Early rolling bearing fault diagnosis in induction motors based on on-rotor sensing vibrations

Zuolu Wang, Dawei Shi, Yuandong Xu, Dong Zhen, Fengshou Gu, Andrew D. Ball

Research output: Contribution to journalArticlepeer-review

2 Citations (Scopus)


The traditional on-house sensing (OHS) accelerometer for vibration measurements causes poor signal-to-noise ratio (SNR) and complicated fault modulations, which increases the difficulty and complexity for early bearing fault diagnosis. To overcome these challenges, this paper develops a wireless triaxial on-rotor sensing (ORS) system to largely improve the SNR and deduces fast Fourier transform (FFT) and Hilbert envelope analysis for accurate early rolling bearing fault diagnosis, which largely improves accuracy and efficiency for early fault diagnosis. First, the development of the ORS system for wireless vibration measurements is given. Second, the theoretical diagnostic relationships between dynamic ORS signals and rolling bearing faults are derived for FFT and Hilbert envelope analysis for the first time. Finally, the induction motor tests with outer and inner race faults successfully validate that both simple FFT and Hilbert envelope analysis can achieve more robust early rolling bearing fault diagnosis compared to OHS measurements.
Original languageEnglish
Article number113614
Number of pages15
Early online date4 Oct 2023
Publication statusPublished - 30 Nov 2023

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