PDF(Pubmed)
Abstract：
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.
摘要：
随着时间的推移，牙科空气涡轮手机（DATH）的内部机制变得越来越复杂。为了提高牙科手术的操作可靠性并确保患者安全，本研究建立了具有时间序列因果关系函数的时间卷积网络(TCN)预测模型，传输内存，学习,存储,和快速收敛监测的健康和诊断DATH的转子和夹头故障。模仿牙医的手负荷为100g的手机被用来反复研磨玻璃瓷块以进行切割。在手持件自由运行时，采用加速度计来捕获振动信号。旨在辨别这些振动的特征。然后利用这些数据来创建诊断健康分类(DHC)，以进一步开发TCN。一维卷积神经网络(CNN)，和长短期记忆(LSTM)预测模型。这三个框架被用于机器学习并进行比较，以建立DATH的DHC预测模型。实验结果表明,就实验数据集预测的DHC而言，TCN框架的平方分类交叉熵损失函数误差普遍低于1DCNN，它没有内存框架或消失梯度问题的缺点。此外,TCN框架优于LSTM模型，这需要更长的病史才能提供足够的诊断能力。尽管如此，通过振动信号，在进给驱动铣削方向和机头重力方向均实现了高精度。总的来说,当嵌入式传感器可用时，故障分类预测模型可以在使用DATH之前准确预测牙科手机的健康和故障模式。因此,该模型可以被证明是预测实际牙科手机在剩余使用寿命中的劣化模式的有益工具。
