PDF(Pubmed)
Abstract：
Prognostic and health management technologies are increasingly important in many fields where reducing maintenance costs is critical. Non-destructive testing techniques and the Internet of Things (IoT) can help create accurate, two-sided digital models of specific monitored objects, enabling predictive analysis and avoiding risky situations. This study focuses on a particular application: monitoring an endodontic file during operation to develop a strategy to prevent breakage. To this end, the authors propose an innovative, non-invasive technique for early fault detection based on digital twins and infrared thermography measurements. They developed a digital twin of a NiTi alloy endodontic file that receives measurement data from the real world and generates the expected thermal map of the object under working conditions. By comparing this virtual image with the real one acquired by an IR camera, the authors were able to identify an anomalous trend and avoid breakage. The technique was calibrated and validated using both a professional IR camera and an innovative low-cost IR scanner previously developed by the authors. By using both devices, they could identify a critical condition at least 11 s before the file broke.
摘要：
在降低维护成本至关重要的许多领域中，预后和健康管理技术越来越重要。无损检测技术和物联网(IoT)可以帮助创建准确的、特定监控对象的双面数字模型，实现预测分析并避免风险情况。这项研究的重点是一个特殊的应用：在手术期间监控牙髓文件，以制定防止破损的策略。为此,作者提出了一种创新的，基于数字孪生和红外热成像测量的早期故障检测的无创技术。他们开发了NiTi合金牙髓文件的数字孪生，该文件从现实世界接收测量数据，并在工作条件下生成物体的预期热图。通过将此虚拟图像与红外相机获取的真实图像进行比较，作者能够识别异常趋势并避免破损.使用专业的红外摄像机和作者先前开发的创新的低成本红外扫描仪对该技术进行了校准和验证。通过使用这两种设备，他们可以在文件破裂前至少11秒确定一个临界条件。
