Abstract
Infrared Air Turbine Dental Handpiece Rotor Fault Diagnosis with Convolutional Neural Network
Highlights
Dental handpieces are medical devices that are commonly used by dentists
Signal analysis techniques were used with the fast Fourier transform (FFT), histograms, and convolutional neural network (CNN) algorithms to detect the fault status of a dental handpiece under idling and drilling conditions
Experimental results indicated the condition of the dental handpiece when a typical dentist uses it with a handy mobile phone and a plug-in infrared device within 1 min is feasible in the near future
Summary
Dental handpieces are medical devices that are commonly used by dentists. Problems with an internal rotor bearing are responsible for 70% of the faults in typical dental handpieces. Dental handpieces require regular maintenance and immediate replacement when damaged. Dentists must avoid using devices with damaged bearings to treat patients. The quality of dental handpieces is crucial. The function of a dental handpiece involves pumping high-pressure gas into the handpiece chamber, which forms an air flow that rotates microturbine blades and drives the rotor. The speed can be changed by changing the pressure of the air and the shape of the rotor blades. The handpieces can be diagnosed with sound signals, this is not easy because of the noisy background of the environment
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