Failure Diagnosis for Dental Air Turbine Handpiece with Payload Using Feature Engineering and Temporal Convolution Network

The internal mechanisms of dental air turbine handpieces (DATHs) have become increasingly intricate over time. To enhance the operational reliability of dental procedures and guarantee patient safety, this study formulated temporal convolution network (TCN) prediction models with the functions of causality in time sequence, transmitting memory, learning, storing, and fast convergence for monitoring the health and diagnosing the rotor and collet failure of DATHs. A handpiece mimicking a dentist's hand load of 100 g was employed to repeatedly mill a glass porcelain block back and forth for cutting. An accelerometer was employed to capture vibration signals during free-running of unrestrained operation of the handpiece, aiming to discern the characteristic features of these vibrations. These data were then utilized to create a diagnostic health classification (DHC) for further developing a TCN, a 1D convolutional neural network (CNN), and long short-term memory (LSTM) prediction models. The three frameworks were used and compared for machine learning to establish DHC prediction models for the DATH. The experimental results indicate that, in terms of DHC predicted for the experimental dataset, the square categorical cross-entropy loss function error of the TCN framework was generally lower than that of the 1D CNN, which did not have a memory framework or the drawback of the vanishing gradient problem. In addition, the TCN framework outperformed the LSTM model, which required a longer history to provide sufficient diagnostic ability. Still, high accuracies were achieved both in the direction of feed-drive milling and in the gravity of the handpiece through vibration signals. In general, the failure classification prediction model could accurately predict the health and failure mode of the dental handpiece before the use of the DATH when an embedded sensor was available. Therefore, this model could prove to be a beneficial tool for predicting the deterioration patterns of real dental handpieces in their remaining useful life.

Use of Long Short-Term Memory for Remaining Useful Life and Degradation Assessment Prediction of Dental Air Turbine Handpiece in Milling Process

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.

Failure analysis of the ball bearings of dental air turbine handpieces

BACKGROUND

The aim of this study was to identify the nature and causes of deterioration and failure in dental handpiece ball bearings and thus provide guidance for clinical handling for service longevity.

METHODS

The bearings of 36 turbine assemblies were dismantled for visual inspection, documented using a digital camera, and examined using scanning electron microscopy, as appropriate.

RESULTS

For the metal parts of the ball bearing assembly, defects observed were mainly wear arising from the running load and corrosion. This was in the form of scratches and discoloured circumferential bands on the balls, and dull or worn surfaces extending around the circumference of the raceways. Cage damage including cracking, fracture, surface rubbing and distortion occurred, in varying degrees, in every failed turbine.

CONCLUSIONS

Dental ball bearing failure modes have been identified. Cumulative effects of damage from corrosion and mechanical factors lead to handpiece deterioration. The cage was found to be very vulnerable to damage, and this may be the key limitation on bearing lifetime. Autoclaving may contribute to that, as it does to corrosion in the absence of adequate lubrication, but this seems to be minor in comparison to the effects of abuse. There is no justification for failing to observe usage and sterilization instructions.