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This paper reports the diagnostic results of a free-running of air turbine dental handpiece (ATDH) with three rotor statuses by applying fast Fourier transform (FFT), Hilbert-Huang transform (HHT), and multiscale entropy (MSE) processes. The proposed method was tested under conditions of additional axial preload on the rotor and ceramic bearings with a damaged outer race supporting the rotor. A laser-Doppler vibrometer, condenser microphone, and portable MEMS system microphone were used to acquire the signals when the ATDH rotor features were changed. The results showed that changes in preload or malfunctioning ball bearings can be discriminated and abstracted using FFT and HHT to analyze the vibration frequencies. The experimental results showed that the proposed method can successfully predict the prognostic status of an ATDH rotor. The smart sensing of the health of the ATDH was achieved through a comparative evaluation of the MSE values. The proposed diagnostic method yielded satisfactory prognostic effectiveness in predicting the health status of the tested ATDH rotor.

Prognostic diagnosis of the health status of an air-turbine dental handpiece rotor by using sound and vibration signals