MR tooth-passage signals. Results demonstrate high classification accuracy across multiple defect conditions and operating speeds.

Comments

Georgia Southern University faculty member, Rami J. Haddad co-authored, "CNN-Based Detection of Bearing Race Defects from Variable Reluctance Sensor Signals."

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