Abstract
The complexity of the internal components of dental air turbine handpieces has been increasing over time. To make operations reliable and ensure patients’ safety, this study established long short-term memory (LSTM) prediction models with the functions of learning, storing, and transmitting memory for monitoring the health and degradation of dental air turbine handpieces. A handpiece was used to cut a glass porcelain block back and forth. An accelerometer was used to obtain vibration signals during the free running of the handpiece to identify the characteristic frequency of these vibrations in the frequency domain. This information was used to establish a health index (HI) for developing prediction models. The many-to-one and many-to-many LSTM frameworks were used for machine learning to establish prediction models for the HI and degradation trajectory. The results indicate that, in terms of HI predicted for the testing dataset, the mean square error of the many-to-one LSTM framework was lower than that that of a logistic regression model, which did not have a memory framework. Nevertheless, high accuracies were achieved with both of the two aforementioned approaches. In general, the degradation trajectory prediction model could accurately predict the degradation trend of the dental handpiece; thus, this model can be a useful tool for predicting the degradation trajectory of real dental handpieces in the future.
Highlights
A dentist cannot identify the state of health (SOH) of and the presence of any damage in the internal components of a dental handpiece
Establishing a prediction model for the health index (HI) of dental handpieces is critical. Such a model can be used to monitor the SOH of a dental handpiece and predict its remaining useful life (RUL) in real time, which can allow dentists to identify the timing of potential equipment faults on the basis of comprehensive factors such as internal bearing damage, cage fracture, or loss of original dynamic balance by the rotor
Logistic Regression Prediction Model for Many to One Structure In Equation (7), the sigmoid function is used for predicting the output of logistic regression (LR) with In Equation (7), the sigmoid function is used for predicting the output of LR with the the inputs features x1 –xn in Equation (8) using model parameters θ0 –θn
Summary
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. Establishing a prediction model for the health index (HI) of dental handpieces is critical Such a model can be used to monitor the SOH of a dental handpiece and predict its remaining useful life (RUL) in real time, which can allow dentists to identify the timing of potential equipment faults on the basis of comprehensive factors such as internal bearing damage, cage fracture, or loss of original dynamic balance by the rotor. The cell state and hidden state obused an accelerometer to obtain the vibration signals of dental handpieces for analyzing tained at time t, namely (c ) and (h ), respectively, are transmitted to the hidden layer at the signal trends and features generated as the rotor of the dental handpieces gradually time (t + 1) This process that progresses with the time series is used for the transmission degraded with time.
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